Roundup

A roundup of where top investors and CEOs appeared this week — interviews, podcasts, events, and the topics they discussed.

Podcast and interviews

Michael Batnick and Ben Carlson covered market breadth and the Mag7 stumble on Animal Spirits Ep. 470, noting nearly every market segment is outpacing the S&P 500 in 2026. Watch on YouTube.

Chamath Palihapitiya sat down with Dan Primack on The Axios Show (June 25), calling AI “the most important economic leveler of our lifetime” while admitting Meta “fumbled” its AI shot and reflecting on his SPAC era regrets. Watch on YouTube.

Jeremy Grantham, GMO co-founder, told Steven Bartlett on The Diary Of A CEO (June 25) that AI has produced the biggest stock market bubble of his 60-year career, and that he’s sold out of US equities entirely. Watch on YouTube.

Events

Jensen Huang, Nvidia CEO, joined Coherent CEO Jim Anderson at a groundbreaking ceremony in Sherman, Texas for an expanded indium phosphide laser fab, backed by a $50M CHIPS Act grant, framing it as part of a broader U.S. AI-driven reindustrialization. Watch on YouTube

Deep Dives

Detailed breakdowns of the week’s must-listen investing and finance podcasts — key arguments, notable quotes, and takeaways from each episode, condensed so you can catch up without listening.

Animal Spirits ep. 470: “Everything Is Outperforming the S&P 500 This Year”

June 24, 2026

On episode 470 of Animal Spirits, Michael Batnick and Ben Carlson discuss: why diversification is working again, how AI is creating more winners and losers in the stock market, why the Mag 7 is underperforming, the triple-digit club, why investors are holding more cash, rich people who complain too much, what makes America great, AI is disrupting self help books, the World Cup and more.

Market Breadth: Everyone Beating the S&P

The core theme: nearly every major slice of the market — Russell 2000, small/large cap value, REITs, midcaps, emerging markets, dividend stocks — is outperforming the S&P 500 in 2026, some by wide margins. This is unusual because it’s happening while the S&P itself is up nearly 10% on the year, not while it’s falling (the more typical setup for “diversification working”). The driver is that the Magnificent 7 have stalled out and are no longer pulling the index higher.

Mag7 Drawdowns

Several mega-cap tech names are deep in drawdown territory even as the broader market does fine: Microsoft down 33%, Meta down almost 30%, Oracle down nearly 50% from highs, Netflix crashing again (Michael holds it, down 30%, says he’d buy more if it falls further), Google down 13%, Amazon down 15%, Tesla down 17%, SpaceX down 25% from its brief post-IPO high. Microsoft, Amazon, and Google are trading at their lowest average P/E in five years. The hosts frame this positively for the overall market: these stocks are shedding the “asset-light, gushing free cash flow” halo as AI capex spending turns them more asset-heavy, and the broadening rally (the “493” outperforming the Mag7) is healthy even if painful for holders of those specific names.

AI Infrastructure / Semiconductor Mania

Morningstar found 42 stocks in its large/mid index have more than doubled over the past 12 months (more than 2x the 10-year average), with names like SanDisk, Micron, Western Digital, Dell, and Intel leading — mostly AI infrastructure plays. SK Hynix is up over 300% year-to-date before a recent pullback. Semiconductor net profit margins are projected to rise from a 2024 low of ~25% to 49% by 2027, which the hosts flag as the real story behind the “bubble” framing — fab capacity (e.g., TSMC) can’t scale quickly, creating persistent bottlenecks despite a perceived era of tech abundance.

A Surprising Hedge Lesson

A Wall Street Journal chart tracking gold, oil, and semiconductors since the start of the Middle East war showed semiconductors up 70%, oil only up ~10% (round-tripped most of its spike), and gold down 20% — the opposite of what conventional wisdom would predict for a war hedge. Takeaway: there’s no universal hedge for every scenario.

Valuations and Return on Equity

Citing Goldman Sachs research (via Meb Faber’s Idea Farm), the hosts revisit their long-standing argument that higher valuations (CAPE) are justified by structurally higher ROE. Trailing four-quarter ROE has risen to a new historical level this decade, breaking a 45-year cyclical pattern — driven by tech business models. Ben notes Buffett wrote in the 1970s that ROE stays roughly 12-14% over time, which no longer holds. They flag this dynamic may now be reversing for hyperscalers as their margins come under pressure from capex spending.

Investor Positioning: Contradictory Signals

A grab-bag of conflicting data points, illustrating the hosts’ broader point that positioning data is noisy and easily cherry-picked to support any narrative:

• Citadel Securities: SpaceX’s IPO day was the largest single-day net retail buying event ever recorded by the firm (handles ~35% of US retail volume); 9 of the 10 largest trading days ever happened in the last month.

  

• Deutsche Bank consolidated equity positioning sits at just the 42nd percentile — “far from crowded.”

  

• Discretionary investor positioning: 41st percentile; systematic: 40th percentile.

  

• Bloomberg data shows speculators getting shorter in S&P, Nasdaq, Dow, and midcap futures.

  

• Hedge fund gross/net exposure to semiconductors has gone parabolic — from 8% at the start of the year to 22% now.

  

• Retail flows into semiconductor ETFs are surging (the DRAM ETF doubled from $10B to $20B in a week).

• Conversely, Vanda Research data (via Kevin Gordon) shows retail single-stock net buying falling to its lowest level since COVID.

Ben’s takeaway: “Consider the source, not the data — what narrative are they trying to spin?”

Household Cash Levels

Citadel reports that household cash as a percentage of total financial assets is at 8%, the highest since 1990. The hosts find this counterintuitive, given the apparent speculative mania elsewhere, and theorize it’s largely a baby boomer phenomenon — retirees who got burned by rising rates in bonds are now happy to hold cash at 3-4% yields instead.

SpaceX IPO Volatility

SpaceX opened around $150 on its debut and has round-tripped back near that level pre-market the day of recording, despite enormous trading volume (including a 2x inverse SpaceX ETF that fell 43% in two days).

AI Bubble Sentiment Survey

Only 8% of surveyed investors said there’s no bubble in AI-related stocks; 51% said some (not all) AI stocks are overvalued. The hosts note persistent “bubble” calls don’t disqualify an eventual bubble, but also point out there’s no longer a single unified “everyone” in the market the way there might have been in past manias — too diverse an investor base today for a classic blow-off top.

Consumer Spending and Credit

Bank of America data shows total card spending up 5.1% year-over-year in May, the strongest growth in nearly four years (partly gasoline-driven). The hosts also riff on the oddity of 0%-APR balance transfer offers for 18-21 months coexisting with 20-25% standard credit card APRs — funded by the high rates charged to riskier subprime borrowers. Bespoke data shows broad-based retail sales strength, with only two sectors showing negative growth.

New Business Formation

Stripe Economics (Ernie Tedeschi) data shows new business applications, after a step-change higher during COVID (driven partly by the PPP loan program’s low eligibility bar), are now accelerating again — this time attributed to AI making it easier to start a business. Applications have roughly doubled versus the stagnant 2005-2018 period. Both hosts frame this as a genuinely underappreciated positive story.

Rich People’s Complaints

A Wall Street Journal piece found rising economic anxiety among upper-middle-class respondents: the share who feel the system is “stacked against people like me” rose from 29% (2017) to 65% (2026), and those lacking confidence that their children’s lives will be better than theirs rose from 64% (2019) to 86% (2026). Michael pushes back hard on this as a pet peeve — wealthy people overstating their grievances relative to those with genuine economic hardship.

AI Disrupting Self-Help Publishing

Tim Ferriss disclosed his book sales (4-Hour Work Week, 4-Hour Body, etc.) are on pace to be down roughly 80% in print copies in 2026 versus 2022, which he and the hosts partly attribute to people now asking AI chatbots directly instead of buying self-help books (with podcasts also cited as a contributing substitute for books).

Miscellaneous Stories

• Ocean data centers: Portland startup Panalasa raised $140M to build floating, wave-powered, seawater-cooled AI data center platforms; backed by Thiel, now valued near $1B.

• Insurance/AI use case: A conversation with David Law (insurance industry) highlighted AI’s ability to analyze hundreds of annuity contracts for a financial advisor’s full client book — work that was previously infeasible at that scale.

• Midjourney launched full-body medical scan analysis; Michael had previously done this with his own bloodwork and got pushback from medical professionals about its limitations.

• World Cup / Americana virality: ABC News story on European visitors going viral on social media expressing surprise at American conveniences (free ice refills, 24-hour retail, friendliness) — hosts frame it as a feel-good counter to “everyone hates each other” social media negativity.

Prediction Markets

Schwab is adding prediction market access, though the hosts are skeptical it’ll be a meaningful part of mainstream brokerage business — prediction market volume remains roughly 90%+ sports betting/parlays, down from over 90% politics/economics two years ago to under 6% today (per Ryan Chern). Notable items:

• Kalshi will require users to disclose their employer for certain trades, following insider-trading/manipulation concerns — prompted partly by accounts linked to military spouses making accurate, well-timed bets on Maduro’s ouster.

• A Google employee was charged with insider trading on Polymarket, allegedly profiting over $1M using non-public information to bet on 2025’s “most searched people.”

• A Wall Street Journal investigation found Polymarket paid influencers to post fake trading videos: of 1,150+ reviewed videos, roughly 70% depicted bets that were never actually real (totaling ~$1.9M in claimed wagers and ~$900K in claimed winnings), and the “real” outcome of those bets would have been a loss of over $166,000.

• The hosts question what moat these prediction market platforms actually have if usage is overwhelmingly sports betting, given established competitors like DraftKings and FanDuel — regulatory access is cited as the main moat, helped by political connections.

Crypto / Ethereum

Ethereum’s price has round-tripped back near its 2017 highs (~$1,400) despite years of bullish narratives (”Own the Internet,” smart contracts, ETH-pilled finance commentators). The hosts admit they can’t fully explain why the “smart contract” thesis didn’t pan out as expected. They note crypto infrastructure (blockchain rails, stablecoins) does seem to be having a real moment even as token prices disappoint — pointing to weakness in Visa and Mastercard stocks (down ~12% from highs) tied to stablecoin disruption fears, and a new joint venture between NYSE’s owner and crypto exchange OKX (co-chaired by former NY Governor Andrew Cuomo) to tokenize NYSE-listed assets. They contrast the current environment with the 2021 mania (e.g., a $69M NFT sale, an “Ethereum rock” selling for $2.2M — the same price as a Florida waterfront house), arguing 2021 was a far more extreme speculative mania than today, with more “real” earnings and margins underpinning current valuations.

Housing

Morgan Stanley data shows mortgage payment as a percentage of income is back to levels seen in 2007, 2004, and 1994 — i.e., affordability has actually improved as incomes have risen, even though transaction activity remains historically low (a low-inventory problem more than a pure affordability problem). Mike Simonson’s analysis suggests a return to “normal” housing activity levels could take until the early 2030s, depending on where rates settle (5.5%-6.5% range). The hosts argue there’s been no genuinely “normal” housing market this entire century. Zillow is cited as a cautionary tale: down 85% from its 2021 all-time high market cap of $48B to under $7B today, despite existing home sales showing some recent signs of life.

Social Security

The latest annual Social Security trustees report shows the program can pay 100% of benefits from current tax revenue through roughly 2032-33, dropping to 83% by 2034, and projected at 65% by 2100. The hosts speculate Congress will likely choose to borrow/spend more rather than cut benefits outright when the shortfall hits.

Pop Culture Corner

• Toy Story (new film, referred to as “Monster”): Opened with $160M — the biggest opening of 2026, the biggest Toy Story opening ever, second-biggest animated movie opening ever, and second-biggest Pixar opening behind Incredibles 2. Both hosts liked it (kids loved it); Michael joked he fell asleep twice.

• The Watchers / “Widow’s Bay” (a buzzy streaming show): Mixed reactions — one host underwhelmed despite the hype, the other (who avoided the hype machine) genuinely liked it and is looking forward to a second season, though both agree the finale left too much unresolved.

• The Hand That Rocks the Cradle (90s thriller, watched via The Rewatchables): A nostalgic but not-actually-rewatchable slow-burn 90s thriller; they note Apple’s Cape Fear series captures a similar modern throwback vibe.

Chamath Palihapitiya on AI: America’s opportunity, Facebook’s fumble, and Trump | The Axios Show

June 25, 2026

Chamath Palihapitiya joins Axios’ Dan Primack to discuss the AI boom, Silicon Valley’s trust crisis and America’s race against China.

In this episode of The Axios Show, Palihapitiya explains why he believes AI could be the most important economic leveler of our lifetime, why backlash against AI is becoming a political force, and how President Trump is thinking about the technology.

The conversation also covers tech oligarchs, OpenAI, Anthropic, SpaceX, Meta’s AI failures, immigration, SPACs, and what Palihapitiya says he learned from getting the incentives wrong.

Silicon Valley Then vs. Now

Palihapitiya, in the Valley for roughly 25 years, framed the current AI boom as the third major cycle he’s lived through, after the dot-com bust and the mobile wave (he considers crypto a minor blip by comparison). The key difference this time: in earlier cycles, Silicon Valley was seen as a quirky, mostly trusted, even endearing community — “exotic animals at the zoo” when they ventured outside the Valley. Now, he said, there’s still admiration but also real distrust, with some figures viewed as outright pariahs. He considers this troubling, given how much more consequential AI is than prior technology waves.

“They were like exotic animals at the zoo... unfortunately what’s happened I think in this cycle is that there’s a good amount of admiration, but there’s equal parts, I think, what people are frustrated, they don’t trust this community anymore. Certain people are viewed as pariahs.”

AI as “the Most Important Economic Leveler of Our Lifetime”

Palihapitiya argued AI is fundamentally different from prior tech advances like having “all the world’s knowledge” on a smartphone, because it converts knowledge into actionable expertise — for example, going from being able to look up what a PN junction is to actually being able to design a working transistor. He sees this as a potential equalizer that could let anyone pursue ambitions without needing large amounts of capital, comparing it to giving “a genius-level co-founder” to every person on earth, regardless of background.

“It's the most important economic leveler of our lifetime. It is the thing that will create the most amount of equality if it's allowed to get to market.”

Wealth Concentration and the “Platform” Question

Pushed on whether AI leveling is in tension with wealth concentrating among a small number of founders/investors, he argued the goal should be ecosystems where the economic value created by users/participants vastly exceeds the value captured by the platform owner — citing a formative early story where Bill Gates told him and Mark Zuckerberg that Facebook Platform wasn’t a real “platform” by that definition, unlike the Google ecosystem. He believes AI has the potential to follow the latter model if “we don’t fuck it up,” but acknowledged this is a potential outcome, not a guarantee.

“A platform is when the economic value that the participants generate greatly exceeds that of the platform maker.”

Founder Psychology and the Roots of AI “Doomerism”

He attributed much of the dysfunction among AI lab leaders to personal psychology — hard-scrabble backgrounds, insecurity, grudges, and a messy, fractious origin story among the major labs (unlike, he says, the comparatively clean founding of Google).

He described a cynical pattern he’s observed: labs hype apocalyptic risk ahead of model releases and fundraising rounds (to attract capital and undercut rivals), then flip to promising the model will “solve everybody’s problems,” cycling back and forth. He called this strategy “deeply selfish” because it drags a broadly valuable technology into the founders’ personal rivalries.

“You're taking this tool that is really valuable to so many people, and you're gonna cast it in all of your own personal issues. And I find that deeply selfish.”

Is AI Doomerism Justified? Jobs and Labor

Asked whether he has any doomer sympathies, Palihapitiya said there’s no scenario where humanity simply does nothing, though there will be major transitions. Pressed on long-term employment (using his 15-year-old daughter as the example), he argued he can’t confidently name a single job — white collar or blue collar, including a “plumber” replaced by robotics — that’s safe from disruption over 35 years. But he rejected pure doomerism, arguing technology has historically expanded how people allocate their time (citing a thought experiment that a person might go from one major daily task 1,000 years ago to 35 today, and possibly 300 a thousand years from now), suggesting future humans will have more, not fewer, ways to spend their time meaningfully.

“I can't think of a thing necessarily that her or her friends... will necessarily be able to do that is gainful employment in 35 years... Because every job I can currently think of, I could make a case potentially that it could absolutely be replaced.”

Existential Risk: Bioweapons, War, and the Case for “KYC” Regulation

On catastrophic risks like bioweapons or AI-enabled conflict, he argued all transformative technologies are “deterministic” — anything possible will eventually be attempted, for good and ill (citing nuclear power’s enormous benefits alongside Hiroshima and Nagasaki as the analogous “tail risk”). He doesn’t believe nefarious uses will dominate but says society needs the right checks and balances — proposing a “know your customer” (KYC) framework for access to frontier AI, modeled on similar systems already used in pharma, Commerce, and Treasury.

“Every single technology that's ever been created is fundamentally deterministic. What does that word mean? It means anything that is possible will be possible and done.”

Trump, AI Policy, and Immigration

Asked about President Trump’s light-touch approach to AI, Palihapitiya said he hasn’t discussed KYC specifically with him but described Trump as unusually sharp and intuitive in their limited interactions — citing a meeting where he walked Trump through a detailed deck on data centers and electricity pricing, and Trump quickly zeroed in on the key underlying issue.

He pushed back on the “oligarchs” framing applied to tech executives appearing with Trump at the G7, instead describing a broader global shift away from a “monocultural blob” (multilateral institutions like the WTO and WEF) back toward national cultural and political independence — with the U.S. AI industry as a competitive advantage in that context (”we’re the dream team”).

On immigration, he (an H-1B visa holder himself, as was Elon Musk) argued the program has been abused by a small number of companies flooding it with applications, crowding out the next great scientist, and that the system needs reform to restore both merit-based access and public trust — while still affirming that attracting top global talent is essential.

“I sure as shit would never have gotten an H1B in the way that the program works today.”

Open vs. Closed Models and the U.S.-China AI Race

He distinguished China’s “open-weight” model strategy from true open source, arguing it offers only a partial, controlled window into how the systems work (”you don’t know how the gears are made”). He advocated for a vibrant ecosystem of many trusted U.S. AI providers — “1,000 flowers to bloom” — as the best way to preserve transparency and accountability relative to cheaper alternatives flooding in from elsewhere.

“No, they're going open weight, they're not open source. You don't know how the gears are made, nor will you ever.”

Betting on AI Companies: OpenAI, Anthropic, and SpaceX

Discussing the leading AI labs, he predicted OpenAI and Anthropic will remain “a plurality” of the market but not a majority, since outsized returns historically attract competition unless a company has true monopoly characteristics. Given a hypothetical choice of equal stakes in Anthropic, OpenAI, or SpaceX, he said he’d choose SpaceX for its broader optionality — citing Anthropic’s strength in enterprise (Social Capital uses it as a foundational model) and ChatGPT’s near-monopoly consumer brand recognition, but noting both are tied to single lines of business tracking global GDP, whereas SpaceX offers exposure to a broken global communications infrastructure and longer-term “off-Earth” optionality. He disclosed he is technically invested in all three companies, including OpenAI and Anthropic via SPVs.

The Political Backlash Against AI

He argued a real, bipartisan anti-AI political backlash has emerged, driven partly by public resentment of wealth and power concentration in Silicon Valley, which he says the frontier labs’ own doomer rhetoric handed to critics “on a silver platter” — creating an opening exploited by both foreign actors and domestic political groups uncomfortable with that concentration of power. He warned this dynamic could backfire by handing even more power to hyperscalers (Amazon, Meta, Google), who could use the frontier labs’ “immaturity” and inconsistency as a wedge to position themselves in Washington as the more responsible, KYC-ready alternative — a scenario he said he doesn’t want but finds “compelling” as an argument. He expressed confidence that Trump and advisor David Sacks favor an open ecosystem and wouldn’t hand control to just a few players.

“I think what the Frontier Labs did with their rhetoric was put on a silver platter the opportunity to weaponize hatred and mistrust against them.”

Facebook’s Missed AI Moment

Reflecting on a past conversation (just before ChatGPT’s release) where he’d argued Facebook was well-positioned for AI/AGI given its trove of emotional/personal data, he said Facebook has “completely fumbled” the opportunity, in a pattern he likened to its earlier failure to become a true “third leg of the stool” alongside Android and iOS with its own mobile OS (a project he worked on before leaving the company). He argued Nvidia, under Jensen Huang, has effectively become the bulwark for open ecosystems that Facebook could have been, and said it’s now “pretty unlikely” Meta can recover that position.

“I mean, they have completely fumbled it... they've profoundly failed.”

Returning to SPACs: “American Exceptionalism Acquisition Corp”

Asked about his new SPAC (American Exceptionalism Acquisition Corp), which has yet to announce an acquisition, he acknowledged his earlier SPAC era produced disappointing, roughly breakeven returns and attributed this to misaligned incentives — he could get paid on a deal regardless of post-merger stock performance. His new vehicle is structured so he earns nothing unless the investment significantly succeeds. He also argued U.S. public capital markets have become “sclerotic,” with too few gatekeepers and companies staying private far longer than in the past (contrasting what it would mean to have bought Google, SpaceX, Anthropic, or OpenAI at early, low valuations in public markets), and said fixing that access problem is a personal mission, separate from his main operating role at Social Capital.

“My incentives were totally and fundamentally misaligned because I could do a deal and still get paid.”

Family and Legacy

Asked what non-financial trait he hopes his children inherit, he said “nothing” — he wants them to pursue fully independent paths without regard to his opinions or legacy. He shared an emotional story about his 17-year-old son’s internship at the White House Office of Presidential Correspondence, where Palihapitiya waited in a call queue to reach the comment line his son was staffing, and broke down expressing pride to him. The family spent the following Father’s Day weekend together in Las Vegas.

“I want my kids to only know me as their dad. And I want them to have a completely independent path in life... I selfishly want these little experiences where I can just be rooting for them. That's the only thing that matters.”

Jeremy Grantham: Inside the Biggest Bubble of His 60-Year Career | The Diary Of A CEO

June 25, 2026

At 87, Jeremy Grantham has been investing for roughly 60 years, co-founded GMO (which managed up to $165 billion at its peak), and helped pioneer small-cap and value investing. He’s also spent decades studying speculative bubbles — and he says we are living through the biggest one of his career. This conversation ranges from portfolio advice to AI risk to a fertility crisis most people have never heard of. Below is the full breakdown, section by section, with his own words.

Topics discussed:

• Why Wall Street will never warn you when to get out of the market, and what to do instead

• The exact portfolio Jeremy recommends to protect your money before the crash

• What everyday chemicals in your food and cosmetics are doing to your fertility

• Why house prices need to fall 30%, and what it means for your finances

• Why the AI boom won’t automatically lead to higher profits, and what to buy instead

The Biggest Bubble of His Career

Grantham’s central thesis: bubbles don’t form around scams — they form around the most important, most obviously world-changing ideas (railroads, the internet, now AI). The bigger and more legitimate the idea, the more capital floods in, and the bigger the eventual bust. He believes today’s AI-driven market is the largest bubble in American history, even bigger than the 2000 dot-com peak.

“The great bubbles always occur around the very most important ideas... The greater the idea, the more obvious the idea, the more money goes in and the bigger the bubble and the bigger the bust.”

Are We on the Verge of a Collapse?

Asked directly whether a crash is coming, Grantham says the timing is the hard part — but the structural setup (extreme valuations, euphoric narratives) matches prior pre-crash periods almost exactly.

“If you look at the data, it would be compatible with history for the peak to be very soon... This is, I think, the biggest investment bubble in American history.”

Don’t Own U.S. Stocks — Including the S&P 500

His single most direct, actionable piece of advice: sell U.S. equities, including the S&P 500, and especially concentrated tech positions. He argues non-U.S. markets are far cheaper and have already started outperforming (emerging markets were up 65% over the prior 12 months versus the S&P’s ~25%).

“Don’t own US stocks. That’s a simple strategy that you can act on... And if you have a big position in US technology stock, my personal advice would be to sell them all.”

Historical Precedents: Japan, the Nifty Fifty, and the Dot-Com Bust

Grantham repeatedly grounds his warning in history. Japan’s 1989 bubble peaked at 65x earnings (versus ~35–40x for U.S. tech today) and the Japanese market took 35 years to recover. The “Nifty Fifty” crash of 1972 fell 65% in real terms and triggered one of the worst recessions since the Depression. Amazon itself fell 92% in the 2000 crash before going on to dominate retail — proof that being right about the technology doesn’t protect you from the bubble bursting.

“In Japan, you went 20 years and you lost money. You went 30 years and you still hadn’t gotten back. It took 35 years for the Japanese market to recover.”

Why Wall Street Will Never Warn You

Investment advisors, he argues, have a structural conflict of interest: telling clients to get out of an overpriced market costs them business long before the crash arrives, because no one can time the top precisely. He tells a vivid story from 1999 in which 400 self-described stock market experts unanimously agreed the market was overvalued and would eventually fall — yet the firms employing them kept telling clients to stay invested.

“You will not receive the advice from investment advisers to get your tail out of the market ever. It is not good business for them to do that, and they will not ever say it to you.”

Practical Portfolio Advice for Everyday Investors

For someone investing their salary, his recommended allocation: roughly 60% in a broad non-U.S. equity index, 5–10% in precious metals (gold or silver, no strong preference), some real estate if convenient, and the rest in bonds. He walks through how bonds work in plain terms — a loan to a government or company at a fixed interest rate, buyable directly via sites like TreasuryDirect.gov or through a broker’s fixed-income desk.

“Buy a broad-based index of non-US equities... for like 60% of your money, and then 5 or 10% in precious metals, and if it’s convenient and sensible, hold a bit of real estate, and the rest I’d put in bonds.”

Crypto: “An Unnecessary Piece of Nonsense”

Grantham has never owned crypto, never will, and won’t recommend it. His critique: it’s too volatile to be a store of value, impractical as a medium of exchange, and mainly useful for speculation and moving money anonymously, including by criminals. He believes Bitcoin will eventually go to zero, though he concedes it “may take a long time.”

“It’s an unnecessary piece of nonsense that facilitates nothing except criminals moving money, but they can’t be seen... it will certainly go to zero.”

Advice for Founders and Startups

For entrepreneurs dependent on investor capital, his advice is to raise and lock up money now, build in conservatism, and prepare for a funding drought — comparing it to a founder he’s heard of who plans to raise aggressively now so he can “vulture” up distressed competitors after a downturn.

“If you can lock up money, I would. If you can build a bit of conservatism in other ways, do it — just brace yourself for impending problems, which is a pretty good principle anytime, but is a better principle than normal today.”

Is AI Itself Overblown — or Genuinely Transformative?

Grantham is unambiguous that AI is not a scam — it’s a genuine, civilization-altering technology on par with the railroads. What fascinates him is the total lack of consensus among experts, even Nobel laureates, on whether AI will make humanity prosperous beyond imagination or pose an existential threat.

“There is absolutely no agreement on whether AI is going to make us all so rich we can sit on the beach and never do another day’s work, or it will wipe us out accidentally or on purpose because it’s a much higher level intelligence one day.”

The “Benevolence” Problem and the Paperclip Risk

Drawing on conversations with Geoffrey Hinton, Grantham raises the open question of whether a superior intelligence can ever reliably remain benevolent toward a lesser one (Hinton’s only proposed example being mothers and babies — which Grantham finds unconvincing). He discusses the classic “paperclip maximizer” thought experiment: a literal-minded AI given a vague, open-ended goal could pursue it catastrophically and without malice, simply through unintended consequences taken to an extreme.

“When was there ever a case where a higher intelligence was benevolent in a sustainable way to a lower intelligence?... I don’t see how it can’t be [risky]. Unless you make it programmed completely to be benevolent.”

The Mag 7: From Comfortable Monopolies to a Winner-Take-All Brawl

Grantham makes a sharp observation about the seven dominant tech companies (Alphabet, Nvidia, Tesla, Microsoft, Meta, Apple, Amazon): historically, each owned a clean, separate monopoly (Apple/smartphones, Google/search, Meta/social, etc.). Now all seven are pouring unprecedented capital into the same AI battlefield, with no guaranteed winner — a complete departure from their previous “well-behaved, separate monopolies” structure.

“Now they have no monopoly. There are seven potentially sharp-elbowed, ruthless players determined to fight out with each other until they win.”

Robots, Job Disruption, and an Uneasy Hope That SpaceX Fails

Citing a livestreamed demo where a Figure AI humanoid robot reportedly outlasted a human worker on a sorting task (the human needed sleep and bathroom breaks), Grantham sees significant job disruption as highly likely. More strikingly, he says he hopes SpaceX’s most ambitious promises — data centers in space, mining asteroids — fail to materialize, because their success would mean an explosion of energy-hungry chips and robots that he believes the world isn’t ready to manage safely.

“I’d much prefer them to fail, and the idea to move much more slowly, to buy time for humans to work these things out.”

SpaceX as “the Defining Bubble Story”

Despite being an early SpaceX investor himself (he got in around a $100 billion valuation, before AI was even part of the pitch — it was a Starlink bet), Grantham calls SpaceX’s prospectus — addressing “a quarter of global GDP,” talk of mining asteroids — a textbook signal of market-top euphoria, comparable to the legendary 18th-century South Sea Bubble. He separately argues that Elon Musk’s genius has been less about hitting financial targets and more about talking up valuations to fund real factories — a feat he says cannot necessarily be repeated in a different market cycle. He’s also skeptical that humans could ever sustainably colonize Mars, citing bone and heart deterioration in low gravity and the need for underground, radiation-shielded, artificially-gravitated habitats — arguing humanity hasn’t even managed a sustainable closed-loop dome on Earth yet.

“SpaceX is such a fabulous BS story... It’s the classic description of a market peak. It’s what you look for at the top of a terrific bubble.”

Wealth Inequality — and What History Says Comes Next

Grantham lays out a stark inequality picture: the top 1% of Americans control 31% of national wealth while the bottom 50% hold just 2.5%; the top 10 billionaires saw wealth surge 526% (inflation-adjusted) between 2020–2025. He contrasts this with 1935–1975, when the bottom quarter of earners grew wealth faster than the top. Historically, he says, extreme wealth concentration rarely gets fixed by peaceful policy — it’s typically broken by civil collapse, mass-mobilization war, or revolution. His prescription: a gradual, multi-decade tax shift favoring the bottom over the top, similar to mid-20th-century policy.

“According to historical macro studies, peaceful policy changes almost never fix extreme inequality... a wealth peak is broken by one of three violent or catastrophic triggers: total civil collapse, mass mobilization warfare, or total revolution.”

If He Were 33 Again: Get Into AI and Take Risk

Asked how he’d build wealth if he were starting out today, Grantham’s answer is simple: go all-in on learning AI deeply, take real risk, work hard, and reject the assumption that experts and institutions know what they’re doing.

“I think the simple appeal would be to get your tail into AI and try and be a leader. Try and know more about everything in that area than the next guy.”

The Fertility Crisis: A “Chronic Baby Bust”

In the interview’s most alarming turn, Grantham — whose foundation has studied this for years — lays out research (citing Shanna Swan) showing human sperm counts have dropped from roughly 100 units/mL in 1970 to about 35 today, with the decline accelerating at 2.5% per year. Roughly 17% of young couples currently need fertility help, up from near zero a generation ago. Extrapolated forward, the projection is that the median male sperm count could hit zero by 2045 — meaning the typical couple would be unable to conceive without medical assistance.

“Dr. Swan’s projection indicates that if the current rate of decline continues unchecked, the median male sperm count is on track to hit zero by 2045... half the male population will have zero viable sperm.”

The Toxins Behind the Decline

Grantham points to endocrine-disrupting chemicals as the primary culprit: phthalates (cosmetics, shampoos, food packaging) that lower fetal testosterone production; BPAs (plastics, can linings, receipts) that flood the body with synthetic estrogen; PFAS “forever chemicals” (non-stick pans, rain jackets, carpets) found in over 45% of U.S. tap water; and microplastics now found in human placentas, breast milk, and testicular tissue — one 2024 study found them in 100% of human testicular samples tested. He also cites pesticide exposure: in small Harvard/Mass General fertility-clinic studies, men and women eating the least pesticide-contaminated produce (”the Dirty Dozen” avoided) saw roughly double the sperm counts and live-birth rates of those eating the most contaminated produce.

“BPAs... are synthetic estrogens. They flood the male body with female hormone signals, crashing sperm count and motility... [microplastics are] physically embedded in human placentas... breast milk and human testicles.”

The U.S. vs. Europe Regulatory Gap

Grantham draws a sharp contrast between U.S. and EU chemical regulation: the EU has banned or heavily restricted roughly 1,300+ chemicals in cosmetics versus about a dozen in the U.S.; the U.S. permits 85 agricultural pesticides banned in the EU, China, and Brazil; and life-expectancy gaps between the U.S. and countries like Sweden have widened from two years to six years over 70 years. He frames this as a direct consequence of U.S. corporate power over regulation.

“The US currently permits the use of 85 agricultural pesticides that are completely banned in the EU, China, and Brazil... the US has worse life expectancy [and] it’s the only rich country in the world where 15 years ago they had the same life expectancy as they have today.”

What You Can Actually Do About It

His most practical, “easily acquired” advice: pregnant women in particular should avoid cosmetics entirely for nine months and redirect that money toward organic versions of the “Dirty Dozen” produce (berries, apples, peaches, spinach, etc.). He notes effects may persist across multiple generations, since a woman’s eggs are formed before her own birth. He also points to consumer tools — apps like Yuka, EWG’s Healthy Living, Think Dirty, and ClearYa — that scan products for endocrine-disrupting ingredients.

“No cosmetics, no bad fruit — or make it organic. That’s a piece of cake. That is easy. It will save you a huge amount that you will never appreciate, because you’ll never know how much better your children are than they would have been.”

Detoxifying Capitalism, Not Just Chemicals

Beyond banning toxic chemicals (which he calls “intellectually easy”), Grantham argues for a much harder, generational shift: rebuilding capitalism around family formation as a public good — what he calls “the commons.” He cites 2.1 children per couple as the replacement rate a healthy society needs to sustain itself, alongside clean air, water, and soil, and says no policy intervention tried anywhere in the world (despite some countries spending several points of GDP) has yet produced a lasting increase in birth rates.

“We have to slowly but surely turn our capitalist societal norms into much more family-friendly, children-friendly... 2.1 healthy, well-educated children is a part of the commons.”

Should You Leave the United States?

Asked bluntly whether there are countries he wouldn’t live in, Grantham jokes he’ll “refuse to answer on the grounds it might incriminate” him — then makes clear he means the U.S. itself. His case centers on a frayed “social contract”: declining corporate civic responsibility, a weak social safety net, and what he considers the starkest civilizational indicator of all — maternal mortality. U.S. maternal mortality is roughly 20–21 per 100,000 (44 in the Black population), versus 5 in Britain, 4 in Germany, 2.1 in Sweden, and 0 in Norway — making the U.S. roughly 50% worse than the next-worst developed country.

“How is it possible that a country more or less the richest in the world... 50% more mothers die here than in the second worst country in the developed world? ... It’s a sign of the social contract.”

Closing: The Book He’d Write If He Could Not Fail

Asked what goal he’d pursue if failure were impossible, Grantham said he’d want to write a book in the spirit of Rachel Carson’s Silent Spring — one focused on toxicity and rebuilding a social contract that actually encourages people to have and raise children, arguing that without that shift, “we fail as a society pretty quickly.” His own book on markets and bubbles, The Making of a Permabear: The Perils of Long-Term Investing in a Short-Term World (with Edward Chancellor), covers much of the investing philosophy discussed in this interview.

“If I could write a book that would pull a Silent Spring, I would sit down tomorrow and start it... We have to detoxify. We have to create an environment where people want to have children.”

NVIDIA & Coherent: Reindustrializing America — Detailed Summary by Topic

June 24, 2026

At a groundbreaking event in Sherman, Texas, Coherent () CEO Jim Anderson and NVIDIA () CEO Jensen Huang announced an expansion of Coherent’s indium phosphide laser fab — the world’s first and largest-volume 6-inch indium phosphide manufacturing facility, critical for the optical interconnects that link AI data center processors over long distances.

Backed by a $50 million CHIPS Act award, support from the State of Texas, and NVIDIA’s strategic investment (part of a partnership dating back over two decades), the expansion will double the facility’s size, quadruple its output within a year, and grow the site’s workforce to over 1,000 jobs (550+ direct).

Huang framed AI as a new “industrial revolution” spanning energy, chips, infrastructure, AI models, and applications — one that depends as much on connectivity (optical/silicon photonics, since processors are now separated by hundreds or thousands of feet) as on raw compute.

Both executives positioned the investment as the start of a broader U.S. reindustrialization: bringing back high-skill, high-wage manufacturing jobs in chips, packaging, and AI infrastructure, enabled by pro-energy policy, strong company revenues funding domestic capacity, and government-industry-community collaboration — with the goal of rebuilding a balanced economy of both “builders” and “information workers” over the next decade.

Press Release

The Announcement and the Partnership

The event marked a groundbreaking at Coherent’s Sherman, Texas facility, hosted by Coherent CEO Jim Anderson with NVIDIA CEO Jensen Huang as guest speaker. Anderson opened by thanking the partners who made the expansion possible: the U.S. Department of Commerce (Secretary Howard Lutnick) for a $50 million CHIPS Act award announced that same morning, NVIDIA for its strategic partnership and investment, Texas Governor Greg Abbott and the state’s economic development team, the Sherman Economic Development Corporation and local officials, and Coherent’s own employees, customers, and suppliers. NVIDIA and Coherent have worked together for over two decades, and the companies expanded that relationship through a partnership announced in March. Anderson framed the day as the product of a three-way alliance between industry, government, and the local community, with each layer of investment reinforcing U.S. AI infrastructure and technology leadership.

AI as a New Industrial Revolution

Huang described AI not just as a product but as an industrial revolution, laying out what he called a “five-layer cake”: the energy sector; chips and infrastructure (land, power, shell, cloud services); AI models themselves (spanning language, physical AI for robotics and self-driving cars, biology/chemistry models for drug discovery, and physics models for climate and weather simulation); and finally applications, which he called the most important layer because they touch every major industry — financial services, healthcare, education, transportation, chip design, and more. Because intelligence is a general-purpose capability, he argued AI will both create an entirely new industry and simultaneously revolutionize every existing one, which he said should be approached thoughtfully and responsibly but also with enthusiasm, given its potential to reduce suffering, improve safety, and address labor shortages.

Why Connectivity Matters as Much as Compute

A central technical theme was that AI scales not just through raw computing power but through the ability to connect huge numbers of processors together — what Huang called “the largest, most computing-intensive software program the world has ever processed.” He explained that modern AI workloads are sharded across hundreds of thousands or millions of processors spread across distances of hundreds or thousands of feet, sometimes across an entire data center. At short range, electrical interconnects suffice, but at longer distances, only optical/silicon photonics can do the job. This is where Coherent’s indium phosphide lasers come in: electrical signals activate the lasers, light carries the signal across optical fiber, and detectors on the other end convert the photons back into electricity that drives the chips. Huang described this optical interconnect as fundamental to scaling AI infrastructure and an increasingly important part of overall system architecture.

The Sherman Facility and Manufacturing Scale-Up

The Sherman site is described as the world’s first and largest-volume producer of 6-inch indium phosphide wafers, a key input for the lasers used in AI data center optical networking. Coherent, founded in 1971 as a manufacturing company in Saxonburg, Pennsylvania, has spent roughly 50 years building expertise in lasers; Huang noted it took 50 years to build the facility’s current capacity, and the new investment will quadruple its output within a single year. The expansion will double the physical size of the production facility. At full capacity, the site is expected to support over 1,000 jobs, including more than 550 direct positions, and the facility was actively hiring at the time of the event.

Reindustrialization of U.S. Manufacturing

Both speakers framed the investment as part of a broader effort to bring advanced manufacturing back to the United States after decades of decline in domestic chip and computer manufacturing. Huang argued that competing in mature, decades-old manufacturing categories is difficult, but a genuine technology phase-shift — like the AI-driven demand for chips, packaging, and optical components — creates room for new factories, new skills, and new companies that didn’t previously have a reason to exist domestically. He noted that the number of U.S. chip fabs, packaging plants, computer plants, and AI data centers is now growing for the first time in his career, having already created roughly 600,000 jobs. He emphasized that this reindustrialization targets the most advanced, highest-skill, highest-paying tier of manufacturing, and that growth in U.S. capacity does not come at other countries’ expense since global chip manufacturing is expanding simultaneously worldwide.

Energy Policy and the Economic “Flywheel”

Huang connected the manufacturing buildout to energy policy, arguing that pro-energy government initiatives are essential because fabs, plants, and data centers cannot be built without sufficient power. He described a self-reinforcing “flywheel”: strong corporate revenues and exports generate tax revenue and capital, which fund domestic manufacturing investment, which creates jobs, which in turn requires continued energy investment. He highlighted that, for the first time, market forces alone (without government subsidies) are driving investment in both traditional and sustainable energy sources, crediting overall market demand for the technology sector.

Jobs, Community, and a 10-Year Outlook

Both executives emphasized job creation and quality of life in Sherman, pointing to the area’s lakes, family-oriented community, and the facility’s hiring efforts. Looking ahead ten years, Huang predicted the period would be remembered for three things: a pro-energy growth agenda that revived U.S. economic growth, AI enabling investment in and modernization of the energy grid, and a rebuilt manufacturing workforce that restores “builders and makers” alongside knowledge workers. He argued for a more balanced society where people who want to build things with their hands can find prosperous careers, not just those with advanced degrees, and suggested this moment could be looked back on as reshaping not only the economy and U.S. technological leadership but also the fabric of local communities. The event closed with Anderson and Huang signing a steel beam to be used in the facility’s construction, followed by a groundbreaking ceremony.

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Without Graham’s principle of “investing from a business perspective,” Warren Buffett would never have become one of the greatest investors of all time, as this idea forms the core of his investment philosophy. It is the foundation for applying first-principles thinking to investing. The practice is uncommon, unpopular, and uncomfortable. It requires long-term commitment and dedication despite constant opposition from Wall Street and Main Street to reap its rewards. However, when fully embraced, it gives investors the ability to achieve the highest possible returns on their investments.

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“Investment is most intelligent when it is most businesslike.” – The Intelligent Investor by Benjamin Graham

Warren Buffett emphasized the importance of viewing investing from a business perspective in his 2013 letter to Berkshire Hathaway shareholders, citing his mentor, Benjamin Graham1.

Without Graham’s tenet of “investing from a business perspective,” Warren would have never become one of the greatest investors of all time, as this concept is the bedrock of his investment philosophy.

Warren learned the concept of investing from a business perspective from one of his many teachers and mentors, Benjamin Graham, when he was Graham’s student at Columbia University, and later observed Graham apply it at his investment firm, Graham-Newman Corporation.

Investing from a business perspective is a component of applying first-principles thinking to investing. The practice is uncommon, unpopular, and uncomfortable. It requires long-term commitment and dedication to reap its benefits, in the face of constant attack by Wall Street and Main Street.

However, when you understand what it means to invest from a business perspective and appreciate its benefits, you will no longer allow your emotions or the market to dictate your capital allocation; instead, you will learn to profit from other investors’ follies driven by fear and greed. You will start to notice great investment ideas that others miss. It will completely change how you think about businesses and investments.

Applying first-principles thinking to investing

When Benjamin Graham and Warren Buffett evangelize investing from a business perspective, they are actually talking about investing from first principles. A first principle is a basic proposition that cannot be deduced from any other proposition or assumption. First-principles thinking, therefore, is a process of breaking problems down into their basic components and reasoning from there.

The first principles of investing are the most basic, enduring truths that underlie any sound investment decision: a stock is ownership in a real business, that business is ultimately worth the cash it can generate for its owners over time, which depends on the economics of the business, and your results depend on the relationship between the price you pay and that underlying business value. Reasoning from first principles means returning to these facts whenever you make an investment decision, rather than being led by headlines, price movements, or market sentiment.

Warren Buffett, Peter Lynch, Mohnish Pabrai, and other successful investors break down businesses into their basic components, digging deeper than your average retail investor to understand their economics and the levers that will drive their growth over the long term.

By taking apart potential investments, understanding how each component performs, and selecting only the businesses with the strongest business economics and the most durable long-term competitive advantage, you will discover a handful of great businesses that will deliver exceptional results.

Benefits of investing from a business perspective

If you have never thought about investing from a business perspective, it will open your eyes to the businesses behind stocks and their economics. When you commit to investing from a business perspective, you will:

• think as a business owner, as if you were running the company.

• no longer be tempted to buy stocks because Twitter Finance or FinTok says a stock is going to the moon, or “play” earnings.

• stop looking for opportunities that offer a 25% or even 50% return over the next few months, and instead look for businesses that will continue to grow over 5 to 10 years or even longer and produce an annual compounded return of 15% or better.

• realise that people diversify to protect themselves from their own mistakes, and that investment professionals praise diversification to collect more fees.

• start noticing great investment ideas every time you shop.

• realise that a $1,000 stock could be cheap compared to a $10 stock.

• start thinking of stocks as bonds with variable rates.

• wait and wish for the market and stock prices to fall so that you can buy partial ownership of publicly traded businesses you have been wanting to own.

Take advantage of others’ fear and greed.

The major benefit of investing from a business perspective is the ability to take advantage of minute-by-minute business valuations that publicly traded securities provide.

Investors in stocks have an enormous advantage: they receive offers for their businesses as long as stock exchanges are open. In the future, as more and more securities become tokenized, investors will be able to access minute-by-minute valuations around the clock.

Not only do you have the ability to receive thousands of offers for your businesses and every single publicly traded business on the planet on a minute-by-minute basis, but many of those offers are wildly mispriced. An investor who thinks from first principles can and should take advantage of such mispriced offers.

It is no different if you own a neighborhood grocery store, and nearby store owners come by your store daily and offer to purchase your store or sell theirs. On days when your neighbors feel jolly and offer to buy your store at ridiculously high prices, you may decide to take them up on their offer and sell the store, or choose to ignore them. However, when they offer to sell you their store at similarly observant prices, you have no obligation to purchase it from them.

On the other hand, when your neighbors feel depressed and offer you below-market value for your business, you have no obligation to sell, no matter how depressed the economy and business buyers are. However, if they offer to sell their business at a depressed price, you may take them up on it.

Warren and other successful investors take advantage of other market participants whose emotions about the market, the stock, or something in their personal life drive them to offer prices that are too low or too high. If you think long-term and view stocks for what they are - ownership of operating businesses, you cannot lose money investing in stocks because of variables you cannot control, such as the behavior of other market participants.

Betting on the actions of other market participants is how speculators try to benefit. It is the only thing a speculator bets on: someone else taking action that would benefit them, such as paying more than they did. Expecting someone else to take action that benefits you is a time bomb waiting to explode your portfolio.

Wall Street and Main Street don’t want you to invest from a business perspective.

Although Warren constantly talks about it during shareholder meetings and college lectures, most investors, whether professional investors on Wall Street, Frankfurt, or Tokyo, or retail investors on Robinhood, still ignore it.

The reason this simple concept has eluded so many is that Warren’s approach runs counter to the business model of the investment profession as a whole. If investors had only looked at investments through the business lens, a large swath of the investment management profession would not exist.

Investing from a business perspective is not a formula that can be relied on to produce a profit in every situation. It is, rather, as Graham put it, “a negative art.” It is a discipline that helps investors avoid pitfalls and identify what not to invest in, as much as what to invest in. This is precisely why the financial industry is not promoting it.

Stock price movements, company news, and economic reports provide many reasons to trade a security, which is how a large portion of the investment community makes money, either by providing services and products to trade securities or by providing information to trade on.

Robinhood’s entire business model, like those of all brokerage firms, gambling houses, sports betting operations, and prediction markets, is based on trading volume, which is the opposite of investing from a business perspective. The more transactions those businesses can push through their platforms, the more profitable they are.

All of the above businesses benefit from high trading volume, so it is not in their interest to help investors avoid bad investments—quite the opposite. Regardless of an investment’s outcome, they earn a fee every time investors buy or sell a security, either upfront or on the back end, such as the payments for order flow that “commission-free” brokerages like Robinhood use.

Much of the financial industry promotes ideas that run counter to investing from a business perspective because the more you focus on buying and selling stocks, the less you view investing as committing capital to real businesses. This creates a greater distance in your mind between a company’s business operations and the stock. Which in turn creates space for someone to come in and dictate what you should think and, in turn, do about a stock.

This is precisely why Warren and Graham always said that:

“In the short run, the market is a voting machine, but in the long run it is a weighing machine.” - Benjamin Graham

In the short term, anyone can convince you of what to think and do about any particular stock, but in the long term, the business’s fundamentals will dictate how the stock is priced. This is why it is crucial that investors look at the fundamentals of the businesses they intend to invest in; in other words, look at investments from a business perspective, rather than as speculators guessing where the price will be over a period of time.

Choosing to invest from a business perspective creates a moat in your mind that protects you from your own emotions, daily news, the whims of the market, and the persuasion of the trading platforms and investment analysts. It allows you to focus on the first principles of investing—the business itself.

Investing from a business perspective is uncommon, unpopular, and uncomfortable. If you want to see businesses the way successful investors do and improve your chances of finding and investing in great businesses with strong business economics at low prices, you must commit to seeing investments from a business perspective and have the discipline to stick to it despite constant pressure to trade.

Discipline is paramount

The characteristics of the businesses Warren invests in will vary based on the nature of the investment. The company he invests in for arbitrage purposes will not have the same qualities as the one he invests in for the long term. Regardless of the type of business or the nature of the investment, Warren always uses a business perspective as the foundation for his decisions.

Having a business perspective on investing is more than a philosophy—it’s a discipline that requires absolute devotion. Without discipline, you will succumb to the whims of the markets and your emotions, buying stocks when everyone is excited about them and selling when the market is full of fear. Having the discipline to commit capital to investments only when it makes sense from a business perspective will allow you to use the market’s whims and other investors’ emotions of fear and greed to your advantage and profit from other people’s lack of discipline.

The formula of investing from a business perspective

The nature of the business enterprise and whether it can be bought at a price that will yield a sufficient return will determine the investment’s worth and whether or not we are investing from a business perspective. - Buffettology by Mary Buffett & David Clark

What does it mean to invest from a business perspective? It means one stops thinking of the stock market or other financial markets as something to buy and sell on its own and starts thinking about the economics of businesses that those securities represent.

A common stock is a partial ownership interest in a business organization, a legal entity that offers products and services to its customers, employs people, pays taxes, and tries to outperform the competition.

It was Graham who taught Warren to ask (a) what business? and (b) on what terms is the investment being made? instead of asking (a) which stock? and (b) how much is it? This frames the questions in a more businesslike manner.

Warren’s main innovation was to buy excellent businesses at prices that made business sense. In Warren’s world, business sense means the business he invests in provides the highest possible, predictable annual compounding rate of return with the least amount of risk. The reason Warren is able to do this better than others is that he thinks long-term, like a business owner, unlike Wall Street and retail investors who focus on the next few quarters.2

Thinking like a business owner

Warren can ignore stock price fluctuations and focus on business economics because he understands that stocks have no value on their own and are nothing more than equity-based financial instruments representing legal ownership of a business3. So before Warren makes an investment, he assesses the business’s economics, which helps more accurately value the business, and only then decides which financial instrument of the company will yield the highest return.

Similarly, he doesn’t limit himself to short-term gains. There are many advantages to investing for the long term, including allowing compounding to work in his favor, reducing the number of times he pays taxes, decreasing the number of new investments needed, and others that we will cover in future discussions.

Warren discovered that the best returns come from investments that compound at high rates over the long term. The best investment, therefore, is a business that can reinvest capital at high rates of return over the longest amount of time.

This realization encouraged Warren to think long-term, which in turn led him to see stocks as ownership in businesses. This perspective helps him separate the unpredictable fluctuations of stock prices from the underlying economics of those businesses and to patiently wait for stock prices, in other words, company valuations, to decrease, allowing him to invest in those companies at a low market value relative to their long-term potential, which he can evaluate based on the business economics.

One way to help you think like a business owner is to focus on how much you would have to pay to buy the entire company and whether you would buy it at that price, rather than on the stock’s price. If you believe the business will grow over the long term, it’s simply a matter of waiting until the stock drops enough to produce the rate of return you desire. Warren always looks for an annual compounded rate of at least 15% to ensure it exceeds inflation and provides enough profit after paying capital gains taxes, so 15% is a good starting point.

Comparing investment returns

Warren wants to buy excellent businesses at prices that make business sense. In Warren’s world, an investment makes business sense if it provides the highest predictable annual compounding rate of return possible with the least amount of risk.

Continuing with our example above: if I offered to sell you a neighborhood grocery store, you would review the accounting books to see how much money the business is making. If it is profitable, you would look at whether the profits are consistent, in other words, predictable, or vary year to year. If the store is consistently profitable, you would then ask whether that could change in the future. If the answer is no, given that it’s the only store in the area, you would ask what the store is selling for.

Once you know the asking price, you compare it to the store’s annual earnings to determine the return on your investment. A $100,000 asking price against $25,000 in earnings would yield a 25% annual return ($25,000 / $100,000 = 25%).

Once you know the estimated return, you can compare it with other potential investments to determine if 25% return is a good investment. In other words, you would be comparing rates of return. If something looks attractive, you make the investment.

This is how Warren thinks. Whether he is buying an entire conglomerate or a partial interest in a neighborhood grocery store, he asks himself: How much money can this business predictably earn, and what is the asking price? When he knows the answer to those questions, he can compare it to other opportunities.

Owning a business that yields 25% annually for 20 years is a great investment. Would you consider selling it during a tough year if someone offered to buy it at a depressed price? Or would you sell it for 30% or even 50% more than what you paid for it during a year of market exuberance? A Wall Street manager would undoubtedly do so to show prospective investors a great annual return. But that would invite the IRS, cut the overall return, and leave you with the rest of the capital and potentially worse investment opportunities.

Warren, like a good businessman, prefers to hold on to investments that can predictably produce an annual return of even 15% over the long term. This keeps the IRS at bay, compounding working in his favor, and leaving less capital to worry about. Like Warren, you should prioritize ownership of the powers of production in great businesses over the short-term profits promoted by Wall Street.

Warren’s two small investments from a business perspective

In his 2013 letter to Berkshire shareholders, Warren gives two examples of investing from a business perspective.

Nebraska farm

From the mid-1970s to the 1980s, agricultural commodity prices rose sharply, with wheat prices doubling and corn prices tripling, as the U.S. government reduced its reserves from the large surpluses of the 1950s and 1960s, eventually falling to 8% of annual national production. Additionally, in 1972, the U.S. signed a multi-year trade agreement on wheat and feed grains with the Soviet Union, further increasing demand for American agricultural products.

Lowered grain reserves and increasing demand from international markets, such as the Soviet Union, pushed prices higher and made farming highly profitable. This led farmers to borrow more to expand their operations, local and regional banks to ease lending practices, and the value of farmland to rise, creating upward pressure for farmers to borrow more.

It all came crashing down when the Soviet Union invaded Afghanistan in 1979, and then President Jimmy Carter imposed a grain embargo, halting shipments of grain to the Soviet Union. Additionally, the Federal Reserve raised interest rates, reaching a record 21.5% in 1981 to combat inflation. The combination of these two factors reduced demand for agricultural products and made borrowing more difficult.

By January 1984, the Federal Reserve Board released a report estimating that one-third of all American farmers held nearly two-thirds of the nation’s total farm debt. As commodity prices eventually declined, leveraged farmers found themselves with enormous debts, leading to widespread farm foreclosures and five times higher failure rates among Iowa and Nebraska banks compared to the Great Recession.4

This is where Warren steps in. Seeing prices drop by 50%, Warren buys a 400-acre farm 50 miles north of Omaha. Without any farming experience, Warren learns from his son about the farm’s potential yields and operating costs. Using that information, Warren estimates the farm will generate around 10% normalized returns. He also believes productivity will improve over time thanks to advances in agricultural technology, and that crop prices will eventually recover from their record lows, which both turned out to be true.

Warren didn’t need any special knowledge or intelligence to evaluate the farm or estimate its profitability from growing crops, concluding that the investment involved little risk and had significant upside. Most importantly, he didn’t rush to buy the farm when the market was booming, and everyone was competing for farmland, driving prices higher. Instead, he waited for the right moment when economic conditions aligned, making the investment a no-brainer.

Warren understood that crops would have both good and bad years, but over the long run, the farm would perform well. The low price he paid makes it a profitable investment that beats inflation and covers taxes. Importantly, he will never be pressured to sell it, which allows for long-term, multi-decade growth.

By 2013, Warren still owned the farm 28 years after acquiring it, as mentioned in the Letter to Berkshire Shareholders. The farm had tripled its earnings and was now worth five times his original purchase price. Despite this, Warren had little knowledge of farming, having visited the farm only twice.

Manhattan real estate

Another opportunity presented itself after yet another boom-and-bust cycle, this time in New York commercial real estate. During the 1980s, the U.S. government deregulated savings and loan associations (S&Ls), allowing them to finance risky, speculative commercial and residential projects. This resulted in significant speculative development in Manhattan and led to an oversupply of office space. Heavy foreign investment, particularly from Japanese investors, further drove up property prices. Moreover, lenders used high loan-to-value ratios, assuming property values would keep rising forever.5

Naturally, nothing lasts forever, and the exuberance of lending and development was tested when the large-scale, speculative building boom of the 1980s confronted a market with high vacancy rates, causing rental incomes to decline, about one-third of all S&Ls, or roughly 1,600 institutions, to fail, and real estate prices to drop. The failure of S&L institutions triggered a sudden liquidity crisis, preventing developers from refinancing and forcing them to sell assets at severely reduced prices. From its peak in 1987 to its low in 1997, real house prices in New York City fell approximately 31%6.

Again, investors like Warren Buffett and Peter Lynch, who think from a business perspective rather than a speculative one, were presented with an incredible opportunity to invest in real estate and in struggling savings and loan associations for pennies on the dollar.

In his 2013 letter, Warren explains that analyzing the New York retail property next to NYU was as straightforward as analyzing a farm. He concluded that the property’s unleveraged current yield was about 10%.

The property was poorly managed by the previous owners, and its income is expected to increase once several vacant stores are leased. Additionally, the largest tenant, occupying about 20% of the space, was paying approximately $5 per square foot, while the other tenants paid around $70 per square foot. The expiration of the undervalued lease in nine years will significantly boost earnings. As with any real estate deal, location is crucial. This retail building is located directly next to NYU, a stable institution likely to attract steady future traffic, ensuring good business for the tenants.

As old leases expired, earnings tripled. Annual distributions in 2013 surpassed 35% of Warren’s original equity investment. Additionally, the initial mortgage was refinanced in 1996 and again in 1999, moves that resulted in several special distributions totaling more than 150% of what Warren had invested. Warren had not yet viewed the property as of the time he wrote the letter in 2013, since he had purchased it in 1993.

Lessons from Warren’s two small investments

• You don’t have to be an expert to get good investment results (and we will show you how).

• You must recognize your limitations and follow a reasonable course of action, such as making investments during times of financial downturns and fear, not when prices are inflated by exuberance during market bubbles.

• Keep things simple and don’t swing for the fences.

• When someone promises quick profits, respond with a quick ‘no.’

• Focus on the future productivity of the assets you’re considering; in other words, invest from a business perspective.

• If you’re not comfortable making a rough estimate of the asset’s future earnings, skip it and move on.

• No one can accurately evaluate every investment opportunity. And you don’t need to. You just need to understand the actions you take.

• If you concentrate on the potential price change of an investment, you are speculating. There is no way to accurately predict something’s price. Many variables at any given moment can influence the price over a short period. Therefore, speculation is a fool’s errand. As Warren said in his 2013 letter:

  ”Half of all coin-flippers will be correct in their prediction, but none of them can expect to continue guessing correctly.” - Warren Buffett, 2013 Letter to BRK Shareholders

• The fact that an asset has appreciated recently has no effect on its future appreciation and should never be the reason to purchase it.

• With his two investments, Warren focused solely on what the properties could generate in the future and paid no attention to their daily valuations.

  “Games are won by players who focus on the playing field - not by those whose eyes are glued to the scoreboard.” - Warren Buffett, 2013 Letter to BRK Shareholders

• Forming macro opinions, like trying to predict the government’s policies, international trade agreements, or interest rates, or listening to macro and market predictions, is a waste of time. On the contrary, paying attention to commentators and pundits can obscure your view of the most important facts, such as the investment’s business economics.

  “When I hear TV commentators glibly opine on what the market will do next, I am reminded of Mickey Mantle’s scathing comment: ‘You don’t know how easy this game is until you get into that broadcasting booth’.” - Warren Buffett, 2013 Letter to BRK Shareholders

When Buffett made those two investments in 1986 and 1993, he didn’t care about what market commentators were saying, interest rates, or how the economy would perform in the future. No matter what the pundits predicted at the time, the corn would keep growing in Nebraska and be consumed around the world, and students would continue to attend NYU.

Although the two small investments Warren made performed well shortly after he made them due to their incredibly low prices, they didn’t have to perform well immediately to make financial sense. They will continue to generate cash for Warren, and most importantly, for his children and the children of their children. That is the beauty of investing from a business perspective.

“The two investments will be solid and satisfactory holdings for my lifetime and, subsequently, for my children and grandchildren.” - Warren Buffett, 2013 Letter to BRK Shareholders

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We reserve the right to change our minds about anything published in The Golden Age newsletter as new information becomes available.

Nothing produced under The Golden Age brand should be considered investment advice. The content of this publication is for educational and entertainment purposes only, and does not claim to be, nor is it intended to be, financial, legal, accounting, tax, or investment advice.

The investments or strategies discussed may not be suitable for you, as they do not consider your specific investment objectives, financial situation, or needs, and are not meant to provide personalized investment guidance. Before making any investment or trade, evaluate if it is appropriate for you and consider consulting your own financial or investment advisor.

The authors of this publication are not licensed investment professionals.

Views expressed on The Golden Age are solely those of the editors and not of any affiliated firm or organization.

Always do your own research before investing.

Find all the lessons in the Economics of AI here and the previous lesson below.

The Golden Age’s mission is to build a more peaceful and prosperous world by popularizing and applying first-principles thinking to the problems around us.
Join our community of first-principles thinkers, builders, and investors.

The Enduring Cycle of Automation

Generative AI, for all its unique and powerful characteristics, is the latest chapter in the long and repeating history of technological automation. Each new wave of technology feels revolutionary and unprecedented, yet it follows a familiar pattern of adoption, integration, and eventual invisibility.

A 1955 U.S. government report on automation highlighted the “electronically controlled elevator” as a frontier technology.

This innovation automated a job—the elevator attendant—that was once commonplace. Today, the automated elevator is so ubiquitous that we call it a “lift.”

We have forgotten that the world was ever different. This cycle was perfectly captured by computer scientist Larry Tesler in 1970: “AI is whatever machines can’t do yet.” Once a task is successfully automated and integrated into our daily lives, we cease to see it as artificial intelligence. It becomes part of the baseline, the new normal. The world is permanently changed, the technology disappears into the background, and the cycle of innovation begins again with the next platform shift.

The Old Stuff is Still Here

While generative AI represents the latest platform shift, it exists alongside “the old stuff” that continues to mature and “other new stuff” that has been in development for years. For example, e-commerce is not a new concept, yet it still only accounts for 30% of U.S. retail sales (excluding gas and grocery) and continues to grow.

At the same time, long-gestating technologies like autonomous “robotaxis” are finally beginning to work after a decade of investment, with Waymo seeing a significant rise in monthly trips.

Other emerging “new stuff” includes the development of humanoid robots and augmented reality glasses.

It is Still Too Early

We are currently in an early and uncertain phase of Generative AI, characterized by a significant “engagement gap” where users experiment with tools like ChatGPT occasionally rather than making them a part of their daily life.

This stage is similar to the internet in 1997, when it was similarly unclear which business models or ideas would eventually succeed, leading to high levels of noise, hype, and “vibes-based forecasting”. Because the physical limits of AI are unknown, predicting its future is difficult, bubbles are expected, and early leaders are likely to disappear before the “dust settles” and the technology becomes a basic part of daily life.

More lessons from The Economics of AI course

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• Did we miss any other ways we should be thinking about AI?

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Disclaimer

We reserve the right to change our minds about anything published in The Golden Age newsletter as new information becomes available.

Nothing produced under The Golden Age brand should be considered investment advice. The content of this publication is for educational and entertainment purposes only, and does not claim to be, nor is it intended to be, financial, legal, accounting, tax, or investment advice.

The investments or strategies discussed may not be suitable for you, as they do not consider your specific investment objectives, financial situation, or needs, and are not meant to provide personalized investment guidance. Before making any investment or trade, evaluate if it is appropriate for you and consider consulting your own financial or investment advisor.

The authors of this publication are not licensed investment professionals.

Views expressed on The Golden Age are solely those of the editors and not of any affiliated firm or organization.

Always do your own research before investing.

Find all the lessons in the Economics of AI here and the previous lesson below.

The Golden Age’s mission is to build a more peaceful and prosperous world by popularizing and applying first-principles thinking to the problems around us.
Join our community of first-principles thinkers, builders, and investors.

Strategic Horizon: Unbundling, Disruption, and New Economic Models

The primary strategic challenge for businesses today is to think beyond simple automation and envision how generative AI will fundamentally innovate and disrupt existing markets.

This requires asking more profound questions: What existing product and service bundles will large language models (LLMs) unbundle? And what new forms of aggregation and value creation will they enable?

The “Infinite Interns” Paradox

Generative AI is often described as providing businesses with “infinite interns”—a virtually limitless source of cognitive labor. This concept stems from the famous Jevons Paradox, which holds that when something becomes more efficient, its usage increases. This presents a critical strategic choice: Do you do the same work with fewer people? Or more work with the same people? The answer has disruptive implications. For industries where employing large numbers of people was a competitive moat, that advantage may soon evaporate.

This shift mirrors the impact of the steam engine in 19th-century Britain, which gave the nation the labor equivalent of “250 million extra working pairs of hands” and powered the Industrial Revolution. Generative AI promises a similar multiplier for knowledge work, forcing a complete rethinking of business scale and defensibility.

The Future of Aggregation and “Agentic Commerce”

The internet era was defined by new models of aggregation, where algorithms fed by user activity (e.g., clicks, views, purchases) determine what we see. Generative AI has the potential to create a new paradigm. An LLM can understand products and user needs at a deeper level of abstraction, potentially without needing a massive user base for training.

“Our new AI recommendation model drove 5% more ad conversion on Instagram and 3% on Facebook” - Meta, Q2 2025

“Advertisers that activate AI Max in Search campaigns typically see 14% more conversions” - Google, Q2 2025

This capability poses a direct threat to the $1 trillion global advertising industry.

While the current “absorb” phase focuses on using AI to optimize the creation of ad assets, the truly disruptive phase points toward “agentic commerce.” In this future, a user might simply ask an AI what to buy, bypassing traditional search and discovery platforms like Google and Amazon entirely, creating a radically different, more direct path to purchase.

Redefining the Customer Relationship in the Age of AI

At its most fundamental level, AI has the potential to unbundle the very concept of a purchase by better understanding a consumer’s underlying intent.

It can serve two distinct types of needs in novel ways:

• Utility: For tasks focused on pure function and logistics, AI can solve the problem with maximum efficiency.

Old: “half of AI will be turning three bullet points into email, and the other hald will be turning emails into three bullet points”

New: half of AI will be turning three bullet points into 300 ads, and the other half…

• Experience: For needs centered on curation, enjoyment, and authenticity, AI can offer highly personalized recommendations that cut through the noise of infinite choice. The example of a Tokyo shop that sells only one curated book illustrates a powerful counter-trend to the internet’s “infinite shelf.” AI could deliver this level of hyper-curation at a global scale, fundamentally changing how consumers discover products and how businesses build relationships with them.

Next lesson in The Economics of AI course:

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• Did we miss any other ways we should be thinking about AI?

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Disclaimer

We reserve the right to change our minds about anything published in The Golden Age newsletter as new information comes to light.

Nothing produced under The Golden Age brand should be construed as investment advice. The contents of this publication are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax, or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation, or needs, and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. This publication’s authors are not licensed investment professionals. Views expressed on The Golden Age are exclusively those of editors and not of any affiliated firm or organization. Do your own research before investing.

Find all the lessons in the Economics of AI here and the previous lesson below.

Enterprise Adoption: The Slow Path from Automation to Disruption

Historically, enterprises deploy transformative new technologies in a predictable, three-stage pattern: Absorb, Innovate, Disrupt. The initial “Absorb” phase involves automating obvious, existing tasks. The “Innovate” phase sees the creation of entirely new products and services. Finally, the “Disrupt” phase redefines entire markets. Currently, the vast majority of enterprise activity in generative AI is concentrated in the first phase.

The “Absorb” Phase: Automating the Obvious

Enterprises are primarily applying generative AI to absorb it into existing workflows where the return on investment is clear and immediate. The most common use cases are:

• Coding: Automating software development and testing.

• Marketing: Generating ad copy, images, and campaign ideas.

• Customer Support: Powering chatbots and summarizing customer interactions. This phase is being heavily driven by consultants, with firms like Accenture reporting over $1.5 billion in new generative AI bookings per quarter as they help large companies identify and implement these initial automation opportunities.

The Reality of Deployment Timelines

While the hype cycle is moving at lightning speed, the reality of enterprise technology adoption is far slower and more methodical. Several data points provide critical context:

1. Cloud Precedent: Cloud computing is now considered an “old and boring” technology, yet it still only accounts for approximately one-third of all enterprise workflows. Major technology deployments take time.

   

2. Current AI Deployment: Recent surveys show that while about a third of large companies have at least one generative AI product in deployment, another quarter state they have no plans to do anything “just yet.”

   

   

3. Pilot Challenges: An MIT report found that “95% of generative AI pilots at companies are failing.” However, these failures are not typically due to the AI technology itself but rather to the classic challenges of any major IT implementation: security concerns, data integration with legacy systems, privacy, and legal issues.

   

The Power of “Just Automation”

Dismissing the “Absorb” phase as mere incrementalism is a strategic error. Simple automation, deployed at scale, can be profoundly disruptive. The introduction of the barcode in the U.S. grocery industry in 1974 is a powerful historical analogue. This seemingly simple automation technology enabled retailers to manage five to ten times more unique products (SKUs), which in turn fundamentally restructured the entire consumer packaged goods industry. This historical precedent demonstrates that today’s “simple” automation is not the end goal, but rather the foundational layer upon which true market disruption will be built.

Next lesson in The Economics of AI course:

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• Did we miss any other ways we should be thinking about AI?

• What other businesses and industries do you think will be disrupted? How and why?

  Leave a comment

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We reserve the right to change our minds about anything published in The Golden Age newsletter as new information comes to light.

Nothing produced under The Golden Age brand should be construed as investment advice. The contents of this publication are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax, or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation, or needs, and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. This publication’s authors are not licensed investment professionals. Views expressed on The Golden Age are exclusively those of editors and not of any affiliated firm or organization. Do your own research before investing.

Find all the lessons in the Economics of AI here and the previous lesson below.

The Golden Age’s mission is to build a more peaceful and prosperous world by popularizing and applying first-principles thinking to the problems around us. Join our community of first-principles thinkers, builders, and investors.

The Competitive Landscape: Commoditization, Engagement, and Strategy

Despite the massive capital expenditures and rapid engineering progress, the competitive landscape for generative AI remains highly uncertain. Three years into the current wave, no clear, durable moats or dominant long-term business models have emerged. The market is characterized by a race toward technological parity, a significant gap between user awareness and deep engagement, and divergent strategies among the key players.

Model Performance: A Race to Parity

While AI models continue to improve at a remarkable pace, they are also converging in capability. For most general-purpose tasks, the leading models are effectively becoming commodities. Data from common benchmarks shows that the top models are within 5-10% of each other in terms of performance, and the leadership position changes almost weekly as new models are released, indicating that a sustained technical advantage is extremely difficult to maintain.

The Engagement Gap: Usage vs. Awareness

There is a stark contrast between the high public awareness of AI tools and their actual integration into daily life and work. ChatGPT is a prime example. While ChatGPT boasts an impressive 800 million weekly active users, the data shows that “most people who are using it don’t use it very much.”

Typical usage frequency is once a week or even once a month, indicating a pattern of occasional experimentation rather than deep, daily reliance for most users. Multiple consumer surveys confirm this “engagement gap,” confirming the market remains in an early, exploratory phase, defined by broad but shallow engagement.

Strategic Postures of Key Players

In response to this fluid environment, the central players are pursuing distinct and aggressive strategies to secure a long-term advantage.

• OpenAI: With immense mindshare but a largely commoditized product, OpenAI is in a frantic race to build a defensible business before its commodity product and reliance on partners’ capital becomes an existential vulnerability.

  

  Its approach is “everything, everywhere, yesterday (on other people’s balance sheets),” aggressively pursuing both bundling and unbundling across the entire technology stack.

  

  There are two ways to make money. You can bundle, or you can unbundle. -Jim Barksdale

• Microsoft: The company is undergoing a fundamental strategic shift, moving from a business model built on the network effects of software to one that competes on access to capital. This is evidenced by its capex soaring to 45% of revenue as it invests billions to build the foundational infrastructure for AI.

  

• NVIDIA: Leveraging its dominant market position and $77 billion in trailing twelve months free cash flow, NVIDIA’s strategy is to use its financial might to “buy demand, FOMO, and platform lock-in,” investing heavily across the ecosystem to solidify its central role.

• Oracle: As a legacy business, Oracle is employing a classic strategy of using immense capital expenditure to “burn your way into the new thing,” leveraging its balance sheet to build relevance in the new cloud and AI paradigm.

These divergent strategies reflect a fundamental disagreement on where value will accrue: OpenAI is betting on product and platform ubiquity, Microsoft on capital-intensive infrastructure, Nvidia on ecosystem lock-in, and Oracle on brute-force relevance. The market has not yet decided which bet is correct.

This landscape presents a central strategic dilemma for every company in the space: will long-term value be captured “down the stack” through superior capital, scale, and infrastructure, or “up the stack” through superior product, distribution, and traditional software business models?

Next lesson in The Economics of AI course

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• Did we miss any other ways we should be thinking about AI?

• What other businesses and industries do you think will be disrupted? How and why?

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Disclaimer

We reserve the right to change our minds about anything published in The Golden Age newsletter as new information comes to light.

Nothing produced under The Golden Age brand should be construed as investment advice. The contents of this publication are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax, or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation, or needs, and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. This publication’s authors are not licensed investment professionals. Views expressed on The Golden Age are exclusively those of editors and not of any affiliated firm or organization. Do your own research before investing.

Find all the lessons in The Economics of AI here and the previous lesson below.

The Capital Tsunami: Analyzing the AI Investment Surge

The fundamental uncertainty surrounding AI’s ultimate potential has triggered a capital expenditure (capex) boom of historic proportions. This ambiguity is the primary psychological driver of the intense FOMO (Fear Of Missing Out) gripping the world’s largest technology companies1.

The prevailing sentiment, as articulated by Google CEO Sundar Pichai

“The risk of under-investing is significantly greater than the risk of over-investing.”

and Meta CEO Mark Zuckerberg

“The very worst case would be that we have just pre-built for a couple of years.”

This mindset has unleashed a torrent of capital aimed at building the foundational infrastructure for the AI era.

The Scale of Investment

The numbers behind this investment surge are staggering. The four largest platform companies are on track to spend nearly $350 billion on infrastructure in 2025 alone, a fourfold increase from just a few years ago.

This spending is expected to accelerate, with Microsoft, Alphabet, and Meta signaling that their capex growth rates in fiscal year 2026 will be even higher than in 2025.

“We now expect the FY26 growth rate to be higher than FY25” - Microsoft

“Capex dollar growth will be notably larger in 2026”- Meta

“We expct a significant increase in 2026” - Alphabet

To put these figures in perspective, the potential annualized capex for generative AI infrastructure, estimated at $500-750 billion, would rival or exceed the spending of entire global capital-intensive industries, such as telecommunications (~$300 billion annually) and upstream oil and gas (~$540 billion annually). The value of data center construction will soon overtake that of office and warehouse construction.

Supply Chain and Infrastructure Bottlenecks

This unprecedented demand is placing immense strain on the global technology supply chain, creating significant bottlenecks that constrain the pace of deployment.

• Chips: Nvidia, the dominant provider of AI chips, “can’t keep up” with demand, reporting a quarterly revenue run rate of $40 to $50 billion. Its manufacturing partner, TSMC, is similarly unable to expand capacity fast enough.

  

• Power: For data center developers, particularly in the United States, “getting access to electricity is actually a bigger problem than getting chips from Nvidia.” Power backlogs have become a primary constraint on new construction.

  

  This is not an issue in China, as it has surpassed the U.S. in energy production for years.2

  

  Since 2005, China’s power output has increased nearly fivefold, with an average annual growth rate of about 8 percent. Meanwhile, the U.S., with a more mature infrastructure, has grown by less than 1 percent each year. In 2005, the U.S. generated about twice as much electricity as China; today, China surpasses the U.S., producing more than twice as much power.

  

  Especially in the industrial sector. Per VanEck: in 2023, industry absorbed nearly 60 percent of China’s final energy consumption, while in the U.S., commercial and residential accounted for more than 70 percent.

  

• Construction: The physical challenge of building is immense. As Microsoft’s CTO, Kevin Scott, stated, it has been “almost impossible to build capacity fast enough since ChatGPT launched.”

  

The Shift in Funding and “Circular Revenue”

Initially, this capex boom was funded by the massive cash flows of highly profitable technology giants. However, the sheer scale of investment is now forcing a shift toward external financing.

• Microsoft is reportedly engaging in approximately $50 billion of leasing in a single year to supplement its cash flow.

  

• Meta has been cited in two separate data center expansion leasing deals, each valued at around $30 billion.

• Oracle has seen suggestions that its cloud capex commitments might exceed 100% of its revenue, indicating a massive leveraging of its balance sheet.

• OpenAI also joined the club, announcing commitments for 30GW+ of capacity at $1.4T3, aiming for 1GW/week of new construction at $20bn/GW or $1T annually4, which is equivalent to 2/3 of the total current global base every year. Part of it is the 10GW+ multi-year partnerships to deploy tens of gigawatts of capacity, including a 10 GW partnership with NVIDIA, a 6 GW agreement with AMD, and a 4.5 GW deal with Oracle as part of the “Stargate” project.

This dynamic creates a dizzying “circular revenue” loop: Nvidia’s own cash, derived from hyperscalers, is loaned to OpenAI. OpenAI then uses that money to buy chips from Nvidia to compete with the very hyperscalers who funded the transaction, while simultaneously funding Nvidia’s direct competitors, such as AMD and Broadcom. It’s a high-stakes, financially engineered ecosystem built on speculation and mutual dependence. This unprecedented capital burn forces the critical question: What, if any, defensible moats have been built with this mountain of cash?

Next lesson in The Economics of AI course

Let us know what you think

• How do you use AI for work or personal reasons? Is it successful? What about your company?

• Did we miss any other ways we should be thinking about AI?

• What other businesses and industries do you think will be disrupted? How and why?

  Leave a comment

Share our community with others

We are building a community of innovators and proactive problem-solvers. Our aim is to establish a business and innovation incubator that supports individuals eager to innovate—helping them get started, connect with like-minded people, form teams, secure funding, and transform their ideas into tangible solutions.

Disclaimer

We reserve the right to change our minds about anything published in The Golden Age newsletter as new information comes to light.

Nothing produced under The Golden Age brand should be construed as investment advice. The contents of this publication are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax, or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation, or needs, and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. This publication’s authors are not licensed investment professionals. Views expressed on The Golden Age are exclusively those of editors and not of any affiliated firm or organization. Do your own research before investing.

Find all the lessons in The Economics of AI here.

The Golden Age’s mission is to build a more peaceful and prosperous world by popularizing and applying first-principles thinking to the problems around us. Join our community of first-principles thinkers, builders, and investors.

Technology platform shifts

If we think about the progression of technology over the last 50 or 60 years we've tended to move in cycles, so we have a platform shift every 10 or 15 years. We had mainframes and then we had PCs and then the web and then smartphones and you could put other things on here. You could put databases or client server or open source. And each of these pulled in all of the investment and innovation and company creation. The old thing is still there. Airlines still run on mainframes, banks still run on mainframes, but the new thing is where all the innovation, all the investment, all the change, all the company creation happens. And so the view is that now this is the next platform shift. Generative AI is the next thing after smartphones, after the web, after PCs.1

Although we will focus on Generative AI this time, let us not forget that we are going through multiple technological platform shifts: robotics continue to revolutionize warfare, manufacturing, and healthcare; machine learning and computer vision is revolutionizing transportation; new technologies in rocket science will open up the final frontier of space exploration and allow everything from tourism to drug development and manufacturing in microgravity.

We want to focus on three questions about technology platform shifts to understand not only Generative AI but all innovations, technology or otherwise, that will be developed in the future and change the way we live:

1. What happens in a platform shift?

2. What lessons have we learned from the previous platform shifts?

3. What should we expect from the current platform shift of Generative AI?

What happens in a platform shift?

Meta is a perfect example of it. Remember when smartphones became the main thing, and Mark Zuckerberg pivoted the entire company to mobile, only to pivot again when Augmented and Virtual Reality became the new hot things? Meta reportedly spent ~$50B on developing its metaverse and AR/VR products, and in the process, booked ~$45B in operating losses, per its own disclosures. Only now to pivot their focus again to LLMs and Generative AI.

When mobile became the new technology platform, practically every start-up was trying to build for mobile; that is how we got Uber and Uber-like services, Social media applications, and millions of other mobile-first applications we can’t live without.

Across the economy, a few things happen:

• All of the technology innovation and

• the investment (think Meta shifting focused to applications for smartphones, then building out the Meta-verse), and

• company creation (startups, think new smartphone apps like Uber, Instagram, and now OpenAI, Grok etc) moves to the new thing2

Secondly, inside the tech industry, a few things happen:

• new gatekeepers emerge,

• old gatekeepers fall,

• new value capture emerges,

• new and bigger markets appear, and

• old things get destroyed.

A note on value capture

New value capture refers to how value and profits shift to different parts of the ecosystem during a platform shift. In every major technology transition, the way companies make money and who captures the most value changes dramatically.

Examples from past platform shifts:

• PC era: Value shifted from hardware manufacturers to software companies (Microsoft, Adobe) and microprocessor makers (Intel)

• Internet era: Value moved from traditional media and retail to online platforms (Google, Amazon, eBay)

• Mobile era: Apple and Google captured massive value through app store ecosystems, taking 30% cuts from developers

• Cloud era: Value shifted from on-premise software licenses to subscription models (AWS, Azure, SaaS companies)

In the Generative AI shift, new value capture means we’re seeing:

• Foundation model companies (OpenAI, Anthropic) are capturing value through API access

• Cloud providers capturing compute costs

• New application layers are being built on top of AI models

• Uncertainty about where sustainable margins will settle - will it be the model layer, the application layer, or somewhere else entirely?

The key question is: who will capture the economic value in this new platform? Will it be the infrastructure providers, the model builders, the application developers, or the end-user-facing companies?

Thirdly, outside the tech industry, everyone is trying to figure out what to do with the new technology. Everyone is trying to answer:

• Is this a new tool?

• Is this a new feature?

• Is this an existential threat?

• How much do we have to care about it?

That’s what every big company is thinking about the Generative AI right now.

Dominance is won and lost

Inside technology industry, platform shifts mean that companies gain dominance and lose dominance.

This is Microsoft's share of computing. When the PC was the center of the tech industry, that was Microsoft. They dominated it. When we moved to smartphones, Microsoft became irrelevant for a decade because they were no longer a player in the thing that set the agenda.

The Microsoft graph shows the percentage market share of operating systems across all devices, including smartphones. Microsoft failed at developing an operating system for the next generation of computers - smartphones - and paid the price by losing market value for a straight 17 years. It is very costly for technology companies to miss a technology platform shift, as we will see from quotes of prominent technologists below. Also, this is the primary reason Warren Buffett stayed away from investing in technology companies: they are one innovation away from bankruptcy.

For this next Generative AI platform shift, think about business models that are being impacted and are not adopting, like Blockbuster when Netflix launched unlimited streaming. For example, content mills, agencies producing high-volume, low-quality blog posts, SEO articles, and filler content at $0.01–$0.10/word have been entirely replaced by Generative AI. Low-effort / low-value work is the first to be automated, as we will discuss in future posts.

Early leaders often disappear

Going back a generation, there were a lot of people trying to make PCs before Apple and before IBM and Microsoft. And none of those companies succeeded. In fact, the same thing for web browsers, for search, for social, for smartphones. The first companies are very, very rarely the companies that end up getting all the value.

The current technology platform shift to Generative AI is similar to every other technological breakthrough. In the early days of car manufacturing, the auto industry was chaotic, experimental, and crowded with hopeful entrants. Between the 1890s and 1910s, hundreds of small manufacturers appeared in the United States and Europe, testing different technologies, designs, and business models. Most disappeared or were absorbed, but their collective experimentation paved the way for a few dominant players—like Ford—to emerge with scalable production, strong brands, and powerful distribution. It’s a familiar pattern: early leaders often vanish, value shifts to the new platform, and only a handful of companies turn a breakthrough technology into lasting economic power.

There is a lesson on innovation here - you have to have a lot of at-bats. You improve your odds of building something successful by having many attempts (experiments, prototypes, products, companies). Most will fail or be mediocre, but a few wins are enough to distribute the innovation to everyone else. Venture Capitalists use this to spread their bets across hundreds of start-up investments, knowing that only a few must succeed to cover the costs of hundreds of failures. Scientists and engineers understand the same thing: they have to keep trying as many times as it takes, in as many different approaches as it takes, to solve the problem at hand. This is why over-regulation is so dangerous - it prevents innovators from solving problems in new ways, forces entire populations into one solution, and makes people reliant on the government instead of themselves.

It is one thing to create a technological breakthrough; it is entirely different to monetize it. That is why successful start-ups usually have multiple founders: a marketing/product person and an engineer, Steve Jobs and Steve Wozniak at Apple, Paul Allen, followed by Steve Ballmer (although not a founder) and Bill Gates at Microsoft, Marc Benioff and Parker Harris at Salesforce, Jimmy Wales and Larry Sanger at Wikipedia, etc. It is extremely rare to find a person who is both technically and marketing-savvy, like Thomas Edison and Elon Musk, who helped them attract the best talent to help bring their vision to life.

Most things will fail

We all use the web now, but it was not clear in the mid ‘90s how this was going to work. And there were all sorts of other companies and acronyms and ideas and business models that ended up not being the thing. The same thing for mobile. I used to come to Helsinki to talk to a company called Nokia. Some of you may remember it. And talk about IM mode and Java and WAP and none of those things were the future. The same thing with Generative AI now. We should presume that half the stuff we’re working on is not going to be the answer and isn’t going to work.

The reason so many start-ups and early adopters of technologies fail is that we don’t know how the new thing will work or where the value will be captured. The early stages of a new technology are periods of testing. It takes a few years to get enough people onboarded onto the new technology platform, to see how people are using it, and to understand for what purpose. That takes time, and many use cases will not make practical sense.

Hype and bubbles

… that means we get a lot of noise and a lot of hype and a lot of nonsense that you just kind of have to sweep aside and ignore.

Trying new things means we don’t know exactly how it will work, which makes it extremely hard to get someone to give you money, whether to invest in it or buy it. So, to address the uncertainty that comes with new innovation, innovators have to overpromise and exaggerate what the thing will do for investors or buyers. This leads people to overinvest and overpurchase, creating asset bubbles.

You also generally get bubbles. In bubbles people draw straight lines on log scale charts and they say things like: “you just don’t understand how exponential growth works.” And they always say: “this is different,” and the problem is they’re always right. It always is different. The .com bubble was different. This is clearly different to the .com bubble. But it doesn’t mean it can’t be a bubble.

Naturally, when there is a lot of uncertainty, people throw everything at the wall and see what sticks. Through simple experimentation and iteration, people figure out how to use these new tools, what these tools are good at, and what they are bad at. But to even try, innovators need capital. How does someone get capital to test and try something new, unproven, and untested, without any guarantee that any of it will work? By promising and exaggerating what is possible.

Start-ups compete for capital like any other investment, whether it is real estate, a loan, a public or private company, or a piece of art. When investors can put money in an index fund and get 8% annually, or in Treasury bills and get a guaranteed 5%, you have to sell investors on a much larger return. After all, would you rather take 5% guaranteed, an 8% annual average over several years, or 2x or 5x your investment with zero guarantees? At what return would you give your hard-earned money to someone to do something that has never been done before? What would the entrepreneur need to try to achieve to make it worth losing your entire investment over?

To achieve such a significant return, a new technology must revolutionize, mesmerize, and cure all kinds of things, or at least promise to do so; otherwise, the risks of losing your savings do not justify the returns. This brings us to financial bubbles. Entrepreneurs promise a huge impact and hence huge returns to compensate for the risk investors take on. The potential for large returns attracts risk capital, which forces other entrepreneurs to overpromise and exaggerate what is possible as they compete with other innovators to attract investors. Why would you invest in a start-up B that promises a smaller impact with smaller returns instead of start-up A that promises a massive impact with huge returns?

Similarities with politics

This behavior is not much different from politics. Politicians cannot guarantee their solutions will work. What worked in one area at one time does not guarantee that it will work in another area at a different time. Past performance does not guarantee future results. Yet promises are free and don’t cost anything, so there’s no reason not to overpromise. You can always blame the consequences on a scapegoat: greed, billionaires, corporations, the other party, or the previous administration, to name a few.

Furthermore, new untested political ideas are no different than throwing things at the wall and seeing what sticks. The differences between trying new ideas outside of politics and implementing them through legislation are stark. If a political solution has negative consequences (which it always does, as every single action has a reaction that negatively impacts someone), it affects a much larger population, impacting everyone in the jurisdiction and many outside it, regardless of whether constituents agreed to it or not. Those who disagree have to live by the rules they didn’t want and their outcomes, and, what’s worse, they are legally unable to opt out unless they move to a different jurisdiction, which is hardly an ideal solution. This is often the cause of mass migration, as in Venezuela, where around 8 million people left the country because of its socialist government between 2014 and the mid‑2020s. Worst of all, it becomes punishable for others to try different solutions. Can you imagine being punished for trying a better solution?

In business, if you don’t deliver, people leave and take their business elsewhere. In politics, however, we are effectively stuck—either for the duration of the term or until the law is reversed. Reversals are rare and extremely difficult to pass, since legislators, no different from anyone else, including our early technology adopters, would rather focus on the new, shiny object. Take, for example, California’s Prop 47, passed in 2014 and not repealed since. It reclassified many non‑violent drug and property crimes, including some theft under $950, from felonies to misdemeanors, causing larceny theft rates to increase by 9%3, theft of motor vehicles to rise by ~25%,4 in some cases suspects calculated staying under the $950 threshold per incident; some cities (e.g., San Francisco, Los Angeles) have reported visible organized retail theft rings and “smash‑and‑grab” robberies. All of which led to hundreds of store closures and thousands of jobs lost.5 Who would have thought that lowering the consequences of stealing private property would lead people to choose theft over getting a job? With a single law, California pushed out honest job creators and innovators, lost thousands of jobs, made it harder to find a job, and made it more beneficial to steal. Worst of all, they are not in a hurry to fix it. Make it make sense.

But when you sweep aside all the noise and all the dust is settled, the world has changed and it becomes part of the way we all live. Online dating now is about 60% of new relationships in the USA. This has just become part of the way the world works.

Although many innovations and their implementations don’t survive the landing, those that do change the way we live forever.

Explosion of new tools

On the enterprise side, you get an order of magnitude more tools with each generation. When we went from PCs to SaaS (Web/Mobile), we went from dozens of apps to hundreds of apps. We automate more things with every step. We should expect the same thing to happen with LLMs.

Innovations, like LLMs, allow us to solve more problems by modifying innovations to our personal needs and creating tools specific to the issues we want to solve, thus creating an explosion of new tools and businesses.

Is this time different?

However, there is one way that this is all different from all those other platform shifts, which is with all the other ones, you knew what the physical limits were. You knew what the next phone could and couldn't do. You knew it couldn't fly. You knew that Telecomms wouldn't give everybody fiber next year in 1997. With AI, we don't know why these models work so well. So, we don't know how much better they're going to get. All we have is this sort of vibes based forecasting where people say what they feel might happen.

And so it might be that this is only as big as the internet or PCs or smartphones, which seems pretty big to me. Or it might be much bigger than that. It might be a much more fundamental shift. It's something more like electricity or computing or fire or like the next generation of humanity or something else. We just don't know. We'll have to come back in 10 years and find out.

If this is only as big as the internet, that feels like enough to get excited about. And so we ask, well, how is this going to work? How is this new thing going to be useful? Where is the distribution and the value capture and the value destruction going to be as this thing gets rolled out?

Benedict poses several questions that everyone who is looking at this space, including investors, entrepreneurs, business owners, and users, is trying to solve:

1. How does it work? What can users do with it?

2. What problems and use cases does this solve? Ultimately, any technology is simply a tool used to solve problems. Some problems are more important, hence financially more beneficial, to solve than others.

3. What are the economics of it? How does it fit in the current ecosystem, and how does it impact everything around it?

4. I would also add: How does it work technologically? This is important to understand to get the full value out of it and innovate on top of it.

Next lesson

In the following lesson, we will look at what has happened in the technology industry over the past few years in response to the latest technological shift.

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We reserve the right to change our minds about anything published in The Golden Age newsletter as new information comes to light.

Nothing produced under The Golden Age brand should be construed as investment advice. The contents of this publication are for educational and entertainment purposes only, and do not purport to be, and are not intended to be, financial, legal, accounting, tax, or investment advice. Investments or strategies that are discussed may not be suitable for you, do not take into account your particular investment objectives, financial situation, or needs, and are not intended to provide investment advice or recommendations appropriate for you. Before making any investment or trade, consider whether it is suitable for you and consider seeking advice from your own financial or investment adviser. This publication’s authors are not licensed investment professionals. Views expressed on The Golden Age are exclusively those of editors and not of any affiliated firm or organization. Do your own research before investing.

Innovation First

We found The Golden Age because of a profound optimism for the future and a shared excitement about building it. Our confidence in what lies ahead comes from understanding the incredible progress humanity has made. Not so long ago, life was tough for everyone—just 10,000 years ago, our ancestors survived by foraging, hunting, and wearing animal skins.

But history shows that each new invention helped solve the challenges of its time, setting us on a remarkable path forward. These are just a few of the groundbreaking innovations people have created to drive humanity toward greater peace and prosperity:

1. The wheel (circa 3500 Before Common Era) - Ancient Mesopotamia (modern-day Iraq)

2. Writing (c. 3200 BCE) – Mesopotamia

3. Printing Press (c. 1440) – Germany (Johannes Gutenberg)

4. Electricity & Light Bulb (19th Century) – Light bulb: United States (Thomas Edison) and concurrently in the United Kingdom (Joseph Swan)

5. Steam Engine (late 17th - 18th Century) – England (United Kingdom)

6. Vaccination (1796) – England (Edward Jenner)

7. Telephone (1876) – United States (Alexander Graham Bell, Scottish-born inventor living in the US)

8. Automobile (late 19th Century) – Germany (Karl Benz)

9. Airplane (1903) – United States (Wright Brothers)

10. Penicillin (1928) – United Kingdom (Alexander Fleming)

11. Computer (20th Century) – United Kingdom (Alan Turing, Charles Babbage; first programmable computers in Germany and the US)

12. Internet (late 20th Century) – United States

13. Smartphone (21st Century) – United States (first iPhone: Apple, California)

14. Reusable rockets (December 21, 2015) – United States (SpaceX)

15. AI chatbots (Eliza in 1966), Modern AI Chatbots (21st Century) - United States (OpenAI, Google, and others)

These innovations delivered peace and prosperity for all who could access them.

For most of human history, people lacked the means to build homes with modern comforts like climate control to survive harsh winters and scorching summers. Today, we have developed methods and devices that require only a few hours of labor per month to keep homes and buildings at the exact temperature we want, regardless of the weather or season—all thanks to innovations in cooling and heating systems, and in energy production that lowered energy costs.

A few hundred years ago, people didn’t have the tools to communicate with each other, share knowledge on a mass scale, or in real time. Today, we don’t have to travel to another location and sift through countless dusty books to access knowledge; instead, we have real-time, lifelike conversations with devices we build, which contain all the knowledge in the world accessible anywhere on the planet in the palm of our hands, in our laps, via headphones, and even in our glasses. We have the tools to reach the 8.1 billion people worldwide and connect with individuals with specialized experience or knowledge. Thanks to innovations in telecommunications and technology.

On top of it, we can use those devices to create more tools to solve virtually any problem we have or can imagine, not just for ourselves or those around us, but for anyone on the planet.

Transacting and exchanging with people outside one’s town, or delivering goods over long distances, was extremely difficult. Today, we have the tools to transact with anyone across the globe, using thousands of forms of money and stores of value. People can choose from over a billion different products from anywhere on Earth (Amazon alone offers 350 million) and have them delivered to anyone’s doorstep on the planet. Thanks to innovation in banking and supply chains.

Healthcare was out of the picture. Until at least 1800, the average life expectancy worldwide was under 40 years. By 2021, the global life expectancy average was around 71 years. That is almost doubling people’s lives in just over 200 years. Thanks to innovation in medicine, pharmaceuticals, and healthcare.

Our mission

The mission of The Golden Age is to bring peace and prosperity to people everywhere, which is what all of us ultimately seek in every action and decision we make. While true peace comes from within, most of us mere mortals are not willing to become monks and give up the luxuries I described above. A certain level of prosperity is needed to achieve the comfort we seek.

We focus on discovering solutions for underlying causes.

All innovations, including those above, emerged from a need to solve a specific problem. They were not planned in boardrooms or on legislative floors, and if government agencies helped pay for some of them, they were funded by the taxpayers, who could have funded them themselves.

These remarkable achievements have happened in a brief moment of human history—things unimaginable a few centuries ago, and still out of reach for many. Yet, they remind us that progress is possible. Humanity has always risen to solve even the toughest challenges, and there’s a real chance that someone, somewhere, has already found a solution to the challenge you’re facing or one very much like it, or is currently working on it.

At times, it feels like we’ve lost sight of our history and our remarkable ability to solve problems. Instead of trusting our own capacity for innovation, we wait for someone else to rescue us—often looking to the wrong people. Too often, we overlook and fail to support the real changemakers—engineers, entrepreneurs, and business owners—while giving too much attention to those who offer only talk and promises, like politicians trying to win votes.

Laws don’t solve problems—they attempt to shape behavior.

We have become too focused on solving our issues through government intervention and have ended up giving up the exact powers that created progress in the first place. The vast majority of progress didn’t come from legislation. It came from innovators.

We don’t need the right to innovate. The right to innovate is a human right just like the right to think and speak your mind. When the government raises barriers to innovation, the first people who get hurt are the most vulnerable.

It’s the innovators who create progress, not the legislators.

Measures like price controls, for example, address symptoms rather than the underlying causes, such as high production or labor costs. Most laws are passed with good intentions, yet rarely reach the root of the issue and always create lasting, unintended consequences, which politicians are happy to blame on someone else. Real progress happens when we look deeper, encourage innovation, and support those genuinely working to solve the underlying issues.

We often become so focused on passing laws - rules and standards - that we start believing lawmakers alone can fix our problems. While sound policy lays the foundation for order and enables people to build peace and prosperity, laws themselves cannot solve the majority of our issues, as they can only shape our behavior. Laws are simply rules we decide to abide by, but if there are too many of them, people give up innovating altogether. The rules we set for ourselves should encourage problem-solving, not discourage it.

Our method

We use First Principles thinking to do that. First principles is a problem-solving approach that involves breaking down complex ideas, problems, or processes into their most basic, fundamental parts. Once you identify these core truths—things that cannot be reduced further—you reason upwards from there, building understanding and solutions step by step, without relying on assumptions or inherited beliefs. This is what Elon Musk used to found at least nine revolutionary companies and create at least $2 Trillion in value (the sum of all the companies he built at their peak valuations).

To achieve this, we are building a community of like-minded individuals who want to solve the challenges we face and create wealth in the process. This approach solves two issues at once: one, it improves people’s lives without coercion by resolving problems at their root; two, it takes power away from the state and returns it to the constituency. Politicians are masters at spinning issues to put themselves in a positive light and blame the critics. They exploit issues by promising results they cannot deliver and passing laws that transfer power from the people to the state, while securing personal advantages through backroom deals that benefit themselves and their friends.

Dedicated Teams, Not Crowds

Revolutions don’t require everyone’s participation, and innovation doesn’t happen by committee. Real progress never emerges all at once from the masses—it starts with individuals, is championed by leaders, and shaped by small, dedicated groups. The world truly changes when these pioneering ideas demonstrate what’s possible, and others choose to embrace and adopt them.

Rogers’ innovation adoption curve illustrates how new ideas spread, starting with innovators, then early adopters, followed by the early and late majority, and finally laggers. Our goal isn’t to reach or please the masses. Instead, we focus on finding committed individuals who are willing to test and implement the ideas we develop together.

Our role

The Golden Age newsletter will provide the following types of intelligence, news, and analysis:

Defining the problem:

1. A breakdown of complex issues down to their fundamentals using first principles so that we can uncover and address their true root causes.

2. Analysis of research and data from academia, businesses, nonprofits, think tanks, and governments to provide context—so we can focus on building solutions that address root causes rather than getting lost in politics.

Evaluating solutions:

1. Summaries of the most important conversations, interviews, and presentations on current affairs to capture a wide range of viewpoints and ideas.

2. Evaluation of public-sector solutions and their impact by analyzing current and proposed laws, policies, and regulations.

3. Assessment of private-sector innovations, businesses, and organizations providing real solutions to these challenges.

Building and funding solutions:

1. Proposals for businesses and/or policies that entrepreneurs and lawmakers can use to address specific issues.

2. Analysis of businesses, markets, economics, and investments to understand how companies stay competitive, solve problems, and create prosperity. This will help us build organizations that last and identify investments with long-term, durable competitive advantages—the kind Warren Buffett likes.

Long-term goal

Our long-term goal is to create a practical framework that can be applied to challenges of all kinds—one that enables us to identify the root causes of problems, develop and fund effective solutions, and pass laws that support human flourishing and innovation.

If this resonates with you, we would love to hear your thoughts.

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