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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• 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?

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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:

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

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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 regardless of industry, problem, or experience.

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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?

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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 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

Let us know what you think

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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 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 regardless of industry, problem, or experience.

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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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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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Wishing you peace and prosperity!

The Golden Age