2025 Annual Letter
2025 was, in some ways, a historic year for Social Capital. Numerically, we did well. Our portfolio took an important inflection upwards when NVIDIA licensed Groq for $20B. However it felt...
Social Capital Performance Summary
To the supporters and friends of Social Capital:
This is our eighth annual letter, in which we share our investments, our observations on the year, and our thinking on technology, markets, and the future.
2025 was, in some ways, a historic year for Social Capital. Numerically, we did well. At the end of the year, our portfolio took an important inflection upwards when NVIDIA licensed Groq for $20B.
However, 2025 felt neither particularly successful nor unsuccessful for me in investment terms. Investment returns are a deeply lagging indicator, as is revenue of work done. If you had made a good decision, at some point in the past, it may bear fruit today or in the future. For Groq, because I made the decision to invest ten years ago, the results, while valuable and valued, do not accurately represent any meaningful work I did in 2025.
So what did I actually do?
Last year, there were three things I needed to do on the investing side. First, I had to set up my portfolio to thrive through the changes that were coming with the Trump Doctrine and the AI geopolitical reorganization (more on this later). Second, I wanted to create an unexploitable portfolio to thrive in this age of AI uncertainty. Third, I doubled down on a handful of businesses that were working.
On the entrepreneurial side, I really focused on scaling 8090 by clarifying the product strategy, sharpening a crisper go-to-market, closing some important deals, and refining 8090’s flagship product - Software Factory - so that it was in a position to be launched into the wild. We finally shipped it in February 2026.
2025 was about a cascade of tactical decisions. It was about me being in the engine room, tinkering. Did every tactical call pay off? No. But I kept my hands on the controls through all of it, and put us in position to do something big in the future. If Groq’s outcome is a clue, perhaps I’ll know in ten years if I made the right decisions in 2025.
Now here’s the really important part: when it’s raining money on your head, you need to be extremely grateful but realize that it’s temporary. That it’s not you. It’s not an accurate representation of today because it’s a reflection of all the big and small decisions that you got right years ago. You can’t let it disrupt your ability to see clearly. This is true in investing and entrepreneurship.
By the way, the discipline is the same in both directions: do not let the windfalls inflate your judgment, but do not let the losses distort it either. In 2024, I took a $400 million markdown in another portfolio company following a recapitalization after it ran out of capital. It stung but had no lasting emotional impact because I could see it coming. I’d already gone back, assessed the mistake, understood what I could’ve done differently… and moved on. So what have I learned from this?
I’ve said repeatedly that investing is a fundamentally internal struggle. But in many ways, entrepreneurship is as well. The feedback loops for outsized success are so long in both realms that for years at a time, the only person who can judge whether you’re on the right track is you. Everyone else is reading the scoreboard. But the scoreboard tracks lagging indicators. The inputs won’t show up for years. And the scoreboard is everywhere. It’s the press, your X feed, your peers, your team, your friends. The temptation to measure yourself by it is constant. In hindsight, it is clear to me that every major mistake I have made came from taking that scoreboard too seriously and giving in to the crush of outside expectations: when I self-sabotaged my path by replacing my own conviction with someone else’s. While hard to live by, all the greats show that the only proof that matters is your own. Because the moment you outsource the judgment, you outsource the power. And you cannot afford that.
Always think for yourself.
Once you stop outsourcing the judgment, and once you stop relitigating the losses or inflating the wins, once you let go of the future and the past, you’re left with a surprisingly simple question:
What should I do today?
Success is found in the present. Learn from the past, then move forward. Take a curious guess about the future and try something, then come back, see what you learned, and then repeat. There’s not much more to that. Sometimes, people just get in their own way by putting too much weight into these decisions. I try not to. At my best, I have no nostalgia. I think about the future, but I do my best to live in the present.
What the hell is going on – 2025 edition
Superintelligence is coming but it’s not here yet.
In my 2018 Annual Letter, I encouraged our readers to learn about two new technologies: artificial intelligence and computational biology.
“Over the next few decades,” I argued, “...we should see artificial intelligence successfully applied to problems that were previously too difficult using traditional software methods, such as self-driving cars, robotics, and drug design and discovery. AI can truly transform how we work, how we live, and even how we think.”
Good call.
In line with that thesis, I invested in companies applying AI or building the infrastructure that powers them.
Then ChatGPT landed in 2022, and AI became mainstream. A few years of remarkable progress followed: the duration of the tasks an LLM can confidently execute has gone from 10 seconds in 2022 to many hours in 2025. By 2025, AI lab CEOs were forecasting “artificial general intelligence” or even “superintelligence” arriving within the decade. It’s worth pausing here.
Let’s call today’s AI what it really is: an ultra-advanced, mathematically grounded autocomplete and logic checker. It’s computer software that predicts thousands of words into the future instead of one. That gap, from one word to thousands, turns out to be where something unexplainable happens. It is solving incredible problems that stumped us for decades. It is able to perform certain tasks like image classification or code generation as well or better than an expert. How it happens is still, at some level, a mystery.
When we can’t fully understand something, the temptation is to reach for supernatural language. In testing the most effective messaging for fundraising, parts of the AI industry decided that doomerism was the most effective. I get it. Fundraising requires narrative, and “we are building God” is a better pitch than “we wrote some very clever linear algebra, pirated the internet, and threw a bunch of compute at it.” This is what an industry still figuring out its place in society looks like. But let us not confuse the pitch with reality.
The cost of that immaturity, however, is now measurable: a recent NBC News poll put AI’s net favorability at negative 20 among registered voters. Only the Democrats and Iran, a nation we’re at conflict with, are less popular.
Because this feeling of “magic” isn’t exclusive to AI, nor is it new as it seems. We just weren’t alive for most of the last few rounds. Imagine discovering that an invisible force could light entire cities, power machines, and travel across continents (electricity). Imagine hearing a human voice travel across a wire for the first time (telephone). We weren’t alive to remember those. But we might remember the first time we pulled up a webpage and realized the entire world’s information was at our fingertips. That felt like magic too.
AI is no different. It feels unprecedented, but the experience of it is not. Every major technology feels like magic at first. We just forget how strange it once seemed, once it becomes ordinary (when was the last time you stared at your lightbulb in awe?).
To be clear: AI does not need to be superintelligent to be transformational. But it has to be trusted because low public trust leads to lower adoption and worse regulations, which in turn slows diffusion and may put America at a disadvantage relative to China, where 87% of the public views AI positively - or at least that is what we are told.
It would be much better if the AI industry said soberly: there is a lot of experimentation, the revenue is promising, but we do not yet know what is real, we are going to figure it out methodically, and we are going to work within regulated industries rather than flout them. That is a mature message. It is also the truthful one.
The AI Stack
If you look back at the evolution of humanity, there have been moments that enabled exponential expansions of human productivity. Every one of them also came with areas, in that expansion, where economic value concentrated. The assets at those layers are what I call fulcrum assets, because everything else pivots around them.
Historically, for oil, the fulcrum assets were refining and transport. For railroads, it was control of the network. For the internet, Global Crossing and WorldCom bet it was infrastructure and spent billions on fiber in the 1990s (they drastically overbuilt, and both went bankrupt). The fulcrum turned out to be the platforms that owned the users.
The question is: where are the fulcrum assets in the AI stack?
To find the fulcrum, you first need to define the stack. Take, for example, the first generation of the internet. In 1978, the ISO drew a map of the internet economy in seven layers, from physical cabling to application software. It was called the OSI model, and it demarcated where one company ended and the next began. That clarity built some of the greatest technology companies in history, and the framework preceded the industry. We need this for AI.
The AI stack is the complete architecture required to generate intelligence at scale, where every layer depends on the one below it, and fails without it. It begins with raw inputs such as critical minerals and energy and ends at the application layer.
As you can see, in our framework, the AI stack builds on a shared infrastructure base. Above that, it diverges into physical AI and digital AI, because great robots and great digital AI require different boundary conditions.
Digital AI trains on text, code, and images, data of which there’s a massive supply on the internet. Physical AI trains on sensory data: touch, gravity, friction, spatial depth. That data barely exists. It has to be collected from the real world, slowly.
The data are also different in kind. Text is words in a sequence, whereas sensory data is continuous streams of spatial and physical information coming in all at once. Models need different architectures to process each, the same way your brain uses different regions for language versus balance and movement.
Digital AI runs in data centers with abundant power and memory. Physical AI has to run on the robot itself, on a small chip with limited power. The robot can call a cloud model for reasoning, but its real-world model has to be local, because when it’s about to fall, it can’t wait for a server response. It needs to react instantly, the same way your reflexes work faster than your thinking.
Between the model and the application sits an execution layer. On the physical side, that means actuation: sensor fusion, kinematics, batteries. On the digital side, it means agent systems: tool use, orchestration, memory, APIs. Most applications will use both. In these scenarios, you’ll see a dominant model running the application, then calling on specialized models as needed. A hospital robot uses a language model to talk to patients but a spatial model to navigate hallways. A warehouse robot uses a vision model to move boxes but a language model to understand spoken instructions.
On the digital side, the model layer is commoditizing fast. I view foundation models as equivalent to electricity, where the model is not the moat. Rather, what you build on top of it is.
So if the fulcrum is not in the digital layer, where is it?
First, let’s consider silicon. Silicon is a sixty-year-old industry. There are bottlenecks inside it, but the asset itself is not the bottleneck. You can build a GPU, an ASIC, an FPGA, or a CPU on the same fabrication line, you are really just debating the process node. Memory looked like a chokepoint until people found ways around HBM with DRAM and SRAM. Networking looked like a chokepoint until photonics offered an alternative to InfiniBand.
Energy storage and actuation are different. A Panasonic line making NMC cells cannot make LFP prismatic cells. All the machines are different. All the tooling is different. You are operating at the level of chemistry: specific slurries, specific coatings, baked at specific temperatures. And without enough stored energy and fine-grained precision in movement, physical AI does not work. The robot just stops.
Said more simply: you cannot repurpose a factory that makes one type of battery cell to make another. Every battery chemistry requires its own manufacturing line, from scratch. And without sufficient energy storage and precise actuation, the robot does not move. Everything upstream becomes irrelevant.
As a result, I think the fulcrum assets of this era sit where the shared infrastructure meets the physical layer: critical minerals, chemical processing, energy storage, and actuation. But building the intelligence that sits on top of them is not free either. It requires deep talent, massive compute, close-to-infinite energy, and capital markets willing to finance all of it.
So who can actually build this?
Bipolar world
For the last 10 years or so, our geopolitical landscape has been formed more and more by the US-China power competition. Building frontier AI also happens to be so expensive and talent-intensive that only two countries can build it at the frontier.
The boundary conditions for frontier AI are: deep talent pools, massive compute infrastructure, close-to-infinite energy, and capital markets that can finance it. Only the US and China have the economic muscle to spend hundreds of billions of dollars on the computational and energy infrastructure that AI requires. Even if a third player wrote that check, they would still be years behind, because the US and China have been compounding since the beginning.
The result is a simple structure: the two superpowers at the center, each building its own intelligence stack. The US overwhelmingly closed-source, China overwhelmingly open-weight. Around them, a small number of countries that hold critical inputs: lithography in the Netherlands, critical minerals in Australia, energy and capital in the Gulf Countries, fabrication in Taiwan. Two planets and a handful of moons. The moons have leverage because they supply something that cannot be routed around.
Everyone else faces a simpler, less comfortable reality. If you do not control a critical input, you do not have a term sheet. You have a menu. And the menu has two items: rent the intelligence from China, which will come with a certain set of conditions, or rent the intelligence from the United States, which comes with a different set of conditions. Every country will have to go through a sorting function of choosing what they want.
Put simply: AI is the defining technological competition of the 21st century, the U.S. and China are the players, and a bipolar world is taking shape. But how did we get here?
Globalism: cheap goods, expensive consequences
First, some history. For most of the post-WWII era, U.S. trade policy was pragmatic and flexible. Presidents from Truman through Reagan used tariffs, sector-specific deals, and hard-nosed negotiations to solve discrete problems bilaterally. For example, when Reagan faced a flood of Japanese car imports in 1981, he leveraged tariff threats to extract voluntary export limits from Tokyo, eventually pushing Japanese automakers to build their cars on American soil. Trade policy was a tool for advancing American interests, not a rigid ideology.
That changed after the Cold War. The Soviet collapse, the 1991 Gulf War victory, and strong economic growth gave the United States its unipolar moment. With that moment came a rare domestic consensus that free markets, deregulation, and strong IP rights were simply the “right” economics.
As the world’s sole superpower, U.S. policymakers had both the ideological conviction and the hegemonic leverage to set rules for a truly global trade order. Under Clinton, Washington got the world to sign onto the WTO. The IMF and World Bank became vehicles for spreading neoliberal policy worldwide. In December 2001, China was admitted to the WTO despite not meeting many of its standards, on the promise that integration would follow. The zeitgeist was that economic openness would inevitably lead to political reform, as it had in South Korea and Taiwan.
While the U.S. was spending money on its protracted war in Iraq, China focused on scaling its economy and building wealth. It became known as the world’s factory. The short-term winners were American corporations and consumers, who saved an estimated $411,000 for every manufacturing job lost to Chinese competition, with middle and low-income households getting access to a mass supply of affordable goods.
As we know now, it came at a cost: America’s position eroded on two fronts.
Economically, we lost our resilience and domestic ability to make things. Since the 1980s, manufacturing has been claiming a smaller and smaller share of all American jobs. Chinese imports didn’t start that trend, but they made it worse. Between December 2000 and January 2026, manufacturing’s share of total U.S. employment dropped from 6.0% to 3.7%. On aggregate, U.S.-China trade was positive-sum. But China benefited disproportionately.
Geopolitically, the asymmetry was even starker. Between 2001 and 2024, China’s share of world GDP quadrupled from 4.0% to 16.8%, while America’s fell from 31.4% to 25.9%. And geopolitical power, while not zero-sum in absolute terms, is relentlessly relative. If your adversary grows faster than you, you lose comparative advantage, even if you yourself grow.
The return of bilateralism
Unlike previous technologies, AI creates a strategic imperative to gate access once you have it. And the moment you rent that intelligence, you cede sovereignty to whoever provides it.
So we have to see past the historical frameworks that have governed geopolitics and into a new way of organizing: not who protects you from a threat, but who gives you access to prosperity. Put another way, the organizing principle of geopolitics is no longer military alliances, but supply chain position.
So if you are a country that is not the United States or China, what do you write on your term sheet?
For most countries, the honest answer is: nothing. But there are five forms of leverage that still matter: critical minerals, endemic high-tech manufacturing, capital, energy, and data center infrastructure. Countries that hold any of these have something to negotiate with. The more of these layers they control, the better their deal. And countries that hold none will face an uncomfortable question: which version of intelligence do they rent, and what do they concede in return?
As a result, bilateralism is back. Why? Because the superpowers want flexibility: the ability to negotiate bilaterally with the countries that hold critical inputs, on their own terms, without being bound by multilateral frameworks that give equal voice to countries that bring nothing to the table.
Belt and Road was China’s strategic response to the old framework: lock in bilateral agreements, get its tentacles into critical infrastructure across Africa, Central Asia, and Southeast Asia, and become totally unexploitable. But they picked the wrong countries and the wrong infrastructure. They were playing to preserve optionality for long-term military and geo-economics in a world where everyone outsources to China and everyone wants to stay rich in the same way forever.
What they did not factor in is that America would simply tax everybody else and use that money to subsidize its way out of dependency. That is what tariffs are. You put everyone behind the wall first. Then you go country by country and say, “I will let you back in, but here is what I want.”
The natural objection is: won’t this push allies to diversify away from the U.S.? I think not. Because what would they be pivoting to? Leverage exists when options exist. You can’t pivot away from America giving you inter-related abundance,prosperity and productivity without knowing with certainty that China can fill the gap. And if AI is the next critical input to prosperity, there will only be one Chinese and one American option. Each country will then need to do a deal in their best interests.
The Trump Doctrine trumped the Monroe Doctrine
The Trump Doctrine is the American response to this new game. Many have called this a modern Monroe Doctrine, or even the Donroe Doctrine. It is not.
In 1823, President James Monroe warned European powers against further colonization or political interference in the Americas. It was a defensive posture: stay out of our hemisphere. For two centuries, it defined the baseline of American foreign policy in the region.
The Trump Doctrine is more ambitious. It expresses strategic optionality across four dimensions: economic resilience, meaning we no longer accept reliance on anyone but ourselves for what we need to grow and prosper. Geopolitical optionality, meaning engagement in global conflict is a choice, not a necessity. Fundamental security, meaning overwhelming military strength that acts as a deterrent. And internal population health, meaning a society that thrives.
Said differently, America wants the freedom to act on its own terms, in its own time, with no dependency that could be used against it.
Things happen in harmonics. After decades of China-based supply chains, the U.S. impulse is to swing the opposite direction and go fully nationalist and reshore production entirely. In the long-term, I see a more balanced approach with supply chains distributed across trusted allies such as Mexico, Canada, Australia, and more.
America is also training its capital markets to underwrite a new set of economics. Subsidies and tariffs have a direct effect: they make it cheaper to build at home and more expensive to build abroad. But they also set a precedent. Capital markets watch where the state puts its weight and recalibrates what they consider financeable. A decade ago, domestic manufacturing was a bad bet. Once policy signals otherwise, the risk models update. Independent of how politics evolve, American capital markets will carry The Trump Doctrine forward.
Why the race to intelligence is a race to peace
So, does this end in conflict?
Not necessarily.
Think about this geopolitical standoff as a heads-up match in poker. In poker, GTO, game theory optimal, is a strategy where you make your opponent indifferent to your actions. You are not trying to exploit them; you are acting to make yourself unexploitable. When both players adopt GTO, the game reaches a Nash equilibrium. Neither side can improve its outcome by changing strategy unilaterally. The result is stability through mutual indifference.
Right now, neither China nor America is at Nash. Both are exploitable. Each is racing to close its vulnerabilities, getting a little less exploitable with every move. You start highly exploitable, you change a thousand variables, and eventually you become unexploitable.
In the view of René Girard, we are more likely to compete over things when we are similar and want the same things, and less likely to fight when we are fundamentally different.
Look at America and China. At a deeply broad societal level, we are fundamentally differentially organized. The rewards in America are money. The rewards in China are recognition and power. One is individualist, the other Confucian. These differences are important and good because they increase the odds of peace.
But there is an overlap. Economically, both need energy and critical minerals. Militarily, China may no longer accept the extent of U.S. power projection in the Indo-Pacific, yet the U.S. may equally refuse to accept China’s growing military presence there. That’s a real risk. But reduce it to its simplest form: this is a negotiation. You close negotiations from strength. The U.S. may escalate to improve its position before any deal is reached.
So how does this end? Both sides reach a state where they cannot be leveraged by the other. We have raw intelligence. They have raw intelligence. Both deploy prosperity and abundance to their respective teams. Countries pick which side they want to be on. And because neither side is exploitable, neither side has a reason to fight. That is the Nash equilibrium.
We are not there yet. Both sides are still adjusting, closing one vulnerability at a time. But the trajectory is legible: the race to intelligence is the race to peace.
The collapse of terminal value
In our first annual letter in 2018, I remarked that “large internet companies are truly just hitting their stride,” and that “Big Tech will get bigger and will leave less room for obvious companies doing obvious things. The demands of innovation are going up.”
Big Tech absolutely did get bigger. The Magnificent 7, the world’s biggest tech companies, made up ~17% of the S&P 500’s value in 2018 when I wrote the above. In October 2025, their market concentration peaked at 36.2%.
The demands of innovation also increased by orders of magnitude.
The architecture of modern capital markets rests on a single, rarely examined assumption: that competitive advantages compound over time, and create persisting moats. That’s why, for most tech businesses, 60 to 80 percent of equity value lives in the terminal value, which is the estimated earnings beyond the period you can credibly forecast.
That number has always been a comfortable fiction. But increasingly, I believe AI is ending it.
The question every business has to answer now is:
“How will this not be unbundled by an LLM?”
If someone can replicate your core product in weeks, your moat is not a moat. And if your moat is not a moat, your terminal value does not exist. And if your terminal value does not exist, then 60 to 80 percent of your equity value simply ceases to exist. What remains is what you can prove you earn over the next handful of years and, consequently, a world where the future is worthless until it arrives.
We saw the first inning of this in 2025: a compression of multiples, the market quietly applying a larger discount factor to future cash flows. Most clearly, we saw this in public markets, which went long on silicon, short on SaaS.
Silicon companies, the ones building the physical infrastructure AI runs on, went up. Broadcom, SK Hynix, and the semiconductor supply chain. Software companies, the ones whose products can be replicated by a better prompt, went down. Duolingo fell 67.5 percent from its mid-2025 peak to the end of year. Adobe fell 24.6 percent from its 2025 peak.
For a decade, SaaS companies were the perfect private equity target: recurring revenue, contractual lock-in, predictable cash flows. From 2015 to 2025, private equity buyers bought over 1,900 software companies in transactions worth $440 billion. By year-end 2025, software and technology companies made up roughly 25 percent of the entire private credit market.
Private equity buys these companies with borrowed money. The SaaS cash flows pay back the loan. At least, they used to. If AI makes the product replaceable, the cash flows disappear, and the loan defaults. Put simply, the default rate on those loans tells you how far along the AI disruption actually is. Private credit is the canary in the coal mine.
But we are not yet in that new regime. Right now, we are compressing the price-to-earnings ratio. The real shift is when the market flips entirely from P/E to multiples of current free cash flow. We will know it’s there when companies only report GAAP, when there are no more adjustments to EBITDA, and when stock-based compensation gets treated as the real cost it always was.
If that transition happens, the implications cascade quickly. Growth investing stops working. The entire logic of growth equity is a bet on tomorrow: sacrifice free cash flow now, build a dominant position, harvest it for decades. That logic breaks when tomorrow is no longer financeable. Who funds a pre-revenue company at a billion-dollar valuation if there is no terminal value to grow into? Capital rotates to the physical world. The corporate world restructures around shorter horizons. M&A logic inverts. Short-termism stops being a pathology and becomes the correct response to the actual incentive structure.
I have written about these implications at length elsewhere, so I will not belabor them here. But the central paradox is worth stating plainly: the companies driving AI disruption are spending $300 to $500 billion per year on infrastructure that only makes sense over a seven-to-fifteen-year horizon. If markets reprice to short-duration multiples, that capex becomes very difficult to finance. Put another way, the disruption engine disrupts itself. The likely outcome is not a permanent new regime but an oscillating one: shorter cycles, higher volatility, terminal values compressing and recovering as AI investment ebbs and flows.
The partial move already underway is significant on its own. A full repricing would be the most consequential structural shift in capital markets since the postwar era.
Long-term investing in a short-term world
If AI shortens the window in which any business can call itself defensible, two questions follow. First, where does private capital go when it cannot trust ten-year projections? Second, who finances the things that still take twenty years to build?
Where private capital goes
First, to the fulcrum assets. Earlier in this letter, I argued that the lasting bottlenecks in the AI stack are physical: critical minerals, energy storage, and actuation. The same logic applies to capital allocation. Energy infrastructure. Farmland. Toll roads. Batteries. If your moat requires atoms, not bits, a better language model cannot unbundle it overnight. For now.
Second, to the small number of companies making history rather than reacting to it.
The logical endpoint of Big Tech hitting its stride is the vertically integrated megacorp: a firm so embedded in physical supply chains that it becomes too big to disrupt. Elon seems to be assembling exactly this, with Tesla, SpaceX, and xAI converging into a single industrial entity.
Some of these companies are already financing themselves like sovereign entities. Microsoft, Amazon, and Apple have issued 40-year bonds. In 2026, Google even issued a 100-year bond that was oversubscribed tenfold.
At the same time, megacorps could face regulatory headwinds. In our 2019 annual letter, I argued Big Tech had become a modern railroad monopoly – hoarding talent, stifling innovation, and concentrating wealth and power in ways that demanded trust-busting.
That was seven years ago. Now look back at the Mag 7 chart earlier in this letter. We’re almost twice as concentrated now. But a new variable has entered the equation, which is that these same companies are spending hundreds of billions on the AI infrastructure that America needs to win the race against China. Much of that spending goes into data centers, silicon, and energy systems that will hold value regardless of which company built them. American regulators now face a genuine dilemma: the same concentration they once wanted to dismantle is now the engine driving national competitiveness. Trust-busting Big Tech and winning the AI race may be incompatible goals.
Who finances the long term
That leaves the second question: who finances the things that still require a twenty-year horizon if private capital will not?
Historically, governments have financed the things a country needed but that private markets refused to touch: the Interstate Highway System, the Apollo program, and the early internet. When the payoff horizon is too long or too uncertain for private capital, the state steps in.
That is what is happening now. The U.S. is already doing this, taking stakes in companies like Intel, negotiating tariff concessions for domestic investment, and subsidizing reshoring. China, through its model of capitalism with Chinese characteristics, always had deep state involvement in financing its strategic industries, and has committed to “never deindustrialize”. States holding large sovereign wealth funds – such as Norway, the Gulf States, and Singapore – are at an advantage. So are nations with low debt levels, should they choose to invest.
Put another way, industrial policy is making a comeback because AI is increasingly changing the world too fast for private capital alone to underwrite.
A closing observation
I opened this letter by mentioning that investment returns, as well as revenue, are deeply lagging indicators. What are the leading indicators? Curiosity, work ethic, being temporally present. And above all: thinking for yourself.
I had a young, very smart guy in my office recently. In our conversations, he’d always bring up others’ viewpoints: so-and-so tweeted this about rates. This person thinks that about China. He had a list of people he’d decided were smart, but what he was actually doing was was outsourcing his thinking to them. He was borrowing somebody else’s conviction.
Here’s what I told him:
Dismantle the idea that social proof matters. The only proof that matters is your own proof. Don’t trust anyone blindly. This is a key mental break you have to make. It doesn’t mean to look down on them, it doesn’t mean to judge them. But it doesn’t mean to blindly believe them either. It just means they are simply a human, over there saying something that may or may not be true.
We all went through this. I went through this evolution. First, I’d think: “This person is theoretically smarter than me, so I need to think what he thinks.” Then, I made a small change: “This person has a point of view that I should pay attention to. Now let me go and break this down from first principles and see where they’re right and where they’re wrong.”
When you do this over and over and over again, you notice patterns, you come to your own conclusions, and you make better decisions.
Put another way, don’t think you’re smarter than anybody, but also don’t think that anybody is smarter than you.
If you look at the great investors and the great entrepreneurs, that is the crucible test. They simply don’t care what anybody else says. Not because they think they’re better, but because they refuse to let somebody else carry what is fundamentally their burden.
I have many smart friends, but I can’t outsource the thinking to any of them. Because if I outsource the thinking, I outsource the decisions, and if I outsource the decisions, I inevitably outsource the power. And outsourcing power will make me miserable.
Which means you should do exactly what I told that young man to do: break this letter down from first principles. See where I’m right and where I’m wrong. If you just take my word for it, you’ve missed the entire point. Always think for yourself.
God is in the details.
Respectfully,
Chamath Palihapitiya
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We’ll also do Q&A on this letter in the members group chat:
Appendix
2024 Social Capital Performance Summary
2023 Social Capital Performance Summary
2022 Social Capital Performance Summary
2021 Social Capital Performance Summary
2020 Social Capital Performance Summary
2019 Social Capital Performance Summary
2018 Social Capital Performance Summary
Do what I mentioned in the last paragraph and share your thoughts below. Looking forward to doing some Q&As in the subscriber group chat.
Congratulations on a great year and for contributing to the free exchange of ideas without fear.