Cliff Sosin
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Invest Like the Best with Patrick O'Shaughnessy · Cliff Sosin - Owning the Complements to Intelligence
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Why Is Cliff Sosin Comfortable With 80% of His Portfolio in Carvana?
Cliff Sosin is comfortable keeping most of his portfolio in Carvana because he thinks about the stake the way an owner thinks about a company, not the way a study of price swings tells a typical investor to diversify. Patrick O'Shaughnessy describes the portfolio as roughly 80 percent Carvana, a vacations holding, and about 5 percent Meta, with one position at 80 percent, and Sosin agrees. Asked whether an AI sizing tool would put less in Carvana, he says he is underweight and that it should be 90 percent.
He would not hold this much if he did not believe Carvana's infrastructure and brand left few scenarios that work out really badly. He compares it to finding Walmart, Home Depot, or Faste
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Will AI Agents Push Customers to the Lowest-cost Provider?
Cliff Sosin says that once people hand routine choices to an AI agent, the customer moves to the lowest-cost provider. He puts decisions on a spectrum. An insured bank deposit can be delegated: take the highest rate. A favorite sweater cannot. The agent can find options, and the person still has to choose. Garbage collection, in his stylized example, sits near the deposit. A cheaper hauler still struggles to win a switch today, because many people do not know what they pay and will not spend attention on it. An agent can hear an offer of 50 cents a month, confirm the trash still gets picked up, and switch.
A second effect is coordination on a street. One truck serving every house is cheaper
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Who Makes Money When AI Intelligence Gets Cheap?
Cliff Sosin says the businesses that benefit when intelligence gets cheap own the complements to intelligence, not the intelligence itself. Assume everyone already has very good intelligence, and ask what is left. The complements he names are data, attention, affection, physical proximity, scale, and, more cynically, regulatory capture. A product that is only a hardened piece of intelligence can be hurt, because a general model does that work. A company held back by the cost of intelligence can be helped once that cost falls. A company whose profits depend on customers being too tired to shop around can be hurt.
Meta is his example. He calls the core business personalized TV. It used to be
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Why Should Kids Learn Math Without AI?
Cliff Sosin argues that children should learn foundational skills apart from models so they can direct a model. You cannot ask a model to treat a problem as a stochastic process unless you already know what a stochastic process is. A model could teach the term, but figuring it out that way adds a lot of friction. You do not have to work out the math, but you do have to know what it is.
Foundational skills build on one another, and children have to be able to do them without models. The point is not that later work will happen without models. If they cannot multiply, matrix multiplication is very hard to understand. They need not perform matrix multiplication especially well. They do need a
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How Does Cheap AI Affect Income?
Cliff Sosin tells two stories about how cheap, ubiquitous intelligence affects wealth and income distribution. One story, already widely discussed, holds that income concentrates: people made money on labs, data, and NVIDIA, with increasing returns to small groups.
The counterargument starts with rent takers. A software company that charges bowling alleys gets smoked. Losing the rent lowers the cost of bowling and raises living standards for everyone who bowls. Spending on bowling is a small share of a high income and a larger share of income for people who make less, so the effect flattens income. When the little rent pieces go away, corporate profits decline. Money the average American gi
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How Much of the Economy Moved Into AI?
Cliff Sosin says American capital markets redeployed 3% of GDP into a highly speculative AI venture on the turn of a dime. Worry about a decline in American dynamism ran into a market that moved capital at a scale and speed no other country has matched. He allows the fitting share might be two or six.
An inability to move investment into productive resources can bind productivity growth, especially when technology is advancing quickly. Capital spending is tied to people doing different work. Every day, people wake up and go to Pennsylvania to build a data center. The buildout depends on infrastructure that moved an enormous amount of capital.
Taking risk on speculative ventures and slicing
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Does a Person Working With an AI Model Beat a Person or a Model Alone?
Cliff Sosin says a person working with a model now beats either one alone. Computer-aided chess had a long stretch of freestyle chess, where the human plus the program out-competed the human alone and out-competed the computer alone, because each side thought differently and the mix was synergistic. Investing is in that era.
A person still understands things a model misses by having lived a life, and not everything is on the internet. Using a model and then judging makes better judgments while the gap remains, which he thinks will last a while. Later the gap can shrink until it does not matter. Models will not predict the future, because the future is intrinsically unpredictable, but the mo
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Why Does AI Infrastructure Get Overbuilt?
Cliff Sosin says AI infrastructure gets pushed until it is overbuilt because a strong price signal is what makes a buildout of a huge technology possible at all. Super smart, talented people are focusing their lives on the project, and they will make, and deserve, lots of money. Without that money, people would not keep pushing.
American society looks like a household saving money for a house. Electricians, plumbers, and other resources go into the AI infrastructure, so costs rise elsewhere in the economy. The work keeps going because nobody really knows the right stopping point, and it continues until the buildout is overdone. Lead times and a winner's curse mean the last person to stop wi
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When Does AI Reach Its Limit?
Cliff Sosin says the limit arrives after the papers are finished. Models read all the papers and figure out connections that a person who had read all three papers and thought about them would have seen, though nobody had. All kinds of great results fall out of the reading. At some point the mining of published work is done, and what is left is a hypothesis about how to think about things outside what people already know. Even a super smart model will not necessarily be better at guessing the hypothesis. The model will still guess better than the smartest person, but the smartest person's odds are super low anyway.
The model can come up with great ideas for experiments, and the experiments
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When Do Price Cuts Shrink a Market?
Cliff Sosin says a market shrinks once further price cuts stop creating enough demand, and a price of zero takes the market size to zero. Jevons paradox is only a narrow spot on a U-shaped demand curve, and markets in general take that shape. When a product is terrible, there is no demand. As the product gets better, people will pay a lot but will not buy very much. As the price goes down, the market grows.
Moving information once cost a huge amount, and people moved very little of it. A series of innovations cut that cost a lot, the amount of information moved rose sharply, and the market grew for a long time. Growth then stopped. The cost kept falling and the market kept shrinking. Cogent
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Why Can't Even Superintelligent AI Predict the Future?
Cliff Sosin says even superintelligent AI cannot predict most of what people want to know, because complex systems amplify tiny errors until a forecast falls apart. Intelligence fills in a thin scattering of facts. Mathematics can reach a closed form. Weather, mixing cream into coffee, and how groups of people interact cannot. A simulation carries error in the starting conditions or in each truncated step, and that error builds.
The weather model behind chaos theory is the concrete case. A forecaster ran it, reset it, and ran it again. The second run matched at first and then diverged. A few days out, one forecast was 70 degrees and sunny and the other was 20 degrees and pouring. The only c