AngelSpan

America needs a natural aristocracy. Aristocracy just means “rule by the best” and Joe Lonsdale argues that it is an idea that is unduly demonized. Artificial, hereditary aristocracies are indeed a corrupting force, but to reject the idea that the most competent should rule is equally dangerous. By rejecting Aristocracy altogether, we don’t unleash democracy we get bureaucracy: a geriatric slop that will suffocate a civilization if not eradicated. This is what is plaguing America today. @JTLonsdale is the closest thing you’ll find to a Roman senator, someone who acts courageously in politics, commerce, and the military informed by a classical understanding of virtue. And this interview will tease out Lonsdale’s political philosophy that underlies all of his ventures: Palantir, UATX, Cicero and 8VC. Timestamps: 1:50 Change Requires a Demanding Philosophy 3:17 America’s Problem: No Aristocracy 8:34 In a World of Machiavellis, Be a Cyrus 10:51 The World Can’t Afford Our Absence 21:43 Democracy Dies in Bureaucracy 30:48 How Cicero Institute Writes Philosophy Into Bills 32:38 Measuring Who Gets Help First 44:04 When Buyers Become Too Powerful 50:04 The Philosophy Behind Palantir

Venture doesn't run on leverage. It's asymmetric: you can only lose 1x, but you can potentially make 1,000x. In the math, overwhelmingly the error that matters is the error of omission.

The biggest scandal is that top Silicon Valley VC firms went to China and “taught them how to do early stage investing.” Venture capital is American Dynamism. “It is the thing that allows America to be dominant in the global economy.” And we just gave it away. “VCs went over there and shared information, shared limited partners, shared all of the aspects and the functions of how you build an incredible legacy defining venture firm.” Twenty years later, China has a full-stack tech ecosystem aligned with the CCP. The real scandal is not just Apple but Silicon Valley itself. @KTmBoyle

"All models are wrong, but some are useful" The post quoted below has ruffled enough feathers to require further explanation: TL;DR for the lazy: The implication is simply that a trend towards larger portfolios is likely to lift the median to top quartile performance of venture capital. This is a general observation, not a recipe for success. Nobody is trying to make the case that diversification is always and strictly better, or vice-versa. Before going further, I think this quote (and the others below) from Daniel Kahneman summarises many of the responses: "What we told the directors of the firm was that, at least when it came to building portfolios, the firm was rewarding luck as if it were skill. [...] The illusion of skill is not only an individual aberration; it is deeply ingrained in the culture of the industry. Facts that challenge such basic assumptions — and thereby threaten people’s livelihood and self-esteem — are simply not absorbed. The mind does not digest them. This is particularly true of statistical studies of performance, which provide general facts that people will ignore if they conflict with their personal experience." (source: "Don't Blink! Hazards of Confidence") Unequivocally true: Portfolio construction is a poorly understood topic, and too many GPs (and LPs) blindly follow the common wisdom about concentration without giving it proper thought. There are factors the model cannot represent as a simple reflection of probabilities, and many of them are counter-intuitive or require deep analysis: For example: - Overconfidence is the most well-studied bias in investing, and is endemic to venture capital. It is a large part of what leads the majority of investors to believe they can pick well and concentrate. More here: xitter.cf/credistick/status/1940… - Overconcentration manifests as broadening the distribution of returns with a negative skew. That's exactly what we see in VC: it has the widest distribution of returns and the worst median. More here: xitter.cf/credistick/status/1939… - Diversification is shown to benefit fund performance by enabling investors to take more idiosyncratic risk per investment, which is fundamentally the source of outperformance. Concentration drives consensus. More here: xitter.cf/credistick/status/1934… A lot of the group-think around concentration versus diversification is based on the assumption that concentrated firms must be better at picking because they spend more time per investment. This is fundamentally untrue, as is described in Kahneman's article. When faced with difficult judgements on limited information, people will habitually change the question to something simpler. That is, if investors spend more time on decisions where that extra time doesn't actually yield access to more useful information, they're likely to construct a process that produces greater confidence rather than greater accuracy. "The confidence we experience as we make a judgment is not a reasoned evaluation of the probability that it is right. Confidence is a feeling, one determined mostly by the coherence of the story and by the ease with which it comes to mind, even when the evidence for the story is sparse and unreliable. The bias toward coherence favors overconfidence. An individual who expresses high confidence probably has a good story, which may or may not be true." In venture capital this typically manifests as overconfidence in founder-focused human capital, which produces predictably bad investments (and missed good ones). More here: xitter.cf/credistick/status/1844… The distribution of outcomes in venture capital, and the unpredictability of those outcomes, means it is fundamentally a portfolio discipline more than it is a picking discipline. The only point at which that changes is when an investor has enough genuine experience to consistently beat the odds — which is very rare. Just as every pitch to LPs somehow demonstrates top quartile performance, every investor believes they can beat the odds. ~90% of VCs lose that fight. Finally, there is another important element to this portfolio construction question: reserves. I don't have anything written on this topic (yet), but I know two people who have: First is @AngelSpanInc's brilliant paper on 'Process Alpha' in venture capital. Essentially, his proposition is that a diversified portfolio with a robust follow-on strategy can reap the benefits of both strategies: broad exposure to more potential outliers, and the ability to concentrate. It is represented in the diagram below. "Poker players and statisticians sorted this out years ago, and the economics of gambling and venture capital are similar: If you are wrong (particularly at the seed stage of investing), you lose all your money. A recognized optimization framework that is instructive as to how much investor capital to deploy at each stage of the startup life cycle (or funding round) is called the Kelly Criterion, also referred to as 'The Scientific Gambling Method'" (source: "Process Alpha: How to Construct and Manage Optimized Venture Portfolios") Another great read on this topic is from @LauraLPThompson of @SapphirePrtnrs. It covers multiple angles on reserve strategies, with some interesting math, but there's one particular quote that is relevant to this theme: "Reserve dollars have to be concentrated into winners to improve fund performance and there usually aren’t many of them. Early-stage venture remains a power-law business, and many portfolios come down to one or two winners." (source: "Dirty Secret: Venture Reserves are Not Always a Good Thing") To conclude a post that is already too long: "Diversification versus concentration" is a complex and multivariate question. There is no "right" answer, and certainly not one that can be reached by a simple model of distributed outcomes. However, the general understanding of this topic and the underlying factors is shockingly poor, which speaks to the institutional insecurity of venture capital and the unwillingness to engage in self-scrutiny. If you are a GP, you owe it to your LPs to be a student of venture capital. Read the research, familiarise yourself with the data. Build a well-informed and rational strategy. Do not engage in group-think. If you are an LP, you are paying GPs significant management fees to invest your money. Learn how to find competent investors, and then don't get in their way if they have a rational strategy. Beware anyone who blithely dismisses questions like these; they are charlatans. To finish with another great quote from Kahneman: "In general, however, you should not take assertive and confident people at their own evaluation unless you have independent reason to believe that they know what they are talking about. Unfortunately, this advice is difficult to follow: overconfident professionals sincerely believe they have expertise, act as experts and look like experts. You will have to struggle to remind yourself that they may be in the grip of an illusion."

the more ambitious you are, the harder it is to feel proud of anything you’ve done

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.