Another thing I can trace back to Peter is the idea of talent bat-signals. Having started my own company now (in stealth for the moment), I appreciate this a lot more: recruiting good people is hard, and you need a differentiated source of talent. If you’re just competing against Facebook/Google for the same set of Stanford CS grads every year, you’re going to lose. That means you need a set of talent that is (a) interested in joining you in particular, over other companies (b) a way of reaching them at scale. Palantir had several differentiated sources of recruiting alpha.

But doesn’t the military sometimes do bad things? Of course - I was opposed to the Iraq war. This gets to the crux of the matter: working at the company was neither 100% morally good — because sometimes we’d be helping agencies that had goals I’d disagree with — nor 100% bad: the government does a lot of good things, and helping them do it more efficiently by providing software that doesn’t suck is a noble thing. One way of clarifying the morality question is to break down the company’s work into three buckets – these categories aren’t perfect, but bear with me: Morally neutral. Normal corporate work, e.g. FedEx, CVS, finance companies, tech companies, and so on. Some people might have a problem with it, but on the whole people feel fine about these things. Unambiguously good. For example, anti-pandemic response with the CDC; anti-child pornography work with NCMEC; and so on. Most people would agree these are good things to work on. Grey areas. By this I mean ‘involve morally thorny, difficult decisions’: examples include health insurance, immigration enforcement, oil companies, the military, spy agencies, police/crime, and so on.

The critical case against Palantir seemed to be something like “you shouldn’t work on category 3 things, because sometimes this involves making morally bad decisions”. An example was immigration enforcement during 2016-2020, aspects of which many people were uncomfortable with.

I don’t believe there is a clear answer to whether you should work with category 3 customers; it’s a case by case thing. Palantir’s answer to this is something like “we will work with most category 3 organizations, unless they’re clearly bad, and we’ll trust the democratic process to get them trending in a good direction over time”. Thus: On the ICE question, they disengaged from ERO (Enforcement and Removal Operations) during the Trump era, while continuing to work with HSI (Homeland Security Investigations). They did work with most other category 3 organizations, on the argument that they’re mostly doing good in the world, even though it’s easy to point to bad things they did as well. I can’t speak to specific details here, but Palantir software is partly responsible for stopping multiple terror attacks. I believe this fact alone vindicates this stance.

This is an uncomfortable stance for many, precisely because you’re not guaranteed to be doing 100% good at all times. You’re at the mercy of history, in some ways, and you’re betting that (a) more good is being done than bad (b) being in the room is better than not. This was good enough for me. Others preferred to go elsewhere. The danger of this stance, of course, is that it becomes a fully general argument for doing whatever the power structure wants. You are just amplifying existing processes. This is where the ‘case by case’ comes in: there’s no general answer, you have to be specific. For my own part, I spent most of my time there working on healthcare and bio stuff, and I feel good about my contributions.

by making the company about something other than making money (civil liberties; AI god) you attract true believers from the start, who in turn create the highly generative intellectual culture that persists once you eventually find success.

Palantir does data integration for companies, but the data is owned by the companies – not Palantir. “Mining” data usually means using somebody else’s data for your own profits, or selling it. Palantir doesn’t do that - customer data stays with the customer.

As a SaaS startup scales, sales and customer support staff pay for themselves, and the marginal cost to serve your one-thousandth versus one-millionth user is near-zero. The result? Some SaaS companies achieve gross profit margins of 75 to 90 percent, rivaling Windows in its monopolistic heyday.

Rent-seeking has become an explicit playbook for many shameless SaaS investors. Private equity shop Thoma Bravo has acquired over four hundred software companies, repeatedly mashing products together to amplify lock-in effects so it can slash costs and boost prices—before selling the ravaged Franken-platform to the highest bidder.

In the Kafkaesque realm of health care, software giant Epic’s 1990s-era UI is still widely used for electronic medical records, a nuisance that arguably puts millions of lives at risk, even as it accrues billions in annual revenue and actively resists system interoperability. SAP, the antiquated granddaddy of enterprise resource planning software, has endured for decades within frustrated finance and supply chain teams, even as thousands of SaaS startups try to chip away at its dominance. Salesforce continues to grow at a rapid clip, despite a clunky UI that users say is “absolutely terrible” and “stuck in the 80s”—hence, the hundreds of “SalesTech” startups that simplify a single platform workflow (and pray for a billion-dollar acquihire to Benioff’s mothership). What these SaaS overlords might laud as an ecosystem of startup innovation is actually a reflection of their own technical shortcomings and bloated inertia.

Over 1,500 software startups are focused on billing and invoicing alone. The glut of tools extends to sectors without any clear need for complex software: no fewer than 378 hair salon platforms, 166 parking management solutions, and 70 operating systems for funeral homes and cemeteries are currently on the market. Billions of public pension and university endowment dollars are being burned on what amounts to hackathon curiosities, driven by the machinations of venture capital and private equity. To visit a much-hyped “demo day” at a startup incubator like Y Combinator or Techstars is to enter a realm akin to a high-end art fair—except the objects being admired are not texts or sculptures or paintings but slightly nicer faces for the drudgery of corporate productivity.

As popular as SaaS has become, much of the modern economy still runs on the humble, unfashionable spreadsheet. For all its downsides, there are virtues. Spreadsheets are highly interoperable between firms, partly because of another monopoly (Excel) but also because the generic .csv format is recognized by countless applications. They offer greater autonomy and flexibility, with tabular cells and formulas that can be shaped into workflows, processes, calculators, databases, dashboards, calendars, to-do lists, bug trackers, accounting workbooks—the list goes on. Spreadsheets are arguably the most popular programming language on Earth.