Accuracy, Precision, Economy

One of my long-time friends and portfolio founders, Dan Siroker of Limitless AI recently posted about how first-time founders brag about how much they’ve raised and how big their team is; second-time founders brag about how little they’ve raised, and how small their team size is. Artificial Intelligence (AI) as we see it, and as my book pointed out in 2017, obviates the need for rote and routine tasks (not jobs). Anything rote, routine, and repeatable, goes to a machine. What’s left for people are the creative, collaborative, and complex tasks that generally require greater depth of knowledge, ability to ask questions, and structure problems, like a lawyer. It’s no wonder that many lawyers, like many entrepreneurs, and future founders, will actually be deep structured thinkers who come from humanities backgrounds like Philosophy (as many have already before i.e. Chris Dixon, Reid Hoffman, Peter Thiel, etc).

First-time founders brag about how much they’ve raised and how big their team is; second-time founders brag about how little they’ve raised and how small their team is.

In a world where all the information is universally available, as it already is, we are rate limited by our ability to ask and structure questions to retrieve answers. At one point we asked questions to Larry Page’s blinking cursor on Google; now we’re all prompt engineers, Socratic ping-pong players with Sam Altman’s ChatGPT. What’s marginally changed are the formats of the answers; what hasn’t changed is the premium we pay to smart questioners, structured thinkers, and those able to laser cut to the bone of signal in a world of endless disinformation and noise. Judge a person by their questions, not by their answers, philosopher Voltaire once advised (and every hiring manager, might today heed).

David Deming, a Harvard economist, published years ago about the importance of soft skills in business, but from an analytical and interesting angle. Generally large organizations solve complex problems. Complex problems require some compartmentalized knowledge, or specialization among workers, and so functional large organizations must “task trade” between individuals. You do one thing, and I’ll do another. In an organization requiring task trading, there are coordination costs, or friction points between individuals and teams. If an organization doesn’t place a value on interpersonal skills, there is a high degree of friction in task trading, so less cooperation leads to slower problem solving. Soft skills like communication actually materially impact the terms of trade between individuals, reducing friction, coordination costs, and thus enabling greater frequency of task trading to solve increasingly complex human capital challenges. So in a world where more rote and routine tasks are going to AI, and where complex tasks remain the bread and butter of what human workers do, it makes sense therefore that there are a premium for these soft skills that enable greater task trading, and reduce coordination costs. This was the thesis of my book, and why “the Liberal Arts will rule the digital world.”

In many domains, economy is a sign of mastery. Verbosity is the result of lack of command on language. Economy of language is the badge of honor for writers like Steinbeck and Hemingway, something we point at to define their greatness.

Hemingway’s The Old Man and the Sea, which was around 27,000 words, won him the Pulitzer Prize. Abraham Lincoln’s Gettysburg Address was 275 words, and is remembered 160 years later as one of the greatest speeches in American history. Similarly, Instagram was 13 employees when it sold to Facebook for $1 billion, and WhatsApp was 55 individuals when it sold to Facebook for $19 billion. In a world where plenty of companies solve the problem, or get it done, the greats get it done with the utmost efficiently. They’re the ones who quietly ace the test, not the ones who brag about studying hard.

With the rise of AI taking on more rote and routine tasks, more companies will become what I call “Thin Companies,” or companies where tremendous value will accrue to very few individuals who master this trifecta of accuracy, precision, and economy. Accuracy refers to nailing product market fit and customer demand. This is the question of can you find the bullseye of what people want, and where there’s willingness to pay. Precision refers to your ability as a team to execute against this vision (accuracy) repeatedly with a low error rate. Once you know where the bullseye is, how do you hire, scale, and maintain culture such that you can continue to hit this target (knowing that the target also moves depending on market dynamics, the competition, and even where you are in the product adoption lifecycle. To Geoff Moore’s great book Crossing the Chasm, selling to an early adopter is dramatically different than selling to the early or late majority or to a laggard). The reality is that the target moves, and as a leader you need to both maintain accuracy and precision. Moreover, as you scale beyond your ability to hire directly, your leadership manifests itself as culture when you’re not in the room, and culture is all that keeps precision functioning when you as the leader no longer have your hand in every decision. Many leaders discount culture, but culture is what’s in the room when the CEO leaves it. It’s the determinant of values, and how people act when you are no longer in the room.

1. Accuracy - Locating demand, and ever-changing product market fit
2. Precision - Laser focused execution, even at scale
3. Economy - Getting it done as efficiently as possible

Finally to Dan’s point, and to the above points of David Deming, and the examples of Hemingway, Steinbeck, Instagram and WhatsApp, economy matters. How well you can achieve accuracy, precision, and then economy will determine a lot. Economy allows you to, all things considered, stay alive longer on the same amount of capital. This means one of two things, 1) you can raise less money, thus preserving greater equity for the founders and employees, or 2) you can generally survive as others die, thus harnessing a market that materializes around you. You have more time to find the target (accuracy), and streamline the team and execution (precision), on less input of labor and capital (economy). Better founders won’t adopt AI because this is the latest buzzword, or en vogue thing to do, but because it is a tool that helps them zero in on these three levers of accuracy, precision and economy. AI may mean you hire fewer, more senior workers, highly specialized, and to Deming’s point, those with the soft skills that enable streamlined task trading for efficient complex problem solving.

Thin startups of tomorrow will ruthlessly prioritize accuracy, precision, and economy, meaning they will prioritize an ability to listen and the deep humility to find product market fit, the internal culture to continually refine precision and execution, and leverage new tools such as AI to trim and refine the inputs necessary to achieve more company on less, aka greater economy.