Elon Musk's "Algorithm" Full Analysis: How to Turn Crazy Ideas into Reality

Anyone can draw on the powerful management playbook behind Elon Musk’s success.

At least, that’s the core argument in the new book The Algorithm just published by Jon McNeill, the former president of Tesla. The book argues that Musk’s requirements for how the teams at Tesla and rocket manufacturer SpaceX operate can be summarized into five steps.

“Many of the geniuses displayed by Musk’s companies come from a large pool of elite talent empowered by the ‘algorithm,’” McNeill writes in the book. “They are given absolute authority to question everything and innovate boldly, so they can pursue goals that are out of reach for ordinary people.”

When I watch Musk’s recent event, this philosophy comes to mind. At the event, he announced a planned joint project between Tesla and SpaceX aimed at building the world’s largest artificial intelligence (AI) chip factory.

Musk said that the project, called Terafab, will have production capacity far beyond the combined total capacity of all existing chip manufacturing plants worldwide. This is not a domain that an automaker or a rocket manufacturer would typically venture into—especially considering that entering a fiercely competitive industry that is completely different in nature carries its own risks.

And yet, AI chips are at the heart of Musk’s grand vision; in his blueprint, every year around the world will produce billions of robots, and humans will carry out space missions to the Moon and Mars. As he told audiences recently in Austin, Texas, the goal is simple: “Turn science fiction into scientific fact.”

So what exactly is “the algorithm”? It includes a series of seemingly simple steps with deep meaning: 1) Question every requirement; 2) Cut out all unnecessary steps (or components) in the process; 3) Simplify and optimize; 4) Compress the cycle time; 5) Automate.

This approach was first described in detail in Walter Isaacson’s 2023 book Elon Musk. McNeill says it was Isaacson who encouraged him to write his own book, to dig into how the “algorithm” works.

McNeill left Tesla in 2018; before that, he had been Musk’s right-hand man, witnessing the period when Tesla struggled through the development of the groundbreaking Model 3 sedan and worked to increase Model X SUV production.

As McNeill describes it, during that time, this problem-solving way of thinking became second nature—so much so that a Tesla executive suggested naming it “the algorithm” in order to spread the method more efficiently across the company.

McNeill told the writer in an episode of the Bold Names podcast that the method is rooted in the “first principles” thinking Musk champions.

McNeill explained: “In my view, first-principles thinking is breaking a problem down into its most basic elements—that is, I break the problem down… to the atomic level.”

However, putting this theory into practice perfectly is far from easy—even for Musk himself.

Estimates suggest the Terafab project could cost as much as $20 billion or more, and it has all the hallmarks of “the algorithm.”

Right now, Musk and other investors are pouring enormous sums into building more powerful computing power to drive AI forward. And the key bottleneck at present lies in the supply of AI chips and the energy needed to run data centers.

Part of SpaceX’s recent AI strategy is to build data centers in space. Musk believes that solar resources in space are abundant, and that operating costs will ultimately be lower than on Earth.

But a chip supply shortage is tying up this vision. Musk says that to realize his big ambitions in AI with xAI, Tesla’s autonomous vehicles and humanoid robots, as well as SpaceX’s AI data centers, all need a large number of chips—and currently, the total production capacity of global suppliers can meet only about 2% of their demand.

Musk says he has been trying to urge suppliers to rapidly expand capacity, but these suppliers often have their own expansion cadence that they are unwilling to break.

Most people in the business world might be forced to wait it out in this situation. But Musk refuses to wait.

Musk said, “That speed is far below what we expect, so there are only two paths in front of us: either build Terafab or there won’t be any chips available. And we need chips, so we’re going to build Terafab.”

McNeill told the writer that this is the essence of “the algorithm”: if Musk wants to take control of his own fate, there is no dogma requiring him to rely on other people’s chip supply.

“Elon has three businesses that all depend on chips, and he knows that this dependency is a ‘single point of failure,’” McNeill told the writer in a follow-up email.

Outside observers have doubts about what Musk will do next—especially as he plans to push for SpaceX to go public this year. Why would these companies get involved in such complex and money-burning chip manufacturing?

Beyond that, some of Musk’s grand plans in recent years have ultimately not panned out as hoped—for example, he had planned to expand Tesla’s annual production capacity to 20 million vehicles, but last year’s actual deliveries were 1.6 million. That also reduces the persuasiveness of building Terafab.

But supporters believe that Musk’s past record of successfully turning Tesla into a giant of electric vehicles, and shaping SpaceX into the dominant player in the emerging space economy, is enough to show that once Musk succeeds, he can create miracles.

This set of “the algorithm” has been forged through years of trial and error. Supply-chain bottlenecks have been a major challenge faced by Musk’s manufacturing companies. This is especially true when dealing with new technologies, because in those areas not everyone has absolute confidence—like Musk does—in the scale of potential new markets.

For example, shortly after the Model S sedan was successful, Musk began planning to build a massive battery factory. Similar to the situation today, Musk then predicted that the amount of batteries needed for electric vehicles would exceed global production capacity, so he decided to break through first.

In the end, Tesla persuaded its battery supplier Panasonic to open a large plant in Nevada, which became a key foundation for the Model 3’s journey to success.

McNeill told the writer that one core element of “the algorithm” is that it injects a sense of urgency into everyday work. For Musk, this means grabbing one or two matters that determine survival and then keeping a relentless focus on them week after week.

“When I used to attend those meetings, I was really confident that the CEOs of our competitors would never personally show up for weekly engineering reviews, and they would never move the company forward at that pace,” McNeill said. “So compared with these competitors, the advantages we’re building keep stacking up.”

Now, Musk’s new sense of urgency is clearly focused on the AI space domain.

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责任编辑：刘明亮