Elon Musk’s Lessons From Hell: Five Commandments for Business

Simply put: Elon Musk can be a real jerk.

And that has probably helped and hurt him in business, according to a new biography by Walter Isaacson.

In “Elon Musk,” out Tuesday, Isaacson puts forth the idea of “demon mode” to explain the temperamental impulses behind some of the tycoon’s successes—and setbacks. But it isn’t just demon mode that has fuelled his rise. Isaacson details other teachable ways the billionaire’s methods have helped make him the world’s richest man.

Both sides of Musk are sure to become part of B-school lore for a new generation of would-be entrepreneurs and business managers picking and choosing which traits and tactics to emulate.

Isaacson had previously made the concept of the “reality distortion field” popular with his bestselling 2011 book about Apple co-founder Steve Jobs and his ability to bend perception to motivate others.

Demon mode was on display in 2018 as Musk struggled to ramp up production of Tesla’s Model 3 sedan, which nearly destroyed the electric-car company and which the CEO dubbed production hell.

That experience through hell, the book says, also helped Musk shape five commandments for how he wants problems solved by his workers across his companies, from rocket maker SpaceX to social-media platform X, formerly Twitter.

Musk, in the book, calls the framework for problem solving “the algorithm.” In short, Musk urges his employees to:

Question every requirement
Delete any part or process you can
Simplify and optimise
Accelerate cycle time
Automate

“His executives sometimes move their lips and mouth the words, like they would chant the liturgy along with their priest,” Isaacson wrote of Musk’s mantra.

In the book, Musk acknowledges he talks about the approach often. “I became a broken record on the algorithm,” Musk is quoted as saying. “But I think it’s helpful to say it to an annoying degree.”

The approach builds off a long-held method for problem solving touted by Musk called first principles, a reasoning that breaks tasks into their very basics without simply reverting to what has been done before.

“The algorithm is a five-step process for not only making good products and designing good products, but manufacturing them,” Isaacson said in an interview Monday.

Throughout his book, Walter Isaacson chases the question of whether Elon Musk could be successful any other way. PHOTO: ARIEL ZAMBELICH/THE WALL STREET JOURNAL

“It begins with first principles. He says, question every requirement, and, by first principles he means, look down at the physics. If somebody says, no, we can’t build it at this price, he says, tell me how much the materials cost. Tell me exactly what’s involved here and then tell me you can or can’t do it.”

There are other lessons in the book that Musk has long practiced, such as never asking an employee to do something you aren’t willing to do (hence his sleeping on factory floors), hiring employees based on their attitude, and saying “it’s OK to be wrong. Just don’t be confident and wrong.”

Telling Musk bad news, however, has been seen by some employees as dangerous to one’s career.

“One of his problems is people sometimes are afraid to tell him the bad news,” Isaacson said. “Those who succeed around Musk are those who figure out you got to give him the bad news even if it’s going to result in some unpleasant scenes.”

Their fear is often rooted in demon mode.

Claire Boucher, known as the musician Grimes and the mother of three of Musk’s children, coined the term in an interview with Isaacson.

“Demon mode is when he goes dark and retreats inside the storm in his brain,” Boucher said in the book. “Demon mode,” she added, “causes a lot of chaos but it also gets s— done.”

And Musk has gotten a lot done, helping usher in the electric-car era as Tesla chief executive and igniting the commercial space race with SpaceX, which he founded. His messy stewardship of X, however, is testing public perception of his business genius.

Isaacson, who shadowed Musk for two years in reporting the book, saw demon mode in person several times along with other personalities that he described as ranging from silly to charming. He suggests the roots of the dark clouds come from the 52-year-old’s childhood in South Africa.

“It’s almost like Dr. Jekyll and Mr. Hyde where a cloud comes over and he gets into a trance and he can just be tough in a cold way,” Isaacson said. “He never gets really angry, never gets that physical, but coldly brutal to people and he almost doesn’t remember afterwards what he’s done. Sometimes I’ll say, why did you say that to that person? And he’ll look at me blankly as if he didn’t quite remember what happened while he was in demon mode.”

In one instance, Isaacson described seeing demon mode emerge when Musk saw SpaceX’s launchpad in South Texas empty late one evening.

“He orders a hundred people to come in from different parts of SpaceX from Florida, California so they can all work for 24 hours a day getting this thing done even though there was no need to,” Isaacson said.

Such surges seem to play in tandem to Musk’s need for drama.

“He is a drama magnet,” Musk’s younger brother, Kimbal, said in the book. “That’s his compulsion, the theme of his life.”

Isaacson cautions that readers shouldn’t come away thinking they can be just like Musk and automatically succeed. Rather, he said, readers should see both how leaders such as Musk and the late Jobs were effective and also take away cautionary tales.

“You don’t have to be this mean,” he said.

Still, throughout his book, Isaacson chases the question of whether Musk could be successful any other way.

“I try to show how that’s one of the strands in a fabric and as Shakespeare said, we’re moulded out of our faults,” Isaacson said. “If we pull that strand out, you might not get the whole cloth of Elon Musk.”

A sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.

The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.

The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.

The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.

This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.

The debt behind the AI build-out

Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.

Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.

JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.

The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.

That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.

A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.

When interest rates rise, those assumptions become less forgiving.

Inflation changes the calculation

September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.

The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.

CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.

Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.

Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.

The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.

China adds another source of pressure

Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.

Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.

The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.

That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.

A reset rather than the end of the boom

The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.

What has changed is the market’s willingness to overlook the price and financing structure of that growth.

The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.

More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.

For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.

Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.

The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.

Fact box

Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026

Philadelphia Semiconductor Index: Down approximately 11.4 per cent

Micron Technology: Down approximately 14.6 per cent

Samsung Electronics: Down approximately 18 per cent

SK Hynix: Down approximately 24 per cent

US September inflation: 3.7 per cent annually

Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion