What Does NVIDIA Actually Do?

In simple terms, NVIDIA operates in four main areas, going from large to small: data centers, gaming, 3D design, and automotive technology.

Let’s start with the one driving all the hype today: Data Centers. The segment accounts for 89.7% of total revenue, in FY2026 $193.7 billion. These are our beloved AI chips, networking equipment, and entire computing systems to cloud providers and companies that need the massive computing power required to train and run AI models. Of course, this segment has exploded with the AI boom.

Then there’s Gaming, NVIDIA’s original business. Think GeForce graphics cards for PC gamers. Gaming created NVIDIA, but it now accounts for just 7.4% of revenue, or $16.0 billion. Still a big business, just overshadowed by the hype.

NVIDIA also has Professional Visualization, which provides technology for 3D design, rendering, and demanding creative and engineering work. This business generated $3.2 billion, or 1.5% of revenue.

Its Automotive business focuses on in-car computing and autonomous driving platforms, including NVIDIA DRIVE, which automakers can use to develop smarter vehicles. It brought in $2.3 billion, representing 1.1% of revenue.

Just mentioning this for completeness. :D there’s OEM and Other, a small category covering products and arrangements outside NVIDIA’s main market platforms. It generated $619 million, or just 0.3% of total revenue. Hey, $619 million is something a mid-sized company would be pretty proud of. But next to NVIDIA’s core business? Peanuts. ;D.

So, what’s the big picture? NVIDIA has evolved from a company best known for graphics cards into a company that powers much of the AI infrastructure being built today. Data Center alone generates nearly 9 out of every 10 dollars the company earns. Gaming remains a meaningful business, while Professional Visualization, Automotive, and OEM and Other make up relatively small pieces of the business.

Here’s an excerpt from NVIDIA’s annual report.

You might be wondering: We’re still in October 2026, so how do we already have NVIDIA’s full-year 2026 revenue figures? Shouldn’t those numbers come out in early 2027? Nope. Here’s why: NVIDIA’s fiscal year doesn’t follow the calendar year. It ends in late January, so FY2026 actually ended on January 25, 2026. That means almost the entire fiscal year fell in calendar year 2025, with just the final few weeks in January 2026. So when you see NVIDIA reporting $215.9 billion in revenue for FY2026, remember: that’s revenue earned over the fiscal year ending in January 2026, not revenue earned between January and December 2026.

Now, let’s look at how NVIDIA’s revenue has evolved over the years. To keep things simple, I’ve focused on the last six fiscal years. Not a surprise.

The chart below shows how each segment’s share of NVIDIA’s total revenue changed between FY2020 and FY2026. The shift is hard to miss: Data Center has grown dramatically as a share of revenue, while Gaming’s share has shrunk. The other segments have remained relatively small, with only modest changes over the period.

For a better understanding, here are NVIDIA’s revenues in dollars for the last six years.

The Main Business: Data Centers

As we’ve seen, Data Center is NVIDIA’s main business, generating roughly 90% of its revenue. So, if we want to understand what makes NVIDIA so successful, this is where we need to look.

Let’s take a step back. What actually happens inside a data center? And what makes NVIDIA’s chips so valuable for AI? To understand that, we need to look at the different types of chips and what sets them apart. A GPU (graphics processing unit) is a chip designed to perform huge numbers of calculations in parallel. That might sound a little technical, so let’s make it simple.

Imagine you have 10,000 easy math problems to solve. A CPU (central processing unit) is like a small team of brilliant professors. Each one can tackle complicated problems, switch between different tasks, and handle instructions that need to be carried out in a specific order. But there are only a limited number of them, so working through thousands of problems can take time. A GPU is like a stadium full of thousands of students, all solving simple problems at the same time. Each worker is less versatile, but together they can get through a massive amount of work in parallel.

Of course, real processors are more complicated than this analogy suggests. CPUs can also perform parallel calculations, and GPUs aren’t limited to simple arithmetic. But the comparison captures the basic idea: CPUs are built for flexibility, while GPUs excel at massive parallel workloads.

Think about a video game. Every frame on your screen contains millions of pixels, and the computer needs to calculate their colors, lighting, textures, and other visual details over and over again. Much of this work can be split into smaller calculations that happen simultaneously. That’s exactly the kind of job GPUs were designed to handle. Originally, their main purpose was to make video games look better and run faster. Then AI came along.

Training an AI model involves performing enormous numbers of mathematical operations on grids of numbers called matrices. These operations need to be repeated across huge amounts of data. Sound familiar? It’s the same basic principle: lots of calculations that can be broken into smaller tasks and performed in parallel. That made GPUs a natural fit for AI. A technology originally developed to render realistic game graphics turned out to be incredibly useful for training and running AI models. And NVIDIA was already making GPUs. It had spent decades developing the hardware and software needed to make them work efficiently. When demand for AI computing took off, NVIDIA was in a strong position to benefit.

Now imagine thousands upon thousands of chips inside those computers, all connected so they can work together like one enormous computing system. That’s essentially an AI data center. Let’s break it down.

The chips inside an AI Data Center. Long story short! GPUs (or TPUs, Google’s custom-built AI specialists) are the workhorses, CPUs are the managers, coordinating the work, while GPUs handle much of the number-crunching. Memory chips HBM (high-bandwidth memory) and DRAM, RAM store the data that processors need to access quickly. Networking chips, helping thousands of GPUs work together. And finally storage devices, including SSDs, hold datasets, software, and model files for the long term.

A chip sits on a circuit board. Multiple boards and components make up a server. Servers are installed in racks, tall cabinets designed to hold and connect the equipment and racks are arranged in large data center halls. The buildings, electricity supply, and cooling systems are obviously essential (and very essential, bit it is a different topic) too. But NVIDIA’s business is focused on the computing technology inside those facilities, and that technology is in tremendous demand.

AI Data Center

NVIDIA’s Competitors: Four Groups to Watch

NVIDIA faces competition from four different directions. And not all of its competitors are trying to beat it in the same way.

1. Direct competitors: AMD and Intel

AMD is NVIDIA’s closest major rival in the market for general-purpose AI GPUs. Its accelerators offer customers an alternative to NVIDIA’s hardware. Intel competes in processors and AI accelerators, including its Gaudi product line. Both companies want customers to buy their chips instead of NVIDIA’s. AMD is the more prominent challenger in the market for general-purpose AI accelerators.

2. Big Tech companies building their own AI chips

Google has its TPUs (I mentioned earlier), Amazon has Trainium, Microsoft has Maia, and Meta has MTIA.

Here’s the interesting part: these companies are also NVIDIA’s customers. But they’re increasingly developing their own chips, too. Why? They want to reduce their dependence on NVIDIA and potentially run AI workloads more cheaply using hardware designed specifically for their needs. In other words, NVIDIA’s biggest customers are also working on ways to need less of NVIDIA and potentially destroy the moat.

3. Companies designing custom chips

Broadcom and Marvell help technology companies design specialized chips for particular workloads. Think of it this way: NVIDIA makes powerful, flexible GPUs that can handle a wide range of tasks. But sometimes, a company doesn’t need all that flexibility. It needs a chip designed to do one specific job as efficiently as possible. Custom chips can be a better fit for those workloads, potentially offering advantages in performance, power consumption, or cost.

4. Specialized AI-chip companies

Cerebras, Groq (please don’t confuse with Grok by Elon’s xAI), and Huawei are other players worth watching, with Huawei particularly relevant in China.

These companies take different approaches to AI hardware or focus on specific markets. They aren’t all direct replacements for NVIDIA across every type of AI workload. But they can compete with NVIDIA in particular applications or regions, giving customers alternatives to its technology.

Moats?

Moat, not outdated Michael Porter's Five Forces.

NVIDIA dominates the AI chip market. Is it simply making the best chips, or is there something else going on?

The answer is a mix of software, hardware, speed, and scale. NVIDIA has several advantages that make it difficult for competitors to catch up. But none of them is guaranteed to last forever! Let’s look at what gives NVIDIA its edge, how long those advantages might hold, and where the real vulnerabilities are.

1. CUDA: Developers Don’t Like Switching

NVIDIA’s first major advantage is its software ecosystem, especially CUDA, the real moat.

CUDA allows developers to program NVIDIA GPUs for complex computing tasks. Over nearly two decades, generations of engineers have learned to use it, and companies have built their AI infrastructure around it.

Think about the QWERTY keyboard. Maybe there’s a better layout out there, but most people aren’t going to switch. They already know where every key is, and relearning everything would be a pain.

CUDA works in a similar way. Developers know it, companies rely on it, and switching to a different platform can mean rewriting code, adapting tools, and retraining people. That’s a real advantage for NVIDIA.

How long will it last? Probably longer than most of NVIDIA’s other advantages, but it’s not untouchable! Many developers now use higher-level software tools that make it easier to work across different hardware platforms. And companies like Google and Amazon have the engineering talent and financial resources to build their own software ecosystems.

2. NVIDIA Sells the Whole AI System, Not Just the Chip

Imagine you’re building a massive AI data center. You don’t just need powerful chips. You also need CPUs, networking equipment, memory, and software that allows thousands of components to work together. NVIDIA offers much of this as an integrated system, from individual GPUs to complete rack-scale computing systems. That matters because getting all these components to work together efficiently is a serious engineering challenge. A competitor might build a great chip, but matching NVIDIA’s entire system is another story. NVIDIA’s customers aren’t just buying hardware. They’re buying a system designed to work together.

How long will the moat last? The advantage should remain meaningful over the next few years, but competitors are catching up. AMD, for example, is developing its own rack-scale AI systems. It has also announced major AI infrastructure agreements with companies including OpenAI and Meta. The more competitors can deliver complete systems rather than individual chips, the harder it becomes for NVIDIA to stand out on integration alone.

And as mentioned earlier sometimes customers don’t need the whole system but individual chips that can perform specific tasks efficiently. So this moat doesn’t hold for every customer.

3. Speed and Scale: NVIDIA Keeps Moving

Another advantage is NVIDIA’s ability to develop new generations of chips and systems at an extraordinary pace. The company has enormous (yes, enormous!) financial resources to invest in research and development. It also has deep relationships with suppliers, including TSMC, the world’s leading advanced chip manufacturer, and suppliers of high-bandwidth memory. These relationships matter because building advanced AI chips isn’t just about having a good design. You also need access to scarce manufacturing capacity and specialized components. NVIDIA’s scale helps it compete for those resources while investing in the next generation of products. Jensen Huang has projected at least $1 trillion in demand for NVIDIA’s Blackwell and Vera Rubin platforms through 2027. That’s a projection, not guaranteed revenue, but it illustrates the scale of the opportunity NVIDIA is targeting.

How long will it last? As long as NVIDIA keeps executing wisely. This advantage isn’t something the company can establish once and then forget about. It has to keep developing better products, securing supplies, and delivering on time. Falling behind by just a few generations could put NVIDIA’s dominance at risk.

4. NVIDIA Works Across Clouds

Here’s another important advantage: NVIDIA’s hardware is available across major cloud providers. A company can use NVIDIA GPUs through Amazon Web Services, Microsoft Azure, Google Cloud, and other providers. It doesn’t have to commit to one cloud ecosystem just to access NVIDIA’s technology. That flexibility makes NVIDIA attractive to businesses that want to avoid becoming too dependent on a single provider. By comparison, custom chips such as Google’s TPUs and Amazon’s Trainium have traditionally been more closely tied to their respective cloud ecosystems, although access to these chips is expanding.

How long will it last? NVIDIA’s broad availability is a meaningful advantage, but competitors are becoming more accessible, too. Anthropic has committed to using up to 5GW of Amazon’s Trainium capacity. OpenAI has also committed to using 2GW of Trainium capacity through AWS. Google’s TPUs are gaining traction beyond Google’s own internal workloads as well. These agreements show that major AI companies are willing to invest heavily in alternatives to NVIDIA, especially when they believe those alternatives can improve performance or reduce costs.

Note from the author.

Do we want it or not? AI applications will expand beyond today’s data centers. Robotics, autonomous systems, industrial simulation, and edge computing are all potential growth markets, and they’re coming. I genuinely believe these areas will create new growth opportunities for NVIDIA and not only for NVIDIA.

Risks

The Cost Question.

Can customers run AI more cheaply without NVIDIA? Google’s TPUs and Amazon’s Trainium are designed for specific workloads and may offer better economics in certain cases. Broadcom helps companies like Google design these custom chips.

For companies running AI at massive scale, even small cost savings can add up. If a specialized chip delivers the same results for less money, why pay more for NVIDIA? A valid question, right?

Custom chips won’t replace NVIDIA GPUs everywhere, but they give customers another reason to consider alternatives.

TSMC: The Factory Behind the Chips

NVIDIA relies heaviest on Taiwan Semiconductor Manufacturing Company, or TSMC, to manufacture its advanced chips. TSMC has the expertise, technology, and manufacturing capacity needed to produce some of the world’s most advanced semiconductors. Building comparable capabilities takes years and enormous investment.

The problem is geography.

A substantial share of the world’s advanced chip manufacturing is concentrated on a tiny island far away in Taiwan. If a military conflict or blockade disrupted production there, the consequences would extend far beyond NVIDIA. It would affect the entire global technology industry, including companies that depend on advanced chips for smartphones, data centers, and AI systems.

Think of it as the world’s most important bakery supplying nearly every high-end restaurant. Those restaurants might design their own menus and create their own recipes, but if the bakery stops delivering, they’ve got a problem.

The semiconductor industry isn’t standing still: TSMC is expanding manufacturing capacity outside Taiwan, including in Arizona, where its first US fabrication plant is already operating, but as for now not completely independently. Samsung and Intel are also investing in advanced manufacturing and seeking to become more competitive alternatives to TSMC. These efforts take time. Building a new fabrication plant doesn’t immediately create a fully operational alternative to Taiwan’s manufacturing ecosystem. Advanced production requires specialized equipment, experienced workers, reliable suppliers, and years of investment. Diversification can reduce the risk. It can’t eliminate it in short run.

Besides, ASML, based in the Netherlands, is the only supplier of EUV lithography machines, which are essential for manufacturing the most advanced chips. NVIDIA also depends on companies such as Samsung for some chip manufacturing, SK Hynix and Micron for memory, and other suppliers for packaging and assembly.

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Being the CEO of a huge company when you didn’t start it is a different thing. And don’t get me wrong: I’m not diminishing the significance or importance of hired CEOs. Look at Tim Cook. He took over Apple after Steve Jobs and did some pretty remarkable things: grew the company’s value, optimized the business, and turned it into an even bigger machine. Or Satya Nadella. In my opinion, he’s done a much better job at Microsoft than Steve Ballmer did.

But for me, there’s something especially fascinating about founders. I’m more interested in the early years, the uncertainty, the struggles, the risks, and those moments when there was no guarantee that any of it would work. So you’ve probably noticed that I keep writing about them, founders: Elon Musk, the Collison brothers, Jeff Bezos, Travis Kalanick, Sam Altman, Elisabeth Holmes… And today, I want to talk about NVIDIA, with a valuation of more than $5.6 trillion as of October 2026.

It started at Denny’s

Denny’s is definitely not my favorite restaurant. But there’s something pretty interesting about one particular Denny’s. One of the most valuable companies in the world, NVIDIA, was founded there in 1993. The Denny’s was located at 2484 Berressa Road in San Jose, California. And yes, NVIDIA really started as a scrappy little startup. The company was founded by three engineers: Jensen Huang, Chris Malachowsky, and Curtis Priem. And Jensen Huang had actually left a stable job as a division head to take the risk.

Denny’s — Jensen’s former employer and the birthplace of NVIDIA

There’s another part of Jensen Huang’s story that I find interesting. He wasn’t born in America. Huang was born in Taiwan in 1963, and when he was around nine, his family sent him to the United States. He grew up partly in Kentucky and later in Oregon. Eventually, he became a U.S. citizen and today is generally described as both Taiwanese and American.

And no, Jensen Huang wasn’t some 20-year-old kid with no experience who suddenly decided to start a tech company. He had a serious technical background. He earned a bachelor’s degree in electrical engineering from Oregon State University and then a master’s degree in electrical engineering from Stanford University.

After that, Jensen worked at semiconductor companies including AMD and LSI Logic. And then, 1993, at 30, he founded NVIDIA. Thirty! I actually find that detail surprisingly interesting because we often have this image of startup founders as extremely young people in their early twenties who drop out of college and immediately build a billion-dollar company (I know some of my friends don’t agree with me on this point). Anyway, that wasn’t Jensen Huang. He and the other NVIDIA founders were already experienced engineers when they decided to take the leap.

There’s a funny detail about the Denny’s story that makes it even better: Jensen Huang actually worked at Denny’s when he was 15. He started as a dishwasher, then became a busboy and eventually a waiter. Years later, he said the diner was a great place to think because there was unlimited coffee and nobody was going to kick you out.

NVIDIA

And then there’s the name. NVIDIA comes from “invidia,” the Latin word for envy. Pretty fitting, considering where the company ended up, don’t you agree, huh? 😄

But the early years were definitely not glamorous. NVIDIA’s first product was the NV1, launched in 1995. And it was… not exactly a hit. The NV1 was an unusual graphics card. It combined 2D and 3D graphics with audio and a game port, and used a technology based on quadratic texture mapping. NVIDIA was betting on a particular way of rendering 3D graphics at exactly the moment the industry was moving in another direction. Microsoft’s Direct3D approach, which was built around triangles, was becoming the standard, and NVIDIA’s technology wasn’t well aligned with it. The result? Not enough software support. Poor sales. And a serious problem for a very young company. The first product had basically flopped and NVIDIA was running out of time.

Then came Sega. This is where the story gets really interesting. NVIDIA had been working with Sega on a chip for its next-generation game console. But Jensen Huang eventually realised that NVIDIA’s technical approach was wrong and that the chip wasn’t going to work. Instead of pretending everything was fine, Huang told Sega. Basically: “We screwed this up”. And then he asked Sega to continue paying NVIDIA so the company could survive. Sega agreed to keep supporting NVIDIA, providing roughly $5 million that helped keep the company alive. According to Huang, by 1996 the company was only about a month away from running out of money. NVIDIA had to lay off more than half of its employees, leaving only a few dozen people, from roughly 100 people to around 40. So when you look at NVIDIA today, it’s worth remembering that this wasn’t some perfectly executed master plan. At one point, they were basically fighting to stay alive.

And this is Sega. Even I had one. 😂

And here’s the funny twist: Sega never actually ended up using NVIDIA’s chip in its console. NVIDIA helped develop the technology for Sega’s console, but the final console didn’t use an NVIDIA graphics chip.

Lesson learned, my dear subscribers: Even in the middle of a struggle, you have to be honest. Jensen Huang realized that NVIDIA’s approach wasn’t working. Instead of hiding the problem, he admitted it to Sega and was honest about the situation. That honesty helped NVIDIA survive.

Don’t confuse optimism with refusing to see reality. Sometimes leadership means having the courage to say, “We got this wrong. Here’s the problem. Now what can we do about it?”

And then came the GPU

The thing that ultimately changed NVIDIA’s trajectory was the GPU, graphics processing units originally designed primarily for rendering graphics and powering video games. But GPUs turned out to be incredibly good at something else: doing huge numbers of calculations in parallel. And that became incredibly important for artificial intelligence.

NVIDIA developed the technology and software ecosystem around GPUs for years before the current AI boom exploded. Its CUDA platform, introduced in 2006, opened up GPU parallel processing to scientific and other computing workloads. Then, in 2012, NVIDIA GPUs powered the breakthrough AlexNet neural network, helping spark the modern AI era. And suddenly, the chips that had originally been built to make video games look better became some of the most important infrastructure in the AI industry. That’s the part of NVIDIA’s story I find fascinating.

The company didn’t start out knowing it was going to power the AI revolution. It started with three engineers, a bet on 3D graphics, a product that nearly failed, layoffs, and a company that was reportedly only weeks away from running out of money. And somehow, that company survived.

NVIDIA still dominates the AI accelerator market. Reuters reported earlier this year that the company held more than 90% of both the AI training and inference markets. And the market capitalisation tells the rest of the story. More than $5 trillion.

Jensen’s company

There’s another thing I find interesting about NVIDIA: the way the company is structured. Jensen Huang is still both President and CEO, and he has a remarkably broad span of control. Finance, sales, operations, and legal all ultimately sit directly under him. There isn’t a COO sitting between Huang and those functions, and NVIDIA has a relatively wide group of senior executives reporting directly to the CEO rather than everything being pushed through several layers of management.

That’s pretty unusual for a company of this size. And it says something about Huang’s role. He isn’t just the founder whose name happens to be attached to the company. He’s still very much in the middle of the business.

From $12 to… well, NVIDIA

NVIDIA went public on January 22, 1999, at $12 per share on the NASDAQ under the ticker NVDA. The share structure is actually pretty straightforward, too. Unlike companies such as Google or Meta, NVIDIA has one class of common stock. There isn’t a special dual-class structure giving Huang or other founders extra voting power. NVIDIA has also split its stock six times since the IPO, most recently with a 10-for-1 split in 2024. And that’s one reason the original $12 IPO price can look so misleading when you compare it with today’s share price.

And Jensen still owns a huge piece of it. As of 2026, Jensen Huang owns roughly 870 million NVIDIA shares, or around 3.6% of the company. At NVIDIA’s current valuation, that stake is worth well over $200 billion. That makes Huang the company’s largest individual shareholder and puts him among the richest people in the world. Not bad for a guy who started the company at 30. 😄

But here’s another interesting detail. When NVIDIA went public in 1999, Huang owned a much larger percentage of the company: roughly 10–13%, depending on the point in time you’re looking at. Over the following decades, his percentage ownership fell mainly because NVIDIA issued more shares and the company grew enormously.

So the funny thing is that Huang owns a smaller percentage of NVIDIA today, but that smaller percentage is worth an absolutely insane amount of money. That’s one of the strange mathematical realities of building a company that goes from a tiny startup to the most valuable companies in the world.

I’m trying not to make this article long, so I’m going to stop here. There’s still a lot more to unpack: the fascinating story of GPUs, NVIDIA’s competitive moat, the risks the company faces, and the things that could ultimately affect its value. Those deserve their own article. So, NVIDIA Part II is coming. 😄

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Btw, Elon, if you are reading this, hey there!

Despite the constant controversies and his political involvement, I really admire Elon Musk. Why? Because he refuses to just accept traditions or the way things have always been done.

Instead of following the crowd, he questions everything and challenges the status quo. That kind of thinking is what actually drives human progress forward, do you agree?

I won’t bother listing his whole life story here. Elon is still relatively young, though you could already fill libraries with books about him. But his mindset of questioning how things work is what makes him stand out. Here in short:

Elon Musk is a South African-born American entrepreneur, engineer, and billionaire businessman known for his work across the automotive, aerospace, artificial intelligence, and infrastructure industries.

He started his first company, Zip2 (1995), an online business directory and web software provider for newspapers, alongside his brother Kimbal. Elon was not that young, he was 24! At the age of 27 he was already a millionaire. Zip2 was sold to Compaq in 1999 for over $300 million.

Young Elon and Kimbal Musk

While he is heavily associated with Tesla (which he joined early as an investor and chairman in 2004, though it was originally founded by others), Musk has founded or co-founded several major companies throughout his career:

• X.com (1999): An online financial services and email payments company that later merged to become PayPal.

• SpaceX (2002): Space Exploration Technologies Corp., focused on reducing space transportation costs and enabling the colonization of Mars (including the Starlink satellite internet network).

• OpenAI (2015): Co-founded as a non-profit artificial intelligence research lab.

• Neuralink (2016): A neurotechnology company developing implantable brain-machine interfaces.

• The Boring Company (2016): An infrastructure and tunnel construction company designed to ease urban traffic.

• xAI (2023): An artificial intelligence company focused on understanding the true nature of the universe, creator of the Grok chatbot.

He also acquired Twitter in 2022, subsequently rebranding and restructuring it as X Corp.

Have you noticed Elon’s obsession with the letter “X”? There’s x.com, SpaceX, xAI, and X Corp!

However, this article is dedicated entirely to his venture SpaceX.

SpaceX. A Rocket Company? Not Only.

Elon Musk founded SpaceX in 2002 with the explicit long-term goal of making humanity multi-planetary and dramatically cutting the cost of space travel. The idea is simple: you don't throw away the car after a single ride. That's exactly how Musk believed rockets should work.

In the beginning, SpaceX faced a brutal battle. The aerospace establishment and government regulators weren’t welcoming, they viewed space exploration as exclusive government territory and put up massive roadblocks for a private startup.

The early days were a survival test. The first three rocket launches all failed, pushing the company to the brink of bankruptcy. With money running completely out, the fourth launch was a moment. Had it failed, SpaceX likely would not exist today.

For over two decades it stayed private, funded through 32+! rounds totaling roughly $12 billion from investors like Andreessen Horowitz, Founders Fund, Sequoia, Fidelity, and Google Ventures. Its private valuation climbed steadily, $137B in early 2023, $350B in late 2024, $400B by mid-2025, and around $800B by December 2025 via an insider share sale.

Where SpaceX stands now?

At its core, the company is built on three pillars: Space, Connectivity, and Artificial Intelligence.

• Space (Falcon, Dragon, Starship): Dominates the global stage, executing more orbital launches each year than any other competitor, beating out even major national space programs.

  

  SpaceX rocket

• Connectivity: Powers Starlink, a massive global satellite internet network.

  

  Starlink sattelites

• Artificial Intelligence: Manages data centers, X, and the Grok AI model. These assets were originally part of xAI before being acquired by SpaceX in early 2026, ahead of its IPO. Important to notice: X (formerly Twitter) ended up under xAI, not that xAI was under X.

SpaceX has about 22,000 employees and recently completed a 13th Starship flight test, described as largely successful.

Despite the heavy-hitting presence of the space and AI divisions, Starlink is the undisputed financial engine of the entire enterprise. While the AI venture is currently burning through cash at a rapid pace, Starlink remains the sole profitable division.

SpaceX made 18.7 billion in revenue for 2025 but lost 4.9 overall, and that loss actually widened further into Q1 2026, losing 4.1 billion in just 3 months. Despite strong top-line growth, the company was solidly in the red for the year, primarily driven by the AI segment's losses.

Are There Any Moats? Clearly!

The Space Division

• The Ultimate Price Cut: By figuring out how to land and reuse rockets instead of throwing them away after a single flight, this division slashed the cost of getting into orbit. Competitors and traditional government space agencies simply cannot compete on price.

• The Go-To Transport: Because they fly more often than anyone else on Earth, they get safer, faster, and cheaper with every single launch. It has essentially become the global highway to space, trusted by both commercial giants and the military for critical missions.

The Connectivity Division (Starlink)

• Beating Everyone to the Sky: Starlink put thousands of internet satellites into low-Earth orbit before anyone else could even get their plans off the ground. That massive head start is nearly impossible for a newcomer to match.

• The In-House Advantage: Unlike traditional telecom companies that have to buy expensive third-party gear, Starlink gets its rides to space built and flown by its own sister division at cost. That keeps expenses remarkably low while building a massive, global base of paying subscribers.

The Artificial Intelligence Division

Some analysts may disagree with me and argue that xAI has no moat at all. That’s a fair point. Grok is still not particularly popular, accounting for only about 3% of AI chatbot web traffic, and its share has remained largely flat over the past year. Meanwhile, ChatGPT, Google’s Gemini, and Anthropic’s Claude have all expanded their user bases.

Even so, I believe there is meaningful long term potential. And I’m not referring only to the chatbot itself. The real opportunity may lie in the broader ecosystem, particularly xAI’s AI infrastructure and data centers, which could become a significant competitive advantage as demand for AI computing continues to grow.

• Brute-Force Compute: Backed by enormous data centers, they possess the raw processing muscle needed to go with the biggest names in tech. Down the road, they even have the unique potential to move data centers right into space, tapping into endless solar power and natural cooling to bypass Earth’s crowded energy grids.

• A Firehose of Real-Time Data: While other AI labs struggle to find fresh, high-quality data to train their models, this division has direct, exclusive access to a massive global stream of live human conversation and news.

Going Public (IPO)

A private company sells shares to the public for the first time. In exchange for cash, investors get ownership stakes (shares). The company gets listed on an exchange like the NYSE or Nasdaq, and after that, anyone can buy or sell those shares.

Share types
Most public companies keep it simple with one class of “common stock” , one share, one vote. But some companies (especially tech, like Google or Meta) use multiple classes:

• Class A sold to the public, usually 1 vote per share

• Class B held by founders/insiders, often 10 votes per share

• Preferred stock sometimes exists too, usually held by early investors,gives priority on dividends or payout if the company is sold, but often little or no voting power

This lets founders raise public money while keeping control.

Governance structure

• Shareholders: own the company, vote on big things (electing the board, mergers, major changes). Voting power depends on shares held.

• Board of Directors: elected by shareholders. Sets overall strategy, hires/fires the CEO, oversees major decisions. Usually includes independent (outside) members plus a few insiders.

• Board committees: smaller groups handling specific jobs, like the Audit Committee (checks the finances), Compensation Committee (sets executive pay), Nominating/Governance Committee (picks future board members).

• Executives (CEO, CFO, etc.): run day-to-day operations, report to the board.

Chain of accountability
Shareholders → elect → Board → hires/oversees → CEO and management → runs the company

So the short version: shareholders own it, the board oversees it, executives run it and share classes decide how much say each owner actually gets.

SpaceX Remains Fully Controlled by Musk

SpaceX went public on June 12, 2026, on the Nasdaq under the ticker SPCX, in what became the largest IPO in history, priced at $135 a share and raising roughly $75 billion.

Here’s the model they picked:

Dual-class structure, tilted hard toward Musk. SpaceX used the same trick as Google, Meta, and Ford before it: public investors get one class of shares, insiders get another with outsized voting power. But SpaceX pushed it further than almost anyone: Musk controls about 85% of SpaceX’s shareholder voting power, even though he doesn’t own anywhere near 85% of the economic value. Pretty unfair to regular shareholders, don’t you agree?

Unusually generous retail allocation. Most IPOs hand only a sliver of shares to ordinary individual investors and save the rest for big institutions. SpaceX flipped that: roughly 30% of the offering was set aside for retail investors, distributed through everyday brokerages like Schwab, Fidelity, Robinhood, and SoFi. Basically anyone could buy it at the IPO.

So! One class for the public, one super-voting class for Musk, a big slice deliberately handed to retail buyers, and a valuation that made it the biggest debut on Wall Street.

Let me explain ownership vs. control: It’s genuinely one of the most concentrated ownership setups of any major public company I’ve ever seen! Musk owns roughly 42% of SpaceX’s equity, but that’s not the number that matters. Through SpaceX’s final S-1/A filing, Class B shares carry 10 votes per share versus 1 vote for Class A, giving Musk approximately 85.1% of the voting power despite owning less than half the company. He holds this by owning 12.3% of Class A shares and 93.6% of Class B shares. Elon is CEO, CTO, and Chairman of the Board. Per the prospectus, Musk controls the election of a majority of the board directly through his Class B shares.

Who can fire Musk?
Musk can only be removed from his roles as Chairman, CEO, and CTO by a vote of Class B shareholders. Since he personally controls the vast majority of Class B votes, this provision essentially lets him veto his own removal. In practice, no one can fire him against his will under this structure. Pretty smart, huh?

Bottom line: public shareholders own a large slice of the economics but essentially none of the control. It’s a governance model, not an accident.

Because Musk controls the majority vote, SpaceX qualifies as a “controlled company” under Nasdaq rules, which exempts it from several standard governance requirements, like needing a majority-independent board.

My Take

Shares priced at $135, opened around $150 and the company debuted at a valuation near $1.8–2.3 trillion depending on the source. Musk reportedly made a deliberate push to give retail investors meaningful access to shares rather than the usual Wall Street-heavy allocation.
The stock has been volatile. It peaked intraday around $225.64 on June 16, then fell hard. As of late July 2026 it’s trading around $113–115, roughly 15–22% below the IPO price and about 49-51% off its post-IPO high, putting the market cap near $1.49 trillion.

Reaction is split. Analysts remain broadly bullish on paper: around 27 “buy” ratings vs. 1 “sell”. Firms like Raymond James have reiterated strong buy ratings. But there’s real skepticism too: Morningstar (my former employer! Love you, guys) called it “significantly overvalued” before the IPO, valuing it closer to $780B versus the $1.5-2T+ private/IPO targets. Morgan Stanley has flagged a valuation gap.

SpaceX has genuine, durable competitive advantages, it dominates commercial launch. Starlink is a real and growing revenue engine, and it has a long head start on reusable rocket technology. That’s different from, say, a hype-only IPO. But the stock’s volatility since debut suggests the market is still trying to figure out how to value a company that’s part rocket company, part satellite internet provider, and now part AI company (via the xAI merger), three very different businesses bundled together, each with its own risk profile.

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Disclaimer: I hold only a tiny amount of SpaceX shares. Just for fun!

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Uber is no longer just the app you open when you need a ride home after a night out. Over the past decade, it has transformed into a global technology platform that operates across transportation, food delivery, and logistics.

Headquartered in San Francisco, Uber employs around 35,000 people worldwide, according to PitchBook. This is a grown-up public company with real scale!

Uber’s Services

What makes Uber genuinely interesting right now is how far it's stretched beyond its original idea. The core ride-hailing business is still going strong, but delivery and freight have become major growth engines in their own right. Uber has effectively turned itself into a platform that moves both people and things and that's a much bigger business than anyone expected when it launched.

Check the latest Annual Report of Uber here.

And that’s not all…

For companies, Uber for Business simplifies corporate travel and team meal management. For patients, Uber Health solves a quiet but real problem: getting people to and from medical appointments without the stress of arranging a car. On the consumer side, Uber One is the company’s subscription play (membership that ties together ride discounts and free deliveries), making it harder to imagine life without the app. Need a car for the weekend? Uber Rent lets you book one directly without ever dealing with a rental desk. And for everyday errands, their growing Local Commerce and Grocery service means groceries, pet supplies, or a last-minute bottle of wine can show up at your door in under an hour.

Uber has gone from getting you from A to B, to basically handling the physical logistics of your life.

Uber: Behind the Stock

Uber’s journey as a public company has been anything but smooth.

When the company went public in May 2019 at $45 a share, expectations were sky high. Investors, however, were unconvinced. The stock stumbled immediately, closing its first day below the IPO price and beginning life on Wall Street with disappointment rather than celebration.

Then came 2020. As the pandemic brought cities to a standstill, Uber’s shares collapsed to around $13.70, the lowest level in the company’s history. At the time, few could have imagined that the business would eventually emerge stronger from the crisis.

But that’s exactly what happened. As travel resumed and consumer habits shifted, Uber transformed itself. Ride demand recovered, Uber Eats became a major business, and perhaps most importantly, the company finally learned how to make money. By September 2025, Uber shares had climbed to nearly $102, an all time high.

Today, in June 2026, the stock trades around $73. That places it roughly 62% above its IPO price and more than 430% above the pandemic lows. Yet it remains almost 30% below the peak reached last year.

In other words, Uber’s stock sits somewhere in the middle of its own history. Neither particularly cheap nor excessively expensive.

What’s fascinating is that the business underneath the stock has changed dramatically. Back in 2019, Uber was losing billions of dollars and profitability seemed like a distant dream. Today, the company generates more than $50 billion in annual revenue, produces billions in free cash flow, and delivers consistent earnings.

Ironically, even though the share price is lower than it was at the 2025 peak, the company itself is arguably stronger than at any point in its history.

Sometimes the market price and the quality of the business don't move in perfect sync, dear reader. Perhaps because the future is driverless, and it’s not a guarantee Uber wins this war.

Sure, right now, companies like Waymo own the tech and the actual self-driving fleets, while Uber is just trying to be the middleman app that connects them to riders. Uber’s whole strategy is to let other companies do the heavy lifting while they take a cut.

But will that actually work? If tech giants build their own massive robotaxi fleets and launch their own apps, they could totally cut Uber out. At the end of the day, users just want a cheap, fast ride from point A to point B. If a competitor offers cleaner, cheaper, fully autonomous rides directly on their own app, Uber’s massive network of human drivers won’t matter anymore. It’s all about who wins the customer, right?

The Robotaxi Map

Let’s be honest: Europe has already lost the AI race. While European bureaucrats busy themselves writing compliance checklists :D, the US and China are busy writing the future. The entire AI ecosystem from capital to computing power is now a strict duopoly split between Silicon Valley and Beijing.

Right now in mid-2026, the global robotaxi map is highly exclusive. Commercial autonomous vehicles (AVs) are primarily scaling across the US, China, the Middle East, and selected Asian hubs.

In the US, Alphabet’s Waymo is the undisputed heavyweight champion, but the competitive landscape is shifting fast. Amazon’s Zoox is pushing hard into new cities with its custom-built pods, while Tesla continues its aggressive push to take its software fully driverless. Meanwhile, deeply funded players like Motional (backed by Hyundai) and Avride are quietly gaining massive ground by embedding their fleets directly into Uber’s network.

Waymo

Waymo started back in 2009 as Google’s secret autonomous car project when they brought on AI pioneer Sebastian Thrun. Today, the Mountain View-headquartered firm remains with Alphabet, operating privately with a lean team of roughly 2,500 employees.

And they are scaling at an unprecedented velocity.

As of mid-2026, Waymo is a fully driverless, commercial reality. They have expanded broad public access across 11 major US metropolitan areas, including San Francisco, Los Angeles, Phoenix, Austin, Atlanta, Miami, and a massive Texas footprint covering Dallas, Houston, and San Antonio.

Waymo robotaxi

They have over 3,700 robotaxis out there, handling about 400,000 paid rides every week. They’ve logged 170 million fully autonomous miles, pretty massive, huh?

For expansion plans, Waymo is being quite ambitious. They raised a $16 billion funding round in February 2026 that valued the company at $126 billion. They are targeting to be in 20 or more cities by the end of 2026, and they are also planning international expansion into Tokyo and London, although regulatory approval is still pending in those places.

Safety: If you’re skeptical about riding without a driver, the data might change your mind. Waymo’s safety performance is statistically superior to humans. According to their real-world data, Waymo vehicles are involved in 92% fewer serious injury or fatal crashes than human drivers in identical conditions. Check it out here.

When you look at the raw numbers per million miles, the drop-off is striking:

• Serious Injuries: Humans clock 0.22 incidents per million miles; Waymo sits at a near-zero 0.02.

• Any Injury Crashes: Waymo marks a 0.41 rate vs. the human average of 2.8 (an 85% reduction).

• At Intersections: Waymo’s tech proves to be 96% safer than a human driver.

While the cars drive flawlessly, using them requires a bit of patience. Waymo strictly adheres to posted speed limits, capping out at 65 mph on freeways and hovering around 25 mph on city streets. Because they use incredibly defensive routing to avoid tricky situations, studies show a standard Waymo trip can take significantly longer than an Uber or Lyft, sometimes averaging over 30 minutes for a route a human rideshare driver would zip through in 15.

Look, ma, no steering wheel!

And you may ask, “What about the price?” A valid question!

Waymo operates as a premium product. With a median ride cost hovering around $19.69, it sits about 13% higher than Uber ($17.47) and 27% higher than Lyft ($15.47).

However, the premium gap is closing. Just a year ago, Waymo carried a massive 30% to 40% premium. Since then, Waymo has optimized costs while Uber and Lyft fares have steadily climbed. Longer rides are almost equal. On trips longer than 8 miles, Waymo often wins on price because you don’t have to tip a robot (saving you that standard 15% to 20% driver gratuity). The main pain point remains evening rush hour (5 PM–6 PM), where surging demand can cause a Waymo to cost $9 to $11 more than its human-driven counterparts.

The wildcard is Tesla. Tesla’s robotaxi average is just $8.17 and rarely exceeds $10, compared to Lyft’s $15.47, Uber’s $17.47, and Waymo’s $19.69, so Tesla is using ultra-aggressive pricing to grab market share, similar to what Uber and Lyft did when they first launched.

Next Stop: Tokyo and London

In Tokyo: Waymo has partnered with Japan’s leading taxi apps, GO and Nihon Kotsu. They are currently conducting mapped testing with safety drivers across seven major wards (including Shibuya, Shinjuku, and Minato), aiming for full commercialisation.

In London: Marking their first major footprint in Europe, Waymo is actively running autonomous trials on London roads this year with specialists behind the wheel. Partnering with fleet operator Moove and utilizing customized, all-electric Jaguar Land Rover vehicles, the goal is to launch a direct-to-consumer, fully driverless service via the Waymo app.

Tesla’s Robotaxi

Tesla currently operates in only three Texas cities: Austin, Dallas, and Houston, which, haha, are the very same cities where Waymo is already driving. It’s quite a bit less than the seven cities Elon Musk promised by now :).

And the actual fleet size is way smaller than you’d think. They have roughly 20 to 59 vehicles actively driving out there. Quite low, huh?

Tesla’s robotaxi in Austin, Texas

On pricing, Tesla is being aggressively cheap. Rides are running around $7 to $8, which is about half the price of an Uber or Lyft, and nearly a third of Waymo’s average.

But the trade-off is absolutely brutal. Because the fleet is so tiny, you’ll be waiting 15 to 16 minutes for a Tesla robotaxi, compared to just 3 to 6 minutes for a Waymo, Uber, or Lyft. Hope you aren’t in a rush :).

When it comes to safety, Tesla is currently going in the wrong direction. Their driverless robotaxis are crashing roughly 3 to 4 times more often than human drivers. The data from their public testing is pretty wild: Tesla Robotaxi: 1 crash every ~55,000 miles; while Human Drivers: 1 crash every ~500,000 miles. Tesla has clocked around 17 crashes total across 800,000 driverless miles driven. So while Waymo is genuinely safer than human drivers, Tesla’s driverless tech is still significantly worse.

The Roadmap (Or Lack Thereof): Looking ahead, Tesla promised to expand to 7 more cities in the first half of 2026, but they are significantly behind that timeline. Mi querido Amigo, Elon, is still targeting a 1,000-vehicle fleet by the end of the year, which honestly seems like a massive stretch given where they are sitting right now.

Tesla’s global plans look pretty ambitious on paper, but they are entirely at the mercy of regulators:

• Europe (excluding Switzerland): Tesla is eyeing a broad rollout right now mid-year. But let’s be clear: this is for Full Self-Driving SUPERVISED capabilities (meaning a human still has to hold the wheel) rather than a true, driverless robotaxi service. They just picked up historic approvals in the Netherlands, Denmark, and Belgium, with Germany and the UK up next. But a fully driverless Tesla robotaxi in Europe? Zero operations exist yet; it all hinges on rigid regulatory type approvals. Considering the notoriously stale and strict European bureaucracy, this feels like Mission: Almost Impossible, right?

• China: They’ve secured initial nods to test, but what broader commercialisation actually looks like there remains completely up in the air.

For now, Tesla is a budget-friendly science experiment, while Waymo is a premium chauffeur.

Robotaxis in China

In China, the robotaxi market is growing rapidly, driven by government-backed pilot zones in cities like Beijing, Shanghai, and Guangzhou. Baidu’s Apollo Go leads with around 1,000 vehicles, while Pony.ai has about 250, and WeRide operates roughly 100 vehicles in Guangzhou. The goals are big: Baidu aims to expand to 100 cities by 2030, Pony.ai plans to grow to over 10,000 by 2028.

The major players (Baidu, Pony.ai, and WeRide) do have their own fleets. They’ve invested heavily in autonomous vehicles, so they’re operating and managing their own cars rather than just partnering with existing ride-hailing drivers. They do sometimes collaborate with automakers, but the core fleet is theirs.

Baidu’s Apollo Go robotaxi

When it comes to actual trip times, Chinese robotaxis are catching up to traditional rideshares, but they still struggle to beat a human driver.

Baidu’s vehicles can comfortably cruise up to 70 or 80 km/h on open routes. But in dense, chaotic city centers, human drivers are still noticeably faster. Why? Because humans take aggressive, flexible shortcuts and split-second risks, while the robotaxis remain rigidly locked into extra safety measures and defensive routing. They are close, but the human hustle still wins out for now.

Escaping the Homeland

The most exciting shift right now mid-2026 is that Chinese AV tech is aggressively breaking out of its domestic borders:

• The Middle East: WeRide has already successfully launched a fully driverless robotaxi service on the streets of Abu Dhabi.

• Singapore: In a massive milestone for Southeast Asia, WeRide teamed up with the region’s super-app, Grab. They’ve rolled out a public autonomous shuttle service (called “Ai.R”) in Singapore’s residential Punggol district.

• Switzerland: In a stunning regulatory first, Baidu’s Apollo Go just locked down an official Level 4 autonomous permit in Switzerland. Operating under the name “AmiGo” in a joint venture with the Swiss PostBus, they’ve officially deployed fully electric pods to navigate the tricky terrain of the Alpine foothills. They are targeting full public commercial rollouts by 2027.

Europe might be strict, but China’s tech is officially putting wheels on European soil.

Uber: Game Over?

It’s a fascinating dynamic. For a long time, the narrative was that Waymo would eventually put Uber out of business by removing the need for human drivers. However, Uber is trying to shift its strategy to ensure it remains the “central nervous system” of the ride-hailing world, regardless of who owns the robot.

As of early 2026, Uber is moving from being a simple partner to a direct competitor with its own “asset-heavy” fleet strategy.

The “If You Can’t Beat ‘Em, Host ‘Em” Phase (2024–2025)

Initially, Uber pivoted from building its own tech (after selling its ATG unit) to becoming a platform for others. Just to offer robotaxis services through their app. Sorry, that's not a strong argument for me.

The main advantage: Uber doesn't have to buy expensive vehicles, maintain depots, or pay for cleaning and charging. They generate high-margin revenue purely from software matching, but it is not sustainable.

Why it won’t! work: A large user base alone is not a moat if your service is expensive. Building an app is relatively easy. By giving the app, Uber is giving its most valuable asset its customer base to its chief competitor. Once customers get used to autonomous rides, they might realise Waymo offers a more consistent experience and cut out the Uber middleman entirely.

The Shift to “Direct Competition” (Late 2025–2026)

Great that Uber realised that the app alone, even with a large client base, won't be enough. It really won’t! In a major move, Uber is now transitioning from “platform dependency” to owning and operating its own autonomous fleets. This signals a strategic split from Waymo in several key markets:

• The Lucid-Nuro Partnership: At CES 2026, Uber unveiled a purpose-built robotaxi developed with Lucid (the vehicle) and Nuro (the AI driver). Uber plans to deploy these in San Francisco by late 2026, taking the fight directly to Waymo’s home turf. Good.

• Asset Ownership: For the first time in years, Uber is buying its own vehicles (over 20,000 units committed) rather than just being a middleman. This allows them to control the “in-cabin” experience and potentially lower costs further. Good.

• NVIDIA Collaboration: Uber is working with NVIDIA to build a massive “AI data factory” to help scale their global autonomous fleet to an estimated 100,000 vehicles by 2027. Great.

What is good in this strategy: By owning the fleet (the Lucid/Nuro vehicles), Uber is no longer at the mercy of Waymo's pricing or platform rules. They control their own destiny. Uber can fully control the "in-cabin" experience, entertainment, cleanliness, and branding, which is highly variable. By building an AI data factory with NVIDIA, the more miles Uber's fleet drives, the smarter their routing and dispatch algorithms become, unlocking massive efficiency gains over time.

Why it might not work: Buying 20,000+ custom electric robotaxis destroys Uber’s hard-earned profitability. They shift from a lean software company to a heavy logistics and maintenance company, which Wall Street traditionally dislikes. Moreover, Waymo has over a decade and billions of miles of real-world testing. Nuro and Lucid are incredibly capable, but launching a brand-new autonomous stack directly into San Francisco (Waymo’s stronghold) risks high-profile software failures or accidents that could damage Uber’s brand.

The Global Multi-Partner Strategy

Unlike Waymo, which mostly uses its own proprietary “Waymo Driver,” Uber is playing the field with a “multi-AI” approach:

• In Europe: Partnering with Wayve and Volkswagen (using the ID. Buzz) to launch robotaxi services in London, Madrid, and Munich.

• In the Middle East: Deploying over 1,000 robotaxis in Dubai and Abu Dhabi through partnerships with Baidu (Apollo Go) and WeRide.

• In Freight: Partnering with Waabi and Aurora to integrate self-driving semi-trucks into Uber Freight.

Why it can work: Geopolitics make it impossible for an American company like Waymo to launch in China, or for a Chinese company like Baidu to dominate the US. By partnering with local heroes (Baidu/WeRide in Dubai, Wayve/VW in Europe), Uber bypasses political friction. Uber isn’t putting all its eggs in one autonomous basket. If Nuro’s software hits a development bottleneck, Uber’s business survives because they are also integrated with Wayve, Waabi, and Aurora. Bringing autonomous tech to Freight (trucking) opens up a completely separate multi-billion-dollar revenue stream where Waymo has significantly scaled back its focus.

Why it might fail: Managing many different autonomous vehicle APIs, safety protocols, and hardware requirements across different continents can be difficult. Because Uber is using third-party AI drivers everywhere globally, they never truly own the core intellectual property. If Volkswagen or Baidu decides to launch their own competing ride networks locally, Uber can be booted out of those markets overnight.

What do you think, will Uber actually pull this off? Are they going to win the self-driving race because everyone already has the Uber app on their phone? Or are we watching the beginning of the end for Uber, destined to go down in history like Blockbuster or Kodak because pure AI companies like Waymo are just too fast?

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If you open the Uber app, you aren’t just looking at a map, you’re looking at a breathing mathematical model. For Uber, data isn’t just a “business asset”, it is the literal oxygen of the company. Without it, the cars stop, the prices break, and the system collapses into chaos.

As the company moves further, the “Magic of Uber” has shifted from simply finding a ride to orchestrating a massive, multi-vertical ecosystem of mobility, delivery, and advertising.

Here is how data fuels the world’s most famous “logistics company that owns no vehicles.”

The Invisible Engine: How Data Actually Runs Uber

The Michelangelo of Algorithms

At the heart of Uber’s data empire is Michelangelo, an internal Uber's Machine Learning-as-a-Service (MLaaS) platform, designed to democratise AI and make scaling machine learning as easy as requesting a ride. While it started in 2015 (under leadership of Travis Kalanick) by predicting ETAs (Estimated Time of Arrival), it now handles everything from GenAI-driven customer support to detecting fraudulent “phantom” rides. As of 2026, Michelangelo manages over 400 active ML projects and more than 5,000 models in production. At peak times, it serves roughly 10 million real-time predictions per second.

And connected to it Hyper-Local Demand Prediction. Uber’s ability to tell a driver where to go before a rider even requests a trip is uncanny. Uber’s ML systems ingest historical data and real-time signals to predict demand at a hyper-local level, sometimes even down to specific city blocks. The models don’t just look at Uber data; they factor in weather patterns, local concerts, flight delays, and even public transit strikes to anticipate a spike in demand before it happens.

Surge Pricing: The “Relief Valve”

Most people (including myself) hate surge pricing, but from a data perspective, it is a masterpiece of economic equilibrium. Since its introduction in late 2011 under leadership of Travis Kalanick Uber treats its marketplace like a liquid.

Surge isn’t just “more people = more money.” The algorithm recalculates every five minutes based on the cross-correlation between supply, demand, and Estimated Wait Times (EWT). It’s a “relief valve.” By increasing the price, the system simultaneously encourages more drivers to head toward a busy zone while filtering out riders who aren’t in a rush. This ensures that the person who really needs a ride can always get one.

Data-Driven Advertising

In 2026, Uber is proving that it’s not just a taxi service, but an advertising giant. Because Uber knows exactly where you are and where you’re going, it possesses “high-intent” data that Google and Meta can only dream of.

Example: If you’re taking an Uber to a French restaurant, the app might show you an ad for a specific wine or a nearby dessert shop.

Uber’s goal for 2026 is to scale this advertising without “breaking the vibe.” The data ensures that ads feel like helpful suggestions (relevance) rather than intrusive clutter.

Controversies

Alongside these developments, it’s also important to mention several controversies, unethical projects, and data breaches. Most of them took place during the time Uber was led by Travis Kalanick, a highly complicated and complex figure, I wrote about previously.

The God View

In the early days of Uber, “God View” wasn’t just a cool name, it was a literal, real-time map of every single person using the app.

Think of it as a video game interface for the real world. From a laptop, an Uber employee could see a “ghost” icon for every car on the road and click on it to see exactly who was inside.

The “Wild West” Strategy: At the time, Uber’s strategy was “growth at any cost.” God View was technically built to help managers track car supply, but it quickly became the ultimate insider party trick. The Creep Factor is that executives reportedly used it to track journalists’ locations in real-time or show off the travel habits of celebrities at launch parties. There were almost no internal locks. If you worked at Uber, you could basically stalk your ex or a high-profile politician just by opening your laptop.

The “God View” era eventually crashed into reality. After a series of privacy scandals and investigations by the FTC, Uber was forced to lock down the tool, rename it “Heaven View,” and submit to 20 years of privacy audits.

It remains the ultimate case study of what happens when a tech company’s data power grows way faster than its ethics.

The Greyball

While God View was about spying on riders, Greyball was about hiding from the law.

If God View was a “video game” for the office, Greyball was a stealth mode designed to trick government officials trying to catch Uber operating illegally.

The “Ghost Map” Strategy

In cities where Uber wasn’t yet legal (like Portland or Seoul), police would try to hail a ride to impound the car or ticket the driver. Uber’s solution? A secret software tool called Greyball.

Here’s how it worked. Identifying the “Enemy”: Uber used data to spot undercover stings. If someone opened the app near a government building or used a credit card linked to a police union, they were “Greyballed.” When a Greyballed official opened Uber, they didn’t see a real map. They saw a fake version of the app populated with “ghost cars” that didn’t exist. If they actually tried to book a ride, the app would either show no cars available or simply cancel the request instantly. The driver never even knew the “sting” was happening.

Uber’s strategy was “Ask for forgiveness, not permission.” By using Greyball, they could launch in a city, build a massive fan base of riders, and stay one step ahead of regulators until the laws were eventually changed in their favor.

The New York Times exposed the program in 2017, sparking federal investigations. Uber eventually banned the use of Greyball to dodge local regulators, but it remains the ultimate example of weaponized code.

And that is not all!

The Hell

If God View was about watching riders and Greyball was about dodging the law, then “Hell” was about destroying the competition. I’ve already outlined in my last article what Uber did to Lyft, but the Hell was only slightly mentioned. Here is more about it.

While the other tools were defensive or internal, Hell was pure corporate espionage. It was a secret program designed specifically to stalk, target, and poach drivers from Uber’s arch-rival, Lyft.

The “Spyware” Strategy

Uber didn’t just want to know where its own cars were, it wanted to know where Lyft’s were, too. Between 2014 and 2016, they built a “ghost” system to infiltrate Lyft’s app. Uber created thousands of fake Lyft rider accounts and spread them across a city in a “grid”. These fake accounts tricked Lyft’s system into showing the locations of the eight nearest drivers. By comparing this data with their own internal maps, Uber could identify “double-app” drivers, the people working for both platforms at the same time.

The “Hellish” Playbook

Once Uber knew which drivers were flirting with the competition, they moved in for the kill: Uber would send special bonuses and ride offers specifically to those “double-app” drivers to keep them busy on the Uber platform. The goal was to dry up the supply of Lyft drivers. If all the drivers were busy with Uber, Lyft’s wait times would skyrocket, and their customers would eventually switch to Uber out of frustration.

The program earned its name because it was the “flip side” to the Heaven (God View) program. It was so top-secret that even most Uber city managers didn’t know where their competitive data was coming from.

When the program was exposed in 2017, it triggered an FBI investigation and multiple class-action lawsuits for unfair business practices and computer fraud.

Hell is the ultimate example of Uber’s early “win at all costs” era, where the code wasn’t just built to help you find a ride, but to make sure you couldn’t find one anywhere else.

When the Data Engine Failed

There’s a familiar pattern in tech: move fast, scale aggressively, and worry about the consequences later. In its early years, Uber followed that script, and when it came to data protection, the consequences were real.

2014: The First Major Incident

In May 2014, an attacker used an access key found on a public GitHub repository to enter Uber’s Amazon Web Services (AWS) server. The names and driver’s license numbers of approximately 50,000 to 100,000 drivers were accessed. Uber didn’t discover the breach immediately and only disclosed it to the public in February 2015. 9 months later!!! Cringe, right?

But that was only a preview.

2016: The “Great Cover-Up”

In October 2016, hackers again used stolen credentials from a GitHub repository to access Uber’s AWS servers. This was massive, 57 million users and drivers had their data stolen, including names, email addresses, phone numbers, and driver’s license numbers. Instead of reporting the breach to the authorities, Uber’s security team paid the hackers $100,000 through a bug bounty program to delete the data and stay quiet.

The CEO at the time, the notorious Travis Kalanick, didn’t even have the guts to disclose the breach. The public didn’t find out until the new CEO, Dara Khosrowshahi, revealed the truth in November 2017. This led to massive fines ($148 million) and criminal charges against the former Chief Security Officer.

Following the change in leadership, Uber began to treat its previous security failures as a roadmap for improvement. While data incidents haven't vanished entirely, they have become far less frequent and have shifted in nature. Rather than attackers breaking directly into Uber’s primary "vault," more recent vulnerabilities have originated from "secondary" leaks, usually involving a breach at a third-party vendor or a clever social engineering trick that targets a single human employee rather than the system's core infrastructure.

2022: The “MFA Fatigue” Attack

In September 2022, a hacker (linked to the Lapsus$ group) gained access to Uber’s internal network through a social engineering trick called “MFA Fatigue.” After stealing a contractor’s password, the hacker sent constant login approval requests to their phone. The contractor eventually clicked “Approve” out of frustration. The hacker gained access to Uber’s internal Slack messages, Google Workspace, and cloud environments. While internal systems were “fully compromised,” Uber stated that no customer trip history or credit card details were stolen.

2022/2023: The Teqtivity Third-Party Leak

Shortly after the MFA attack, a separate leak occurred via Teqtivity, a third-party vendor Uber used to manage its IT assets (like laptops). Data belonging to roughly 77,000 Uber employees was leaked on a hacking forum, including company email addresses and mobile device info. Again, customer data was not affected, but it highlighted the risk of “third-party” vendors in Uber’s ecosystem.

As of today in May 2026, there have been no major new reports of customer data being leaked this year. However, the company remains a “tier-one” target for hackers because of the sheer volume of data I discussed in the article, specifically the high-intent location and payment data they hold.

Uber’s current strategy focuses on “Zero Trust” security model, essentially trying to train their thousands of employees to spot the social engineering tricks that caused their past headaches.

Closing

I hope you enjoyed reading this deep dive into Uber’s data ecosystem, controversies, and the decisions that shaped Uber. But the story is far from over.

In my next story, I’ll shift the focus from Uber’s past to its future. The big question is no longer whether Uber can dominate ride-sharing, it’s whether the company can survive the next technological shift. With autonomous driving advancing rapidly, competitors like Waymo and Tesla are positioning themselves to potentially reshape the entire transportation industry.

Could self-driving technology make Uber stronger? Or could it slowly turn the company into a middleman with shrinking influence and margins? And what happens if companies controlling the autonomous vehicles no longer need Uber at all?

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This time, I want to go deeper into something even more interesting: his leadership. Not the usual “Uber founding story” you’ve probably read a hundred times. Instead, I want to look at how Travis actually led the company, the mindset, the energy, the style that pushed Uber from a scrappy startup into the aggressive and influential companies in Silicon Valley.

As mentioned in my previous article, love him or hate him, one thing is hard to argue with: Uber wouldn’t have become Uber without Travis Kalanick.

So in this article, I’m diving into the leadership style of Travis Kalanick: what made it powerful, what made it controversial, and how it helped push Uber to the very top.

The Short Creation Story of Uber

The story starts in Paris, December 2008. Both Travis Kalanick and Garrett Camp were in the city for the annual tech conference LeWeb. At that point, neither of them was a struggling founder anymore. Both were already successful entrepreneurs, having sold previous tech companies, and were financially well off. So both guys arrived in Paris successful, wealthy, and probably not expecting to start another company anytime soon. But startups rarely begin with a perfectly planned idea. They start with something much simpler: a problem.

Travis Kalanick, Garrett Camp, and Gary Vaynerchuk at the LeWeb conference in Paris, 2008

One cold evening in Paris, the two founders were standing outside trying to get back to their apartment. Sounds easy, right? Not that night. No taxis. No available cars. Just the streets of Paris and two frustrated entrepreneurs trying to get home. Earlier that week they had paid around $800! for a private driver on New Year’s Eve, which felt ridiculous. And now they couldn’t even find a normal taxi on a snowy night.

That moment planted a simple thought: What if you could just press a button on your phone and get a ride? That idea, simple, obvious in hindsight, would eventually become Uber.

Garrett Camp started working on an early prototype. Together with his friends Oscar Salazar and Conrad Whelan, he built the first version of the mobile app. At that stage, Travis Kalanick wasn’t officially running the company yet. His role was something like an Advisor, the experienced founder helping shape the direction.

The company slowly started to take form. In February 2010, Ryan Graves became the first employee of the startup. Fun fact: Travis found Ryan through Twitter, Ryan responded to Travis’ tweet. A few months later, in May 2010, he was named CEO. But that arrangement didn’t last long. By December 2010, Travis Kalanick stepped in as CEO, while Graves moved into the role of Chief Operating Officer.

And that’s when the real Uber story began.

Kalanick’s Leadership Style

Uber. It didn’t happen overnight. Travis Kalanick, who stepped down as CEO in 2017, laid the foundation for much of what we see today. His focus on aggressive expansion, innovative tech, and a vision for a flexible platform set Uber on its path. Now, as we look closer, we’ll dive into how each piece was shaped by his bold vision and daring moves as a CEO.

Kalanick had always been known as a hustler. As a teenager he even sold knives door-to-door an experience that probably taught him more about persistence than any business book ever could. But he wasn’t just the loud founder or the marketing face of the company. Travis could actually build technology too. He studied computer engineering at UCLA (although he never graduated) and was known as a strong coder long before he became a CEO. During the difficult early days of Red Swoosh (the story of Red Swoosh please read here), he reportedly spent months coding the product himself just to keep the company alive until he could hire a proper team. That engineering mindset shaped the way he approached business. At the same time, Travis was highly skilled at storytelling. His narrative was compelling and helped recruit employees, investors, and early adopters

Below are the most important leadership traits that defined his impact.

Competitive Drive. What Travis did to Lyft?

One of Travis Kalanick’s defining traits was his relentless competitive drive. He approached the transportation market not as a typical startup opportunity, but as a global battle for dominance. From the very beginning, his focus was on outperforming competitors, especially early rival Lyft, while pushing Uber into new markets at an aggressive pace.

Many drivers operate on both platforms simultaneously, switching between them depending on demand and pricing. Travis didn’t like it. He treated Lyft as crap. He wasn’t interested in sharing the market. And his behaviour remains highly controversial.

• The Ghosting Campaign: Uber employees reportedly created many fake Lyft accounts to book rides, only to cancel them at the last minute. This left Lyft drivers stranded, frustrated, and earning zero dollars, effectively breaking the platform's reliability.

• The Investor Blockade: If true, it’s deeply cringe, Travis reportedly called Lyft’s potential investors personally, leaning on them to stay away from Lyft’s funding rounds to starve the company of the cash it needed to survive.

• The Talent Raid (even though common industry practice): Uber went after Lyft’s brain trust. They scouted Lyft’s top engineers and lured them away with massive salary bumps and “golden handcuff” bonuses that Lyft simply couldn’t match.

• Poaching the Fleet: Uber launched aggressive recruitment drives specifically targeting Lyft drivers, promising them shorter hours and fatter paychecks if they ditched the pink mustache for the Uber “U.”

• Last but not least: Uber’s “Hell” program was a secret spy mission to crush Lyft. If Uber’s tool for tracking their own riders was “God View,” this was the opposite, designed to make life a living hell for the competition. Here is how they did it: The “Spy” Tech - Uber created a bunch of fake rider accounts and spread them across a city in a digital grid. This tricked Lyft’s app into showing exactly where their drivers were. Because every Lyft driver had a unique ID, Uber’s software could track them 24/7. By watching these drivers, Uber figured out who was “double-apping” (working for both Uber and Lyft at the same time). Once they caught someone doing this, they’d strike: the bribe (they’d send that specific driver massive bonuses to stay on the Uber app), or the trap (they’d give those drivers more rides so they were too busy to ever switch over to Lyft.)

  It eventually leaked, and it was a total mess. The FBI investigated them for “hacking” Lyft, and it became the ultimate example of Uber’s old “win at all costs” culture. It wasn’t just business, it was digital warfare.

Travis treated Lyft like a trash. Yet, despite years of being bullied by a giant, Lyft survived. Today, they remain a multi-billion dollar player, a rare example of a company that stared down a serious threat and refused to go under. Btw, from time to time, I use both services and the price difference can be striking. For the exact same trip, with the same car and the same driver, a ride through Lyft can cost significantly less than Uber.

Bold Vision and Market Disruption

Travis Kalanick was driven by a bold vision that technology could fundamentally reshape transportation. Rather than building a traditional taxi company, he positioned Uber as a technology platform and a logistics network, eventually describing it as an “operating system for transportation.” This expansive way of thinking pushed Uber far beyond simple ride-hailing and opened the door to new business lines such as Uber Eats, freight logistics through Uber Freight, and early investments in autonomous vehicle research.

This vision did more than shape strategy, it helped Uber attract massive amounts of venture capital and some of the world’s top engineering talent. By framing the company as a transformative force rather than just a service, Kalanick created the narrative that Uber was not just participating in the future of transportation, but actively building it.

Speed Over Perfection

Travis Kalanick was known for a strong bias toward action, operating under the belief that speed is what ultimately wins markets. At Uber, this translated into a “launch first, fix later” approach, where services were rolled out quickly and then refined through rapid, data-driven iteration. The company frequently entered new markets even when legal frameworks were unclear or not yet established.

A clear example of this mindset was Uber’s decision to launch in many cities without traditional taxi licenses, effectively forcing regulators to respond only after consumer demand had already taken hold. This willingness to move fast, even in uncertain conditions, allowed Uber to scale at a pace that traditional transportation companies simply could not match.

This approach had a powerful impact. Uber expanded into more than 70 countries at remarkable speed, reshaping urban transportation along the way. By launching early and forcing regulators to react, the company gained a first-mover advantage in many regions. Internally, this mindset became part of Uber’s identity, captured in the phrase “always be hustlin’,” which reflected a culture driven by urgency, ambition, and constant competition.

Data-Driven Execution

One of the things that really set Uber apart was how intensely it ran on data. Under Travis Kalanick, the company didn’t just operate like a normal service, it behaved more like a machine constantly learning and optimising itself. Every city had its own detailed metrics, every ride added new insights, and everything was tracked in real time.

This is where things like surge pricing came in: prices changing instantly based on demand, and where Uber could predict when and where people would need rides before they even opened the app. Behind the scenes, there was deep analysis of both drivers and riders, from behavior patterns to peak hours to route efficiency.

All of this turned Uber into something much bigger than a transportation company. It became a logistics engine, quietly optimising movement in cities every second, using data as its fuel.

When I first started exploring this topic, I didn’t expect to run into this level of tension: conflicting narratives, uncomfortable facts (God View, Grayball, Hell), and even traces of data leaks. The deeper I went, the clearer it became: there’s more going on here than most people realise. And trying to squeeze it all into one article would not be the best. So I’m going to break this out into a separate piece and take it much further. So catch you on the next one dear readers!

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Very High Risk Tolerance. Ask for Forgiveness, Not Permission

Another one of Travis’s traits is a very high risk tolerance.

Uber has spent much of its history in courtrooms as much as on roads, facing lawsuits that challenge the very foundation of its business model.

I clearly remember the interview where Travis sits there, almost casually, and says:

“I got threatened by the city of San Francisco and the State of California three months after we launched Uber with 90 days in jail for every ride that had occurred.”

Uber has intentionally broken many laws and has spent years intentionally testing legal limits by claiming it isn’t a taxi service, but a technology company. This distinction is the core of its strategy: by arguing they simply provide an app to connect people, they avoid the strict regulations and costs that traditional car companies must follow.

This “tech company” label allows Uber to call its drivers “partners” or independent contractors rather than employees. Doing so saves the company billions because they don’t have to provide standard benefits like healthcare or a minimum wage. While Uber claims this offers drivers freedom, many courts are now pushing back, ruling that Uber controls the work so closely that these “partners” are actually employees who deserve legal protection.

In places like California around 2013–2014, Uber drivers were cited for operating at airports without proper authorisation. Uber reassured drivers that they would cover fines and legal costs, effectively encouraging them to keep driving despite regulatory pushback. This created a sense of protection, but also shifted risk onto drivers operating in uncertain legal territory.

Uber didn’t just fight these restrictions on its own. It used its large base of riders and drivers as a kind of support network. When it was banned or limited in a place, it encouraged users and drivers to speak up, sending messages to regulators, signing petitions, and showing public support at protests or marches. The message was simple: Uber was being held back by outdated rules. This public pressure often made a difference, helping Uber shift public opinion and eventually get approval from regulators in many places.

At the same time, Uber has faced waves of lawsuits over passenger safety, including serious sexual assault claims.

Beyond that, the most common outcome across all these cases is settlement. Uber has repeatedly settled disputes rather than fight them to the end, especially with taxi industries and drivers, often paying large sums without admitting wrongdoing. Uber frequently chooses to resolve disputes financially to avoid prolonged legal risk and reputational damage. Where cases do reach final judgment, the trend shows increasing regulatory pressure and occasional losses that force Uber to adapt its operations.

Overall, the story is less about one big lawsuit and more about a pattern: continuous legal challenges, costly settlements, and a gradual reshaping of how Uber operates worldwide.

Corporate Culture under Travis Kalanick

What was it like to work for Uber?

The corporate culture Travis build was demanding, intense, and, in some ways, toxic.

One core idea was “All in or not at all!” If something was worth doing, you committed fully with time, energy, and resources. Half-measures weren’t really accepted, which is a good thing.

But …. the work environment was extremely demanding. Long hours were common, and the pressure contributed to burnout, stress, and a poor work-life balance for many employees.

Another belief was “Let the builders build.” Teams were given a lot of freedom to move quickly and make decisions without waiting for approval. The upside was speed and innovation, sounds really great, doesn’t it?

But …. the downside was that rules and regulations could get ignored along the way. Breaking the law? Handled as a risk, not a barrier. Ethical lines? Flexible, depending on the target. The real currency was growth and almost everything else could be negotiated away in its name.

Bro-like culture

The early workplace culture also became known for issues often described so, where inappropriate behavior toward women and lack of respect for their contributions were reported.

In 2017, Uber hit a breaking point when former engineer Susan Fowler published a viral blog post. She detailed a “horrifying” year where her manager propositioned her for sex on her very first day. When she reported it, HR famously refused to punish him because he was a “high performer” and it was his “first offense”, despite Fowler finding other women he had harassed.

And of course escort: The Information reported that in 2014, a group of senior employees, including Kalanick, visited an escort and karaoke bar in Seoul. This led to a human resources complaint from a female marketing manager. Patrons at the bar select women to sing karaoke with before taking them home.

Crazy Celebrations

And that’s not even the half of it.

According to a report by Recode, Uber CEO Travis Kalanick once sent an internal email laying out “ground rules” ahead of celebrating Uber’s 50th city launch in 2013. The so-called “Miami letter” included a mix of corporate guidance and party survival tips: don’t get arrested, don’t do drugs, and (ideally ;-) don’t throw up at the company party. The closing instruction, however, was less restrained: “have a great fucking time.”

As Recode pointed out, it wasn’t exactly standard CEO communication. The letter even included an entire section dedicated to setting “guidelines” around sexual relationships between employees, because apparently, that also needed a policy memo.

4) Do not have sex with another employee UNLESS a) you have asked that person for that privilege and they have responded with an emphatic “YES! I will have sex with you” AND b) the two (or more) of you do not work in the same chain of command.

What do you think?

According to Daily Mail, in 2015, Uber didn’t just organise a company event, it staged something closer to a corporate spectacle, quietly flying in around 4,800 employees from across the globe to Las Vegas for what was framed as “team building.” Drivers, notably, were not on the guest list.

What followed looked less like a work gathering and more like a curated excess. Employees were checked into a lineup of Strip hotels: Paris, Planet Hollywood, Flamingo, Bally’s, the LINQ, all covered by the company. On arrival, they were handed branded swag, free merchandise, and even prepaid $50 VISA cards, setting the tone before the first drink was poured.

Uber party

And there were plenty of drinks. Poolside happy hours rolled into long nights, with open bars and private parties where the only real requirement was a wristband. Uber branding was subtly stripped away, replaced by cryptic double X symbols, as if the company itself preferred to stay just out of sight. Security kept things tightly controlled, reinforcing the sense that this wasn’t just a celebration, it was a closed world.

Inside, the mood was euphoric. “I haven’t spent a penny,” one attendee reportedly said. “Uber has paid for everything.” And that, in a way, was the point: a high-gloss, no-expense-spared experience designed to reward, impress, and maybe distract, while the real engine of the business, the drivers, remained firmly outside the velvet rope.

And what do you think about that?

Kalanick’s Ride of Shame

Another day, another embarrassing story!

Uber found itself back in the spotlight when Bloomberg News released dashcam footage of CEO Travis Kalanick arguing with one of his own Uber drivers over the company’s impact on drivers’ livelihoods. You can see the video here:

The video, reportedly recorded on 5 February 2017, shows Kalanick in the back seat, wedged between two female friends, shimmying his shoulders to the Maroon 5, until the mood shifts.

The driver, Fawzi Kamel, takes the opportunity to raise a grievance that many drivers had voiced before: “You’re raising the standards, and you’re dropping the prices.” What starts as a calm exchange quickly turns into a tense back-and-forth about a collapsing earnings reality. Kamel pushes further: “People are not trusting you anymore. I lost $97,000 because of you. I’m bankrupt because of you… You keep changing every day.”

Kalanick pushes back, dismissing the claim about falling rates: “Bullshit.”

And then the conversation takes a sharper turn. He did respond harshly:

Some people don’t like to take responsibility for their own shit. They blame everything in their life on somebody else. Good luck!

to which the Uber driver said: “Good luck to you too, I know you won’t go too far!”

The clip went viral almost instantly, forcing a full-scale public apology. In an internal email later shared with staff, Kalanick wrote: “To say that I am ashamed is an extreme understatement.” He admitted his behavior did not reflect the leadership Uber needed, and acknowledged he had to “grow up” and seek “leadership help.”

Kalanick reportedly met with Kamel later and eventually paid him $200,000 out of his own pocket to settle the bad blood.

What was meant to be just another ride ended up becoming a defining moment fueling public backlash and putting Uber’s culture under even harsher scrutiny.

This incident, combined with the sexual harassment scandals and legal battles mentioned earlier, led investors to revolt, resulting in Kalanick’s resignation as CEO just a few months later in June 2017.

What Did We Learn Today?

A strong vision, clear storytelling, and the courage to act quickly can help you build something big. Being determined and willing to take risks is important if you want to create change. But this story also shows that how you grow matters just as much as how fast you grow.

As a leader, you can never compromise on ethics. Once you start bending rules, ignoring fairness, or treating people poorly just to win, it slowly damages trust, culture, and eventually the whole company. Shortcuts might bring quick success, but they create bigger problems later.

In the end, real success is not just about being the fastest or the biggest. It’s about building something strong, trustworthy, and lasting and that only works if you lead with integrity from the start.

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It’s easy to critique Kalanick from a distance. The headlines make it simple: Toxic, Aggressive, Unethical. But leadership isn’t black and white. It’s context. And yes, let’s say it clearly: without him, Uber wouldn’t be what it became. He pushed it like a man possessed. Brilliantly, recklessly, sometimes unethically, occasionally illegally. That duality is exactly what makes him fascinating.

On December 24, 2019 (yes, Christmas Eve) he announced he was stepping down from Uber’s board, effective December 31. The new CEO of Uber is Dara Khosrowshahi.

Travis Kalanick is currently the CEO of City Storage Systems (CSS), the parent company of the ghost kitchen startup CloudKitchens, the company buys old buildings and turns them into professional kitchen spaces specifically for food delivery apps. He also manages Otter, a software tool that helps restaurants organize all their online orders in one place. Additionally, he is working on a project called Lab37 to build robots that can handle repetitive kitchen tasks and save on hiring costs.

In 2026 Kalanick has an estimated net worth of around $3.6 billion.

And by the way, Travis, if you’re reading this article, hi there! 😀

Early Life

When I started digging into Travis Kalanick’s life, I realized something fast: if you want every detail, you’re signing up to write a 500-page book. The man doesn’t do “small chapters.” He does eras.

Leaders, like all of us, also used to be kids. They are shaped by family, by hustle, by early wins and early scrapes. And in Kalanick’s case, the origin story matters a lot.

Travis Kalanick was born on August 6, 1976, and raised in the Northridge neighborhood of Los Angeles. His mother, Bonnie Renée Horowitz Kalanick (died in 2017), worked in retail advertising for the Los Angeles Daily News. His father, Donald Edward Kalanick, was a civil engineer for the city of Los Angeles. He grew up in a family that wasn’t flashy, but it was driven. He has two half-sisters and a brother Cory who became a well-known firefighter.

As a teenager, he sold knives door-to-door for Cutco. That’s not glamorous work. It’s rejection-heavy, sweaty, and humbling. You knock. You pitch. You get told “no” more times than you can count. Then you knock again.

First Lesson Learned. That experience teaches you something fundamental: resilience isn’t a mindset; it’s a muscle. You build it by getting uncomfortable repeatedly. You learn how to read people fast. You learn how to sell value. And maybe most importantly, you learn that effort directly connects to income.

At 18, he didn’t just work for someone else. A test preparation company called “New Way Academy,” launched with the father of a classmate.

After graduating from Granada Hills Charter High School, he went to the University of California, Los Angeles (UCLA), studying computer engineering and business economics. In 1998, just a few credits shy of graduating, he dropped out to work full-time at a startup called Scour. That moment, leaving without the diploma, isn’t just a cliché Silicon Valley move. It signals something about his risk tolerance. He was willing to walk away from a nearly finished degree for a shot at building something bigger.

The First Failure: Scour (1998–2000)

Travis Kalanick’s first major project was a failure, as often happens in the startup world.

So let me take you back to 1998. I always imagine that moment like this: the internet is still wild, everybody thinks they’re about to reinvent the world from a dorm room. And in the middle of that chaos you’ve got Travis Kalanick, a UCLA student thinking, “Yeah, this is it. This is the future.”

Not alone. Alongside Michael Todd and Vince Busam, he drops out to work with Dan Rodrigues, the founder of Scour Inc. Scour was building a multimedia search engine and something even more explosive: Scour Exchange, a peer-to-peer file sharing service.

Kalanick took on sales and marketing. He was out there pushing the story, selling the vision, convincing people that this new digital world was inevitable. Later, he would refer to himself as a co-founder. Others disputed that. And already, you can see a pattern forming: ambition, ownership, control.

Scour started gaining traction. Users were coming in. The growth curve looked sexy. But like most dot-com darlings of the late ’90s, the company was burning cash like there was no tomorrow. And when you’re young and growing fast, you need fuel, venture capital fuel. After a long back-and-forth, the company was ultimately forced to accept unfavorable investment terms and, in the process, lost control.

And then came the hammer. In 2000, the Motion Picture Association of America, the Recording Industry Association of America, and the National Music Publishers Association filed a $250 billion! lawsuit against Scour for copyright infringement. Scour was actually the first to allow users to trade movies (DivX files) and photos, not just MP3s. This is why the MPAA (movies) was involved, making the lawsuit much “scarier” than the ones Napster was facing at the time. Scour wasn’t just in trouble. It was existentially threatened. By September 2000, the company filed for Chapter 11 bankruptcy. Game over. The assets of Scour were sold at auction for a mere $9 million to CenterSpan Communications. For a company once valued at many times that, it was a total fire sale.

Now here’s where I’ll be honest with you. Even without the lawsuit, I don’t think Scour would have survived the dot-com crash. The market was overheated, valuations were fantasy, companies with zero revenue were being treated like royalty. When the bubble burst, it swallowed a lot of “next big things.” Scour was playing in a legally explosive space, burning cash, and negotiating from weakness. The storm was coming either way.

Interesting fact: The dot-com crash was a major financial downturn that started on March 10, 2000, when the NASDAQ reached a peak of 5,048.62. Investors were overly excited about internet companies, many of which weren’t making any profit. When the bubble burst, it took about 2.5 years for the market to reach its lowest point, wiping out roughly $5 trillion in value. By October 2002, the NASDAQ had fallen 78%, and it took the tech industry 15 years to recover those peak levels.

But here’s the thing about failure: whether it’s inevitable or not doesn’t matter when you’re inside it.

Second Lesson Learned. For Kalanick, this wasn’t just a failed startup. It was a masterclass in three brutal realities of entrepreneurship:

1. Growth without a moat is fragile.

2. If you don’t control the cap table, you don’t control the company.

3. Incumbents will not smile while you eat their lunch.

You can draw a straight line from Scour to Uber’s later “ask forgiveness, not permission” approach. At Scour, the incumbents crushed them with legal force. At Uber, Kalanick built a company that moved so fast and scaled so wide that regulators and taxi unions were constantly on their heels. Coincidence? I don’t think so.

Scour hardened him. It probably taught him that if you’re going to disrupt, you better be ready for war. And if war is coming anyway, you might as well scale before they can stop you.

But here’s the uncomfortable part, and we have to talk about it if we’re serious about leadership. Early trauma in business can create two kinds of leaders. The reflective kind, who slow down and build guardrails. Or the combative kind, who say, “Never again. I will never lose control like that again.” Kalanick leaned toward the second. That doesn’t make him a villain. It makes him shaped by experience.

When I think about Scour, I don’t see a reckless kid chasing piracy money. I see a young entrepreneur stepping into a battlefield he didn’t fully understand yet. I see someone learning about power the hard way. And I see the birth of a leadership style that would later fuel one of the aggressive expansions in tech history. Failure at 24 can break you. Or it can recalibrate you.

Scour went bankrupt. The dot-com bubble burst. The headlines faded. But the lessons, they definitely stuck. Kalanick’s first big failure didn’t end his story. It rewired it.

Takeaway for founders: And if you experienced failure with your first business, Scour is a reminder, you learn your lesson, you recalibrate, you pick yourself up, and you get back in the race. Just like in a song by Frank Sinatra. 😀

Red Swoosh (2001–2007): Revenge Matters

If Scour was the punch to the face, Red Swoosh was the swing back.

In 2001, Travis Kalanick teamed up again with Michael Todd and launched Red Swoosh, another peer-to-peer file-sharing company. But this one had a different vibe. Kalanick himself called it his “revenge business” against “the old friends” MPAA and RIAA, the very forces that had crushed Scour?

The idea was sharp: instead of fighting media companies, why not work with them? Instead of distributing pirated content, Red Swoosh would help deliver legitimate music and video files more efficiently. The tech focused on optimising large file transfers, using peer-to-peer architecture to lower distribution costs.

In theory, it was genius. But timing? Brutal. The dot-com bubble had just burst. Investors were shell-shocked. Result – no money!

Red Swoosh was struggling financially, with employees going unpaid for months, when in September 2001 about $110,000 in payroll tax withholdings was used to cover operating expenses. The incident fractured the co-founders’ relationship, escalating tensions and ultimately leading to Todd’s departure, along with several other colleagues. Btw Michael Todd and Kalanick's relationship never recovered.

After Todd left, Kalanick (in his late twenties) moved back into his parents’ house to save money. He later said it “sucked,” joking that he “wasn’t getting ladies.” Poor Travis, zero salary, zero hookups, just startup life. 😀 Seriously, no joke, he went without a salary for over three years!

And then it gets worse. In early 2005, Todd helped recruit Evan Tsang (the last employee and the only engineer) to Google. Publicly embarrassing and strategically devastating resulting in a loss of a potentially lucrative deal with AOL around that time. To be even more precise, the loss of Evan Tsang was what specifically crippled the technical due diligence for that deal, because Kalanick suddenly had no one to show the code to the buyers.

But Kalanick stayed in the fight. Later that year, after a heated exchange on an internet forum, American investor Mark Cuban got to know Kalanick. Yes, you read that right, a contentious online argument led to a relationship. Cuban ended up investing $1.8 million. That investment unlocked further capital from Cuban’s network. And just like that, oxygen, life!

With fresh funding, Kalanick hired more programmers. Around 2006, he even moved to Thailand with his software team for two months to focus on development. That’s scrappy founder energy at its peak. No corporate campus, no polished boardroom, just code, hustle, and belief.

In 2007, competitor Akamai Technologies acquired Red Swoosh for approximately $19 million. After taxes, Kalanick walked away with around $2 million. Not a unicorn, not generational wealth! But after years of living on fumes? That exit was validation!

Red Swoosh wasn’t a massive headline success. It didn’t dominate the world. But from a leadership perspective, it’s very important.

Why?

Third Lesson Learned: Because this is where Kalanick learned endurance.

He learned what it feels like when co-founders split. When payroll collapses. When reputations get bruised. When you’re down to two employees and everyone else thinks you’re done.

He also learned something else, persistence compounds.

Scour taught him about legal risk and investor control. Red Swoosh taught him about survival, loyalty, and staying power. It also likely deepened his instinct to centralize control and push forward aggressively.

Afterword

This isn’t the story of a lucky founder who struck gold, but the story of someone who absorbed pain and converted it into edge.

By the time Uber entered his life, Kalanick wasn’t some naive disruptor. He was battle-tested, skeptical of investors, hardened by lawsuits, and allergic to losing control. And that mindset - stubborn, relentless, controversial - would later power one of the most aggressive growth stories Silicon Valley has ever seen.

Next up, we get to the article where every lesson, every unresolved tension gets poured into a black car app that rewrites an industry.

Please remember this: Uber didn’t create Kalanick’s leadership style. Scour and Red Swoosh did.

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It’s about why commercial companies give away some of their valuable intellectual property. What they really gain and risk by open-sourcing technology. Why “open source” is neither altruism nor charity, but a strategic, cultural, and sometimes deeply personal choice. And why Airflow’s path to becoming Apache Airflow says more about the modern software economy.

Airflow’s Origin Story

Back in 2014, Maxime Beauchemin was working at Airbnb. Not just booking cool places to stay but quietly cranking out massive amounts of data every single day. And the tools the data team had? Honestly, not great.

Some were too simple to be useful. Others were so heavy and complex they slowed everyone down. Scheduling jobs was painful, failures were easy to miss, and figuring out which task depended on which often lived in someone’s head. As the system grew, everything got harder to manage.

So Maxime did what engineers tend to do when they’re frustrated: he built something better.

Maxime Beauchemin.

Before Airflow, most data teams were duct-taping things together with cron jobs, custom scripts, or giant enterprise schedulers. Cron would run things on time, sure, but it had no idea how tasks related to each other. When something broke, you’d go digging through logs. When jobs depended on one another, you’d invent your own rules and hope everyone remembered them. There was no clear picture of what was running, what failed, or why.

It worked, until it really didn’t!

Airflow changed the game. It made workflows explicit. Dependencies were clear. You could actually see what was running, what failed, and what needed to retry. Scheduling finally respected order, timing, and reality. No guesswork. No tribal knowledge required.

And that’s often how the best tools are born: someone hits a wall, rolls up their sleeves, and builds the thing they wish already existed.

So… What Is Airflow and What Exactly Does It Do?

Airflow is a tool designed to help you automate, schedule, and monitor workflows that consist of multiple steps. These steps can include moving data from one place to another, running data processing jobs, cleaning or transforming information, training machine learning models, calling APIs, or sending reports. In short, Airflow keeps all the pieces of a complex workflow organised, running in the right order, and on schedule. Airflow shines when one task depends on another, and everything must happen in the right order, at the right time and please reliably, no excuses!

If one step fails, Airflow knows it. If something needs to retry, Airflow handles it. If a task must wait for another task to finish, Airflow enforces the rules.

Check out how Apache Airflow’s interface looks.

Think of Airflow as a very smart conductor, standing in front of a chaotic orchestra. Don’t get me wrong: Airflow doesn’t play the instruments. It doesn’t crunch the data. It doesn’t store the results. Instead, it tells everyone when to play, in what order, and what happens if someone misses a note. That’s orchestration list.

1. Defines workflows as code

In Airflow, workflows are written in Python code. This means:

• You can version them in Git

• Review changes

• Reuse logic

• Use conditions and loops

These workflows are called DAGs.

2. DAGs (Directed Acyclic Graphs)

A DAG is:

• A set of tasks

• Connected by dependencies

• With no circular loops

Example:

Extract Data → Clean Data → Transform Data → Load Data

Airflow uses DAGs to:

• Know what runs first

• What can run in parallel

• What depends on what

3. Schedules jobs

Airflow can run workflows:

• Every hour

• Every day

• Every week

• On specific dates

• Or triggered manually or by events

It replaces simple cron jobs with smart scheduling.

4. Executes tasks

Each task can:

• Run Python code

• Execute shell commands

• Run SQL queries

• Trigger Spark jobs

• Call APIs

• Launch Docker containers

• Run Kubernetes pods

Airflow itself does not process data, it orchestrates other tools.

5. Handles failures and retries

If something fails:

• Airflow detects it

• Retries automatically (if configured)

• Sends alerts (email, Slack, etc.)

• Stops dependent tasks from running

This is critical in production systems.

6. Monitoring and visualization

Airflow provides a web interface where you can:

• See all workflows

• Check task status

• View logs

• Re-run failed tasks

• Track historical runs

This makes debugging much easier.

7. Scales to large systems

Airflow can:

• Run thousands of tasks

• Distribute work across many machines

• Integrate with cloud services (AWS, GCP, Azure)

This is why it’s popular in large companies.

What Airflow is NOT

It’s important to understand what Airflow doesn’t do: It is not a data processing engine, it does not replace Spark, Hadoop, or databases, it does not move data by itself.

Instead: Airflow tells other tools when and how to run.

The Bakery Explanation (Because Everything Is Clearer With Bread)

Imagine you run a bakery. Your workflow looks like this:

1. Buy ingredients

2. Mix dough

3. Bake bread

4. Package bread

5. Deliver bread

   

You cannot bake before mixing, right? You cannot deliver before packaging, right?
Airflow is the system that makes sure: Step 3 never runs before Step 2, Step 5 doesn’t happen at 3 a.m. or if the oven breaks, someone actually notices. Simple idea but very important. Got it?

Why Airbnb Gave Away Airflow for Free (and Somehow Still Won)

What started as an internal tool at Airbnb turned out to solve a universal problem. As companies collected more data and built more complex systems, they needed exactly what Airflow provided: a reliable way to orchestrate work without losing their sanity.

Airflow didn’t arrive as a grand vision to change the world. It showed up because one company had too much data, too many jobs, and not enough control.

In 2015, Airbnb did something that sounds deeply suspicious in business terms: it gave away one of its valuable internal tools for free. No invoice. No licensing deal.
Airbnb released Apache Airflow as open source by donating it to the Apache Software Foundation (ASF). Yes, donated! As in, zero dollars changed hands.

If you’re thinking, “That seems… uncapitalistic,” you’re asking the right question. So why would a profit-driven company do this?

By 2015, Airflow had already proven itself inside Airbnb. It worked. It scaled. It solved real problems. But maintaining it alone was expensive. Debugging, extending, integrating, and keeping up with new use cases required serious ongoing effort.

Airbnb realized something important: they weren’t the only company drowning in data pipelines. Other companies had the same problems, and many very smart engineers wanted better tools.

By donating Airflow to Apache, Airbnb unlocked a powerful trade-off. Thousands of developers could now contribute to the project. Bugs would get fixed faster. Features would grow. Integrations would multiply. And no single company (including Airbnb) would control the project. Airflow would be free for everyone.

You might ask: “But where’s the money?” There wasn’t any. And that’s the point. Airbnb didn’t profit directly in dollars. They profited indirectly, which in infrastructure land can be even more valuable. As Airflow improved, Airbnb benefited from better features and broader integrations without having to build everything themselves.

Then there was the prestige factor. Open-sourcing Airflow gave Airbnb a serious boost in engineering credibility. The company became highly visible in the data engineering world. Airbnb engineers became Apache committers, giving the company long-term influence over a tool the entire industry was adopting. No cash exchanged hands, but the return on investment was very real.

Giving away a tool as open source sounds generous, even visionary, but it’s not without risk. When Airbnb handed Airflow to the Apache Foundation, they weren’t just donating code. They were placing a piece of their competitive advantage into the public arena. Here’s what’s at stake:

Loss of exclusive control
Once code is open, anyone can see, modify, and use it. Features your engineers carefully built for internal efficiency can be copied by competitors. Imagine a small airline giving its flight-scheduling software to the public, suddenly, competitors could replicate workflows that took months to perfect.

Support burden and reputation risk
Users expect the project to “work.” Bugs, vulnerabilities, and misconfigurations become public problems, not just internal ones. If the open-source community hits a serious bug, it reflects on your company even if your internal team didn’t create it. Think of how Facebook faced scrutiny over React vulnerabilities, even though the core team wasn’t at fault, the brand felt the pressure.

Opportunity cost
Open source can divert talent and resources. Engineers spend time documenting, reviewing PRs, and managing community contributions instead of building proprietary features. Airbnb had to dedicate engineers to Airflow’s open-source success, delaying some internal projects.

Strategic leverage by others
Companies can monetize your open-source work while you don’t. Cloud providers, for instance, often take popular open-source projects, package them as managed services, and profit, sometimes more than the originator. Apache Spark’s originators at UC Berkeley saw their technology adopted widely in industry, but companies like Databricks monetized it heavily.

Legal and licensing challenges
Open source introduces legal exposure. Contributors could introduce code with problematic licenses, or patents could be inadvertently infringed. Airbnb had to carefully navigate Apache licensing rules to protect itself and future users.

Yet, here’s the twist: the same risks that sound scary are often the reasons open-sourcing can be strategically brilliant. It builds a community, drives adoption, and turns your tool into an industry standard. like Airflow did. Competitors may see your code, but the real advantage often lies in knowing how to integrate, operate, and evolve it faster than anyone else. In short, open-sourcing is like letting everyone use your secret recipe. Yes, they can see it, but if you run the kitchen better than anyone else, you stay ahead.

Airbnb wasn’t the first (and definitely not the last)

This move followed a well-worn path. Yahoo donated Hadoop to Apache between 2006 and 2008, laying the foundation for modern big data systems. LinkedIn donated Kafka around 2011–2012, which went on to become the backbone of real-time data streaming. Facebook donated Cassandra to Apache in 2008 after building it for inbox search at massive scale. Google did the same with Kubernetes in 2015, donating it to the Cloud Native Computing Foundation, where it became the industry standard for container orchestration.

In all these cases, companies gave away powerful internal tools but gained influence, trust, and ecosystem momentum in return.

Why Apache Airflow Took Over the Data World and Who Actually Uses Apache Airflow

Apache Airflow became popular because it quietly solved real problems in a way that made sense to engineers.

One of the biggest reasons for its success is that Airflow is Python-based. That alone lowered the barrier to entry. Python was already everywhere in data teams, so people didn’t have to learn a new language just to define workflows. If you could write Python, you could understand Airflow. That familiarity made adoption feel natural rather than forced.

Airflow is also code-driven, which turned out to be a big deal. Instead of clicking through fragile interfaces, teams could define workflows as code, version them, test them, and review them like any other part of their software. This gave engineers a level of control and confidence that was missing from many earlier scheduling tools.

Then there’s the community. Once Airflow was open-sourced, it attracted a growing ecosystem of contributors who fixed bugs, added integrations, and shared best practices. Under Apache governance, the project gained credibility, transparency, and long-term stability. Companies could adopt it knowing it wasn’t owned by a single vendor or tied to one company’s priorities.

Another quiet superpower of Airflow is that it works with almost everything. Databases, cloud services, data warehouses, APIs, if something exists, Airflow can probably orchestrate it. That flexibility made it useful across industries and use cases, not just inside Airbnb.

Finally, Airflow gave teams something they desperately needed: visibility and control. You could see your workflows, understand dependencies, track failures, and know exactly what was running and why. For teams drowning in invisible pipelines, this felt revolutionary.

Apache Airflow isn’t just a tool for tech giants, it’s the secret engine behind some of the world’s most data-driven companies. Airbnb relies on it to keep millions of bookings flowing smoothly, Netflix orchestrates its vast content pipelines with it, and Uber uses it to make sense of ride data in real time. Companies like Stripe, Spotify, LinkedIn, and PayPal also trust Airflow to ensure their complex workflows run reliably every day.

It’s not only big names that benefit; countless startups and growing enterprises turn to Airflow to make their data smarter and more actionable. Its popularity is strongest in areas where precision matters most: data engineering, analytics, and machine learning pipelines. Anywhere there’s a need to move, transform, and orchestrate data in a structured, reliable way, Airflow is often the tool of choice.

Airbnb quickly realized something important. Open-sourcing it wasn’t just generous, it was strategic. Open source brought trust, rapid improvement, and widespread adoption.

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Note from the author 😊: If you are an expert in an open-source tool and understand how companies can use it to solve real problems, don’t underestimate the value of your expertise. Open source gives you visibility, credibility, and a massive user base but it doesn’t automatically generate revenue for you. Be brave: leverage your knowledge to build consulting services, SaaS platforms, training programs, or integration solutions. Your insight into how the tool works in real-world contexts is something no cloud provider can fully replicate. In short, open-source expertise is a launchpad (not a ceiling!) for your own business strategy.

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Intro

The CEO of a company called Astronomer Andy Byron enjoyed a brief and entirely unrequested brush with fame after appearing on a Coldplay concert kiss cam with someone who was, inconveniently, not his wife. Most of you have already seen the clip, possibly against your will, so I’ll spare you a replay. The internet has done enough.

Let me state the obvious, get it out of the way, and then promptly move on without drama: ADULTERY IS BAD. It is ugly and it does not become more impressive when performed in front of 60,000 people, in front of Chris Martin or later in front of the whole world. End of moral commentary.

The genuinely interesting part is the company. Before this moment, Astronomer was not a household name, unless your household is unusually enthusiastic about data orchestration. It remained largely unknown, but at least people were googling it for reasons unrelated to Apache Airflow or enterprise infrastructure.

This article is not about a kiss cam, a marriage, or Coldplay. It’s about Astronomer, the company I learned about entirely by accident, like most things on the internet, and what it actually does once you scroll past the memes.

Astronomer: Not About Stars

Believe it or not, as of today (January 3, 2026) a company called Astronomer doesn’t have a Wikipedia page (yet), and no, it doesn’t run telescopes or chart constellations. In fact, it’s far away from space exploration you can imagine.

Astronomer (sometimes called Astronomer.io) is a privately held tech company that lives in the cloud-data cloud, that is. It specializes in DataOps, data infrastructure, and cloud software. Astronomer’s flagship product, Astro, is a unified platform powered by Apache Airflow®, designed to orchestrate complex data workflows so teams can focus on analytics, machine learning, and other cool data-powered stuff.

Founded in 2015 in Cincinnati, Ohio (yes, Cincinnati, not Silicon Valley, BECAUSE INNOVATION HAPPENS EVERYWHERE!), Astronomer eventually moved its headquarters to New York City, where it continues to help organisations make sense of their data.

According to Contrary Research, the company Astronomer actually started as a response to frustration. The original “main product” didn’t quite work as intended, but the byproduct of figuring out why ended up being the solution that would put Astronomer on the map. Sometimes the stars align even when you’re not looking at them. 😊

Founding Story

In 2014, Ry Walker (then CEO, who previously founded web agency Sharkbytes and Differential, a startup studio and MVP builder), Greg Neiheisel, and Tim Brunk crossed paths while building USERcycle, an engagement analytics product. On paper, it worked. In practice, onboarding users was painful. Every conversation revealed the same underlying problem: companies didn’t actually control their own data. It was scattered, locked away, and stubbornly hard to move, making tools like USERcycle far less powerful than they should have been.

Ry Walker and Tim Brunk.

Instead of fighting the symptoms, the team started questioning the disease. The turning point came in May 2015, at a conference in Las Vegas. Somewhere between sessions and hallway conversations, they made a spontaneous but decisive call: USERcycle wasn’t the future. The real opportunity lay deeper, fixing how data is extracted, moved, and made usable in the first place.

Here it is - the simple and powerful idea of Astronomer: Apache Airflow was becoming the industry standard for data orchestration, but running it in production was… kind of a nightmare. Especially for enterprises.

Within days of returning home, they acted on that insight. The trio applied to AngelPad with a brand-new idea: Astronomer, founded in 2015.

Andy Byron, who made headlines for a scandal at a Coldplay concert, was never a founder of Astronomer and not even an early employee. In 2023, he was hired and found himself appointed CEO. Before Astronomer, Byron was President of Lacework, CRO of Cybereason, and President of Fuze. Under his leadership, Astronomer hit a milestone: a massive $93 million Series D funding round. Things were looking up… until July 2025, when a viral Coldplay concert scandal involving Kristin Cabot, Astronomer’s Chief People Officer, ended Byron’s tenure days later.

Andy Byron is third from the left, and Kristin Cabot is on the right.

Since then, DeJoy has stepped back in as CEO, guiding the company through its usual focus: orchestrating complex data workflows, scaling DataOps, and making pipelines behave.

Apache Airflow’s Origin Story

At some point while writing about Astronomer, it became clear to me that cramming the entire origin story of Apache Airflow into a single post would be doing it a serious injustice. Some stories need room, especially the ones that explain how a tool quietly reshaped an entire industry.

So this is that pause and the promise of a sequel.

The short version goes like this: Airflow was a tool created at Airbnb by Maxime Beauchemin to deal with a growing problem: too much data, too many jobs, and not enough reliable ways to manage them. Airbnb wasn’t just hosting travelers anymore, it was orchestrating massive amounts of data every single day. Airflow started as an internal solution, built to keep workflows sane and dependencies under control.

Then something interesting happened. In 2015, Airbnb donated Airflow to the Apache Software Foundation, officially open-sourcing it and making it free for anyone to use. No licensing fees. No hidden catch. Just a powerful tool released into the world.

Which naturally raises the question: why would a profit-driven company give away something so valuable? That question deserves a story of its own. And yes, it’s coming next! Because the real magic of Airflow isn’t just how it was built, but why it was set free. If you like origin stories, quiet technical revolutions, and the moments where business strategy meets open-source idealism, you’ll want to stick around.

The Idea of Astronomer

By the mid-2010s, Apache Airflow had clearly won. Data teams loved it. Companies adopted it enthusiastically. Job descriptions casually listed “Airflow experience” as if everyone just naturally had it. If you were orchestrating data workflows, Airflow was no longer a bold choice, it was the obvious one.

And yet, behind the scenes, something wasn’t quite right. Using Airflow was one thing. Running Airflow in production was another story entirely. Getting it set up securely often felt like assembling furniture with missing instructions. Scaling it as workloads grew introduced new and exciting ways for things to break. Monitoring required a mix of dashboards, logs, and intuition…

Airflow itself was doing exactly what it was designed to do. It scheduled tasks, respected dependencies, and kept workflows moving. The real struggle was everything around it: the infrastructure, the reliability concerns, the operational overhead. Airflow had become essential, but it had also become demanding.

For enterprises, this pain was amplified. More data meant more workflows. More workflows meant more things to monitor, secure, and keep running without interruption. What started as a powerful open-source tool was slowly turning into a full-time operational responsibility.

And this is where Astronomer’s founding idea quietly clicked. The problem wasn’t Airflow. Airflow was great. The problem was that teams were being forced to become Airflow experts, infrastructure specialists, and on-call firefighters all at once. What they needed wasn’t a replacement, it was a better way to run the thing they already relied on.

Sometimes the biggest opportunities don’t come from inventing something new. They come from making something powerful finally feel manageable. Astronomer asked a practical question: What if we made Airflow actually easy to run in production? That was it. That was the whole idea.

When Airflow works the way it’s supposed to, life gets noticeably better. Data teams stop sprinting from one fire to the next and actually move faster. Pipelines stop breaking for mysterious reasons at inconvenient hours. Downstream tools finally behave. And, perhaps most importantly, people get to sleep through the night without being woken up by an alert.

Long story short: Astronomer didn’t try to change what Airflow was. They focused on making it work the way everyone hoped it would.

Astronomer’s Secret Weapon: The People Who Knew Airflow

Great ideas are nice. Execution is better. But execution without the right people is just optimism with a deadline. When Astronomer decided to build a business around Apache Airflow, they understood something crucial early on: you can’t successfully commercialise an open-source project unless you deeply respect it and the people who build it. Instead of treating Airflow as just a dependency, Astronomer made it the foundation of the company and invested heavily in open-source expertise.

Apache Airflow isn’t just used at Astronomer. It plays a central role there. Several members of Astronomer’s early team became deeply involved in Apache Airflow as the project was gaining momentum. These weren’t casual users. They were contributors who spent their time writing code, reviewing pull requests, and shaping how workflow orchestration evolved in practice.

Ash Berlin-Taylor, a member of Astronomer’s founding team, became a prominent contributor to Apache Airflow, served on the project’s Project Management Committee, and later held a leadership role at Astronomer. Kaxil Naik, who also joined early, went on to become Senior Director of Airflow Engineering while remaining a long-time PMC member and core committer. Pete DeJoy, co-founder and CEO as of 2026, has worked for years at the intersection of Airflow’s open-source development and the operational realities faced by enterprise users.

In other words, Astronomer didn’t just build products around Airflow. Its team became deeply embedded in the project’s evolution. That strategy paid off. Since the late 2010s, Astronomer has been one of the most influential commercial contributors to the Apache Airflow ecosystem. The company employs a significant number of Airflow committers and PMC members, and its engineers frequently serve as release managers and major contributors to new features and improvements. When important milestones occur in the project, Astronomer is often closely involved alongside the broader community.

This is how Astronomer built its business: by aligning itself closely with an open-source project, contributing consistently, and earning trust over time. No forks. No attempts to control the project. Just sustained participation, deep expertise, and a clear understanding that credibility in open source is earned through contribution, not claims.

It turns out that hiring the people who actually know how the engine works is a pretty solid business strategy.

Astronomer: Status Quo

As of 2026, Astronomer is a privately held technology company with roughly 386 people across multiple locations, including United States, Europe and India.

In May 2025, Astronomer closed a $93 million Series D funding round led by Bain Capital Ventures, with participation from Salesforce Ventures, Insight Partners, Meritech, and Venrock, bringing its total capital raised to over $370 million since its founding. This funding is being used to accelerate product development, expand global reach, and invest in research and development.

As a private company, its exact valuation fluctuates based on secondary market activity and is not publicly listed on a stock exchange like the NYSE.

According to data aggregated from PitchBook and other private market sources, Astronomer’s valuation around the Series D was approximately $775 million, reflecting investor confidence even amid market volatility and leadership transitions. The 2025 valuation of ~$775 million represents a "down round" compared to its March 2022 Series C valuation, which was estimated at $1.2 billion to $1.5 billion.

Lessons Learned From Today’s Reading

One thing becomes very clear after today’s reading: opportunities exist everywhere. Astronomer is a great example of this. The company didn’t invent Apache Airflow, didn’t try to replace it, and didn’t chase the latest shiny trend. Instead, Astronomer used Airflow as its foundation and built something valuable on top of it.

Astronomer’s success came from solving a very real, very boring, and very painful problem that many data teams had. Historically speaking, this is exactly how many great infrastructure companies are born. Not flashy. Not glamorous. Just essential.

And that leads to today’s main lesson. The opportunities are everywhere. New tools are released constantly, and many of them work well in theory but struggle in practice. Very often, they need help. They need a supplemental product. They need someone to make them easier, safer, or more reliable to use.

Dear future or current startup founders: look around you! Pay attention to what doesn’t work very well. Ask yourself which tools cause frustration, confusion, or late-night alerts. That frustration might be your chance to build something valuable!

And yes, valuable for society too! Every legitimate business employs people, pays taxes, and contributes to the broader economy. Building something useful is not only good for users, it’s good for the world and for you. So be brave!

There’s another lesson here as well, it’s the silver lining kind: negative attention can sometimes be turned into a branding opportunity.

Astronomer operates in a highly competitive market with major players like Google Cloud Composer, Amazon Managed Workflows for Apache Airflow (MWAA), Azure Data Factory’s orchestration tools, and many smaller service providers. The company has also faced a downward valuation trend even before the viral Coldplay concert scandal. On top of that, Astronomer experienced a very public crisis when then-CEO Andy Byron resigned following the viral Coldplay kiss cam incident, along with the resignation of Chief People Officer Kristin Cabot.

The situation was serious, but the company responded quickly, distancing itself from the scandal, launching investigations, and even leaning into the unexpected attention with a bold PR move by hiring Gwyneth Paltrow (BTW Chris Martin's ex :-D !!!). As a result, far more people now know the name Astronomer. More people are talking about it. More people are curious. While the long-term effects are still unfolding, the company managed to turn a negative moment into widespread brand awareness.

The takeaway? You can’t always control what happens but you can control how you respond.

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If this story made you rethink where real opportunities come from, I’d love to hear your thoughts. Drop a comment, share your own “this tool is great but…” moments, or tell me what problem you’d love to see fixed.

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Most founders obsess over market size, product strategy, or fundraising. The truth is that teams collapse far more often than ideas. As I wrote in my post before, roughly 65% of high-potential startups fail because of conflicts between cofounders. Many people rush into partnerships with individuals they barely know. This often leads to early fractures, slow decision making, and in painful cases, companies that fall apart just when they start gaining traction. Strong cofounders should be steady under pressure. They should be decisive, resilient, and ideally known or tested over time. Domain expertise matters. Character and compatibility matter more.

Do You Even Need a Cofounder?

Before you start drafting job posts as “Cofounder Wanted” ads, ask yourself an honest question:

Do I actually need someone right now?

The most successful founders hire only when a role becomes absolutely unavoidable. If you can survive without that person, you probably should. Early hiring must be brutally selective. Every cofounder becomes part of the company’s DNA. Every personality adds or subtracts momentum. Every wrong hire multiplies complexity.

When you do hire, hire for mission, dedication, and cultural fit. Skills can be taught. Methods can be learned.

The Cofounder Rule #1: The Right One Is Usually Someone You Already Know

The person you choose as a cofounder will influence every part of your journey. The longer you have known them, the better. There is a romantic idea that two strangers meet at a hackathon and build the next unicorn. It looks perfect in a documentary. It makes for high-energy headlines. It almost never survives real life. Startups demand trust at a level that is difficult to manufacture on short notice.

Real success usually grows out of relationships that have already been pressure tested. Think about the origins of Robinhood. Baiju Bhatt and Vlad Tenev were not random acquaintances. They had worked together, studied together, and built side projects. Their collaboration had already endured late nights, technical disagreements, and the small frictions that reveal who stays calm and who cracks. When they decided to start a company, they were not just betting on an idea but betting on a relationship they already knew they could depend on.

Baiju Bhatt and Vlad Tenev co-founded Robinhood in 2013 to “democratize finance” by creating a commission-free stock trading app. Their story began after the 2008 financial crisis, where they saw inequalities in the system and believed that leveraging mobile technology could make investing more accessible to ordinary people. Bhatt studied physics, then mathematics, at Stanford University. Tenev also studied mathematics at Stanford, then went on to earn a master’s at UCLA. At Stanford, the two became friends.

They launched two earlier ventures: Celeris, a high-frequency trading startup, and Chronos Research, a low-latency trading software company before founding Robinhood. While Chronos generated some revenue and had clients, neither venture scaled into a long-term business, and both were ultimately shut down as the founders moved on. Working in that space, they saw just how costly and exclusionary investing remained for “regular users” and decided to create a platform engineered for everyday people. The idea: eliminate fees, eliminate barriers.

BTW Baiju Bhatt stepped down from his executive role as chief creative officer at Robinhood in March 2024 to focus on his “next enterprise”, but he remains on the company’s board of directors.

This pattern repeats across many breakout companies. The Airbnb founders had lived together and weathered financial stress long before Airbnb took off. The Canva founders had collaborated for years. Even at Facebook, the earliest team members were classmates and long-time peers. The common thread is trust built over time. Not trust declared, or trust hoped for. Trust earned through shared work, shared failure, and shared conflict.

When you choose someone you have known for years, you skip months of uncertainty. You already understand how they think under pressure, how they respond to disagreement, how they handle responsibility, and whether they can stay emotionally stable when things go sideways. This matters because startups are emotionally volatile. You will be celebrating one day and doubting everything the next. The pace is unpredictable. The stakes feel personal. In that environment, personality and character matter far more than domain mastery.

You do not need a cofounder who looks perfect on paper but someone whose strengths complement yours, whose temperament you trust, and whose behavior remains consistent even when the situation is not. The longer you have known the person, the more data you have. And in early-stage companies, that data can protect you from misjudgments that cost months, or even the entire company.

Choosing a cofounder is not a hiring decision. It is a long-term commitment. The more history you share, the stronger the foundation you build on.

The Cofounder Rule #2: Select, Test, Hire Slow

If you do not already have someone you have known for years and trust instinctively, the next best option is to look for cofounders through strong referrals. Ask your smartest friends. Ask former colleagues. Ask people who have actually seen this person under pressure. Great referrals are not just names on a list but early signals that someone has character, work ethic, and a reputation worth protecting.

But even with referrals, you should never skip the slow, deliberate process of choosing a partner. Interview them. Then interview them again. Then work with them on something tiny and real. A one-day prototype. A miniature product spec. A weekend fixing bugs or building a demo. Nothing reveals compatibility like shared work. Ten interviews will not show you how someone reacts when the API breaks at midnight. One day of real collaboration will.

Skipping this step is gambling with the entire future of your company. Many founders convince themselves they “see potential” and rush ahead anyway. Months later, they realize the person is brilliant but fragile, or talented but negative, or charismatic but unreliable. A few hours of real work together early on would have exposed these traits immediately.

Short projects are powerful because they compress reality. You see how someone communicates. How they make decisions. How they handle ambiguity. Whether they take ownership or wait to be asked. These signals are far more predictive than a résumé full of prestige.

References matter too. Not superficial references. Deep references. Ask people who worked alongside them in stressful moments. Ask what the person is like when they are tired. Ask how they behave when their ideas are challenged. You are not looking for perfection. You are looking for truth.

Early employees and early cofounders must be resilient, proactive, and comfortable with risk. They must enjoy the chaos that comes before clarity. They must be able to say, “I do not know the answer, but I will figure it out.” As a founder, you should genuinely want to spend a lot of time with them. You should respect their thinking. You should feel energized, not drained, after working together.

You do not need to be best friends. You need to be able to collaborate without friction and communicate without tiptoeing. Mutual respect is non-negotiable.

And do not overvalue experience. Early-stage startups are often better served by curious graduates or people at the beginning of their careers. They come without rigid habits. They learn fast. They bring energy instead of ego. What matters is not where they have worked. What matters is how they think, how they adapt, and how deeply they care about building something that lasts.

Choosing early partners is not about filling roles but about choosing the people you want next to you when everything feels uncertain. That is the real test.

The Cofounder Rule #3: Cofounders Cannot Be Mediocre

If you are building a tech company, at least one cofounder must be truly technical. It is nearly impossible to ship high-quality software quickly without deep technical ownership inside the founding team.

When you are choosing a cofounder, the margin for error is almost nonexistent. Mediocrity at the cofounder level is not a small obstacle. It becomes a structural weakness. It slows every decision. It fractures trust. It quietly drains momentum until the company stops moving altogether.

Think about what happens when one founder avoids hard decisions or resists taking ownership. The other founder begins carrying extra weight. This compounds day after day. Many early-stage companies die because one cofounder simply could not keep pace.

The difference between okay and extraordinary at the cofounder level changes the entire trajectory of the company. Competent is not good enough at the cofounder level. You need exceptional.

Once a startup reaches 10 employees, average performers can survive inside well-defined structures. Processes compensate. Teams balance workloads. Roles narrow. But a cofounder operates before any of those systems exist. They build the culture. They shape expectations. They set the speed. They model how to handle conflict, failure, and ambiguity. A mediocre cofounder sends a signal to the entire organization that “good enough” is acceptable. It is not. Not at the beginning.

A cofounder must be someone who thinks sharply, communicates clearly, acts decisively, and stays resilient under pressure. They must be capable of learning at extraordinary speed. They must be ready to do work far outside their background. If they hesitate, you slow down. If they avoid difficult conversations, the culture becomes fragile. If they cannot adapt, you lose precious time.

The startup world is full of stories about breakthrough ideas and bold fundraising. Fewer people talk about the quiet reality. Many companies fail because the founding team included one person who was simply not strong enough. That is why the cofounder bar must be higher than the hiring bar. It is not elitism. It is survival.

Choosing a cofounder is one of the most consequential decisions you will ever make. Be selective. Be thoughtful. Be uncomfortably honest. A startup can overcome dozens of problems. It cannot overcome a mediocre cofounder.

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What Great Cofounder Matches Have in Common

Every startup has one critical decision: choosing the right person to build it with. A strong cofounder can significantly increase a startup’s chances of success, while the wrong one can create serious challenges. After speaking with early stage teams over the years, I have noticed the same patterns among cofounder pairs that succeed compared with those that eventually fall apart.

Complementary strengths
Great cofounders don’t clone each other’s skills. They cover each other’s blind spots. Think of Airbnb. Brian Chesky and Joe Gebbia were the design minds. Nathan Blecharczyk built the technical and growth foundation. When strengths overlap too much, boundaries blur, decisions stall, and conflict shows up way earlier than revenue.

Shared long-term direction
You don’t need the same personality, but you do need the same destination. If one founder dreams in blitz-scale mode and the other wants a calm, modestly growing business, you’ll feel that tension in every major decision. It’s much easier to tweak a business model than to fix mismatched values about pace, scale, and what “success” even means.

Working style compatibility
Some founders move fast and break things, others think deeply before touching the keyboard. These rhythms shape budgeting, product release cycles, even how you handle investor updates. A lot of smart teams try a tiny project together first. I wrote about it in the Cofounder Rule #2.

Emotional resilience
Pressure comes for every team: botched features, missed payroll, investors ghosting, markets shifting. The real tell is how each founder behaves when things crumble. When the first version of Dropbox struggled to gain traction, Drew Houston and Arash Ferdowsi didn’t turn on each other, they doubled down on clarity and communication. You learn more about a future cofounder by watching how they respond to a broken prototype than by reading their résumé.

Signal values
A strong cofounder elevates your credibility with both hires and investors. Someone who communicates the model convincingly, shows command of financial logic, and brings a trusted network gives the company an early advantage.

Equity clarity and real roles
Before paperwork is signed, founders should clearly define responsibilities, decision domains, ownership percentages, vesting rules, and governance structure. For instance, one founder may lead product and engineering, while another oversees finance, accounting logic, and customer strategy. A vesting schedule ensures equity reflects contribution over time rather than just enthusiasm on day one.

Integrity and financial discipline
You can’t build a company with someone who treats money or authority casually. A cofounder who signs contracts without telling you or overspends “just to move fast” becomes a liability as soon as customers and investors start paying attention. The best teams document decisions, communicate openly, and treat ownership like a responsibility, not a perk. That’s how companies survive audits, due diligence, and the messy middle between product and traction.

Final thought

If you are building something, choose partners with care. Hire slowly. Work closely. Communicate relentlessly. Protect your culture like your life depends on it. Because in a startup, it actually does.

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The Two Cofounder Model

The most common startup structure after solo founders is a two-cofounder team, usually with a 50-50 equity split (please keep in mind that 50-50 is not an ideal split). This model is popular because it combines complementary skills with shared responsibility, while keeping decision-making relatively fast and friction-free.

• In a two-person team, each founder brings different strengths to the table. One might be a tech person who can build the software, while the other focuses on product vision, user experience, and design. This setup allows founders to consult each other, challenge ideas, and refine strategies in a way that is often more productive than working alone.

• Despite having two voices in the room, decisions can still move quickly. Unlike larger teams, there aren’t multiple approvals or layers of debate. Each person has enough independence to act decisively, but still benefits from the checks and balances a partner provides.

Potential Pitfalls

• This kind of partnership has real risks. If the two founders start wanting different things or working in very different ways, the relationship can break down. With only two people, there’s no one to help calm things down when they disagree. Arguments about the direction of the company, what’s right or wrong, or what to work on first can slow everything down if they’re not handled well.

This structure works best when a startup can be built around two complementary roles, often combining technical expertise with business or market execution. The “dual-founder symmetry” one founder focuses on building the product, the other on building the business is a classic high-efficiency setup.

Take Stripe: Patrick Collison and John Collison built a payment platform that grew into a global fintech powerhouse. Patrick led product vision, strategy, and external partnerships, while John handled technical execution and operations. Their shared analytical mindset and overlapping engineering skills allowed them to operate independently while staying aligned on the company’s direction.

John Collison and Patrick Collison.

Here you go, Uber, Travis Kalanick and Garrett Camp conceived Uber in 2008–2009. Garrett Camp, an engineer with prior startup experience, built the early prototype of “UberCab,” while Travis Kalanick focused on product, operations, and market strategy. Together, they combined technical and business strengths to bring the first version of Uber to market quickly.

These examples illustrate the power of distinct yet overlapping skills. When one founder handles technology or product, and the other handles market, operations, or partnerships, the team can move fast, adapt, and scale while maintaining a unified strategy.

Two-person founding teams are often considered the ideal setup for a startup. Each founder brings different strengths, they share responsibility, and they can communicate easily. There’s enough variety to handle big challenges, but the team is still small and flexible enough to move quickly.

Please keep in mind that a 50-50 split isn’t always ideal. Often, one founder ends up contributing more time, effort, or resources than the other, so it’s usually fairer to divide equity based on each person’s actual contributions and long-term commitment. This approach helps prevent resentment, aligns incentives, and reflects the real value each founder brings to the startup.

The Three Cofounder Model

When a startup has three cofounders, the advantages are easy to see. More minds around the table often lead to stronger ideas, richer problem-solving, and a healthier mix of skills. This model allows a team to combine product vision with technical depth, and add operational or financial expertise on top of that. It also brings more stability, because responsibility and decision-making are not resting on only one or two people. With three people involved, the team can challenge each other in productive ways, which typically leads to better decisions and a more resilient company.

However, bigger teams come with their own challenges. Decision making can slow down because more opinions need to be heard, and disagreements become more complex to navigate. Equity splits can turn into sensitive conversations, especially if contributions are uneven or expectations differ. Internal politics can also appear if roles are unclear, or if the team has not defined from the start who makes final calls in specific areas. For a trio to work well, roles and responsibilities must be laid out clearly from day one. Without that clarity, conflict becomes more likely.

The three-cofounder model works especially well for startups that need multiple areas of expertise. Think of hardware companies that require engineering plus supply chain plus software. Or startups in heavily regulated industries where the team needs legal, technical, and commercial talent working together from the start. When complexity is high, three deep skill sets can be a major advantage.

A strong example is YouTube, founded by Steve Chen, Chad Hurley, and Jawed Karim. Hurley drove design and the overall look and feel of the product. Chen focused on engineering and scaling the platform. Karim shaped early product direction, user behavior insights, and growth concepts. Together, they built a platform that grew faster than nearly anyone expected. Their shared skills created balance, while their separate strengths allowed them to move quickly in their own areas.

Chad Hurley, Steve Chen and Jawed Karim.

Other well-known companies started with three founders too.
The unicorn Zapier, founded by Wade Foster, Bryan Helmig and Mike Knoop, combined product, engineering, and operations to build a product that now automates workflows for millions of users.

Wade Foster, Bryan Helmig and Mike Knoop.
The French example BlaBlaCar, created by Fred Mazzella, Nicolas Brusson and Francis Nappez, blended marketing, strategy, and engineering to scale a massive long-distance ride-sharing network across Europe.

Nicolas Brusson, Fred Mazzella and Francis Nappez.

These examples show how three cofounders can provide a rich mix of skills while still maintaining enough agility to move fast. The key is simple. Define roles clearly, communicate openly, and make sure everyone knows where their authority begins and ends. When that structure is in place, three-person founding teams can be some of the strongest in the startup world.

The Four or More Cofounder Model

When a startup begins with four or more cofounders, it often feels like an upgraded version of the three-founder model. The major advantage is obvious. A larger team brings a wider range of skills, perspectives, networks, and industry connections. Each founder usually knows different people, comes from a different background, and opens different doors. This can be extremely valuable in early hiring, fundraising, product strategy, and business development. With more expertise on the table, the team can cover technical tasks, product work, finance, partnerships, and operations from day one.

The challenge is that this richness comes with a real cost. Governance becomes harder. Ownership becomes more diluted. Decision making slows down because more voices need to be heard. The potential for conflict rises sharply, especially when personalities, values, or visions clash. When a team grows beyond three founders, the risk of internal politics increases and disagreements can become deeply personal if roles are not clear.

For this model to work well, every founder must bring an essential and non-overlapping capability that the startup cannot function without early on. That is the only reason to justify such a large founding group. If the skills overlap, or if roles are fuzzy, the structure becomes unstable quickly. This is why the four-plus model is far less common and generally less sustainable unless the startup operates in an extremely complex or deeply technical field.

Still, some globally influential companies began exactly this way.

A well-known example is PayPal. The early team included Peter Thiel, Max Levchin, the Sweetheart Elon Musk, Luke Nosek and Ken Howery. Several of these founders arrived through the merger of two separate startups. X.com and Confinity. Despite the power struggles that later made headlines, the mix of technical talent, financial vision, security expertise, and product thinking created a foundation strong enough to build a global payments platform. The company’s success shows that a large founding group can work if each person’s role is vital and if the market opportunity is big enough to require such a range of skills.

Young Peter Thiel and Elon Musk.

After eBay acquired PayPal in 2002, many of the early employees left and went on to found other influential tech companies. In 2007, Fortune magazine featured these alumni in an article dubbing them the “PayPal Mafia,” complete with a stylized photograph. The photo, with a mafia scene, was taken years after the PayPal sale and does not feature the same founding group, nor was it taken during the company’s early days. Elon Musk was not present for that photoshoot.

Another example comes from the biotechnology world. Moderna began in 2010 with five cofounders. An unusually large number even for a biotech startup. The size of the founding team reflected the complexity of what they were building at the intersection of academic science, medicine, engineering, and venture creation.

Noubar Afeyan provided the venture-building structure, incubation environment, and early funding. Derrick Rossi supplied the core scientific discovery around modified mRNA. Kenneth Chien contributed medical and translational expertise. Robert Langer added engineering and biomedical innovation at world-class levels. Timothy Springer brought immunology expertise and early financial backing.

None of these roles overlapped. Each founder contributed something that the others simply could not. This is why the company’s large founding team worked. The problem they were solving was so complex that no smaller combination could have covered all critical areas.

These stories highlight an important truth. Startups with four or more founders succeed only when every founder brings a uniquely essential skill set, when the team aligns on mission from the start, and when roles and decision rights are defined with absolute clarity. Without those elements, size becomes a burden rather than an advantage. With them, it becomes a powerful engine for breakthroughs that smaller teams could never achieve.

Choosing the Right Founder Model

Whether you build your startup alone or with two founders, three founders, or a larger team, each model comes with its own strengths and weaknesses. Two-person teams often feel like the perfect balance. Fast, focused, and built on complementary skills. Three-founder teams add more expertise and stability. Especially valuable when a product sits at the intersection of several disciplines. Four or more founders can unlock an incredible range of capabilities, networks, and ideas. Yet they require exceptional clarity, governance, and alignment to avoid internal friction.

There is no universal “best” structure. The right choice depends entirely on your market, your product, your personal strengths, and the level of complexity your company needs to handle from day one. What matters most is not the number of founders. It is the quality of the collaboration, the clarity of roles, and the ability of the team to navigate uncertainty together.

In the next article, I will go even deeper into these models. How to choose the right cofounder for your specific idea, how to avoid the most common traps, and how to build a founding team that investors trust. If this topic speaks to you, make sure to subscribe, share the post, and leave a comment. I would love to hear your experiences and perspectives on building a startup from the ground up.

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Every founder eventually faces a defining question: Should I go solo, or should I build with others?

There’s no universal answer. Some of the world’s greatest companies were built by one visionary founder; others thrived because of perfectly balanced teams. Whether you choose to build alone or with partners will shape every part of your startup: from how fast you move to how you make decisions and how investors perceive your business.

Let’s explore the main models (and their trade-offs) starting with the solo founder path.

The Solopreneur

The solo founder model is the most independent and the most demanding. You’re the architect, builder, and driver of everything. You own the vision, the strategy, and the execution.

Advantages

• Full control and clear vision
  You make every decision without debate or delay. There are no disagreements, no compromises, and no need to align multiple opinions. This can be incredibly powerful when you have a clear understanding of your market and product.

• Complete ownership
  Equity is yours alone. You’re not sharing the pie, and your rewards reflect your risk and effort. For founders who have deep conviction in their idea and confidence in their abilities, this model can be both liberating and lucrative.

• Speed and decisiveness
  Without needing consensus, you can move fast. You can pivot in a day, launch in a week, or scrap an idea without a meeting. Solo founders often outpace their competitors in the early stages simply because decision-making is frictionless.

Disadvantages

• The burnout trap
  The same independence that gives you speed can also lead to exhaustion. Many solo founders find themselves working endlessly, wearing every hat (product, sales, marketing, fundraising) until they hit a wall. Without someone to share the emotional and operational load, burnout becomes a real threat.

• Skill gaps
  No one can be an expert in everything. You might be brilliant at product design but weak in finance. Or great at vision but inexperienced in operations. Without complementary skills around you, your blind spots can quietly become your biggest risks.

• Investor hesitation
  Investors often see solo founders as higher risk. They worry about what happens if you get sick, lose motivation, or simply can’t scale the business alone. That’s why many venture capitalists prefer teams of two or three cofounders, just because they see stability and resilience in numbers.

The solo model can be powerful for founders with deep domain expertise, financial independence, or access to strong advisors. If you’re confident in your skills and can hire or consult experts when needed, building solo can offer unmatched creative control.

Think of Michael Dell, who started Dell Technologies from his university dorm room. He saw an opportunity to sell customized computers directly to consumers, bypassing middlemen. That hands-on, end-to-end control gave Dell a competitive edge that reshaped the industry.

Michael Dell started his computer company in 1984 from his dorm room at the University of Texas at Austin, specifically room 2713 at the Dobie Center dormitory. At 19, he was assembling and selling custom computer upgrades out of this room with an initial investment of $1,000, a move that led him to drop out of his pre-med studies to pursue the business full-time.

Or take Jeff Bezos, who built Amazon from a single idea: to make it possible to buy books online. In the early days, Bezos managed nearly everything logistics, website operations, and even packing orders himself. His relentless focus on customer experience and long-term vision became the DNA of Amazon’s success.

Jeff Bezos rented a home in Bellevue, Washington, and the business in 1994 was run out of the garage.

These examples show that solo founders usually have a strong understanding of their field, a clear sense of what they want to achieve, and the determination to keep going even when they’re working alone and facing uncertainty.

However, both Bezos and Dell eventually surrounded themselves with exceptional people early on, leaders who were as capable and driven as they were. That’s the secret most solo success stories share: even if you start alone, you don’t stay alone.

Being a solo founder is not about doing everything yourself forever. It’s about having the conviction to start and the wisdom to know when to bring in others.

If you have the expertise, discipline, and endurance to handle the early chaos and the humility to build a strong team once you gain traction, the solo path can be your launchpad to something extraordinary.

Partnered Founders

Here’s a surprising truth that most founders don’t talk about. Roughly 65% of high-potential startups fail not because of bad ideas, market timing, or lack of funding but because of conflicts between cofounders.

According to research by Harvard Business School professor Noam Wasserman, who studied over 10,000 founders, interpersonal issues and disagreements among founding teams are the leading cause of startup failure. In other words, the most common reason startups die isn’t product-market fit, it’s people problems.

Think about that. Two people can build the next billion-dollar idea and still watch it crumble because they couldn’t agree on how to lead, grow, or even talk to each other.

This isn’t just a statistic. It’s a mirror. Founders often underestimate the emotional and strategic complexity of building a company with someone. Yet, the cofounder relationship is the single most important partnership you’ll ever have in business and sometimes, in life.

When you’re starting a startup, one decision can shape almost everything that follows. It’s not your product. It’s not your pricing model. It’s the person you choose to build the company with.

Choosing a cofounder is one of the most important decisions a founder makes. Yet many first time founders still think of a cofounder simply as someone to share the workload with.In reality, the role goes much deeper. Your cofounder influences how the company makes decisions, handles disagreements, responds to setbacks, divides responsibilities, and grows over time. The right partnership can make difficult challenges easier to navigate. The wrong one can create problems that affect every part of the business.

The Hidden Architecture of a Great Cofounder Relationship

A startup is a living organism made of skills, resilience, and trust. Very few people are built to handle all the demands of early-stage chaos alone.

Think about Brian Chesky and Joe Gebbia of Airbnb. Chesky was the visionary storyteller, the one who could inspire investors and users alike. Gebbia, on the other hand, was the designer who understood user experience at a deep level. Together, they turned a quirky idea about renting air mattresses into a multibillion-dollar brand that reshaped travel. Without that balance of vision and design, Airbnb might have been another startup that never found its footing.

That’s what skill balance and expertise mean in real life. One founder might handle technology and product development. The other might take on finance, marketing, or partnerships. Together, they close gaps, accelerate decision-making, and turn uncertainty into progress.

Sharing the Load When the Nights Get Long

Building a startup isn’t just hard, it’s emotionally draining. There will be nights when you question everything. When investors pass, customers churn, and your product doesn’t work as you hoped.

Having a cofounder isn’t just about dividing tasks; it’s about dividing emotional weight. When Ben Silbermann was building Pinterest, he faced rejection from nearly every investor he approached. But his cofounder, Evan Sharp, shared the belief that they were onto something bigger. That shared conviction kept them going through hundreds of quiet, uncertain days. The ability to lean on someone who understands your vision from the inside is priceless.

The Power of Honest Conflict

One of the most underrated advantages of having a cofounder is access to truth. Great partners don’t just agree, they challenge each other.

When Steve Jobs returned to Apple, he and Jony Ive didn’t always see eye-to-eye, but their creative tension produced some of the most iconic designs in history. Honest, respectful disagreement often sharpens decisions. A solo founder risks falling into an echo chamber. Two grounded minds, on the other hand, can pressure-test ideas before the market does.

What Investors Really See

When investors meet founding teams, they’re not just looking at the product. They’re assessing chemistry. A cohesive team signals stability, accountability, and execution capability. Investors know that early startups pivot constantly. A balanced founding duo can adapt faster because the company isn’t dependent on one person’s expertise or emotional bandwidth.

That’s why many VCs quietly prefer to invest in strong pairs or trios over solo founders. They know startups don’t just fail from bad ideas, they also fail from founder exhaustion, misalignment, or internal conflict.

The Invisible Hand of Founders - Culture

Every company inherits its founders’ DNA. If one founder values transparency and the other prioritizes control, tension will eventually seep into hiring, decision-making, and company culture.

When Google was young, Larry Page and Sergey Brin built their partnership on intellectual curiosity and long-term thinking. That mindset defined Google’s culture for decades. Shared values at the top become the compass for everyone else.

Continuity When Life Happens

No one likes to talk about what happens if a founder steps away. But continuity matters. If something unexpected happens, health issues, family changes, burnout - whatever, a cofounder ensures the company doesn’t collapse overnight. This continuity gives employees confidence and investors peace of mind.

The Real Cofounder Test

Choosing a cofounder isn’t about finding someone who agrees with you. It’s about finding someone who complements you. Someone who will call you out when you’re wrong but still share your conviction about why you’re building what you’re building.

Before you shake hands, ask yourselves:

• Do we challenge each other’s thinking in a healthy way?

• Do we share the same vision for where this company should go?

• Can we handle failure together without blaming each other?

If the answer is yes, you’re not just choosing a cofounder—you’re choosing the backbone of your startup’s future.

In my next post, I’ll dive deeper into the world of cofounder teams, exploring the dynamics of two, three, four, or even larger founding groups, along with their advantages, challenges, and real-life examples from some of the world’s most successful startups.

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I’m not going to write the next incriminating article about Theranos and its CEO Elizabeth Holmes and what bloody business she created. The world already did that.

Of course, I don’t want to belittle the harm that Theranos and Elizabeth caused. Lives were affected. Patients suffered. Investors lost fortunes. And the startup world got a brutal reminder of how thin the line between ambition and deceit can be.

It’s not about excusing. It’s about understanding.

Yet, the more I researched her story, the more I found myself, surprisingly, sympathizing with her. Because deep down, I believe that people are good and kind. Even Elizabeth Holmes.

She didn’t get where she is by having evil intentions, she got there by making a series of mistakes, mis-calculations, mis-judgements. And if she had done things differently, perhaps the ending would have been different too.
So instead of another condemnation, let’s talk about the mistakes she made, and what every founder, dreamer, and builder can learn from them.

Origins

The story of Elizabeth Holmes begins with ambition, brilliance, and a dream that captured Silicon Valley’s imagination.

Elizabeth Anne Holmes was born on February 3, 1984, in Washington, D.C., into a family with deep professional roots and social aspirations. Her father, Christian Rasmus Holmes IV, worked in management at Enron (yes, that Enron!). Her mother, Noel Holmes, served on congressional committees on Capitol Hill.

From a young age, Elizabeth was driven. Friends described her as intense and idealistic, even as a teenager. At St. John’s School in Houston, she excelled in math and science and developed a fascination with technology. Holmes later claimed that during high school she launched her first business, selling C++ compilers to Chinese universities, though that story has never been independently verified. Whether this was a spark of entrepreneurial genius or a myth that grew over time, one thing was clear: she wanted to build something big.

In 2002, Holmes entered Stanford University to study chemical engineering. She was drawn to the promise of innovation in biotechnology and soon joined a research lab in the School of Engineering. Her professors noted her relentless curiosity and unwavering determination. Stanford, with its blend of academic rigor and startup culture, seemed the perfect place for someone like her, a place where companies like Google, Yahoo, and Hewlett-Packard had once begun.

But Holmes wasn’t content to stay in the classroom. In late 2003, she made a life-changing decision: she dropped out of Stanford and used her tuition money to start her own company. That company would later be called Theranos.

In hindsight, this was her first critical weakness. Dropping out to build a tech company can make sense if you’re a coder like Mark Zuckerberg, Sam Altman, Bill Gates, or Steve Jobs. But biotechnology is different. To build a company around medical testing, you need deep knowledge of biology, chemistry, and engineering. Holmes had vision and charisma, but not the scientific foundation to make her idea real.

That single decision marked the beginning of one of the truly captivating and eventually, deeply controversial startup stories in modern history.

Behind that were personal choices that revealed something deeper about her character and judgment. This was the next major controversy in her story. Around this time, Elizabeth became romantically involved with Ramesh “Sunny” Balwani, a Pakistani-born technology entrepreneur nearly twenty years her senior. They met in 2002 during a Stanford-sponsored trip to Beijing for Mandarin studies. She was eighteen. He was in his late thirties, married, and already wealthy from an earlier startup success.

Ramesh ‘Sunny’ Balwani and a young Elizabeth Holmes around the time they met.

It’s hard to see this as a story of young love. Their relationship hinted at something else entirely, Elizabeth’s attraction to power and money, her willingness to blur personal and professional boundaries, and her comfort with risk. At nineteen, newly immersed in Silicon Valley’s culture of bold moves and big promises, she was already making decisions shaped by ambition and ethical gray zones.

Still, she was very young, and like many of us at that age, she was learning through her mistakes.

The Mission

Every great startup begins with an idea that sounds impossible until it isn’t.

In 2003, Elizabeth Holmes believed she had one of those ideas. At just nineteen, with limited practical knowledge of chemical engineering and biotechnology, she founded a company called Real-Time Cures, later renamed Theranos, in Palo Alto, California. Her mission was simple yet profound: to democratize healthcare. She wanted to change how people experienced medicine by removing the fear, cost, and complexity of traditional lab testing.

Holmes said her fear of needles inspired her. What if a few drops of blood from a fingertip could reveal everything doctors needed to know about a person’s health? It was a vision that felt futuristic, elegant, and deeply human. Holmes said she wanted to make blood tests so simple and accessible that no one would ever have to fear them again.

Her concept, gathering vast amounts of medical data from just a few droplets of blood sounds partially feasible today. By 2025, new diagnostic devices are beginning to approach that vision, though still not at the scale Holmes imagined. But back in 2003, the technology simply wasn’t there.

Some Stanford medical experts, including Dr. Phyllis Gardner, warned her about the biological and engineering limits of her idea. They explained that the science could not yet deliver what she envisioned. But Holmes pressed on.

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The “Fake It Till You Make It” Trap

Instead of slowing down, she doubled down. Elizabeth Holmes decided to become her vision: to look, sound, and act like the kind of leader Silicon Valley worshipped. That carefully crafted image later helped her attract brilliant talent and secure investment.

Obsessed with Steve Jobs, Holmes adopted his uniform: the black turtleneck, minimalist aesthetic, and calm intensity. She even mirrored his management habits, scheduling key meetings on Wednesdays, just as Jobs did. Hiring Avie Tevanian, one of Jobs’s close colleagues from Apple, seemed to complete her transformation.

Then came the most surprising part, the voice.

Holmes began speaking in a deep baritone, far lower than her natural tone. Some said it was a strategy, way to be taken seriously in the male-dominated world of tech and venture capital. Whatever the reason, it worked. She became unforgettable.

In this video, you can hear the difference between her fake and natural voices.

Instead of embracing her unique self, she started faking, pretending to be someone she was not. This was the next controversy and the beginning of a pattern that would later define her career.

But the voice was only the start. Holmes fully embraced the strategy: fake it till you make it.

And to be fair, it’s a strategy that has fueled many breakthroughs. Thomas Edison famously announced inventions before they actually worked. Steve Jobs (btw a notorious liar!) promised the impossible, then willed his engineers to make it real.

The difference, however, is critical: some products failing to meet expectations is one thing. In healthcare, the stakes are human. Holmes didn’t fully grasp this. Her drive to realize her idea came at any cost.

She wasn’t building a gadget or an app, nice to have. She was creating a device that patients, doctors, and hospitals would rely on for life-or-death decisions. The moment she crossed that line, her dream of democratizing healthcare gained a weight far heavier than ambition alone could bear.

And yet, investors lined up. A young Stanford dropout, charming and charismatic. A visionary woman. A promise to revolutionize medicine with a single drop of blood. No one wanted to ask too many questions.

Prestige over expertise

By 2003, the foundation was set. Elizabeth Holmes had a dream, a company, and a mission that could change the world. What she needed next was credibility, and in Silicon Valley, credibility often wears a suit, sits on a board, and signs a check.

From its incorporation in 2003 until its collapse in 2018, Holmes ruled Theranos as founder and CEO. Her first key recruit was Channing Robertson, a respected Stanford chemical engineering professor who believed in her vision enough to become the company’s first board member. His presence gave Theranos a much-needed stamp of legitimacy.

Then came Sunny Balwani, Holmes’s secret boyfriend and soon-to-be right hand. He officially joined as president and chief operating officer in 2009. Balwani had made millions during the dot-com boom, and though he had no background in medicine, he was confident, forceful, and completely devoted to Elizabeth or perhaps to the carefully constructed illusion they were building together.

In 2011, Holmes was introduced to former U.S. Secretary of State George Shultz, who quickly became one of her most powerful allies. Shultz was captivated by her charisma and her promise to transform healthcare. Soon, he joined the Theranos board and began inviting his friends.

Within three years, Theranos had assembled a board that looked like a Washington reunion. It included:

• William Perry, former U.S. Secretary of Defense

• Henry Kissinger, former U.S. Secretary of State

• Sam Nunn, former U.S. Senator

• Bill Frist, former Senate Majority Leader and heart-transplant surgeon

• Gary Roughead, retired U.S. Navy Admiral

• Jim Mattis, retired U.S. Marine Corps General

• Richard Kovacevich, former Wells Fargo CEO

• Riley Bechtel, chairman of the Bechtel Group

It was a board packed with diplomacy, defense, and political power, but almost no expertise in medicine, diagnostics, or biochemistry. The company’s core promise was revolutionary medical technology, yet the people in charge had no real ability to evaluate whether the technology actually worked.

And here lies one of Holmes’s critical mistakes. In a biotechnology company, scientific and medical expertise is not optional, it is essential. Boards of such companies typically include experienced doctors, researchers, and professors who can rigorously assess the technology, spot flaws, and guide development. By contrast, Holmes’s “all-star” board was impressive on paper but blind to the scientific realities of her claims. This mismatch allowed her to present Theranos as legitimate while critical questions about its technology went unanswered.

For Holmes, it was perfect: a glittering wall of credibility shielding a company that had yet to prove it could deliver on its promises.

The Illusion of Progress

With her deep voice, black turtlenecks, and Steve Jobs–like intensity, Elizabeth Holmes projected the image of a prodigy destined to lead a revolution. Surrounded by a board of global heavyweights, she looked unstoppable. Investors, journalists, and politicians alike were captivated by her vision.

Theranos became the “next Apple,” the new face of disruption in healthcare.

Between 2003 and 2014, the company raised an astonishing $945 million. The list of investors was as glamorous as the board itself:

• Tim Draper, early backer of Tesla and Hotmail

• Rupert Murdoch, who invested more than $120 million

• The Walton family of Walmart, who contributed $150 million

• Betsy DeVos, future U.S. Education Secretary, who invested $100 million

• Dixon Doll, a well-known Silicon Valley venture capitalist

Every investor believed they were buying into the future of medicine. Holmes graced magazine covers, spoke at major conferences, and was featured on television. She appeared alongside A-list celebrities and was celebrated by political elites, from the Clintons to the Obamas, even hosting Vice President Joe Biden at Theranos headquarters.

There were partnerships with Safeway and Walgreens, ambitious advertising campaigns, and sleek demo centers promising faster, cheaper blood tests. At its peak, between 2011 and 2014, Theranos employed around 800 people and reaches $9 billion valuation.

It was a modern fairytale, a young woman conquering Silicon Valley with vision, charisma, and a cause. Except there was no science!

Perhaps at some point, Elizabeth realised she had gone too far, that the image she built no longer matched the reality inside the labs. But it was too late to go back. Millions of dollars had been invested, partnerships were signed, and patients were waiting. The expectations were enormous, and failure was no longer an option she could admit to.

Instead of pausing to fix what was broken, Holmes doubled down on secrecy and illusion. It’s a common psychological trap among founders: once the dream takes on a life of its own, the truth becomes an inconvenience. For Holmes, admitting that the technology didn’t work would have meant admitting that the entire empire she built, the board, the investors, the fame, was nothing.

Behind the Curtain

Inside Theranos, reality told a very different story.

The company’s blood-testing devices, nicknamed Edison and later MiniLab, were nowhere near accurate. When tests failed, they were quietly rerun on commercial machines from Siemens, the very systems Theranos claimed to have made obsolete.

It is completely normal for a technology to be imperfect or under development. The real problem arises when leaders lie about it. Transparency is critical: acknowledge the challenges, communicate that work is ongoing, and keep striving to improve. Holmes chose the opposite path.

Employees who questioned results were silenced. Some were fired without warning. Others were intimidated by Sunny Balwani’s strict oversight. Everyone was bound by non-disclosure agreements, even after leaving the company.

The culture was toxic, secretive, and fear-driven. Former staff described a workplace where speaking the truth could end your career. Tragically, the pressure contributed to instances of severe mental distress, including at least one suicide.

Creating a toxic and secretive corporate culture may deliver short-term control, but it is disastrous in the long run. Innovation thrives on honesty, collaboration, and constructive criticism. Holmes’s choice to foster fear and secrecy undermined both the company and its employees.

Still, Holmes insisted that Theranos’s technology was real. She dismissed critics, blocked auditors, and justified secrecy as “protecting intellectual property.” In truth, she was protecting the illusion.

Theranos wasn’t democratizing healthcare. It was manufacturing it.

While investors saw brilliance and disruption, patients received false medical results, results that could affect treatments, pregnancies, and even lives.

What began as a vision to help the world had morphed into a carefully engineered deception.

Instead of playing fair and building trust, Holmes chose to hide failures, intimidate employees, and operate a fully secretive, closed company.

Beginning of the End

Inside the sleek offices of Theranos, some employees began to notice that the numbers didn’t add up. The technology wasn’t working, yet the company was testing real patients anyway.

Among the first to question the illusion was Tyler Shultz, the grandson of board member and former U.S. Secretary of State George Shultz. Tyler worked briefly at Theranos and soon realized that the data being shown to investors and regulators was manipulated. When he tried to raise concerns internally, he was dismissed and silenced.

But he didn’t give up.

Alongside another brave employee, Erika Cheung, Tyler began sharing what he knew with a journalist who had started asking questions: John Carreyrou of The Wall Street Journal.

Carreyrou was a seasoned investigative reporter with a sharp instinct for corporate deceit. He received a tip that Theranos’s devices didn’t work as promised and that the company was secretly using third-party lab machines to process tests.

When he started digging, Theranos pushed back aggressively. Holmes and her lawyers, led by the famously formidable attorney David Boies, threatened lawsuits, pressured sources, and attempted to suppress the story.

But facts have a way of surviving intimidation.

In October 2015, The Wall Street Journal published its first exposé. The article revealed that Theranos’s revolutionary devices could not produce reliable results and that most blood tests were secretly run on traditional machines. The myth began to crumble.

Once the story broke, regulators moved in and uncovered serious compliance failures in Theranos’s labs. Major partners like Walgreens and Safeway withdrew their collaborations, leaving the company isolated.

In 2016, Theranos voided two years’ worth of test result, an extraordinary acknowledgment that its core technology had never truly worked.

By 2018, the U.S. Securities and Exchange Commission (SEC) charged Elizabeth Holmes and Sunny Balwani with massive fraud. They had misled investors, doctors, and patients about what their technology could actually do. That same year, Theranos was formally dissolved.

Once hailed as “the next Steve Jobs”, the young woman now faced trial, her rise ending in a notorious Silicon Valley scandal.

The Lesson Beneath the Ruins

Elizabeth Holmes wanted to change the world. In a way, she did, but just not the way she intended.

One statement of hers has stayed with me:
“If I were fired or I had to start this company over a lot of times in order to figure out how to get this right, I would, right. And I would just do the same thing over and over again.”

I suspect she genuinely believed that her “fake it till you make it” strategy would work, at least long enough to make the technology real. And in 2025, her core idea isn’t science fiction anymore: it is now possible to run complex tests on just a small drop of blood. Her vision was not flawed, her execution and ethics were.

We all live our first life, and sometimes we wish we could rewind the film and make different choices. I truly believe that if Holmes had approached her dream differently, quietly and honestly, she might have succeeded and her story might have ended as a triumph.

If she had pursued a solid scientific education, refused to compromise her ethics, and surrounded herself with true biotechnology experts instead of powerful names, the outcome could have been entirely different. If she hadn’t faked her image, her voice, or her data, if she had fostered a culture of openness instead of fear, Theranos might have become what she always dreamed it would be.

Had she been honest from the very beginning with investors, employees, and the public, she might never have raised hundreds of millions or graced magazine covers. But she could have raised modest funding, run real experiments, and slowly, steadily, built something that worked.

Her path might have been quieter, humbler, and far less glamorous but it would have been real. No celebrity board, no red-carpet interviews, no illusions. Just authentic progress born from truth, patience, and integrity. And that might have made her the true revolutionary she always wanted to be.

What Every Founder Should Learn

If you’re building something in technology there are powerful lessons to draw from Elizabeth Holmes’s story:

1. Never compromise on ethics.
Shortcuts may look like progress, but they don’t survive in the long run. Integrity is the one foundation that never collapses.

2. Know your domain.
If you’re working in biotech, healthcare, or diagnostics, understand the science. A business model can’t substitute for real knowledge. Vision without expertise is just wishful thinking.

3. Surround yourself with true experts.
Fill your team and board with people who challenge you, not just those who admire you. Real progress comes from debate, not from blind agreement.

4. Choose transparency over myth.
It’s tempting to project perfection, but honesty about what you don’t yet know earns lasting credibility. Transparency builds trust; secrecy destroys it.

5. Build a healthy culture.
A company driven by fear and silence is a company headed for collapse. Innovation only happens where people feel safe to speak the truth.

6. Image isn’t enough.
Charisma might open doors, but only substance keeps them open. Build something real before you build the spotlight around it.

7. Don’t confuse hype with proof.
Investors love stories but great founders back their stories with data. The best pitch is evidence that your product truly works.

Afterword

The fall of Theranos is more than a cautionary tale. It’s a mirror held up to every founder, investor, and dreamer who wants to change the world.

Because behind every billion-dollar dream, there’s always a story worth telling.

If you’ve enjoyed this story, subscribe to follow my series on the truth behind legendary startups, their visions, their failures, and the lessons every founder can learn from them. I’d love to hear your thoughts, please share them in the comments below!

P.S.: Holmes ended her relationship with Sunny Balwani and later began dating hotel heir William “Billy” Evans. The couple married in 2019 and have two children together. Founder & CEO of Theranos Elizabeth Anne Holmes is currently serving an 11-year sentence at a federal prison camp in Bryan, Texas.

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The ultimate goal of every business is to generate profits. But let’s be honest, profits come from only one source, your customers. They decide if your idea matters, if your product is worth paying for, and if your business will survive.

And yet, countless startups fail not because their idea was bad, but because they forgot to listen to the people they were building for.

The Rise and Fall of Blockbuster

Let’s revisit one of the famous lessons in business history.

In the early 2000s, Blockbuster was the undisputed leader in movie rentals. Their stores were everywhere; renting DVDs was part of everyday life. But there was one big problem - late fees. Customers hated them.

Then came Netflix, a small, quiet newcomer with a simple promise: DVD rentals by mail, no late fees. They listened to what customers disliked about Blockbuster and built their entire business around solving that pain point.

By 2004, Blockbuster tried to copy Netflix with their own DVD-by-mail service, but it was already too late. Netflix had built trust and loyalty, while Blockbuster was still trying to protect its old business model.

When streaming became the next big shift, Netflix moved quickly. Blockbuster moved slowly. The rest is history, Netflix thrived; Blockbuster filed for bankruptcy in 2010.

You can watch the story here:

And here’s the ultimate irony: Netflix’s CEO Reed Hastings once offered to sell Netflix to Blockbuster for $50 million in 2000. Blockbuster laughed it off. Today, Netflix is worth over $200 billion.

So, what went wrong? Blockbuster didn’t take their customers seriously. They punished them with late fees, ignored their frustration, and resisted innovation until it was too late.

MySpace: When Monetization Killed the Experience

Remember MySpace? It was the first big social network, the one that came before Facebook. MySpace had the audience, the buzz, and even the head start. But guess what? They focused too much on advertising and not enough on user experience.

Wanna see how MySpace looked? Here you go:

The platform became cluttered, slow, and chaotic. Users felt overwhelmed. Facebook arrived with simplicity, clarity, and care for the user. It wasn’t about perfection, it was about what people actually wanted.

And that’s why Facebook won.

The Client Comes First

When you start a business, you probably believe your idea is perfect. You think people will love it as much as you do. But then reality hits. Your product doesn’t gain traction. You feel frustrated and lost.

Here’s the truth - you don’t decide what the market wants. Your customers do.

According to statistics, nearly 90% of startups fail. And many of them fail because they build something people don’t really need.

1. Start With Paying Customers, If You Can

The best way to start a startup is to find customers first. If you can identify a group of paying customers before you even build the product, you already have validation. You don’t need fundraising. You don’t need endless pitches. You just need to listen and build for them.

This is rare, but it’s the strongest possible foundation for a business.

2. If You Don’t Have Paying Customers, Find Pilot Users

If you can’t find paying customers yet, look for pilot users, early adopters who are willing to test, experiment, and give feedback. They can be small businesses, professionals, or individuals.

Even if your product is incomplete, it’s valuable to start conversations. Ask them what works, what doesn’t, what they wish they had.

Because as a founder, you don’t know what customers need, only customers know that.

3. And If You Have Neither, Don’t Give Up

Sometimes, you might have neither paying customers nor pilot users. It’s hard, but not impossible. If you’re building something truly new, e.g. a product for a market that doesn’t yet exist, you’re taking a harder path. But if your timing is right, you might find yourself in a blue ocean with no competition. That’s how unicorns are born. Still, even then, you must listen.

Perfection Is the Enemy of Progress

Winston Churchill said, “Perfection is the enemy of progress.”

Don’t get stuck chasing perfection. You don’t know what “perfect” looks like anyway. Your customers do.

Act. Learn. Iterate. Improve. Don’t waste years refining something in isolation that no one needs. Every hour you spend talking to customers is worth more than a week of perfecting code or polishing design.

How to Listen to Customers

There are simple ways to do this:

• Surveys and interviews: Keep them short and specific. People don’t like long forms, but they will answer if you make it clear and easy.

• Social media feedback: Watch what people are saying. Respond. Engage.

• Prototyping: Build quick mockups or MVPs and get reactions.

• Data: Measure how customers use your product, where they drop off, what features they ignore.

Then, iterate. Quickly.

Solve Real Problems

Your goal isn’t to impress investors or win design awards. Your goal is to solve real problems for real people. That’s how loyalty and revenue grow.

And when things go wrong, and they will, handle your customers with care. A good experience with support can turn a frustrated user into a lifelong fan.

There’s a simple rule:

1. The customer is always right.

2. If the customer is not right, read point one again.

Even if the customer is mistaken, make them feel heard and respected. That emotional connection pays off in the long run. Word of mouth is still the most powerful marketing strategy ever created.

Build a Customer-Centric Culture

Make your entire company care about customers. Every employee should think about how their work affects the user.

The best example of this is Amazon. Jeff Bezos famously left an empty chair in meetings to represent the customer. Whenever a decision was discussed, that chair reminded everyone who truly mattered, his customer, his real boss.

Amazon’s success didn’t come from technology alone; it came from customer obsession.

Customer Obsession Instead of Competitor Obsession – Jeff Bezos

Don’t obsess over what your competitors are doing. That’s a distraction. Your true north is your customer. If you take care of them, they will take care of your business.

Wanna check out the video where Jeff Bezos explains why customer obsession matters?

Customers are never fully satisfied. They always want something better, faster, easier, more personalized. What is “good” for you is rarely good enough for them. And that’s a good thing. Their dissatisfaction is what drives innovation. It pushes you to improve, to question, to evolve.

Your customers are the heartbeat of your business. They can make it thrive or make it disappear. Every piece of feedback, every complaint, every request is a signal, a direction pointing toward growth. Ignore it, and they will quietly move on to someone who listens. Embrace it, and they will stay, support you, and help you build something truly great.

At the end of the day, it’s your customers who decide whether your business lives or dies.

Final thought

Perfection will slow you down; customers will guide you forward.

So, stop chasing flawless. Start chasing useful.

Stop asking, “Is it perfect?” and start asking, “Does it help someone today?”

Because in the end, success doesn’t come from perfect products. It comes from perfect focus on your customers.

If you enjoyed this post, share your thoughts in the comments and subscribe to get more real stories and lessons from the startup world.

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In the startup world, people love to talk about ideas, vision, technology, and funding. But there is one quiet force that decides the fate of even the best ideas: timing.

You can have a world-changing product, a brilliant team, and generous investors. Yet if you enter the market too early, you’re shouting into the void. If you’re too late, you’re drowned out by competition. In both cases, the market simply isn’t ready, or it’s already moved on.

Why timing matters more than we think

Timing is about entering the market at the precise moment when people are ready, not when you are ready. It’s about sensing that subtle shift in collective behavior when society begins to desire something it didn’t know it needed before.

Let’s explore this idea through a real story that perfectly illustrates how timing can make or break even the brightest minds’ ideas.

The Story of Loopt

Before he became the face of OpenAI, Sam Altman was a 19-year-old Stanford dropout with a big dream. Together with Nick Sivo and Alok Deshpande, he co-founded Loopt, a location-based social networking app that answered a simple question:
“Where are my friends right now, and what are they up to?”

The idea sounds familiar today because it’s exactly what we now take for granted in apps like Google Maps, Life360, Find My, or the location-sharing options in Messenger and WhatsApp. But back in 2008, Loopt was revolutionary. It allowed users to broadcast their location to a chosen circle of friends, making spontaneous meetups easy.

You can even find a piece of tech history: the WWDC 2008 demo video, where Sam Altman showcased Loopt for the iPhone.

Loopt never gained widespread popularity. It achieved only modest traction, with low user retention, and in Europe, most people don’t even know it existed. Yet the app still managed to raise $30 million in venture funding and was eventually acquired by Green Dot Corporation in 2012 for $43.4 million in cash. That acquisition closed the Loopt chapter but opened new doors for its team and technology, which later helped shape innovations across mobile and fintech.

Some experts and analysts have labeled Loopt a failure, but I don’t quite agree. It may not have been a superstar, yet it was far from a failure, it was a bold experiment that simply arrived a little too soon.

What went wrong

The problem wasn’t the idea. It was timing.

Back in 2008, smartphones were still new. Most people only had basic cell phones and didn’t use mobile data. Internet on the phone was painfully slow, and sharing your location with others wasn’t something people were used to. Many were cautious about privacy and didn’t really understand what it meant to “broadcast” their whereabouts. Both society and technology just weren’t ready yet for location-based social apps.

If Loopt had launched a few years later, around 2010 to 2012, things might have turned out very differently. By then, Facebook was already encouraging people to post updates about what they were doing, and Instagram made sharing photos and check-ins feel normal. Loopt’s idea could have easily taken off in that environment.

But if you look at it today, the timing still wouldn’t be right. After years of oversharing online, people have learned tough lessons about privacy. Many now want to keep more of their lives offline and draw clear digital boundaries. We no longer want every step we take to be visible to everyone.

So Loopt existed in a strange time gap, too early for acceptance, too late to redefine the culture.

The hidden value of failing at the wrong time

Here’s the beautiful irony: Loopt might not have been a success, but it taught Sam Altman lessons that no classroom could offer.

He learned how to build a product, raise money, hire the right people, and deal with the tough realities of the market. He also discovered that not succeeding isn’t the end, it’s a lesson. Early failure costs less, teaches more, and prepares you better than a big setback that comes later when the stakes are much higher.

Without Loopt, there might not have been Y Combinator’s transformative years under Altman’s leadership. Without that experience, there might never have been OpenAI, the company that is now shaping the future of human–machine collaboration and, according to reports, could be valued in the hundreds of billions of dollars.

In other words: Loopt was Sam’s tuition.

Timing and you

So what does this mean for founders, builders, and dreamers today?

Don’t chase the future too early. The world needs to be emotionally, technologically, and socially ready for your product.
Don’t wait until it’s too late. If everyone’s already there, your window is closing.
Listen deeply. Timing is not luck, it’s awareness. Watch how people behave, what they fear, and what they desire next.
Embrace early failures. The best founders use them as preparation, not punishment.

Sam Altman’s story shows that even a “failed” startup can be the foundation for something world-changing. Every misstep, every off-timed idea, is part of the process of aligning your vision with the world’s readiness.

Final thought

When you build something new, ask yourself, and the people around you,
Is the world ready for this?

Because in startups, timing isn’t everything. It’s the one factor that decides whether your idea becomes a footnote or a phenomenon.

And who knows? Your own “Loopt moment” might just be the lesson that sets the stage for your “OpenAI moment.”

If you enjoyed this post, please share your thoughts in the comments and subscribe to follow more stories about startup lessons, innovation, and timing.

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Starting a startup is not just another job. It’s not a hobby. It’s not even a career. It’s a lifestyle choice that will consume your time, your energy, and your attention in ways you probably can’t imagine right now.

That’s why you need to truly love what you’re building.

Because once you start, your startup might very well become the last, and only, activity you dedicate your life to.

If you’ve ever dreamed about traveling the world, camping deep in the Amazon, or simply ticking off that long to-do list of personal goals… do it now. Once you dive into the startup grind, you’ll have little to no time left for these things. And if your startup actually becomes successful, the time crunch only gets worse.

After all, whether rich or poor, we all get the same 24 hours in a day.

The Foundation of Your Startup

The real foundation of a great startup is doing something you love and are genuinely good at. Without this, your business might function, but it will never be fun. And if you’re not having fun, it’s only a matter of time before the grind breaks you.

This work should feel like your dream made real, the thing you would do even if no one paid you for it. That’s the only way to endure the inevitable challenges ahead.

The Power of an Idea (and Why It’s Overrated)

Here’s a hard truth: people often treat startup ideas like fragile secrets, terrified someone will “steal” them. But the reality? An idea alone isn’t worth much.

Execution and timing are what separate failed concepts from billion-dollar companies.

Take Facebook. It wasn’t the first social network, remember Friendster, MySpace, or Orkut? They had the same basic idea. But Facebook’s execution and timing were simply better.

Or look at Uber. Ridesharing wasn’t new. Zimride (which later evolved into Lyft), Sidecar, and other early players already existed. Yet Uber managed to scale faster, execute aggressively, and dominate the space.

It wasn’t the idea. It was how they pulled it off.

* The next part isn’t applicable to every startup, it mainly applies to platforms whose value grows as more people use them.

The Magic of Network Effects

This phenomenon is called the network effect: the value of the product or service increases as the user base expands.

• Airbnb: More hosts mean more options for guests. More guests mean more bookings for hosts.

• Uber: More drivers mean shorter wait times for riders. More riders mean more fares for drivers.

• Instagram: More friends, creators, and communities make the platform more engaging for everyone.

Not every business benefits from network effects, but for platforms that do, this compounding loop creates momentum that makes them incredibly hard to replace once they reach scale.

A Personal Note

Here’s a funny twist: despite everything, Facebook is still the largest social media platform in the world.

I (a millennial) opened my account back in 2008 while at university, used it actively until around 2014, and then drifted away when it started to feel stale, a place mostly for older generations. Yet I still keep my account today, mostly as a login for Instagram and other services.

What about you? Do you still use Facebook, or has it become just another app collecting digital dust in the background of your life?

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For some people, the path seems smoother. If your parents ran a shop, if your uncle owned a factory, or if your best friend built a company, you grow up with business all around you. You hear the stories, you see the risks, you understand the rewards. For these people, starting their own venture feels almost natural.

But for the rest of us? For those who have always been employed, who never had close examples of entrepreneurship in our families or circles of friends — the road feels twice as difficult. You don’t have a “blueprint” to follow. You only have your courage, your questions, and your willingness to learn by trying.

And that’s exactly why I want to tell you something simple, but life-changing: if you’re thinking of starting, start now. Don’t wait.

The Illusion of the “Perfect Time”

There’s always an excuse. Always.
Maybe you’re telling yourself:

• “I’ll start in a year when I have more savings.”

• “Maybe in six months when things calm down at work.”

• “After this vacation, after this project, after this…”

But here’s the truth: the “perfect moment” doesn’t exist. Life will always throw new obstacles in your way. If you’re waiting for the stars to align, you’ll wait forever.

The most dangerous word for anyone with a dream is later. Later becomes next year, then five years, then never.

Regrets Come from What You Didn’t Do

When people look back at their lives, they don’t regret the risks they took, even when those risks failed. They regret the chances they never gave themselves.

If you try and fail, you gain something priceless: knowledge, resilience, experience. Every failure makes you sharper, braver, and better prepared for the next attempt.

But if you never try, you’ll never know. Maybe you could have been the next big success. Maybe you wouldn’t. But at least you would have lived fully, instead of wondering “what if?”

Just Start

So here’s my message to you: stop waiting. Stop bargaining with yourself. Stop telling yourself “maybe next year.”

Do it now. Start small if you have to. Write the first page of your business plan. Register the domain. Make the first call.

Because once you take that first step, the next one becomes easier. And before you know it, you’re no longer dreaming about starting a business — you’re running one.

And even if the road is messy, even if it’s tough — at least it’s your road.

What do you think? Have you ever postponed something important because you were waiting for the “right time”? Do you believe it’s better to take action and learn, even if it means failing along the way? I’d love to hear your thoughts in the comments.

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If this resonates with you, subscribe to this newsletter. I write about the messy, real, and often surprising journey of building something from scratch. Together, we’ll share lessons, failures, wins, and the motivation to keep going.

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Insight Hawk empowers companies through a comprehensive blend of business consulting and advanced technology expertise.

On the business side, we specialise in financial analysis, business analysis, and strategy consulting, helping companies understand performance drivers, improve decision-making, and build resilient long-term strategies. Our approach focuses on optimising processes, strengthening operational efficiency, and delivering structured project and process management that aligns execution with strategic goals.

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By integrating financial insight, analytical rigor, and cutting-edge technology, Insight Hawk bridges the gap between strategy and execution. We enable organisations to optimise operations, reduce risk, and accelerate transformation through intelligent, secure, and scalable solutions tailored to modern business environments.

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Why I’m Starting This Newsletter

I’m starting this newsletter because I want to share the real story of building my company, the good days, the tough ones, and all the experiments along the way. Think of it as a behind-the-scenes look. I’ll tell you what worked for me, what didn’t, and maybe you’ll pick up a few ideas you can try for yourself.

Ultimately, this newsletter is about community. A place where entrepreneurs, creators, and curious minds can share insights, test strategies, and grow together. I’ll bring my experiences, you bring your ideas, and together we’ll build something greater than the sum of its parts.

Welcome aboard, let’s get started.

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