Jim Cook

The year is 2000. Two men walk into Blockbuster's Dallas headquarters in shorts & sandals. Their company was in $50 Million debt. They asked this mammoth($6B company) to buy them. Blockbuster laughs at their ask of $50 Million. That laugh wiped their existence off the planet. Here's how: Blockbuster had $6 billion in revenue, 9,000 stores worldwide, and 60,000 employees. Netflix had $5 million in revenue, 150 people, and $50 million in debt. But still, this ant(Netlix) was able to eat this elephant(Blockbuster). It started with a phone call at a dude ranch. Marc Randolph, co-founder of Netflix, was on a corporate retreat at Alisal Ranch, deep in the mountains outside Santa Barbara. Horses. Dirt roads. No reason to dress up. He was in shorts, a t-shirt, and thong sandals. That's when Blockbuster called. "We'd like to see you. Tomorrow. In Dallas." Randolph turned to Reed Hastings and said there was no way. Different time zones. No direct flight. Impossible. Then they remembered they were $50 million in debt and had been trying to get this meeting for months. They chartered a private jet. The next morning, they walked into the 27th floor of a glass and steel skyscraper in Dallas. Enormous conference room. A hardwood table the size of a small country. Blockbuster executives in suits filing in from one side. Marc Randolph standing there in sandals. He made the pitch anyway. "Combine forces. You run the stores. We run the online business. Build a blended model. Our research shows it's a game changer." The executives leaned in. Questions were flowing. Things felt good. Then came the big question. "How much?" Randolph had rehearsed this on the plane. They were $50 million in the hole. The number was $50 million. Silence filled the room. He watched their faces carefully, trying to read the reaction. Then it hit him. They were trying not to laugh. This tiny company, drowning in debt, at the lowest point of the dot-com meltdown, had just asked to be bought for $50 million. To the people running a $6 billion empire, it was almost comical. The meeting ended shortly after. Quiet cab ride to the airport. Quieter flight back to Santa Barbara. Randolph sat with his head down the entire way, thinking one thing: They are not going to save us. They are going to compete with us. What happened next is where the story gets interesting. Most people assume Netflix simply outworked Blockbuster. Built a better product. Won on merit. The truth is messier and far more human. When Blockbuster finally decided to take Netflix seriously, they nearly destroyed them. They built exactly what Randolph had pitched years earlier. The blended model. Rent online. Return by mail. Or return in-store. Or pick up in-store. It was everything Netflix could not offer because Netflix had no physical locations. Randolph admits it plainly. They could not compete with that. Blockbuster came frighteningly close to taking Netflix down entirely. So why didn't they? Here is where a single human decision changed everything. Blockbuster had been targeted by corporate raiders. Investors who bought large chunks of stock, took seats on the board, and began pushing for short-term profits over long-term survival. John Antioco was the CEO driving the fight against Netflix. He understood the threat. He had pulled a team out of the building, funded them properly, and told them to go after Netflix with everything they had. Then the board denied him his contractually promised bonus. He said: then I quit. And he did. The replacement CEO came from retail and convenience stores. His vision for Blockbuster was not winning the streaming war. It was asking why their 9,000 stores were not selling gum and clothing. The online operation was abandoned. Randolph describes it using a scene from an old Spielberg student film. A robot chases someone, getting closer and closer, almost close enough to grab their ankle. Then a cost calculation hits break-even and the robot just stops, turns, and walks away. One second before victory. That is what Blockbuster did. Netflix scampered to safety. On why Blockbuster never moved fast enough: Imagine you are the CEO sitting on $6 billion in annual revenue. Someone walks in and says let's build an online component. You ask how much it will make in year one. They say $2 million. Do you pull your best engineers off working products and bet them on a $2 million experiment? Of course not. So the B team gets it. Then the C team. Each time underpowered. Each time failing. Meanwhile Netflix was not a movie company. It was a software company built in Silicon Valley with people who had spent their entire careers writing software. Even Blockbuster's A team would have struggled to compete. By the time Blockbuster committed fully, it was almost too late. And then one bonus dispute ended it. Blockbuster did not lose because Netflix was inevitable. They lost because changing a $6 billion business model requires a kind of courage that is nearly impossible to find inside a company that is still winning. They lost because the person with the will to change things was replaced by someone who did not believe change was necessary. They lost because they were one grab away from winning and walked away anyway. Netflix did not kill Blockbuster. Blockbuster killed Blockbuster. Netflix just showed up to the funeral.

Driven by AI, we are entering a new era of enterprise software, ushering in systems of intelligence.  In the mid 1980s, driven by the growth of the client/server architecture, we saw the dramatic rise of systems of record. These are the back office software applications that helped enterprises run their ERP, HR, CRM, and core IT workflows. These technologies were relatively specialized, and helped automate any of the most critical tasks in the enterprise. They were defined by structured data, back office automation, and leveraged by only by select users in an enterprise. With the rise of cloud and mobile in the mid 2000s, we saw a new era of systems of engagement, as coined by Geoffrey Moore. In a world of much more dynamic and ad-hoc work in the enterprise, systems of engagement were tools for collaboration, communication, video, work and project management, social and intranets, and more. These tools dealt with all the messy, unstructured data in an enterprise - the conversations, collaborative docs, and media that began to drive a shift in how the entire enterprise worked.  Now, in the mid 2020s, we are firmly entering a new era of enterprise software, which gives rise to systems of intelligence. Systems of intelligence combine enterprise data, workflows, and AI, to deliver insights and automation to an organization. Importantly, because of the ability for AI to process unlimited unstructured data - like documents, video, or communications - we also get the same benefit from this messy data as we did our structured data. We can query, synthesize, calculate, and automate all the work around thus unstructured data just as easily as we could query a database before. Unlike systems of engagement that generally broke down the more information that goes into them, we see the reverse now with AI, where software can become more powerful and useful the more data it has access to. And with AI Agents being a native property of systems of intelligence, these systems aren’t only leveraged by every employee, they dramatically expand the output of the workforce. Systems of record are where people work by largely themselves. Systems of engagement let users work collaboratively with other people. Now systems of intelligence let us work seamlessly with people and AI.  These systems will also talk to each other in completely new ways. Instead of deterministic APIs and clear handshakes, with Agentic AI, these systems will communicate with each other much like a humans do. A user will make a request in one system, and it will fan out the ask to a variety of other similar systems relevant for the desired information. And if it didn’t get what it wanted, it will simply request again in a different way, just as a person would. We’re going to see systems of intelligence in every domain of work - across every line of business and every vertical. Some of these technologies will be incumbents that evolve, and many offerings will be brand new startups that fill a new gap between existing systems. Wild times ahead.

90%+ of startup scaling & alignment issues can be solved in one shockingly simple way: write it down.