“How did you go bankrupt?”
“Two ways. Gradually, then suddenly.”
― Ernest Hemingway, The Sun Also Rises
The core growth process in the technology business is a mutually reinforcing, multi-step, positive feedback loop between platforms and applications. This leads to exponential growth curves (Peter Thiel calls them power law curves), which in idealized form look like:
The most prominent recent example of this was the positive feedback loop between smartphones (iOS and Android phones) and smartphone apps (FB, WhatsApp, etc):
After the fact, exponential curves look relatively smooth. When you are in the midst of them, however, they feel like they are divided into two stages: gradual and sudden.
Singularity University calls this the “deception of linear vs exponential growth”:
Over the past decade, computing resources that were previously available only to large organizations became available to almost anyone. Using cloud-scale development platforms like Amazon Web Services, developers can write software that runs on hundreds or even thousands of servers, and do so relatively cheaply.
But it is still difficult to write software that makes efficient use of this abundant computing. For some projects, like creating websites, there are well-known software architectures that work reasonably well. In other areas, there’s been progress building generalized tools (for example, Hadoop in data processing). For the most part, however, developers need to solve the parallelization problem over and over again for each application they develop. New tools that help them do this are sorely needed.
Today, I am excited to announce that a16z is investing $20M in Improbable, a London-based company that was founded by a group of computer scientists from the University of Cambridge. Improbable’s technology solves the parallelization problem for an important class of problems: anything that can be defined as a set of entities that interact in space. This basically means any problem where you want to build a simulated world. Developers who use Improbable can write code as if it will run on only one machine (using whatever simulation software they prefer, including popular gaming/physics engines like Unity and Unreal), without having to think about parallelization. Improbable automatically distributes their code across hundreds or even thousands of machines, which then work together to create a seamlessly integrated, simulated world.
The Improbable team had to solve multiple hard problems to make this...
Steve Jobs in 1985:
I felt it the first time when I visited a school. It was third and fourth graders, and they had a whole classroom full of Apple II’s. I spent a few hours there, and I saw these third and fourth graders growing up completely different than I grew up because of this machine.
What hit me about it was that here was this machine that very few people designed — about four in the case of the Apple II — who gave it to some other people who didn’t know how to design it but knew how to make it, to manufacture it. They could make a whole bunch of them. And then they give it some people that didn’t know how to design it or manufacture it, but they knew how to distribute it. And then they gave it to some people that didn’t knew how to design or manufacture or distribute it, but knew how to write software for it.
Gradually this sort of inverse pyramid grew. It finally got into the hands of a lot of people — and it all blossomed out of this tiny little seed.
It seemed like an incredible amount of leverage. It all started with just an idea. Here was this idea, taken through all of these stages, resulting in a classroom full of kids growing up with some insights and fundamentally different experiences which, I thought, might be very beneficial to their lives. Because of this germ of an idea...
An “idea maze” is a map of all the key decisions and tradeoffs that startups in a given space need to make:
A good founder is capable of anticipating which turns lead to treasure and which lead to certain death. A bad founder is just running to the entrance of (say) the “movies/music/filesharing/P2P” maze or the “photosharing” maze without any sense for the history of the industry, the players in the maze, the casualties of the past, and the technologies that are likely to move walls and change assumptions.
– Balaji Srinivasan, “Market Research, Wireframing and Design”
I thought it would be interesting to show an example of an idea maze for an area that I’m interested in: AI startups. Here’s a sketch of the maze. I explain each step in detail below.
“MVP with 80–90% accuracy.” The old saying in the machine learning community is that “machine learning is really good at partially solving just about any problem.” For most problems, it’s relatively easy to build a model that is accurate 80–90% of the time. After that, the returns on time, money, brainpower, data etc. rapidly diminish. As a rule of thumb, you’ll spend a few months getting to 80% and something between a...
A popular strategy for bootstrapping networks is what I like to call “come for the tool, stay for the network.”
The idea is to initially attract users with a single-player tool and then, over time, get them to participate in a network. The tool helps get to initial critical mass. The network creates the long term value for users, and defensibility for the company.
I’m going to give two historical examples and leave it to readers to think of present-day examples (there are many): 1) Delicious. The single-player tool was a cloud service for your bookmarks. The multiplayer network was a tagging system for discovering and sharing links. 2) Instagram. Instagram’s initial hook was the cool photo filters. At the time some other apps like Hipstamatic had filters but you had to pay for them. Instagram also made it easy to share your photos on other networks like Facebook and Twitter. But you could also share on Instagram’s network, which of course became the preferred way to use Instagram over time.
The “come for the tool, stay for the network” strategy isn’t the only way to build a network. Some networks never had single-player tools, including gigantic successes like Facebook and Twitter. But starting a network from scratch is very hard. Think of single-player tools as kindling.
The holy grail of virtual reality, the one that’s always been out of reach until now, is presence.
In the VR community, “presence” is a term of art. It’s the idea that once VR reaches a certain quality level your brain is actually tricked — at the lowest, most primal level — into believing that what you see in front of you is reality. Studies show that even if you rationally believe you’re not truly standing at the edge of a steep cliff, and even if you try with all your might to jump, your legs will buckle. Your low-level lizard brain won’t let you do it.
With presence, your brain goes from feeling like you have a headset on to feeling like you’re immersed in a different world.
Computer enthusiasts and science fiction writers have dreamed about VR for decades. But earlier attempts to develop it, especially in the 1990s, were disappointing. It turns out the technology wasn’t ready yet. What’s happening now — because of Moore’s Law, and also the rapid improvement of processors, screens, and accelerometers, driven by the smartphone boom — is that VR is finally ready to go mainstream.
Once VR achieves presence, we start to believe.
We use the phrase “suspension of disbelief” about the experience of watching TV or movies. This implies that our default state watching TV and movies is disbelief. We start to believe only when we become sufficiently immersed.
With VR, the situation is reversed: we believe, by default, that what we see is real. As Chris Milk, an early VR pioneer,
Stack Exchange is a network of 133 sites (and growing) where people can ask and answer questions about topics related to engineering, science, hobbies, and more. The biggest site on the network is Stack Overflow, which alone gets over 40 million unique visitors per month. The other sites cover a very wide variety of topics, including: math, gardening, English language usage, graphic design, physics, cryptography, chess, astronomy, Buddhism, data science, martial arts, home improvement, photography, bicycles, board games, economics, to name a few. Most likely, you’ve used Stack Exchange without even knowing it —the network had over 300M unique visitors last year. Many users come in through Google, get their answer, and then leave, usually a little bit smarter.
One of the major startup opportunities of the information age is: now that more than two billion...
I’m excited to announce today that Andreessen Horowitz is leading a $3M financing of Skydio, a startup developing artificial intelligence systems for drones.
The Skydio team is awesomely qualified. They worked on drone vision systems at MIT and then co-founded a drone project at Google[x] called Project Wing. The company’s mission is to create smart drones. As cofounder Adam Bry says:
Drones are poised to have a transformative impact on how we see our world. They’ll enable us to film the best moments of our lives with professional quality cinematography and they’ll also change the way businesses think about monitoring their operations and infrastructure. This grand vision is starting to come into focus, but existing products are blind to the world around them. As a consequence, drones must fly high above the nearest structures or receive the constant attention of an expert operator. “Flyaways” and crashes abound. These problems must be solved for the industry to move forward.
Smart drone operators will simply give high-level instructions like “map these fields” or “film me while I’m skiing” and the drone will carry out the mission. Safety and privacy regulations will be baked into the operating system and will always be the top priority.
This is my second drone investment – the first one was Airware. I see Airware and Skydio as complementary (and I’d like to make more drone investments – at any stage including seed investments – as long as they don’t compete with Airware or Skydio). You can...
“How to hit home runs: I swing as hard as I can, and I try to swing right through the ball… The harder you grip the bat, the more you can swing it through the ball, and the farther the ball will go. I swing big, with everything I’ve got. I hit big or I miss big.” – Babe Ruth
One of the hardest concepts to internalize for those new to VC is what is known as the “Babe Ruth effect”:
Building a portfolio that can deliver superior performance requires that you evaluate each investment using expected value analysis. What is striking is that the leading thinkers across varied fields — including horse betting, casino gambling, and investing — all emphasize the same point. We call it the Babe Ruth effect: even though Ruth struck out a lot, he was one of baseball’s greatest hitters. — ”The Babe Ruth Effect: Frequency vs Magnitude” [pdf]
The Babe Ruth effect occurs in many categories of investing, but is especially pronounced in VC. As Peter Thiel observes:
Actual [venture capital] returns are incredibly skewed. The more a VC understands this skew pattern, the better the VC. Bad VCs tend to think the dashed line is flat, i.e. that all companies are created equal, and some just fail, spin wheels, or grow. In reality you get a power law distribution.
The Babe Ruth effect is hard to internalize because people are generally predisposed to avoid losses. Behavioral economists have famously demonstrated that people feel a lot worse about losses of a...