Some Lessons for Startups (ppt)
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My talk at the Stanford Technology Ventures Program on March 6, 2013. I talk about some technical and business lessons from Square, Uber, AirBnB, and the Google Autonomous Vehicle that are applicable to today's startups.
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Some Lessons for Startups (ppt)
- 1. Some Lessons for Startups Tim O’Reilly O’Reilly Media @timoreilly Stanford Technology Ventures Program March 6, 2013
- 2. “The skill of writing is to create a context in which other people can think.” -Edwin Schlossberg
- 5. Lesson #1: Do Less
- 6. Lesson #2: Get creative with hardware, not just software
- 7. Lesson #3: Build “software above the level of a single device”
- 8. Lesson #4: Harness network effects in data
- 9. ` Lesson #5: Rethink workflows and experiences
- 12. Lesson #6: Rethink the possibilities in man-machine symbiosis
- 13. The Google Autonomous Vehicle
- 14. 2005: Seven Miles in Seven Hours
- 15. “We don’t have better algorithms. We just have more data.” - Peter Norvig, Chief Scientist, Google
- 16. AI plus the recorded memory of augmented humans
- 18. To what extent can reputation systems replace or augment regulation?
- 19. Lesson #7: Close the loop
- 20. “What I learned from Google is to only invest in things that close the loop.” - Chris Sacca
- 21. Lesson #8: Create More Value Than You Capture
- 22. “There’s a wonderful section in Les Miserables about the good that Jean Valjean does as a businessman (operating under the pseudonym of Father Madeleine). Through his industry and vision, he makes an entire region prosperous, so that “there was no pocket so obscure that it had not a little money in it; no dwelling so lowly that there was not some little joy within it.” And the key point: “Father Madeleine made his fortune; but a singular thing in a simple man of business, it did not seem as though that were his chief care. He appeared to be thinking much of others, and little of himself.”
- 23. I call it “the big lie” of modern business
- 27. Lesson #9: Work on stuff that matters
- 28. Open Source Web 2.0 The Maker Movement Open Data Open Government
- 29. Lesson #10: Idealism is the best marketing
- 30. Why I love hackers
Notes de l'éditeur
- I like to begin my talks with a quote, because, as Oscar Wilde once said, “Quotation is a serviceable substitute for wit.” Edwin Schlossberg once said... Today, I ’m going to look at a couple of startups or tech projects that I find really interesting, and try to explain why I find them interesting, both from a technical and a business perspective, and as a way of helping you to develop your own “interestingness” filters.
- I want to start out by talking about Square. There ’ s so much to learn from this business. How many of you have ever bought something from a store with Square ’ s iPad cash register? How many of you had the Square Wallet app running on your phone when you did that?
- It automatically checks you in when you walk into a participating merchant. Your name and face appear on the register, and since your payment details are already on file, all the retail clerk has to do is confirm your identity, as shown in this screen shot.
- This is so key. The phone already knows you ’ re there. Why make you “ check in ” manually? This makes sense for apps like Foursquare, but it ’ s so important to think through what the sensors in the phone let you take out of the UI. This is going to be one of the big voyages of discovery over the next few years, as we design interfaces for devices that have “ senses ” of their own.
- Square started with this creative hardware hack, a little free dongle that uses the phone ’ s microphone jack to turn it into a credit card reader.
- But with the addition of the cash Register app, Square saw the possibilities of building a system that actually connected buyer and seller in a more profound way. The software system includes both an app on your phone, and an app on the merchant ’ s ipad, and a cloud database and services in between.
- When I first talked to Jack about Square, he talked about it as a data business - using social network data to make better credit scoring decisions. Long term, once square has millions of participating merchants and consumers, they have built a powerful data system that literally gets better the more people use it. But even apart from this banking angle, think how Square transforms the way a small merchant operates, bringing “ knowing your customer ” to a new level. Square has my face, my credit card info, and, potentially for a repeat buyer, my preferences, like what kind of coffee I normally order.
- And that leads to a profound rethinking of the retail experience.
- Another example of someone rethinking the workflows in retail is the Apple Store. Where most stores (at least in America) have used technology to eliminate salespeople, Apple has used it to augment them. Each store is flooded with smartphone-wielding salespeople who are able to help customers with everything from technical questions to purchase and checkout. Walgreens is experimenting with a similar approach in the pharmacy, and US CTO Todd Park foresees a future in which health workers will be part of a feedback loop including sensors to track patient data coupled with systems that alert them when a patient needs to be checked up on. The augmented home health worker will allow relatively unskilled workers to be empowered with the much deeper knowledge held in the cloud.
- This may be the real opportunity for new information retrieval UIs like Google ’ s Project Glass - in specialized settings where access to a computer can be seen as a powerful kind of human augmentation. I expect it to be used in professional settings before it becomes popular as a consumer device. (In social settings, it will require even more profound resets of behavior than the “ always-on ” mobile phone.)
- In this context, I can ’ t help but mention the Google Autonomous Vehicle project.
- The Google autonomous vehicle is thought-provoking on a number of levels.
- But that ’ s not the most important lesson from the Google autonomous vehicle. You see, back in 2005, the winning vehicle in the DARPA Grand Challenge went seven miles in seven hours.
- Yet only five years later, Google announced that they had a car that had driven hundreds of thousands of miles in ordinary traffic. Was this a triumph of AI? It was surely that. But there ’ s another important factor that is easy to overlook. Google ’ s chief scientist, Peter Norvig, says that the algorithms aren ’ t any better. Google just has more data. What kind of data?
- It turns out that the autonomous vehicle is made possible by Google Streetview. Google had human drivers drive all those streets in cars that were taking pictures, and making very precise measurements of distances to everything. The autonomous vehicle is actually remembering the route that was driven by human drivers at some previous time. That “ memory ” , as recorded by the car ’ s electronic sensors, is stored in the cloud, and helps guide the car. As Peter pointed out to me, “ picking a traffic light out of the field of view of a video camera is a hard AI problem. Figuring out if it ’ s red or green when you already know it ’ s there is trivial. ” So this is a unique and unexpected application of the notion of human-machine symbiosis, which was originally called out as an important thread in computing by JCR Licklider in a paper all the way back in 1960.
- Many of the notions that I highlighted about Square also show up in an app like Uber. A driver and a passenger both augmented with a smartphone changes our expectations about transit, and has the ability to change the way we organize public transit. Uber also shows us the principles of Software Above the Level of a Single Device, the use of sensors (both you and the driver have phones that know where you are), a data back end as part of the system, and “ doing less. ” Because your credit card is already on file, they ’ ve taken payment out of the workflow. And replaced it with reputation - they ask you to rate the driver, and the driver to rate the passenger.
- That leads me to an interesting question. Uber asks every passenger to rate each driver. Drivers who don ’ t do well are eliminated from the service. This actually leads to better results than a system that licenses drivers up front.
- But there ’ s one other great lesson from Uber.
- Investor Chris Sacca, who used to run special projects for Google, and who is an early investor in Uber, once remarked “ What I learned... ” This is what Google did with advertising, figuring out how to predict what ads people would click on. And in the case of Uber, it ’ s fundamental to the value proposition. With a taxi, you wait and hope to find one. With Uber, you know where the car is, when it ’ s going to arrive, and can even watch its progress towards you. Uber closes the loop and takes the uncertainty out of the experience.
- But I want to return to Square. There ’ s one other great lesson there. Create value for more than yourself. Jack ’ s original inspiration for Square was that he wanted to make it possible for anyone to take a credit card. He wanted to enable a fairer, more evenly distributed economy. Don ’ t just think about how much value you can create for yourself, your company, and your investors. Think about how much value you can create for your customers.
- I ’m reminded of this wonderful quote from Les Miserables.
- This is in sharp contrast to the dominant ideology of modern capitalism over the past few decades, which says that the only responsibility of a company is to make money for its shareholders. Leaving aside the fact of excessive executive compensation as prima facie evidence that no big company really believes that principle, this notion misses the point that an economy is an ecosystem.
- This desire to build value for a community of stakeholders also shapes companies like Etsy, AirBnb, and Kickstarter.
- Perhaps the more general lesson here is to work on stuff that matters.
- But there ’ s another lesson here. Let me point to some of the things that matter that I ’ ve worked on. In each of these cases, I did some good for my business, but I was mainly concerned with telling the story of an industry movement, and trying to create awareness and value that benefited many people besides myself and my own company.
- People are hungry for meaning. When you really care about creating value for more than yourself, and work hard at it, people understand it. So don ’ t be afraid to talk about your values, and why what you do matters. Tell it to yourself, and then tell it to your customers.
- One of my best experiences with doing this was when I gave a talk at my Emerging Technologies Conference in 2008 entitled, “ Why I love hackers. ” They work on what is hard. I recited a poem by Rilke, the Man Watching, which talks about Jacob wrestling with an angel. He knew he couldn ’ t win, but came away strengthened from the fight. The poem ends with something like this: “ What we fight with is so small, and when we win, it makes us small. What we want is to be defeated decisively by successively greater beings. ”
- A great example of this is a company called Makani Power, which is building drone aircraft for high altitude wind farms. One of the early employees left a Wall Street hedge fund not because he thought he ’ d make more money, but because, as he said, “ the math is harder and more interesting. ”
- Let me come at this idea from another angle. At our Emerging Technology Conference in 2005, Amazon CEO Jeff Bezos recounted a conversation he ’ d had with computer scientist Danny Hillis, in which Danny said [quote above]. Now this is nothing new. Speech, the written word, printed books and newspapers, the telephone, radio and television are all technologies for passing knowledge from mind to mind.
- There are lots of ways to work on stuff that matters. Code for America, a non-profit I ’ ve been working with, brings talent from the tech industry to work with local governments to build simple, beautiful and easy-to-use interfaces to government services and challenging government to reinvent the way it engages with citizens.
- This coming year, we ’ re going to be working with New York City and Louisville KY on a project that Anne Milgram from the Arnold Foundation, calls Moneyballing Criminal Justice. It turns out that pre-trial incarceration is one of the biggest costs for cities. Using predictive analytics to figure out who to release on bail can save huge sums for cities, but more importantly, it can save jobs and families. Keep someone in jail unnecessarily and they may lose their job, forcing them into the very life of crime we ’ re trying to avoid. This is an incredibly meaningful application of today ’ s “ big data ” technology to an important real-world problem.
- The White House Presidential Innovation Fellows offers similar opportunities to bring technology expertise into the Federal Government. I encourage any of you to apply to either of these programs.
- Health care is another area where today ’ s skills can be put to use working on stuff that really matters. Here ’ s a report I co-authored recently that covers some of my ideas on the subject. I don ’ t have time to go into all the details today, but the report is a free download.
- And there are also amazing entrepreneurial opportunities building companies that also solve interesting social problems. Jen Pahlka, who founded Code for America, wrote a blog post recently that summarized one of these opportunities, which we ’ ve been brainstorming recently. How do you reinvent the corner store so that it delivers what people really need, at affordable prices, in a walkable city?
- These are the kinds of opportunities that we ’ re looking for at O ’ Reilly AlphaTech Ventures, our early stage venture firm. If you want to apply the principles I ’ ve outlined here to build a great business that also just happens to make the world a better place, we ’ d love to hear from you. [email_address]