Deepak Sharma & Nischala Kaushik
The driving force behind the technologies that have powered some of the biggest business ideas of the last two decades - e-commerce, e-books, the AWS Cloud and many others - opens up about leadership, innovation and the trends that will matter.
WOOL:Thank you for speaking to WOOL. Going by your blog, All Things Distributed, your weekend reading has a lot of fundamental research packed into it. Is that the academic in you?
WERNER VOGELS:My weekend reading often helps me solve work problems by going back to the basics. At the size and scale of Amazon's operations, many theoretical assumptions of underlying algorithms become irrelevant. Whether we are talking about log based file systems or the S3 service we launched in 2006, real systems can fail in many ways theoretical models do not account for.
When Amazon engineers try and tackle these problems on scale, they need to use both practical and theoretical approaches. At the size and scale of our operations, with trillions of storage transactions processed each day, the practical solutions can reach their limit.
But, a lot of academic work has limitations too - they are written with a lot of assumptions. Unlike academics, real life systems don't fail by coming to a stop, they fail by spitting out garbage. On Amazon's scale, even the statistically improbable failures will happen.
I was hired partly to bring an academic rigor to problem solving, and going back to basics peels away assumptions that academics pile on to the original research. Looking at the problem that the original algorithm was designed to solve clears the clutter and often suggests a way forward for large scale problems.
WOOL:Experimenting is core to Amazon's DNA. But unlike any other firm, you are remarkable at scaling what works and moving on from your failures. What's your secret sauce?
WV:Jeff Bezos established the philosophy in a famous shareholder letter of 1997. I think the second principle in that letter talks about the importance of experimentation, measuring and learning. Amazon's business philosophy has been built on experimenting continuously and learning from the process relentlessly. And then, scaling and making big bets on the ideas that succeed. So we celebrate experiments and failures because learning is valuable for the business.
There are two things that help us with this.
The technology infrastructure that runs on Amazon Web Services (AWS) helps us deliver new ideas quickly and painlessly, and lowers the cost of experimentation and failure.
The second is how the organization treats people involved in failures - we make sure they don't have diminished career opportunities or a bad reputation. They don't feel they have to keep extending failed experiments to prevent their careers from going down the drain.
Amazon's business philosophy has been built on experimenting continuously and learning relentlessly. And then scaling and making big bets on the ideas that succeed. So we celebrate experiments and failures because learning is valuable for the business.
And now this has inspired one of our customers - ENEL, a utility based in Italy. They have started an internal TV station with a program called My Biggest Failure where engineers and project managers come and talk about things that went wrong.
At Amazon, we encourage innovation and experimentation at all levels, even in smaller teams. For instance, the recommendation engine works differently for different categories. A recommendation engine for books will not work for shoes. Because with a recommendation engine you want to create more opportunities for the customer to shop but minimize avoidable returns. In shoes that can happen because a Jimmy Choo's 8.5 size may be very different from a Valentino's 9.0. But the returns on books have a different motivation. Each category's team will use different data sources and customer feedback in different ways to fine tune the recommendation engine.
Then you have the larger innovations, the bets which require significant capital and significant change in direction. As Jeff says, "The bigger the company, the bigger your failure should be." You should take the bigger risks simply because you can afford to."
When we started selling e-books in the early 2000s, there were few takers for it. It was because reading e-books on laptops weren't a good experience and tablets were new and expensive. But we believed there was big business in e-books, so we built the Kindle and we sold it at cost, to prime the market. The original Kindle is a museum piece now but I am so proud of what we did then. It was a big bet for us - we had no experience in hardware design.
Then we did the phone - not our proudest moment, but a lot of learning came from it. Alexa and Echo both have calling and messaging facilities in them and have benefited from our learning from building the phone. So the larger the company, the bigger experiments, the bigger failures because if you already know the outcome, it isn't an experiment. Real experiments will fail sometimes.
WOOL:How do you keep employees motivated in this environment and culture of incessant innovation?
WV:We hire people who are familiar with this culture (of experimentation and innovation). What we do at Amazon is build small teams of 10-12 people, which are relatively independent of each other and of the organization. And in a team that size, they don't need to have meetings to find out what the others are doing. Teams this size don't need hierarchy or constant direction. We have to hire people who will succeed in this environment. We think people should be motivated by something other than just money. Do you have a bias for action - do you love to just talk about ideas or get around to doing stuff? You can be a great manager and a leader but if you can't deep dive, and your team is writing code in SCALA, which you are not familiar with, how can you earn their respect?
WOOL:Is there tension between being obsessively customercentric and innovating for tomorrow? Do you deal with conflicting priorities all the time?
WV:There is no conflict at all - all our innovation is for today and with keeping the customer first because if we stop innovating, we won't have a business in 10-15 years.
Innovation in small and big steps is part of our culture. We have a process called The Institutional Yes. In most meetings, there is a tendency to kill off new ideas. The Institutional Yes works differently. You have to write a four to six page justification for rejecting the idea before you are taken seriously; you can't wave a hand and dismiss a new idea. When someone takes the trouble to articulate in four pages why an idea is bad for customers or the company, we sit up and take notice.
So that means we do things that are more adventurous. We once built something called Your Digital Soulmate, where we matched customers with others (anonymously, of course) who had identical purchasing patterns. We thought it was brilliant - you could predict what to buy if your digital soulmate just bought it. Customers hated it from the bottom of their hearts. We are all unique in our ways, there is no one exactly like us. In hindsight, we could have hired psychologists who would have predicted that but that is the other thing Jeff wrote in his shareholder's letter - you cannot wait for 100% information and analytics to make a decision, you will be too late.
Of course, ultimately your customers can tell you if it's a bad idea. Here's another one - we thought customers would love to buy more books if we extracted unique phrases that appeared and recommended them on that basis. We called it the statistically improbable phrase. Did customers hate it? No, they found it cute! Did it sell many books? No! When we removed it after a couple of years, no one complained.
WOOL:What's your strategy on Voice? With Alexa doing well and a whole lot of investments in Voice related skill set - what is your vision with Voice?
WV:The interfaces we have today are driven by the capabilities of our digital systems. Screens, keyboards and even the big innovations; the mouse and the touch screen are not the natural way we interact and have conversations. We can be fuzzy in our natural conversations and not very precise, but it works. Conversations and voice are the natural way we interact. As digital natives, we know exactly how to manipulate the question to the search engine and get the exact answers we need. But not by asking a fuzzy question, which is what we do in natural conversations, and using voice. So while smartphones, which provide voice enabled features started off as hands free interfaces for drivers, it was only because we couldn't type while driving, but not meant for continuous interactions.
Alexa, which is one of the first entries in the market and other advanced systems being built are letting people just talk, and you don't have to be that perfect in the way you say things but we can get the answer anyway, without using your hands. In the beginning, we observed two groups of people who immediately flocked to Alexa. One is the youngsters, the other is elderly, say someone who is trying to communicate with their grandkids. They had tablets but had to type, which was not natural. Now you can talk to it and it does what you ask it.
When I am driving back home, I can tell Alexa to open the garage door, switch on the porch lights, play Red Hot Chili Peppers, set the temperature or even interact with WebMD to figure out what to do with a minor rash, all while having a conversation.
Yet, even now, despite the interaction being natural, the back end is still hooked up to page based, digital systems that work on a linear approach. So how you build the back-end systems so that they support the conversational approach opens up exciting new possibilities.
Another driver is that a large part of the population in developing countries, those outside the middle class, cannot access the information they need easily, even if they have access to the internet. Giving them a smartphone is not a solution, but a voice interface might be.
Eventually we will have ubiquitous computing everything connected to the Internet. Using it effectively will make all the difference.
One of our customers, the International Rice Research Institute in Philippines, has information on 70,000 different strands of rice DNA. They have built a system for farmers who want to know which rice to grow, how much fertilizer to use but it would be useless with a web front end. So they built a voice interface, for Indonesian farmers who can go up to it and describe their land. The system uses the information, applies machine learning and responds. They can actually reduce the fertilizer amount by 90 percent and yet double the yield of rice. It's all completely automated and completely on voice. It works on fuzzy information, and can have a back and forth conversation until it understands.
You can see the potential of such systems - they might not change everything in the next two to three years but in the long run, they can have a big impact. Once you go beyond the constraints of the digital system and instead design for the environment, there are lots of possibilities. Why only voice? Johnson & Johnson brought out an Internet enabled toothbrush that tells you if you are brushing your teeth right. Rather than look at your smartphone, it can glow differently, or even vibrate. If you want to monitor energy consumption at home, looking at graphs on your phone may not change behavior - but if the clock in your home starts going red, that might be more effective in changing behavior.
We have a customer in New Zealand - an energy company which was trying to get their customers to pay their bills on time. Putting them on pre-paid doesn't work, sending them reminders by SMS is not effective - it was a difficult problem to solve. Then they put a lamp in the house which starts to glow brighter and brighter if you are overdue. That was effective - because nobody wants to be embarrassed.
Eventually we will have ubiquitous computing - everything connected to the Internet. Using it effectively will make all the difference.
WOOL:Finally, we cannot let you go without hearing your pick of the top technology trends.
Security: Security trends are dramatically changing. We have seen high profile hacking cases with respect to politicians and state sponsored activities. One of AWS's strengths is that it is continuously evolving to find new ways to protect customers from an operational point of view as well as innovating and building new tools by which customers can protect themselves.
Data: The cloud has levelled the playing field for small and large companies with respect to computing capabilities and analytics tools. The difference will be how you use the tools. The quality of your data, the new data streams you can integrate will become key.
Analytics or Artificial Intelligence is the same - it's about making use of data from the past to make predictions about the future. So whether it is traditional machine learning, the stuff that we have been doing like making recommendations, inventory levels, price setting, detecting fraudulent orders or counterfeit goods, all of these can be done based on data from the past. AI is moving into new things like voice recognition, image processing. All of these will be driven by advance software platforms. Analytics works best when you can get data to collaborate irrespective of where it is, whether on premise, Cloud, RDBMS or other places. AWS is absolutely the best place to do your Analytics.
VR: VR to be acceptable needs extremely high quality content and intensive computation so again there is nothing better than AWS if you have to do it on Cloud.
Everything on demand and the sharing economy: That's the principle of the cloud, even if it's a commercial model; you do not have to own things to use them. There is an emotional attachment to owning a car but it's grossly inefficient compared to enterprise data centers, which have 15% - 20% utilization; the car has a peak 5% utilization.
The Cloud has levelled the playing field for small and large companies with respect to computing capabilities and analytics tools. The difference will be how you use the tools. The quality of your data, the new data streams you can integrate will become the key.
Blockchain: I like the decentralized notion of Blockchain but we have miles to go before we have meaningful software components that can be used widely. Like Deep Learning, it’s still dependent on experts. The fact that you need quite a bit of compute-intensive encryption at all levels means that most customers will not do that themselves. Most of the innovators in Blockchain such as Coinbase, are all running on AWS.
Technological Singularity through exponential advances: I think not enough attention has been paid to the negative feedback loops there. We will absolutely have advances that will drive dramatic progress, but there are some complexity constraints. As things get complex, we will encounter dampening effects.
IOT: Everything that draws a current will be enabled and security will play a big role. We love experiments that make homes smart and cities smart but we need to make sure that the data is safe and secure and can't be manipulated. The smart city may not necessarily be the biggest deal because it is often public so access is open, but we also need to ensure no false data is being introduced. Every city can put sensors on poles that measure sound and pollution levels, make it available for everybody, along with software, to use them - all on AWS of course.