My talk for StartupFest in Montreal on July 15, 2016, which had as its theme "Firsts." I start with some history about my business and the "first principles" that drove it, then pivot to the first principles of what I've been calling the Next Economy. Be sure to download the slides and take a look at the speaker notes!
11. @timoreilly@timoreilly
I think it has more to do with architecture
“The book is perhaps most valuable for
its exposition of the Unix philosophy of
small cooperating tools with standardized
inputs and outputs, a philosophy that also
shaped the end-to-end philosophy of the
Internet. It is this philosophy, and the
architecture based on it, that has allowed
open source projects to be assembled
into larger systems such as Linux, without
explicit coordination between
developers.”
11
12. (Control by API)
Desktop Application
Stack
Proprietary Software
Hardware Lock In
By a Single-Source Supplier
System Assembled from
Standardized
Commodity Components
13. Free and Open Source Software
Cheap Commodity PCs
Intel Inside
14. Proprietary
Software As a Service
Subsystem-Level Lock In
Integration of Commodity
Components
Internet Application
Stack
Apache
15. "The Law of Conservation
of Attractive Profits"
"When attractive profits disappear at one stage
in the value chain because a product becomes
modular and commoditized, the opportunity to
earn attractive profits with proprietary products
will usually emerge at an adjacent stage."
-- Clayton Christensen
Author of The Innovator's Solution
In Harvard Business Review, February 2004
34. Text
@timoreilly
“Back in the 1930s, when there were homeless encampments in Washington, D.C., very
much like the homeless encampments that are now under the I-280 in San Francisco, the
federal government invested capital in new industries to create jobs for millions of people.
They created tax codes that redistributed from the rich to the poor….
One obvious step, Hyman says, would be to put to work the "great sloshing pool of
money" that investment banks and companies have been keeping on the sidelines since the
financial crisis. Pay workers more so they have more to spend, and invest in creative risk-
taking and innovative ideas, as happened during the New Deal.
The New Deal’s Reconstruction Finance Corporation not only helped light up America —
moving it from 10 percent of homes having electricity in 1930 to more than 60 percent a
decade later — it also funded research in the Defense Plant Corporation.
“It was fundamentally about investment in edgy technology, so things like
aerospace, aluminum extraction, synthetic rubber were all brought to scale,” Hyman says.
“Aerospace before 1939 had fewer people working in it than worked in candy manufacturing.
And after World War II, the aerospace industry was four times the size of the pre-war car
industry. This is incredible scale and scope of an endeavor, to utterly transform the economy
in about five years, by using idle capital.”
36. @timoreilly #NextEconomy
Some of the grand challenges we face
• Climate change.
• Rebuilding and rethinking the infrastructure by which we deliver
water, power, goods, and services like healthcare.
• Dealing with the “demographic inversion” — the lengthening
lifespans of the old and the smaller number of young workers to
pay into the social systems that support them.
• Income inequality. “The people will rise up before the robots
do.”
• Displaced people. How could we use technology to create the
infrastructure for whole new cities, factories, and farms, where
they could be settlers, not refugees?
39. @timoreilly #NextEconomy
Low wage employers like McDonalds and Walmart are the
new sweatshop, but something different is happening in tech
McDonalds 440,000 employees, 68 million monthly users
Snapchat ~300 employees, 100 million monthly users
41. @timoreilly #NextEconomy
Programmers are actually managers
Every day, you are
inspecting the performance
of your workers and giving
them instruction (in the form
of code) about how to do a
better job
42. @timoreilly #NextEconomy
There are other companies where programs are
managers, while many of the workers are human
At
companies
like Uber
and Lyft,
algorithms
tell people
what to do
Great startups begin with a conviction about how the world ought to be, and then set out to make the world match their vision. Google wanted to make all the world's information accessible. Amazon wanted to bring the world's products to your door. Uber wanted you to be able to summon transportation on demand. At O'Reilly, we set out to change the world by spreading the knowledge of innovators. Your core beliefs about the world form the first principles on which your company is based. Right now, I'm thinking a lot about the first principles of technology in the future economy. Next economy companies don't use technology just to reduce costs; they use it to augment workers so they can do things that were previously impossible. Next economy companies find ways to create great experiences for the people delivering their services, not just for the people consuming them. Next economy companies build a rich circular economy by creating more value than they capture.
My own original mission statement was more modest: interesting work for interesting people.
Later, using what our host emeritus Andy Nulman calls “retrospective intelligence,” I realized that what the company was really about was “Changing the world by spreading the knowledge of innovators.”
I once described my core methodology as “watching the alpha geeks.” One great example of this was when Rob Flickenger, who was a sys admin at O’Reilly, built a wifi antenna out of a pringles can to extend the range of his wifi from his house down to a local coffee shop. That led to an explosion of experimentation by geeks around the world using different kinds of homebrew antennae - I think the record was beaming wifi across the Red Sea - which told us something important about the future of ubiquitous connectivity. It ended up coming more from smartphones than from WiFi - there’s an important lesson there. You can see the future - as William Gibson once said, “the future is here. it’s just not evenly distributed yet.” But even though the broad outline is clear, you often don’t get every detail right. That’s why there are so many startups that are onto something, but still don’t succeed.
A great example of this is the rise of free and open source software. Richard Stallman at the Free Software Foundation, and later, Eric Raymond and Bruce Perens at the Open Source Initiative, were convinced that it was a matter of software licensing.
I’d grown up with Unix, which had a thriving software sharing community even though the license was not open source. AT&T eventually shut down the party, and tried to make Unix proprietary, and that’s when Linux took off. But what that had taught me was that the architecture of software systems and the willingness of developers to share their code was more important than license. I agreed that some systems had what I called an architecture of participation.
In the Wikipedia entry for the book The Unix Programming Environment, I wrote: “The book is perhaps most valuable for its exposition of the Unix philosophy of small cooperating tools with standardized inputs and outputs, a philosophy that also shaped the end-to-end philosophy of the Internet. It is this philosophy, and the architecture based on it, that has allowed open source projects to be assembled into larger systems such as Linux, without explicit coordination between developers.”
Back in 2003, I gave a talk called “The Open Source Paradigm Shift” where I started by talking about the architecture of the PC industry, which looked something like this.
With their mindset shaped by the desktop application stack, open source developers imagined the pattern replaying itself like this. They accept intel inside, and loved the cheap commodity PCs, but they imagined proprietary software being replaced by free and open source applications at the top of the stack. Red Hat or maybe SuSe would displace Microsoft, MySql would displace Oracle, and so on.
But instead, we got a world that looks like this. This is my slide from 2003 - obviously, some of the companies highlighted in the graph would be different today.
Clayton Christensen described this pattern perfectly in a 2004 Harvard Business Review article. He called it “The law of conservation of attractive profits.” “"When attractive profits disappear at one stage in the value chain because a product becomes modular and commoditized, the opportunity to earn attractive profits with proprietary products will usually emerge at an adjacent stage."
So, thinking about the first principles that are driving the future of technology and business can help you to see the future.
That’s what helped me to see that Open Source was just the beginning. I did launch an event about open source software development, now going into its 20th year. Oscon.com
But I also focused my business on big data as a key part of the future of computing, and launch events like our Strata conferences. Strataconf.com
As well as other events focused on the cloud, and web performance and operations. These were implications of the future that I saw. Velocityconf.com
So, what are the first principles that are driving the future of technology and business today?
It’s not just software that’s being commoditized now!
Human labor is being commoditized, but so are physical goods. Walmart exemplifies both trends.
There’s been an awful lot of fear mongering about how AI is going to destroy all human jobs - there was a recent Oxford university study identifying 47% of human tasks - including white collar jobs - now susceptible to automation.
And there’s been a huge furor over self driving cars, with folks like Travis Kalanick of Uber looking forward to getting rid of his drivers, and worries that we’re soon going to put millions of truck drivers out of work. [Note: I gave this talk before I heard about the attacks in Nice. I would have chosen another image had I known of that!]
First off, I want to say that the fears of wholesale replacement are overblown. We all fly in self driving jets, and have done so for decades, but we still have pilots in the cockpit. I suspect the same will be true of trucks. So gradual transitions give us time to adapt.
I got a great taste of this on the flight over here – thank you by the way to Quebecor, who loaned their corporate jet to StartupFest as part of their sponsorship, to fly me and a couple of other mentors to StartupFest along with some lucky startup founders selected through a competitive process. I got to take the controls of the jet - because it was really flying itself! It was fascinating talking with the pilots about how much the autopilot is used - it’s A LOT. In fact, they said that at busy airports, they aren’t allowed to fly manually, because if they make even a small error, they screw things up for everyone. The autopilot lets them schedule planes much closer together.
That’s a key thing about automation - it does take away jobs, but it also expands our capacity. We fly more places and more cheaply because there’s so much automation. And that leads me to my next point.
If we let machines put us out of work, it will be because of a failure of imagination and will to make a better future!
Those weavers who smashed machine looms in Ned Ludd’s rebellion of 1811 didn’t realize that descendants of those machines would make unbelievable things possible. We’d tunnel through mountains and under the sea, we’d fly through the air, crossing continents in hours, we’d build cities in the desert with buildings a half mile high, that we’d put spacecraft in orbit around Jupiter, that we’d smash the atom itself! What is impossible today, but will become possible with the technology we are now afraid of?
I have a small personal story that always reminds me of the power of technology to create new jobs that didn’t exist before. This is a map of the locations providing laser eye surgery here in Montreal. That’s a job that didn’t exist a couple of decades ago. You saw the picture of the young me with the coke bottle glasses. I was legally blind without them. I had the surgery done about 15 years ago, and now I can see perfectly without glasses. But here’s the great story: when I was doing the surgery, the surgeon kept saying “Look at the red dot.” Afterwards, I asked, “What would have happened if I didn’t look at the red dot?” “Oh, the laser would stop if you look away. But the surgery would just take longer.” That made a real impression on me, because it taught me that this was a surgery that could ONLY be done by a computer. And that led me to a key principle, that new work comes when we use technology to augment people to do things that were previously impossible.
And that’s the theme of a new event I’ve been working on, which I call The Next Economy Summit. I’m trying to get people to stop being afraid of the tools of the future, and instead to put them to work on the world’s great problems. http://conferences.oreilly.com/nextcon/economy-us
This is my first Next:Economy principle: Augment people, so they can do things that were previously impossible
And there’s a closely related corollary: Use technology to reimagine current processes and workflows
Uber started out as a great exemplar of both of these principles. There were connected taxicabs long before Uber - but all they did was to recreate the old process. What we got for our connectivity was a credit card reader in the back, and a small screen showing us ads. What Garrett Camp and Travis Kalanick realized was that humans were now augmented by location-aware smartphones, and so you could completely rethink the way you summoned a car. It’s utter magic to someone from the past - that you can click on your phone, and summon a car to where-ever you are, and to know just how long it will take for a car to pick you up.
Another key element to the success of companies like Uber is realizing the power of new technology to completely rethink real world processes - not just the act of summoning a cab, but also things like payment, which are radically simplified in the new model. By the way, this was why I was critical of Apple Pay when it came out, and everyone else was enthusing about it. It’s just a recreation of the old credit card model using technology. Increasingly, with sensors, we pay simply by consuming a service. That’s what happens with uber.
box.net founder Aaron Levie put it perfectly in a tweet. “Uber is a $3.5 billion lesson in building for how the world should work instead of optimizing for how the world does work.” I believe their latest valuation was north of $60 billion, but you get the idea.
This leads to great new experiences for consumers.
But Uber lost the plot when they started talking about self driving cars. Rather than crowing about how they’d finally get rid of those pesky drivers, they should have been talking about an experiment that they’ve run since 2014, delivering flu shots. “Sure, we won’t always have drivers. But just imagine how many other jobs we can restructure and make more magical and on demand once the transportation is even cheaper and more convenient!”
One of my favorite examples of this kind of using technology to do things that were previously impossible, rethinking the structure of an industry is a startup called Zipline, which aims to use drones to deliver blood and medicines in Africa, which not only lacks the infrastructure of hospitals but also transportation to many of its people. One of the leading causes of death among African women is postpartum hemorrhage - getting blood to people is literally life saving. See http://www.nytimes.com/2016/04/05/technology/drones-marshaled-to-drop-lifesaving-supplies-over-rwandan-terrain.html?_r=0
So here’s my vision for the next economy…. For more information, see https://www.linkedin.com/pulse/technology-business-people-matter-tim-o-reilly?trk=prof-post
I mentioned policy makers in that last set of next economy principles - it’s essential that we get policy right. My friend Louis Hyman, who will be a speaker at the Next:Economy Summit. In his new book, he talks quite a bit about the New Deal and some things that people don’t really know about it, like the funding of the Defense Plant Corporation that led to the development of the US aerospace industry.
Climate change might well be for today’s economy what World War II was for the Great Depression - a way to refocus on big challenges, one that needs all of us.
Here are some of the grand challenges we face.
Rebuilding the infrastructure by which we deliver water, power, and goods.
Dealing with the “demographic inversion” — the lengthening lifespans of the old and the smaller number of young workers to pay into the social systems that support them.
Income inequality. “The people will rise up before the robots do.”
Climate change.
Displaced people. How could we use technology to create the infrastructure for whole new cities, factories, and farms, where they could be settlers, not refugees?
The last thing I want to talk about is this idea that I got from Hal Varian, Google’s chief economist. He said to me: “My grandfather wouldn’t recognize what I do as work.”
So he says! I say “The more things change, the more they stay the same!” These programmers at Pivotal bear an uncanny resemblance to workers in a Victorian sweatshop!
I’m really kidding, though, as is illustrated by these statistics. Low wage employers like McDonalds and Walmart are the new sweatshop, but something different is happening in tech. McDonalds has 440,000 employees and 68 million “monthly users,” while a company like Snapchat serves 100 million monthly users with only about 300 employees. How can that be?
It’s because programs are the real workers at companies like Google, Facebook, and Snapchat. This is a portrait of a 21st century worker: a github repo. The programs, not the programmers, are the equivalent of the assembly line workers or the servers at McDonalds.
And all of the people contributing code are the managers! This kind of mental inversion helps you to see the world in a whole new way!
Programmers like you are actually the managers. Every day, you are inspecting the performance of your workers and giving them instruction about how to do a better job. The Build-Measure-Learn cycle is the equivalent of a manager giving feedback to his employees.
Of course, there are still human managers at Google, but the hierarchy is much flatter, because so many of the people who appear to be workers are actually already managers, whose principal activity is not to be told what to do, but to understand the company objectives, and act on their own to tell their “workers” what to do.
It strikes me that that is why OKRs have taken such root at Google and other tech companies. The focus on clear, measurable results fits well with a world in which the workers do exactly what they are told! And managers spend their day trying to figure out how to align their workers better with measurable high level goals.
There are other cases where the algorithmic workers produced by coder-managers like you are in turn managers for human workers. You can see this clearly in a company like Uber or Lyft, where the managerial programs tell people where the demand is, track the performance of the job and how to pay for it, and even solicit feedback from the customers about the human worker’s performance.
This is where things get tricky. When you’re writing a program that serves users directly, measuring user satisfaction is all you have to worry about. But when you’re writing programs that will also manage human workers, you have a real responsibility to make sure that those managerial algorithms are taking care of their workers. Companies like Uber have set their algorithm to optimize for only one factor: passenger pickup time. Until recently, drivers have been considered a throwaway commodity. That’s a big mistake. It’s becoming increasingly clear that many Uber drivers are being paid less than a living wage.
The current state of the ride sharing management algorithms, is somewhat akin to the state of search engines before google, crowded with ads and not doing a very good job of satisfying all of the user needs of the ecosystem. This is a screenshot of Altavista from 1996. I’m old enough to remember how bad search was back then.
Google came along and not only made algorithms that were focused on better search quality, but also algorithms focused on better ad quality. That’s what ride sharing companies need to do today - improve their algorithms to manage their human workers not just for passenger pickup time and customer experience, but also to make sure that the drivers themselves have a good experience and can make a good wage.
This isn’t just a matter of social justice. Worker experience is a matter of competitive advantage, just like user experience. I predicted early this year that competition for better working conditions would shape the competitive landscape for ride sharing companies. And sure enough, Uber has now made a deal with the Mechanic’s Union in NYC, and better wages is a real point of competition between uber and lyft.
As my friend David Rolf of the SEIU said to me before my Next:Economy summit last year, “God did not make being an auto worker a good job.” We have to do the same hard work that was done in the industrial economy to make jobs good for ordinary people again, not just for “managers” like us. Human income inequality is a bug in many of the systems we’re building, and it’s our job as managers of the systems that employ people today to fix that bug.
Workers at companies like Walmart and McDonalds are also managed by algorithm, and it’s a particularly user-hostile algorithm, which tells workers when to show up, gives them very little control over their schedule, and even makes sure that they don’t get more than 29 hours a week so that they aren’t eligible for health benefits. There is a whole industry of algorithmic workplace scheduling systems designed to maximize profits by screwing workers.
Fortunately, even in the old economy, some companies are starting to take notice, and realize that the scheduling algorithms need to take the human workers into better account. For example, in 2014, Starbucks ended the dreaded “Clopen” - in which an employee, who might live an hour away, was assigned to close the store at 11, and reopen it at 4 or 5 am. Many companies still follow this practice, though.
And that’s what allows Uber to attract workers with promises like these, even if they don’t yet live up to them.
Bringing this home to all of you - Being a YouTube creator, or someone who has built and monetized a big fan base on Facebook, is one of those jobs that Hal Varian’s grandfather wouldn’t recognize. In the age of networks, a lot of the people who “work for you” aren’t your employees. The stars who drive all those billions of views on YouTube need to make a living too. The programmers who build the algorithmic workers at YouTube, which in turn manage these outside workers, have a responsibility to think about these workers and how to make what they do pay off.
Consider someone like Brandon Stanton, the heroic photographer and journalist behind Humans of New York, one of Facebook’s most successful feeds.. It’s grown from a hobby into more than a full time job - yet he makes his entire living outside of the platform, via his books and speeches. The platform enables his work, but doesn’t pay him to do it. He decided not to take advertising on his pages, and uses them instead to crowd fund money for charitable causes. He told me earlier this week that he thinks he’s raised about $6 million for charity so far this year. But that was when he was only about halfway through his latest fundraiser, for cancer research, which raised $3.5 million.
Jack Conte told me the story that led him to found Patreon, the crowdfunding patronage site for artists, which now pays out millions of dollars a month.
He and his wife Nataly perform as Pomplamoose, and he got fed up when he realized that 17 million YouTube views had turned into only $3500 in income. “Our fans value us more than that.”
Not finding ways to pay for user contributed content is a bug in the system, because in tomorrow’s economy, a lot of the serious content is being produced by people like Brandon Stanton, and entertainment is being produced by people like Jack and Nataly.
In homage to Norman Mailer, whose wonderful book Why Are We in Vietnam wasn’t about Vietnam at all, but instead was an exploration of American macho, I will end by saying: “And that’s why Donald Trump and Bernie Sanders are dominating the political headlines today.” Human income inequality is a bug in many of the systems we’re building, and it’s our job as managers of the systems that employ people today to fix that bug.
Later, using what our host emeritus Andy Nulman calls “retrospective intelligence,” I realized that what the company was really about was “Changing the world by spreading the knowledge of innovators.”
It’s time to build technology, and companies, as if people matter!