Makers All Framework 0.1

Finally, a new version of the framework! The language is pretty rough but I wanted to get it out there so I can start getting feedback.

Many experts believe that between 2025 and 2040, 25-75% of all jobs will be replaced by robots/AI. Given that the rules of our economy already concentrate wealth and power at the top, this crisis could end up devastating the middle class and the poor and destroying our democracy. The question we need to answer is, how do we:

  • survive this crisis
  • use it as an opportunity to build a more just, prosperous economy
  • ensure that our strategy pays off even if this crisis doesn’t occur by 2040

To succeed, first we need to better understand our values — what do we want this new economy to provide, what kind of behavior do we want this economy’s rules to reward? To do so, we can draw lessons from 2 of the most important recent economic eras: the Mass Consumer Economy of the 1940s-1960s and the Information Economy of the 1990s-2010s.

1) Security

When we talk about security, what most people implicitly have in mind is what the Mass Consumer Economy provided white middle class and upper working-class families: any man who was willing and able to work hard could easily get a good paying job that a family could live on, good benefits, and a home at an affordable price.

2) Creativity

By the late 1960s and early 1970s, even as African-Americans and others’ fight to gain access to this new security was beginning to pay off, many people were starting to ask to ask, is this all there is? Now that for the first time in history large numbers of people no longer had to worry about how they would survive, they began to raise questions about why couldn’t their job offer both security and be more fulfilling, offering them the opportunity to express their full humanity?

The Information Economy gave us a glimpse of what a world of more fulfilling work might look like. It has created an explosion of creativity online, ranging from photos to cooking videos to open-source software. And for a lucky few, it’s also provided the freedom to do a more interesting mix of work via the new “gig economy.”

But while the Information Economy created far more room for creativity than the Mass Consumer Economy did, it offered fewer and fewer workers the kind of security offered by the Mass Consumer Economy. In fact, for many of the most creative workers — songwriters, newspaper reporters — the Information Economy made it much harder to earn a living.

In our future economy, where robots/AI, 3D printing, wearables, etc. will be ubiquitous, creativity won’t just be desirable, it will also play a central role. What makes a robot valuable isn’t the physical robot itself, it’s the creative works that drive it — the cooking recipe, the software that governs what the robot’s arm can do, the patent behind its sensors. And as robots/AI eliminate more and more drudgery, this opens up even more opportunities to shift the balance of work in the economy more towards creative work.

So the question we face in this coming economy is, how can we find a way to combine the best of the Information Economy and the Mass Consumer Economy, offering both creativity and security — and unlike either previous era, how can we make them accessible to every community? The answer: Makers All.

The Makers All Economy

Makers All consists of 2 strategies:

Make Creativity Work
In a world where robots/AI, 3D printing, wearables, etc. become ubiquitous, build an economy where in every community, as many individuals as possible have a shot at earning income from creative work.

  • Create a free/inexpensive system that lets everyone share in the creative bounty and pays creators real $$ based on who uses what they created — a.k.a. “YouTube Done Right.”
  • Create institutions that ensure that every community has ample opportunity for as many individuals as possible to learn the skills they need to participate in this economy — and “smooth the learning curve” by designing robots/3D printing machines/etc and the tools & ecosystems around them so it’s easy to start “coding”, easy to gain more skill as you need it
  • Organize in communities: avoid the mistake of the Internet, which promised it would empower everyone but left many communities behind. Foster organizing in communities so that every community benefits and so there are enough people in every community who understand this new world and have built enough grassroots power so that every community has a seat at the table.
  • Everyone gets to help write the rules of the road: e.g., setting up the payment structure so it isn’t winner takes all and so there’s more room for “mid-list” successes

The End Result: we will unleash massive creativity and wealth that will benefit every community in the US. If we do it right, this approach could also scale globally, making an enormous difference in lives of workers around the world.

Make Community Work
Although every community should benefit from Make Creativity Work, not everyone will benefit enough to pay the bills, and not everything we value will pay. So we will need another way to make sure nobody starves and everyone has a shot at making it:

  • To guarantee everyone security, create a backstop. It could be a mix of small amount of Universal Basic Income and other opportunities to earn additional income that anyone can do and that reinforce the work our society values — e.g., “volunteer bucks.”
  • Provide resources for what the market doesn’t value — e.g., income for taking care of kids/elderly and other work traditionally done by women that our society hasn’t valued — and helping fledgling markets get off the ground
  • Create a continuum of participation that give people more hands-on involvement and that combine the best of bottom-up experimentation with the need to make some choices that we can only choose together. This could include small group experiments (e.g., “Voter Kickstarters”, Tactical Urbanism) and regional opportunities (eg., funding innovation ala DARPA through regional participatory budgeting)
  • If a community is hit hard by current crisis or left behind in previous crises, create a Marshall Plan-style transition

History Shows Us How to Make This Work

It isn’t too hard to imagine how Make Community Work could operate. The most serious concern is how we would pay for it. There’s a pretty straightforward answer: if Make Creativity Work is successful, it should generate more than enough wealth that could be taxed to pay for Make Community Work.

Make Creativity Work is a different story. Because it’s so different from how our world operates today, it could easily feel like pie-in-the-sky. But if we take a closer look at previous economic eras, we can see that while turning Make Creativity Work into a reality won’t be easy, it’s certainly doable.

There are three major objections Make Creativity Work needs to overcome:

1) Could most people actually obtain the skills and knowledge they would need to fully participate in Make Creativity Work?

We’ve Done It Before: Land-Grant Colleges, Agricultural Extension Services, Etc.. This isn’t the first time we’ve had to try to figure out how to help millions of American workers take full advantage of the latest technology. From fostering a network of US Land-Grant Colleges in the late 19th-century to creating the Agriculture Extension Services in the early 20th century, the federal government has repeatedly created institutions to help farmers get and keep up to speed. Cooperative Extension Services, for example, ended up helping to fund staff to support this effort in virtually every county in the US. This massive government intervention in agriculture helped radically drive agricultural productivity, in no small part because it encouraged the spread of mechanization and automation. There is no reason we couldn’t do the same in an era where robotics, 3D printing, etc. are as central to the economy as agriculture was in previous economies.

The other thing that is striking about the US government’s efforts in agriculture was they spent a lot of time thinking about not only how to develop new research in the fields of biology,, chemistry, soil science, etc. and show the results to farmers through demonstration projects but also how to design the output of this research so it was easy for farmers to use. Robotics and AI could do the same, building their programming tools and code so they are designed to make them as accessible as possible. Right now robot researchers are only trying to do that for kids; there’s no reason we can’t take a page from agriculture and make this tech much more accessible for adults too.

Hip-Hop Wasn’t Created by Turntable Engineers
Hip Hop came out of neighborhoods that had lost 600,000 jobs lost to outsourcing and had been devastated by urban renewal. The people who first created it lived in a community that had been stripped bare, and yet out of it this amazing music rose and then transformed not just music but culture around the globe. A key part of Hip Hop’s rise was technical — converting turntables into musical instruments — but this technical brilliance wasn’t the result of the engineers who created turntables.

What the birth of hip-hop shows us is that to unlock the full potential of new technology, the most technically skilled people are only one small part of the story. Even where technical skill is critical, some of the most important work won’t be done by PhD’s. Grandmaster Flash, one of the “holy Trinity” who created hip-hop, had the most technical training of the three, and he’d just attended a vocational high school. But while Grandmaster Flash wasn’t a PhD scientist, he was a mad scientist — a genius with an obsessive drive to experiment and create something new. In short, as much as we want to spread the most technical of skills in every community, plenty of people who aren’t the most technical will still play a core role in the world of Make Creativity Work.

2) But even if many people are able to participate in Make Creativity Work, will it ever pay enough so they could earn a good living?

Today, musicians and newspaper reporters are struggling to make ends meet while Google and other giants flourish. The reason: the rules that govern the Information Economy are designed to funnel wealth and power towards the top. That’s one main reason why between 1980 and 2016, the bottom 90% saw no income growth. But there was nothing natural or inevitable about that outcome. The Mass Consumer Economy shows that an economy can be governed by rules that distribute wealth very differently — between 1930 and 1980, 70% of all income growth went to the bottom 90%.

Similarly, there’s no inherent reason the economy has to be structured so it’s winner-take-all. For example, the rules could be set up so that a creative product pays off at a progressively lower rate beyond 1 million views/uses. People who had a runaway hit would still do well, but there would be more resources in the system for a greater number of people who’d had smaller but substantial successes.

3) The people with power will never let us do it

It isn’t a matter of the wealthy and big corporations “letting us” do it, it’s a matter of us building the power needed to change the rules. In the 1930s and 40s, labor unions helped change the economy’s rules so corporate profits didn’t flow just to the top. The way they did it was by building grassroots power at the heart of the economy — if GM wouldn’t share the wealth autoworkers help to create, cars wouldn’t keep rolling off the assembly line. There’s no reason we can’t follow their lead and build grassroots power at the heart of this new economy. For example, the reason the economy’s rules are rigged against musicians today is that musicians are hopelessly outgunned If we use a community-oriented approach to Make Creativity Work, creating a network of people in every community who understand what’s at stake and how changes in the rules will affect their ability to make a good living, there’s no reason we can’t turn the tide.

Moreover, elites are not a monolith. In the 1930s, you’d have been hard-pressed to find any corporate CEOs who were pro-union. By the 1950s, although there were still plenty of wealthy people who were rabidly antiunion, many corporate CEOs and senior staff took it for granted they would be sharing power with unions. Similarly, while some of today’s 1% will always fight tooth and nail against the Makers All Economy, not all of them will. If we don’t manage to solve the looming AI/robots crisis, the future looks pretty bleak even if you’re a multimillion or billionaire — especially since this new dystopian world would be filled with an awful lot of “peasants with pitchforks”. Like movements in the past, there’s no reason why we can’t take advantage of splits between various elites.

Creating a Makers All Economy won’t be a cakewalk. But with enough grassroots power, there is no reason to assume a movement couldn’t succeed in creating an economy that is far more just than the one we have today.

A Final Note: Working Vs. Not Having to Work

What this argument doesn’t directly address is a key question about our values: should we should fight for world where, given that more and more work no longer needs to be done by humans, no one has to work. I come down on the side that says that for the foreseeable future most people will still want to work if their work lives aren’t filled with drudgery. I also freely admit that I could be completely wrong and that my thoughts on the subject are weighed down by centuries of our habits.

But my gut feeling is we won’t know the answer to this question until we have some practical experience with Make Creativity Work. As I have learned the hard way as a manager of software development, it’s extremely hard to know what you really want a new system to do until the rubber hits the road.

World Go Champion Says Google’s Go-Playing AI is “Improving Too Fast”

It’s a really good thing few people make a living playing Go, because Google’s AlphaGo just stomped the best player in the world, in no small part because it’s been getting better at a remarkable rate.

Mr. Ke, who smiled and shook his head as AlphaGo finished out the game, said afterward that his was a “bitter smile.” After he finishes this week’s match, he said, he would focus more on playing against human opponents, noting that the gap between humans and computers was becoming too great. He would treat the software more as a teacher, he said, to get inspiration and new ideas about moves.

“AlphaGo is improving too fast,” he said in a news conference after the game. “AlphaGo is like a different player this year compared to last year.”

How good has AlphaGo gotten?

“Last year, it was still quite humanlike when it played,” said Mr. Ke after the game. “But this year, it became like a god of Go.”

Not scary at all.

Google Creates AI That Helps Build AI

An interesting development from Google’s dev conference:  

Pichai introduced a project called AutoML coming out of the company’s Google Brain artificial intelligence research group. Researchers there have shown that their learning algorithms can automate one of the trickiest parts of the job of designing machine-learning software to take on a particular task….

On the image task, their system rivaled the best architectures designed by human experts. On the language task, it beat them.

Perhaps more significantly, it came up with architectures of a kind that researchers didn’t previously consider suited to those tasks. “In a sense it found something we didn’t know about,” says Le. “It’s striking.”

But it’s a ways from being a threat to developers’ jobs — at least for now…

When asked if they are on track to put themselves out of a job, Le and Zoph laugh, though. Right now the technique is too expensive to be widely used. The pair’s experiments tied up 800 powerful graphics processors for multiple weeks—racking up the kind of power bill few companies could afford for speculative research.

Still, Google now has a larger team working on AutoML, including on how to make it less resource-intensive. 

How Do We Make UBI Work?  Move All Those Big City Folk to Detroit!

I finally found a UBI advocate who explains how people in the US would be able to make ends meet on the paltry amount most UBI plans offer.  Albert Wenger, the former President of and a managing partner at a NYC venture capital firm, is writing a book online called The World After Capital, and one of its main arguments is that we’ll need a UBI.  In his version, each adult would get $1,000 a month. Here’s how they’d spend it:

 The likely cost allocation for a typical adult would roughly break down as follows: [$300/month] for housing, [$300/month] for food, [$100/month] for transportation, [$50/month] for internet access and associated equipment … [this needs more work and backup].

Given that it’s not 1980, how does he think most adults will get enough housing for $300/month?  First, all the new fangled technology will make building housing cheap.

we need to look at how technology is presently driving down the prices of everything — a process known to economists as “technological deflation.” Technology can make education and health care far more affordable than it is today….

What about shelter? Technology is definitely making it cheaper to put up a building. We now even have the beginnings of houses that are produced in a fully automated way using 3D printers!

Second, buy a lot of folks a bus ticket:

It of course still costs a ton of money to live in certain places like Manhattan or San Francisco, since demand for housing space exceeds the available supply. Here UBI functions quite differently from other solutions that make housing more affordable, such as government subsidies. With UBI, people can live in parts of the country (or the world) where housing is much more affordable.

The city of Detroit is currently giving away houses as an alternative to tearing them down. Or if you prefer a rural setting, you can rent a cottage in North Carolina for only $995/month [40]. Right now, many people can’t take advantage of these opportunities, since they can’t find a job in these locales and would be left with no income. By breaking the connection with a job, UBI makes geographic flexibility possible. People would no longer be geographically trapped by the challenge of providing for their basic needs.

How many people are we talking?  Well, in April there were 39 major cities in the US where the median rent for a one bedroom apartment was over $1,000/mo, aka the total amount provided by his UBI.  That’s a lotta bus tickets.

Luckily, getting all those folks to move to Detroit and other super affordable cities wouldn’t make those cities’ land & housing prices skyrocket, because… he doesn’t say.  

But at least Detroit housing is dirt cheap today.  Or not:

in Detroit, the average rental price for a one-bedroom was $620 (that’s all Detroit – not just downtown, not metro Detroit). That price fluctuated from a low of $602 in May/June [2016], to a spike in December of $725.

It’s clear from the rest of the book that Wenger is a decent person who sincerely wants to find a way out of the crisis we’re facing.  And it certainly makes sense to ask how we can help bring down the  cost of living in the future so that a UBI would go farther.  But betting a UBI-centric strategy on blithely assuming eventually it’ll all work out is just as likely to create the conditions for the kind of despair that leads to fascism as it is to deliver a happy ending.

Amazon’s Latest Patent Shows Why Today’s Automation Is Different

From the New York Times:

In April, Amazon received an intriguing patent for an “on demand” apparel manufacturing system, which can quickly fill online orders for suits, dresses and other garments.

Amazon — the people who started out selling books online and now sell everything from network cables to cable knit sweaters — is now considering using robots to make some of the clothes they sell.  And that, boys and girls, is Exhibit A why the argument that we don’t have to worry about robots/AI because  hasn’t created mass unemployment in the past doesn’t hold water.

Suppose I told you that John Deere was going to automate some of the garment making business.  You’d think that was a little weird, right?  Until recently, most automation was industry specific:  just because you could automate harvesting wheat didn’t mean you could automate harvesting numbers — aka spreadsheets — or making clothes. 

But now the tech that, say, analyzes cat pictures online may end up driving a wide variety of vision systems.  So it’s a much smaller leap from running a massive online store like Amazon to making drones to making clothes.  Amazon’s patent may automate only some parts of the process, and undoubtedly it’ll only cover some types of clothing.   But the speed at which automation has the potential to cross different types of industries and types of work is a little scary.

Or to put it another way:  the mechanization of wheat harvesting took off roughly in the 1930s and 40s, but it wasn’t until the late 1960s that tomato harvesting took off.  Anybody expect it’ll take Amazon 20+ years to go from building drones to automating the manufacturing of some garments?

And that’s why we need to be worried.  In the era of John Deere automation, we could count on  new jobs being created frequently enough to replace the jobs that had been automated away.  But as the speed of automation is starting to accelerate, it is a risky bet to assume that new job creation will keep pace.

Update: how serious is Amazon about AI and robots?  According to one analysis of Amazon’s recent acquisitions and other signs of their intent, Amazon is  “seeking to become the central provider for AI-as-a-service,” where anyone could tap into Amazon’s AI offerings rather than having to build it out themselves.