With the advent of the Internet and the development of new digital technologies, the economy is following a trend of decentralization. The most innovative environments are open source communities and peer production is on the rise. The crowd innovates and produces. But the crowd is organized in loose networks, it is geographically dispersed, and contributions to projects follow a long tail distribution. What are the possible reward mechanisms in this new economy?

Our thesis is that in order to reward all the participants in p2p economic activity, and thus to incentivise contributions and make participation sustainable for everyone, we need to do contribution accounting: record everyone’s contribution, evaluate these contributions, and calculate every participant’s fair share. This method for redistribution of benefits must be established at the beginning of the economic process, in a transparent way. It constitutes a contract among participants, and it allows them to estimate their rewards in relation with their efforts. We call this the contribution accounting system.

For the rest of this article we will try to explain why a contribution accounting system is needed in a more decentralized economy, and unavoidable in a p2p economy.

Contribution accounting and exchanges

First, we need to make a distinction between a contribution accounting system and an exchange system. Suppose that we have 3 individuals picking using one basket. The contribution accounting system keeps track of how many cherries everyone puts in the basket, so that when they sell the basket on the market they can decide to redistribute the revenue in proportion to everyone’s contribution. It describes how contributions from multiple individuals amalgamate into a product, during a co-production processes.

Once a product is created, i.e. once the basket is full and ready to go to market, it can be exchanged using an exchange system: barter, currency, etc.

The contribution accounting system is not a currency, not a barter system. It doesn’t refer to an exchange between our 3 individuals who are picking cherries, or between them and another entity like a company. They are not getting paid a salary in exchange of their work. They are collaborating, they all add cherries into the same basket, which is their product to be. The exchange might occur at a later point in time, once their basket is full and ready to go to the market. Meanwhile, they all share the risk of having their cherries being eaten by birds, or of not getting a good price for their basket.

Production processes

A production process that requires more than one individual can be based on the following 3 arrangements, or on a combination of them:

• stigmergic coordination–  Participants don’t have aligned goals, don’t maintain a relationship other than being contributors to the same process. Ex. this is how Wikipedia is built. 
• cooperation – The goals of participants are not necessarily aligned. Ex. in a corporation employees and business owners usually have divergent interests and goals.
• collaboration – Requires a large degree of alignment in goals. Ex. a group of individuals climbing a mountain together.

The traditional capitalist economy is mostly about cooperation, which doesn’t require an tight alignment of interests and goals. Production is sustained through an exchange process, where workers exchange the time they spend on different tasks against wages. The exchange process transfers risk from workers to the owners of capital, but at the same time, the workers are stripped of their rights to the output of their labor. Workers cooperate (despite some inconveniences and misalignment in interests and goals) with the owners of capital in production processes because there exists an economic dependency between the two groups. Workers need money, which are by far the predominant means to acquire basic necessities. On the other side, the owners of capital need labor to generate more wealth. This economic dependency is not symmetrical and makes the system prone to abuse, which explains the existence (and necessity) of unions to counterbalance the tendency for exploitation.

In peer production we have a blend of the 3 arrangements mentioned above, mostly coordination and some stigmergic collaboration. In general, no one works for anyone else. Everyone involved is a peer, an affiliate of a peer production network. The p2p culture prescribes that the output of a collaborative and participatory process should not be owned or controlled by anyone in particular, but shared among participants in a fair way. Immaterial artifacts that are produced in such way (such as software or hardware designs) are usually released as commons (they are openly shared). Material goods can be exchanged on the market, and the revenue generated is shared among all the participants. Service-based models also exist, where services are exchanged on the market against some form of payment, which is redistributed to everyone involved in the providing the service. A good example of service-bases p2p model is the Bitcoin network. If we focus only on the mining aspect, minors form a open network of peer participants, they collectively maintain the hardware infrastructure of the entire network. Minors are rewarded in proportion to the computing power that they provide to the network.

The normal and the long tail modes of production

normal mode of production

In the traditional capitalist economy wages should be regulated by the free labor market, if we make abstraction of all sorts of mechanisms through which this market can be biased (labor unions and governmental intervention included). The market is responsible for the difference in salary between an engineer and a clerk. The notion of jobimplies that a salary is determined and agreed upon before the employee starts working (with the possibility modify the salary based on performance). Since the amount of $ per hours of work is pre-established, the capital owner needs to make sure that the employee produces enough during the work hours. Therefore, a new role is needed within the organization to guarantee this, the beloved project manager. Traditional organizations spend a lot of energy doing time management, because usually the interest of the worker is not perfectly aligned with the interest of the capital owner (see cooperative production above). Classical organizations operate on the normal mode of production (from the ”normal curve” or ”bell curve”), where the number of workers is minimized, and the majority of employees in a category of roles produce almost the same amount. Very few workers produce less than the norm, because they are eliminated (i.e. fired). Very few produce more, because there are no incentives to do so, the association with the mission of the traditional enterprise is weak, the sense of belonging is usually low (usually fabricated by the HR department), the sense of ownership is almost absent, etc.

long tail mode of production

The situation is very different in a peer production environment, which is open to participation, is decentralized in terms of allocation of resources, and uses a horizontal governance system.

In peer production, we see a log tail distribution of contributions, which means that a very large number of individuals are involved in production, only a very small percentage of those contribute a lot, the great majority of them contribute very little, and most of the production is done by those who make small contributions. A prearrangement on revenue is impossible in this context. First, because the production process is very dynamic and relations of production cannot be contract-based. Second, the process involves a great number of individuals that are distributed all over the planet, therefore it is impossible to do time management. Moreover, no one can force anyone else to work more. In this mode of production we need to evaluate rewards after the fact, based on deliverables or based on the type of activity and its potential to increase the probability of production of valuable products. A system is needed to account for everyone’s contribution, to evaluate these contributions and turn them into rewards.We call this an access to benefits algorithm.

In some sense, the access to benefits algorithm is a distributed solution to time management, which can be applied to large scale and very dynamic peer production processes. It embodies positive and negative incentives, and can contain parameters to influence individual participation and quality of contributions, it can regulate behavior, it gamifies production. For example, a reputation system can be tied to the access to benefits algorithm: a higher reputation results in a higher reward, all other things being equal, and vice versa. Moreover, it can also contain parameters to incentivise periodic and frequent contributions, and to prioritize important processes.

Contribution accounting and network resource planning

The long tail mode of production needs a contribution accounting system in order to allow fair redistribution of rewards. It allows participants to record contributions of various types and it uses an access to benefits algorithm to turn them into benefits. But this is only the first part of the story.

In the OVN model contributions are attributed to the creation of resources, which can be documents, designs, parts or full prototypes, etc. (some contributions go into infrastructure of community development and they lack clear resource or deliverables). From the resource level, contributions aggregate at the project level. A project is an open venture, or a business unit. It is the smallest unit within the OVN that can generate all sorts of benefits, including revenue.

The fact that contributions can be attributed directly to resources (not projects) is very important for commons-based peer production (CBPP), which builds on open source. On Github, pieces of open source software (OSS) can be picked up by someone and remixed into something else. Open source hardware (OSHW) development follows the same path, i.e. designs (mechanical, electronic, optical) are forked and remixed. This ability to fork and remix parts of more complex systems makes open source development a very efficient process. This explains why modularity and interoperability are very important properties of OSS and OSHW. If rewards are envisioned for the work done, CBPP needs to find a way to account for contributions at the resource level and to track the way resources are put together in different contexts (projects are considered contexts). If contributions are only recorded at the project level, projects become silos of economic activity with a reduced possibility of benefits flows between them.

Taking into consideration the structure of OSS development, the solution to the benefit/reward redistribution problem is to attach some information to individual resources created that allows their reevaluation later, when they get remixed and integrated into larger systems, in other contexts. The metrics of evaluation can vary depending on the context. This is the role of the network resource planning system NRP, which allows benefits/rewards to propagate upwards through value streams and the creation of a single resource can generate rewards from many different sources (many projects), depending on how many successful projects are using it.

This goes even further, because this same NRP also provides a growth mechanism for CBPP networks. To illustrate this, imagine that members of a CBPP community decide to attribute equity to resources that are created by other communities. (Example: SENSORICA decides to integrate a piece of open source hardware developed by another OSHW community). First, why would SENSORICA affiliates decide to diminish their revenue by giving equity to other groups when they can just copy the open source design? The economic rationale is to reduce efforts required to internalize new capacity (new knowledge and know how around that piece of open hardware) and to increase the speed of execution (a first to market advantage). CBPP networks grow by affiliation. By offering equity to other CBPP communities they are essentially building bridges to innovate faster and improve production processes. This is the higher-level structure of networks-of-networks (see the Open Alliance).

We believe that in order to sustain the CBPP we need to create infrastructure that allows attribution of value-related properties to individual resources, to allow reevaluation of these individual resources in context, and to facilitate the formation of networks-of-networks that preserve the individuality of every community part of it, but at the same time brings them together on the same economic platform.

Contribution accounting in transition models

As the economy transitions to a networked state, existing organizations are trying to adapt. We already see traditional corporations going from in-house R&D, to outsourcing R&D and more recently to crowdsourcing R&D. This movement is forced by the need to innovate fast, and by the fact that open source lowers the price to a point where traditional high-tech corporations can be put out of business. Crowdsourcing R&D means utilizing all sorts of schemes to attract the participation of the crowd into innovation processes that are sponsored by these corporations. In early crowdsourcing practices corporations tried to control the innovation by signing non-disclosure agreements with the participants. Crowdsourcing platforms were created to match corporate projects with skilled individuals. The practice was competitive, i.e. the company would chose a winner among different proposals, and usually the winner was rewarded with money. This practice gradually became more open, since the first iteration of crowdsourcing platforms were not very successful in attracting highly skilled individuals. In order to attract innovation, in order to grow open innovation communities around them, corporations need to think seriously about the reward mechanisms they put in place. It is not so difficult to understand why the early crowdfunding platforms were not very good attractors. I would not compete in a call by a company to design something for a few bucks, with a good probability of losing the race, knowing that the company will monopolize the work and probably make a lot of profits on it. The trend is to go from closed crowdsourcing to truly open source innovation, which must be accompanied by a broadening of the reward system. Since companies are going to deal with the crowd more and more, they need a contribution accounting system to account for contributions. See this presentation by SENSORICA making the distinction between competitive crowdsourcing and collaborative crowdsourcing.

In parallel to the adaptation of traditional companies we also see the creation of hybrid organizations and models. For example, in the realm or hardware, we have the emergence of ecosystems like Arduino and 3D Robotics/DIY Drones. They are composed of a traditional for-profit organization surrounded by an open source community. This post describes the situation. The difference here is that in most cases the open source community pre-existed the traditional for-profit, the later being created to manufacture and to distribute the products that are based on the innovation created by the open community. These hybrid models, the ones that are sustainable and successful, maintain an precarious equilibrium between the profit motive that can arise within the centralized traditional organization the open and sharing culture within the open innovation community. In some cases, this equilibrium is not maintained and the synergy between the two entities disappears, destroying the ecosystem. This was the case of Makerbot and the RepRap community, well captured in the Netflix documentary Print the Legend.

Photo by Muffet

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The author in front of one of the locations of the MakerBot store — which all closed. Photo via Isaac Anderson.

Once upon a time, MakerBot Industries was the darling of the Brooklyn start-up world. I’d know, because I worked there.

The company this week announced its productions are being outsourced to China, so it’s come a long way from a hackerspace in Boerum Hill. But let’s start at the beginning: MakerBot was the brainchild of a few 3D printing enthusiasts and unabashed nerds who wanted to contribute to an open-source hardware community founded by an eccentric British professor enamored with the prospect of self-replicating machines. It turned out a lot of folks wanted to get their hands on a thing that made things, and it also turned out that the community that made the original MakerBot possible greatly facilitated MakerBot’s rapid expansion, offering useful feedback and oftentimes original design work that was ultimately integrated into the company’s core product.

For a while, MakerBot was equally generous in giving back to this community. They embraced the spirit of openness and freely shared their ongoing improvements to the public. Demand skyrocketed for a product that was improving in real time, so it made sense to double down on Brooklyn, and MakerBot built a full-blown factory for its products in Sunset Park. It was whimsical! The impossible was possible! In the early days, nearly everyone built printers as a rite of passage. They learned the machines inside and out and became ideal candidates for promotion through the ranks to engineering, repair, customer support, and sales roles. In an emphatically post-industrial city in an outsourced 21st-century world, MakerBot fearlessly bucked the trend by building physical things locally, hiring from within, and committing to the mantra that sharing was caring. It was hard not to like them; their fan base ranged from the obvious, like geeks to geeks who like open-source stuff, to hipster geeks, to, uh, local artists previously involved in Occupy Wall Street? Whatever it was they were doing, it was working. Neither MakerBot nor their affable CEO could do any wrong… until they did.

Everyone thinks riding the start-up wave is the ticket to noble success, a mix of idealistic principles and the cutting edge profitability of the new economy. But sometimes that wave crashes, and this is one of those stories.

A year or two before I was hired, the company’s then-CEO Bre Pettis onboarded an outsider who “knew how to run a company,” presumably because the stress of running such a rapidly growing company was getting to him. (Foreshadowing alert: the running joke was later that this woman knew how to run a company into the ground. This outsider eventually maneuvered herself into the position of president of the company and eventually supplanted the very CEO that hired her before cashing out and handing the keys over to the grim reaper — but I digress! More on that later.) I joined MakerBot in May 2013, a bright-eyed and bushy-tailed grad school dropout hellbent on getting my hands dirty at a tech start-up that was changing the world. At that time, MakerBot was still riding high on their goodwill cache; but by the time I came aboard, the company had reached peak cool.

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A little over a month after I’d joined, everyone was called into an all-hands meeting in which it was announced that we’d been acquired by Stratasys, nefarious corporate overlords a company that effectively invented the process integral to the kind of 3D printing MakerBots did, in a deal averaging over $1 million in stock for every employee at the company. Pop the champagne! No, seriously, there were dozens of bottles of sparkling wine provided for us. And why not? This was great! We were one of the lucky startups that made it! We even got a couple of extra vacation days. And who were we to object?

The acquisition didn’t affect us much, or so it seemed at first. For a while MakerBot just kept being MakerBot, and Stratasys kept on being Stratasys, and we got to keep innovating away while the parent company took care of their own business affairs. There was an overarching collective feeling of conviviality and camaraderie between coworkers, who were as eager as ever before to geek out about what they knew and learn from fellow coworkers who knew what they didn’t know.

Come find us in 600+ @SamsClub stores around the US. That’s a lot of industrial revolution. http://t.co/DoXRgqydOy pic.twitter.com/chYkdremoI

— MakerBot (@makerbot) July 7, 2015

The better part of a year before the acquisition, MakerBot pivoted towards closing its source code and keeping its intellectual property cards closer to its chest, and we stayed the course after being bought out. Part of closing our source was scrapping the open-source platform we had previously iterated on every annual product development cycle. Instead of tweaking and improving on a design honed over the course of half a decade, we pulled out all the stops and redesigned our own proprietary hardware, electronics and software from the ground up over the course of a single year. We also decided, while we were at it, to jam-pack our 3D printers with more features than they’d ever had before—cameras, wifi, apps, “smart” everything. Rather than design, test, and build, it was design, design, design some more, and then quick — build!

In the past, with our open-source platform, our customer base had been limited to a smaller group of capable hobbyists who provided tech-savvy feedback and suggestions for improvement. By the time this newest (and under-tested, according to my fellow employees) product had shipped, most people buying our product were largely incapable non-hobbyists with no useful feedback, only unrealistic expectations, and had little idea of how to fix things under the hood when they (inevitably) broke due to flashy new features malfunctioning and debilitating the printers. Customers were up in arms over their multi-thousand dollar paperweights, our repair team couldn’t fix broken printers fast enough, our support department was drowned in an onslaught of complaints, a class-action lawsuit from customers was threatened, and our parent company’s stock price started to decline.

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We just chalked this all up to startup craziness, rolled our eyes, and carried on. The work was still plentiful and engaging! I still felt incredibly productive, learning more than I ever had before as I pushed myself to design the most innovative and robust 3D printer subsystems I could dream up. I trusted my coworkers every step of the way, and we all had a good time shooting the shit while we encouraged and abetted one another along in our respective projects. I even got to play the part of mentor to some interns.

And then, seemingly out of nowhere, work dried up.

While we at MakerBot were distracted either putting out fires left and right or just taking care of business, Stratasys had been going through some mergers and acquisitions of its own, with some corporate restructuring. We were newly barred from making even minor changes to products that were very clearly broken in our eyes and therefore very rapidly damaging the reputation of our company. Morale was dropping fast across the company, and last-ditch efforts were made by management to retain talent they were afraid of losing; in what was in retrospect may have been an obvious bread-and-circuses tactic, my salary as well as those of a few select coworkers was suddenly bumped up by 30 percent, and yet while making markedly more money that I ever had before in my life, I was about the least satisfied I’d ever been with my job.

I started penning what I termed “therapeutic job applications” for positions elsewhere and once cried openly in front of my boss in a one-on-one meeting, telling her plainly that I feared for the future of our department and the company in general considering the course we were on. I was upset because MakerBot was more than just a workplace: it was a community I adored and a dream I’d fallen in love with. And the dream, I could plainly see, was dead.

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Two months later, our former-president-turned-CEO (yeah, remember her?) handed the reins over to the hyper-privileged son of the chairman of the board of Stratasys. His first action as CEO, as shared on the company internal social network, was to learn how to use one of our printers. The second was to fire a fifth of the company. I still remember that Friday: I was ambling through the large glass doors de rigueur for any well-heeled tech company, half-asleep and bracing myself for the daily slog, when someone from HR approached me to tell me I had a meeting with the director of engineering. I made my way over to the cramped meeting room and found the director of engineering sitting next to the head of the company’s legal department. I was given the spiel, told it wasn’t a reflection of my performance, yada yada. Honestly, I couldn’t bring myself to care that much. Between the month and a half of severance and my total job dissatisfaction, I saw getting laid off at that point as getting paid to quit.

Firing a fifth of the company has since become a sort of semi-annual ritual at MakerBot, and this latest round of layoffs complete with outsourcing to China is reported to have cost 100 people their jobs, according to Gothamist. It shouldn’t come as a shock for a company that sold out to the corporate capitalist short-term profit maximization machine. Only a year ago, the company was heralded for opening a bigger factory in Brooklyn. Brooklyn Borough President Eric Adams said then: “For many years folks in this community, instead of making screws, felt that they were being screwed.” Oh, the irony.

It is still, however, an affront to and inversion of the founding principles of the company, a place that put a premium on innovation, was willing to invest in its employees and infrastructure, and wanted to give back to the community from whence it came. As to the question of MakerBot’s continued presence in Brooklyn now that it’s repeatedly shed good chunks of its workforce and sent its manufacturing to China, don’t worry, it’s still here — for now. It’s just shirked back into its corporate shell near the top of a skyscraper in the part of the borough that fancies itself the next Midtown.

Stratasys’s corporate strategy hasn’t made itself more profitable: its stock price bled out in the wake of the first round of layoffs in April 2015 and has flatlined ever since. The majority of MakerBot employees never saw much of that million-dollar-a-head buyout, but then again, maybe Robert Frost — or was that Ponyboy? — was right. Nothing gold can stay: it just gives way to more gold. Gold to line the coffers of the fat cats who don’t give a fuck about dreams, anyway.

Photo by jabella

Photo by Nick Ames

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