For the first time in human history, we can use tools and norms to design organizations that are permissionless, autonomous, and infinitely scalable. Through my research and experiences building the Deep Work design studio, I will juxtapose some traditional centralized aspects of work with newer decentralized manners of working that can increase our collective potential, health, and connections to work and life.
We will look at exciting topics like ending the payroll and hour-tracking pandemic, increasing collective ownership, the potential of sustainable growth models, and the creation of rich, portable digital identities. We all know that change is upon us. It is time for us to collectively contribute to creating more effective, interesting, and sustainable models of working and living together.
Before the rise and spread of agriculture, humans split responsibilities to hunt and gather food, and protect each other but also engaged in non-work-related cultural activities, like making jewelry, painting, and dancing. In the tribal model, work was an inevitable part of life, regardless of age or gender.
In the 16th/17th century chauvinists of the Christian church tied the term “work” to the morality of being a “good person”. When profits were donated to the church, they were distributed to the poor. This cultural norm was transferred to factory work during industrialization, except excess profits were managed by the factory owner, at his discretion.
Over the past few centuries, the definition of work transitioned from being an integrated aspect of life into a necessary disutility in order to be accepted as a functioning member of society. The exponential capitalist growth model has fuelled over-consumption, a distortion of “work” and “life” and the largest disparity of wealth in human history. This model has reached a tipping point socially, culturally, economically, and most importantly, environmentally. If we want to survive and thrive, we need a new way forward.
In the past, rapid economic growth was made possible by centralizing decision-making, power, profit distribution, signal sensing and quality control. In recent decades, the widespread access to quality, low-cost information has made it possible to improve upon strategies for organizational governance and quality control. Furthermore, tools to collectively act upon these improving strategies are becoming more accessible than ever by the minute. Therefore, we will take a look at the possibility of decentralizing various aspects of work that have previously been locked in a centralized bubble. Please note that I do not view decentralization as a panacea for all centralized corporate ills. Rather, decentralization is the natural evolution of particular areas of work as we exit the centralized era.
All organizations rely on matching work with talented individuals who can apply their skills to the agreed-upon purpose. Traditional organizations have employed a job interview model, whereas online communities emerge through the discovery of content. Here I juxtapose both models to give an idea of where we have come from and how online-native organizations (also called Decentralised Autonomous Organizations, or DAOs) can evolve towards a discovery of work model.
Traditional Organisations - Job Interview Model
The job interview model generally operates in one of two manners:
An organization spends resources on tools like social media, recruitment agencies, or job fairs to find high-quality employees. They create a promising job description and hope that someone will be interested and contact them.
People look for work by writing their resumés, actively searching for job openings, and talking about themselves in interviews. They collect responses, negotiate a compensation package when successful, and hope that their future job has a purpose and enjoyable coworkers.
In the early phases of building Deep Work, we spent a lot of time finding people with job postings and received many applications. We quickly realised that this strategy was not beneficial for either party. We were incentivised to overpromise on working conditions and the applicants were incentivised to embellish their skills and experiences.
Online Communities - Discovery of Content
Non-work-related online communities form around a common vision, body of knowledge, or a tokenized financial asset. People gather because:
Communities clearly summarise and communicate their purpose and provide an accessible infrastructure for knowledge exchange. Contributions are transparent and set expectations on the quality of content.
Users with pseudonymous profiles discover communities by searching for content they are interested in, reading existing contributions, and start engaging in conversations themselves. Comments are stored on their public profile, which gives other users an impression of their conversational counterparts.
Online-Native Organizations - Discovery of Work Model
Emerging DAOs combine these two concepts to discover work by:
Clearly explicating their vision, mission, and strategic partnerships. The better DAOs communicate their telos and how they are working towards achieving these purposes, the more effectively they attract like-minded individuals to support their goals.
Maximizing transparency across their community. By creating intricate work and peer-review systems to build robust digital professional identities, DAOs eliminate applicants from being able to embellish their skills and experiences. The more verified past projects and social proof an individual can share, the better their chances to be accepted into a future opportunity, where they will receive a market rate for their work.
In the case of Deep Work, not only has the quality of applicants increased since we switched to the discovery of work model, but they literally offer us free labor (although we don’t accept uncompensated labor) because of their belief in our purpose and interest in learning about web3 design in practice.
The “discovery of work” model can give individuals the self-sovereignty and flexibility of an online user or freelancer with the financial and emotional stability of an employee. But in order to make work more transparent, we first need to re-evaluate and define “work”.
Since Henry Ford invented the eight-hour workday, work has taken up most of our time and has been characterized by hourly compensation. Portions of current younger generations are rejecting this rigid work structure and are attempting to work more flexibly, with fewer weekly working hours, and are willing to compromise a bit of salary to do so.
Hour tracking is an attempt to support flexibility but fails in practice. Those who don’t like their work add more hours. Those who love their work see hour-tracking as a burden and their contribution becomes reliant on unquantifiable and nebulous social recognition.
In essence, there is a movement to do away with the hour-tracking, payroll model of compensation for a select group of the privileged working population.
The payroll and hour-tracking system can create multiple undesired scenarios:
Workers don’t feel encouraged to do their best work. Each time Deep Work hired someone full-time, their quality of work started declining noticeably with time.
Full-time employment creates the expectation that work needs to be done, regardless of whether it’s necessary or not. This leads to employees filling hours with meaningless work, just to satisfy their supervisor.
Doing many meaningless or shallow tasks can keep a worker busy but create an illusion that they are progressing in their career. In reality, they are falling behind the market requirements, unaware of the decline in their capabilities.
For service businesses, economic downshifts can be devastating, especially in countries with employee-centered laws.
Deep Work started out by “projectizing” very specific design services, that we felt comfortable delivering, without making assumptions or allowing client-feedback loops. We calculated upfront that a design sprint project would take us exactly seven four-hour work days to deliver. The deliverable could be assigned a set price our customer audience niche was willing to pay. It was like selling a car for its market value, rather than tracking the hours it would take to create a custom vehicle.
Any service or workstream can be seen as a series of recurring projects (client-facing or internal), as long as we can define the repeatable work, with identical responsibilities and tasks. Since the result of the project has clear value to a client or stakeholder, the compensation will closely resemble the value of the labor, regardless of the time it took.
To quantify the value of the labor when scoping out a project for the first time, we estimate the number of hours it would take to complete the work and compare it to the hourly wage of a similar role on the market. The shorter the negotiation with a client or stakeholder, the more this initial estimation represents the value of the project.
Check out the Deep Skills DAO conception workshop template
Over the next paragraphs, we will see how transitioning from payroll-based workstreams to purely project-based organization can increase the quality of work, minimize the cost of hiring and maximize the resilience of a business.
If a business experiences increasing demand and wants to stay profitable, it needs to ensure the quality of output is consistent at scale. Open source projects and online communities experience a rapid bottom-up drift of workers, but with no structured top-down constraints or long-term incentives. As a consequence these organizations dissolve, their revenue declines or the quality of their work suffers.
CVs, job interviews, hiring exams, and initial training usually cost upfront time but pay off in corporate businesses. A robust recruitment process results in productive and self-driven employees, delivering high-quality work. However, the associated time and cost make good recruitment practices less accessible to smaller businesses and start-ups with limited resources, who end up inevitably sacrificing the quality at scale.
When Deep Work started onboarding new freelancers, their work ended up being inconsistent. Even though the references and past work looked great, on the first project many soft- and hard skills turned out to be missing. It was difficult to retain high quality designers while there was an over-abundance of untrained students.
In progressive organizations quality control and onboarding happens at the same time - as a new person joins for work, they are being taught on the job by a mentor. This concept translates well to project-based work - the mentor selects a suitable student, takes full responsibility for completing a project, and delegates individual tasks to the student, considering their proximal zone of development. Each project offers a unique learning opportunity for both:
Students learn to handle feedback for improvement, become confident in accepting more tasks, and reach their maximum current level of development, with excellence being the ideal.
In addition to sharpening their own skills as a consequence of teaching, mentors improve their competency in talent assessment, project management, communication, and mentorship - soft skills currently either centralized or considered separate from the actual work become an inherent systemic property of the organization.
Once qualified, the onboarded individuals stay interested in delivering high-quality work, and progress in order to leave a positive impression on collaborators and maintain access to future, more interesting work. In case their co-ownership of the organization is represented by token earnings, they are incentivized to invite other high-quality talent from their social network in order to improve the entire organization.
If an organization needs to scale due to increased market demand for its work, project pricing can be increased and the profits distributed as a “recruitment bonus” to mentors. This decreases demand in the short term (like Uber’s surge pricing) while increasing the supply of high-quality work.
Deep Work created descriptive public-facing documentation of the work requirements for each role and project. Not only does it serve as the quality standard for the output of work, but it became a point of attraction for online contributors to discover interesting work and preparing them before they apply.
It costs upfront effort but saves mentors time repeating themselves as they work with many different contributors, who are often encouraged to learn and practice autonomously.
See examples of role documentation at deepskills.io
Transitioning into a different role within the same organization often requires wasteful bureaucratic processes, even if the employee would excel in the new role at a faster pace.
Breaking down the roles into granular workflows not only helps new contributors get onboarded quicker but also creates transparent expectations about career progressions and meritocratic governance within an organization. Role-specific token issuance can represent weighted governance rights, dynamic compensation mechanisms, and progression requirements independent of the humans occupying them.
Beyond the low cost and accessibility of organizational design, supporting it with web3-native technology has highly scalable benefits:
Equity-style token payments can be used to represent a long-term interest in the survival of the organization and incentivize mentoring a new contributor before leaving an organization.
When the organization experiences high demand for work, a role may need to be duplicated. Conditionally issuing a “recruitment bonus” to the experts whose experience is in high demand encourages mentoring new contributors in their role.
Projects become a testing ground for new contributions large enough to prove value but small enough so that failure to deliver wouldn’t negatively impact the deliverable. Role definitions can be dynamically adjusted, expanded, and updated systemically across all members of the organization if new suggestions offer significant value.
Multiple roles can join and scope out a completely new project, in the hope of it becoming valuable on a recurrent basis. If it proves to be true, their effort in the experiment can be rewarded retroactively, if the cost has been defined upfront (as described above).
Similar to open-source software, “open-sourced roles“ not only help decentralize onboarding and quality assurance in existing organizations but will also allow duplication (”forking”) to apply in other contexts and the creation of new markets, increasing diversity in organizational design.
This leads us to the final missing piece - connecting transparency of work with privacy-preserving professional reputation.
Traditionally, roles and skills are hidden from the public and only exist in isolation within an organization, which protects their competitiveness, while preserving the privacy of the individuals.
Most of the creative industry is built on portfolios of past work. Since they are not unambiguously verified by a third party, personal portfolios are often optimized for overpromising on the skills, rather than setting realistic expectations for quality of work.
By owning a transparent role that’s a substantial part of recurring projects of an organization, the verification is done by the organization. It essentially becomes what we know as a skill - they have been applying the same strategy and process to complete a specific deliverable in a specific amount of time, on multiple projects.
In online-native organizations, transparency about skills is mutually beneficial. Decentralized identifiers and zero-knowledge proofs can provide infrastructure to allow users to show the existence of verified skill data, and make it “portable” while being selective about who is allowed to access the details.
The concept of user-owned portable professional reputation can also create new markets for reputation data collection, exchange, curation, prediction, verification, and identification of individuals.
The promise of long-term stability of financial compensation and work requirements disconnects workers from the real world economy and has led to widespread wealth disparity and collective stagnation.
But as we have seen, frequent feedback loops, transparent onboarding opportunities, and role ownership develop a growth mindset and encourage contributors to pay close attention to their environment, plan for the future and make a collective resilient as a whole.
Collaborative organization ceases to be a competitive race and becomes a robust infrastructure for a plurality of unique problem-solving businesses. With novel norms and tools, we can now create meritocratic social networks and simultaneously become participants in the era of decentralized organizations.
