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The Democratization of Work – A Cooperative Exchange for Work

For a generation, the American worker has been under siege. Allison Pugh, Sociology Professor at the University of Virginia, writes about how today’s employment is a “one-way honor system” that ties workers to employers who have little responsibility to employees. Yet, from globalization’s competitive pressures stymying wages, to business’s relentless pursuit of productivity gains, to technology’s constant disruptive influences, the American worker has weathered it all. However, at this moment in time, the American worker stands at a fork with two clear choices for the way forward. Stay the path and let corporate lead, or band together in a new unionism to democratize work. I suggest the way forward is a cooperative work exchange, powered by block chain, defined and owned by the American worker.

With unions no longer looked to for protection, business pensions an anachronism where they still exist, and technology rapidly changing the work relationship from the top down, the American worker must realize that the status quo is both undesirable and unsustainable. The gig economy as it is currently being implemented further erodes the American worker’s pay, rights, and status. Yet, the gig economy could promise something so much more powerful. The gig economy could usher in new freedom and power to the American worker, while at the same time giving business one of the greatest advances in productivity since the advent of the assembly line. The gig economy instantiated as an exchange for work may some day rival the power of the world’s great exchanges for equity.

This exchange for the offering, contracting, managing and paying of work is envisioned to be powered by block chain technology (something like Ethereum). While work will always be identified at the point of need, the terms and conditions for the conduct of that work will now flow from the bottom up (gig worker to employer). To incentivize corporations in this new paradigm, businesses will have the ability to access the entire block chain ledger for analysis and Google-style algorithms for work forecasting and modeling. The Exchange’s mission will be to serve the American worker while enhancing the businesses that employ her by leveraging the power of the crowd. Here’s how it could work.


Recently, the Ethereum Project forked the technology underlying Bitcoin to create a generalized platform for building decentralized applications. Businesses and entrepreneurs quickly took note, with IBM even outlining an Internet of Things management concept using Ethereum. One of the Ethereum Project’s major enhancements was the inclusion of what they call contracts, which is basically a framework for including software logic with each transaction entity.

Thus, in the work exchange, the basic transaction entity would be a piece of discreet work. Discreet as in a task that is part of a user story, waterfall schedule, or even a real time help desk ticket. The work would have metadata outlining the skills needed, timelines, remuneration, and taxonomic elements (epic, program, department, company, etc.). Identity would be encrypted until necessary for consummating a transaction. This would protect businesses from corporate espionage and worker true identity from the negative side of metadata analytics. Everything else would be transparent to all on the work block chain. There are arguments for certain metadata to also be encrypted and maybe even some of the software logic as well, but these are topics that the exchange would decide according to its governance charter.

Whereas the mining concept in bitcoin is the main trust mechanism, algorithm and software contributions, research studies, and other exchange enhancing activities will be the sources of trust and user juice in the Exchange. It’s one thing to be great at a certain type of work or need a ton of a work from the Exchange, it’s another to enhance work for all.

I’m always amazed at how a simple idea rendered in software can become worth $1B plus. Uber, the harbinger of the gig economy that sometimes bears its name, is GPS, a map, a few algorithms, and a software form. With the Exchange, a similar simple worker interface is envisioned – a task or to do list. There is certainly a need for a vitae, yet all the user really needs is list of their current tasks, and a search capability to find new work (gigs) to bid on and the mechanism to so bid.

On the business side, current project management software, using REST/JSON integrations with the Exchange, would pretty much function as they do now for work under their purview. However, the massive data store encapsulated in the Ethereum block chain ledger poses as a litany of opportunities for business performance improvement.


Leveraging the contract mechanism in Ethereum to bundle user defined work contracts is one of the key concepts of this proposal. While Uber gives drivers massively improved efficiency, it also places them in this new and mostly unregulated world of the gig worker. It is clear that business is moving to a post employee era, where all work is contracted on demand. However, the current gig economy has no mechanism or support for contracts that shield the worker. As essentially a company of one, all of the employment laws cease to cover this new business entity. Further, the bulk of case law is premised on the company-employee or company-company relationships, not this new hybrid, company-gig worker relationship.

So, new rules. Why can’t the exchange limit contracts to those defined by the gig workers? As contracts gain favor by workers, and more workers join the Exchange, the contracts become powerful actors in the company-gig worker relationship. For example, work for hire, favored by companies, is not in the best interest of the creative knowledge worker. In the software engineering world, open source (the opposite of work for hire) is rapidly becoming the preferred contract arrangement. Gig worker defined contracts, with enough workers that require them, can accelerate this evolution.

Most contract language today is a series of clauses that have a few variables that lawyers love to re-code. Regardless of the encoding, the clauses end up with the same few variables. Further, certain clauses cannot have certain variables in the presence of other certain clauses. Some clauses even respond to changes in state! Sounds like something a computer is good at doing. Luckily, the legal system is finally undergoing a computing transformation. For example, Stanford University’s law school has a large legal Informatics program that includes work into computable contracts.

One of the reasons the Exchange is proposed to be a non-profit entity owned by the gig workers is in direct recognition of business motivation. What globalization has taught business is that the employee as a resource is not a euphemism, it is reality. Resources in the warehouse is synonymous with employees on the bench. Training is a resource cost. Turnover happens and it is a cost. Friction is to be avoided. These are not behaviors to be saddened by, they are the behaviors that power our modern world and are unstoppable. We should not mourn that business is not motivated by what is good for the worker, we should recognize such, and put in place mechanisms that do consider the worker.

This new form of collective bargaining should appeal to all sides of the political spectrum. It does not need new law making, as it leverages what is already in place. It is not anti-business, as it recognizes what motivates business and agrees. This new form of collective bargaining actually gives business unprecedented forecasting capabilities. This new form of collective bargaining is actually about freedom for the individual via the power of democracy.


Since the Kevin Bacon game popularized what is now known as scale free networks, graph theory has taken center stage of math as an economic value driver. The science has rapidly gone from predictions of nodes joining a network, to actually positing gravity driven information theory. It is hard to argue with the power of network analysis. Google is one of the biggest and most successful companies ever to trade an equity, and they owe it all to the power of network analysis via graph math. Web links describe networks. Search terms by person or by concept describe networks. Each of these networks have metadata that further describe them.

If you have millions, and soon billions of work items traded on a 100% transparent exchange, with a plethora of metadata defining taxonomies of every kind imaginable, what kind of networks and their attendant insights might one expect?

At some point, common work terms by taxonomy will become apparent. I call them lexemes, but you can think of them as types of work. With enough samples, we should start to see common sequencing of these work items. Over time, we will certainly get a good handle on effort estimates. Using graph math, we can create new algorithms. These algorithms can start to make project performance better. They can forecast, they can predict.

Thus, the Work Exchange is not only for the worker, it is for business. A massive data store of work can enable us to take on the great projects that stand as dreams today. The massive data store of work can provide detailed plans of unparalleled fidelity. The massive data store of work can forecast risks and trends with high confidence. The massive data store of work will surprise us with insights we cannot see today. Business will finally see through the fog of tomorrow and strategy will become truly executable.

Exchange Charter

While not an exchange, yet, Linkedin at a $25B market cap shows how valuable a work exchange could become. However, if Linkedin is the exchange, the exchange would be beholding to investors, and then in the end, the gig workers become the product, so rinse repeat, and workers as resources again in short order.

Linkedin, Google, Facebook, et. al, all have the same model. The creators and their data is the product, and advertisers are the customers. If the Exchange is a traditional business, who is the customer and who is the product?

It makes sense to create an entity that is chartered by the gig worker members, charges a micro fee for each transaction, and then gives some portion of profit back to the workers. The product is the work exchanged, as well as its metadata. Businesses are the customers. Making the gig workers the owners, changes their motivation as well. The gig workers will want the massive data store of work to always increase in value. The gig worker owners will want the best contracts for their members that can actually solicit work from business! The gig workers can only change the contract paradigm as far as business can comfortably adjust.

As is readily apparent in understanding this exchange for work, the size of the crowd is pretty key. Both power of negotiation and power of analytics are directly proportional to the numbers of workers and businesses participating. That is one reason that I think that some of the old tactics of unions will not find their way to success in the Work Exchange. A strike is bad for both sides, as the value of the exchange is driven by the number of successfully contracted work items. Work slowdowns, same thing. Outlandish contract claims as a method of negotiating, it might be tried, but the more logical would point out the weakening effect of prolonged negotiating. While these tactics may occur, they will not be effective in this new model, as the workers themselves own what is the most valuable. In old unionism, the power stemmed from the collective ability to stop working, hurting the employer. In the new unionism, workers will coerce business into friendlier contract terms by providing increasing value, not value denial actions.

Whatever the charter, the Exchange must be ruled by its members and not investors.


Business has already started the move to a gig economy, shedding the costs of the employee with the reduced costs of either a vendor or a temporary worker. It is clear that the full time employee is on the road to extinction. So what is the American worker to do? Stand pat, or begin a dialog for a way forward? We think a summit to discuss this Work Exchange concept, even if it does not result in a decision to move forward, will begin to illuminate a positive future for the American worker in the gig economy.

Benchmarking Big Data Business Forecasting Data

The Semantic Approach to Building Accurate Project Plans

An idea.  Alone and unshared, while of great potential, worthless in the wild.  In the consumer space, ideas are tweeted, trending, memes, most popular and can even go viral.  The 140 characters of Twitter and the brief updates on Facebook, singular or compound ideas, are being used to target marketing.  In the consumer space, ideas have value.  It costs over $54 dollars (Google Adwords) to tap into the buy insurance idea.  So what about in the business enterprise?

For over 60 years with the advent of modern project management, ideas happen once, and then the work begins.  No trending, most popular, nothing like a business segment meme, or even the winning idea going viral.  We keep developing a project plan like it is a one off exercise with no recognition that the past shapes a new project’s prospects for success.  The project – one off things we do very poorly (less than 50% full success rates).  Sounding more and more like the definition of crazy.

Last year, I completed an in-depth study of ideas in the workplace.  I consciously decided not to use the idea word, as it sounds so, well, fluffy.  I use lexeme (a basic unit of meaning), a word borrowed from linguistics.  I also use lexemetry to describe the process, measuring lexemes in a context.  For me, that context is projects.

Here’s what I did.  First, I had already developed an engine that does semantic matching.  While it can look at any string, I focused on task titles for this research.  For each task, the matching engine looks for other task titles that semantically match the new task title.  New matches are added to a matchset cache.  Now, we have a subset of all tasks that match semantically.

500px-Semantic_Net The premise is the tasks that match, those that happen more than once in a project portfolio, form the cogs of what makes the day to day project assembly line operate.  These matchsets are the iterating steps of a formal process. They are multiple copies of project templates.  The matchsets are also things that everyone adds to projects based on the culture of the enterprise.  They are the crowd sourced knowledge of the way to get projects done.  Also, iterating tasks allow us to build models based on the matchsets – cost, roles, durations, efforts, movements, relationships, all the metadata of project work.  In fact, I’ve argued that the bulk of our enterprise big data is meta of work/performance.  Thus, these matchsets are actually the Higgs Bosons of enterprise big data – everything else is created from work.  As such, the matchsets can also provide insights into those tasks that don’t match, the value givers.  With models we have benchmarks.  With models we have ranges.  With models, we can even evaluate worst, bad, abnormal, normal, good and best.

While we could look at any other meta of the matchsets, I wanted to do something with time.  Yes, flexing our analytical muscles.  I wanted to show that these matchsets are not just static ideas/lexemes glued to the projects where they occurred.  They move, they materialize in different parts of a schedule.

However, time comparisons are not a simple endeavor.  Projects have different durations, and June 15th is comparable to what?  We need to normalize time across projects.  We chose percentage as our canonical form for time.  Thus, the beginning of the project becomes the 0 percentile of time, while the 100 percentile becomes the end of the project.  Now, all time is directly comparable, and all of our matchsets live in this normalized time.

We have some cogs of commerce iterating in time, so what can we tell from this?  We can find benchmarks, mine process, and even identify best practices.  But first, our data.  I randomly selected data from a few databases. I also ensured that I did not get all similar businesses or industry types.  That resulted in just shy of one million tasks in over 20,000 projects.  I found:

  1. At least 40% of tasks are iterative.  We have a benchmark.  I’d also like to say that the concept that all projects are one offs is a dead one.  Almost one out of two lexemes in your project is iterative.  Plan on it.  Use it to your advantage.  Understand your iterating ideas via measure.
  2. On average, lexemes can deviate in time by 11.52%.  We have a benchmark for a task buffer.  The critical chain folks are going nuts!  To me, this so fundamental.  We have a statistically significant finding of an empirical number that shows how difficult it is to schedule and perform work.  Any piece of work can move by around 10% of the project timeline!  Conversely, we can use this bedrock datum to better plan.  Agile, your stories now have a time scale.
  3. Look at this composite process I mined out:
Matchset Master Title Start Percentile
Develop Project Charter 4
Project Initiation Activities 5
Develop Preliminary Plan 6
Update Charter 10
Complete Charter 12
Provide Detailed Plan 13
Develop Communications Plan 23
Identify Supply Chain 27
Develop Requirements 30
Execution Phase Start 42
Development Complete 43
Deployment 69
Execution Complete 70
Deploy to Production 86
Training 86

At a very large company, I found the commonalities in the plethora of templates across many business units. Additionally, I found over 80 lexeme matchsets (in proper time sequence and proper time distance) that were being consistently added to these project plan templates – sub project assembly lines.  Lastly, their very traditional waterfall approach resulted in actual project work consistently not starting until almost half way into the project!

I believe that knowing the benchmark iteration number (40%), as well as the benchmark time deviation number (11%), project success will increase for any enterprise.  It is time the enterprise got its meme on, as lexemetry is poised to go viral.