Engineering Consumption

By Vinay Gupta — CEO, Mattereum

This is Part 2 of the Mattereum Manifesto. Be sure to read Part 1: “Introducing the Mattereum Asset Passport”, and Part 3: “Knowledge is Power”.

Our goal is to deliver a significant reduction of the environmental and social harms caused by inefficiencies in industrial capitalism, using the Ethereum blockchain.

Although I’m best known for my work in the Ethereum ecosystem since it launched in 2015 (I was the release coordinator), my prior focus had been almost entirely humanitarian and environmental. Over the last few years it took a back seat to the immediate need to help the Ethereum team establish a bridgehead — an embassy of the future — on earth today. But I’ve always been Team Buckminster Fuller, and we found the right analytical framework for Mattereum as an environmental project in work I did at the Rocky Mountain Institute (RMI) 15 years ago, long before the blockchain. It is incredibly satisfying to be able to join two parts of my life together at last: technology and environmentalism as a single cause!

I started to build Mattereum as a sort of companion to projects like uPort and SOVRIN — a digital identity layer for Ethereum, but for things instead of people. During the process of developing Mattereum, we began to understand that the process of collecting accurate data about physical objects had potentially huge environmental benefits, but it took us a while to turn that hunch into a product.

I believe that Mattereum, using blockchain smart contracts, finally has the tools to make a modest extension of how capitalism runs — a relatively gentle upgrade — to get a much, much better world very quickly and without having to sacrifice anything we want along the way.

This “lean consumption” approach will not fix everything, but it will double or triple the amount of slack we have to achieve the rest of the transformation we need. It will free up a lot of personal resources for individual people too. And it’s a relatively modest proposal.

To move forwards, we needed a clear diagnosis of the problem. So let’s do a little more homework, and get a really solid lock on this aspect of the global problem. We have to go back to examine the roots of consumerism, and the role of statistical process control in mass production systems.

The consumer consumption problem is vast and has two aspects.

  • Industrial production has been engineered to respond to demand in sub-optimal ways as we manufacture needs from nothing but hot air and advertising.
  • Economic consumption and waste management have not been upgraded to keep pace with our revolution in manufacturing prowess.

The resulting mismatch in capability between the production and consumption functions in society results in huge environmental and social harms and literally trillions in wasted or lost revenue. Mattereum’s supra-industrial approach, using blockchain and other technologies, goes a significant way to solving it.

So here is the framework for our environmental analysis. I created it with the RMI team for the Danish EPA in 2003 with UN Sustainable Development Goals funding. The resulting paper, A whole systems framework for sustainable production and consumption, is without a doubt a smorgasbord. The core drive was to go beyond the existing early noughties paradigm on the sustainable Production/Consumption interface to also include sustainable Investment at one end of the process, and a very different perspective on Waste at the other. This created an Investment, Consumption, Production, Waste system, modelled as so:

The full IPCW diagram includes a division between natural capital and financial capital, and maps out the ways that waste reduces the efficiency of all the other parts of the system, while also modeling recycling, reuse, and reclamation as a separate set of processes.

In this analysis, investment cascades through production systems, through consumption, and into waste, paying for the production of goods or delivering some services along the way. Resource inputs are financial capital — in a properly functioning system more is made than is consumed — and natural capital. The main casualty here is natural capital; most of the time the natural capital invested in production is lost, thrown away in the name of financial return. In short, into the machine you pour nature and money, and if you are lucky more money comes out. We don’t even account for the natural capital — the nature — that goes into the machine, in most cases. It just gets burned as the price of doing business.

But if we stop the great destructive, over-productive machine of civilization, mass starvation will result. We have a duty to square this circle: to deliver sustainable abundance to all of humanity, and to leave nature alone to its own devices, for the most part. In the future I want to see a self-sustaining closed-loop technosphere (hopefully in orbit… around Proxima Centauri…) and a nature largely left to run wild, which is (more or less) the most productive and environmentally-stabilizing configuration I can imagine.

The rise of consumerism

The invention of consumerism is a long, complex story. In essence Sigmund Freud’s nephew, Edward Bernays, weaponized psychoanalysis to take some of the deep-rooted human needs around identity, sexuality, and religion, and convert them into consistent, persistent, itch-you-can’t-ever-quite-scratch compulsive buying behavior. By coercively deluding us that happiness was to be found in the marketplace, the movement which Bernays came to symbolise has transformed how humans express themselves. In its thrall, we have become a little like the fabled master who loses their keys in the dark, but searches for the keys under the streetlamp “because that’s where the light is.” This process is how we wind up with obsessive shaving of grams off hiking backpacks, and limited edition baseball caps, and a million different car models with 18 cup holders each, all pouring the same poison out the back. Superficial diversity is wrapped around an overwhelming conformity, and a social obligation to consume.

We seem to have taken most of the output of this completely splendiferous industrial and design machinery, and enslaved it to produce the most unimaginably trivial paraphernalia, in gigaton profusion. This is the “horseless carriage” phase of industrial civilization. We are doing the same old thing, so much better and faster that it has become counterproductive. It’s a production system desperately in need of a better understanding of itself, in service to a people who are increasingly alienated, disoriented, and broke. We got really good at making things, but we have not become much smarter at consuming them, and this gap is breaking the world, and us along with it.

Human nature was shaped into consumer identity by people who thought they were advancing civilization by creating markets for industry to serve. Basic drives that could have found many different routes towards expression (for example: playing music, athletic achievement, or martial arts) wound up all too frequently channeled into brand-driven consumerism. Consumerism has become an enormous engine for development, but at a price we can no longer afford, economically or environmentally.

True, our civilization does consumer goods really, really well, but no consumer goods or combination of consumer goods can resolve the deep psychological impulses to which Bernays and his descendents successfully bound our shopping behavior. Psychological needs cannot ever be fully satisfied by material things. We all know this, but we all play the game (for now).

This is the trash treadmill. Every step on the trash treadmill destroys valuable natural resources to satiate human psychological needs that were fanned by advertising into the flames of consumerism.

We do not talk about it

There is a better way.

Engineering Production

Now that we’ve discussed the psychological creation of insatiable demand, let’s talk about the supply side: how are the goods produced to meet these insatiable psychological needs?

Statistical Process Control: we really do get what we measure, and we measure the wrong things.

I’m going to try and simplify several dozen books’ worth of history into two quick stories.

The first is the invention of the production line. Division of labor in an industrial context probably started with the manufacturing of pins. Daniel Foote Taylor built the machine that made pins with minimal labour. Adam Smith wrote about it. Division of labor and production lines became a huge phenomenon, truly the engine of industry, and with all that cheap coal power to be had from shallow and open cast mines, the industrial revolution was off to a good start.

Another Taylor made further improvements, but production lines were lossy and buggy for a couple of centuries. Specifically, stuff would go wrong in the machines or in the manual parts of the production lines. At the end of the line, Quality Assurance workers would test sometimes every single object, and a double digit percentage of them would go back up the line for “rework.”

Like bugs in software, somebody had to go and figure out what was wrong with the gadget, and then fix it by hand; debugging for physical production processes. This kind of analysis and labor was routine and endemic in mass production. Finished goods might be 2% defective when they were sold, and big machines were usually built from large collections of parts which were filed down by hand until the pieces fit together well enough to run.

Everything looked like integrating low-quality software packages filled with bugs, and trying to build large scale working systems from them. This is probably still going on today in some industries. Including software.

For a long time, this was the only kind of mass production we had. In this age — and remember how recent this is for people like high performance car enthusiasts — machines like boats, motorbikes, and scooters were hand-made one-offs, tweaked on the production line until they ran, their collection of imperfect parts hand-tuned until the machine worked as it was supposed to… for a little while. Then a few parts wear down from use, and you have install new parts and hand-tune everything again. Everything was in a continuous process of hand-tuning. Nothing ever just worked. This is what engineers used to do for a living: hand-tune assemblages of irregular parts into semi-stable machines, constantly fighting against parts wear, metal fatigue, and corrosion. It was hell.

Everything was hand-tuned. Even the SR71, the most advanced plane imaginable in the 1950s, was hand-made. Gigantic sheets of titanium in fifty thousand ton presses still varied enough that essentially every one of the 32 SR71s created was unique and had to be maintained individually, often with parts that were hand-made to keep that specific plane flying. There was no simple “pop the bolts out and stick in a replacement part” for these things, even though they were at the absolute height of aerospace engineering at the time.

Hand tuning was how technology worked. Doesn’t it sound a lot like modern software development, where bitrot and the upgrade cycle are our equivalent of friction and corrosion? We are still in the hand-tuning phase of software engineering.

The Age of Quality

The beginning of the end of the age of hand-tuned machines was started in Japan after WW2, when a quality control expert called W. Edwards Deming was shipped out there to help the Japanese rebuild their industry. At the time, Japanese industry was not a place to get quality production done. The industrial revolution had been resisted rather than embraced, and the distrust of foreign methods was legendary. Quite a few great ideas from the West had been absorbed by Japan, and then outright rejected. They did things their way.

But Deming’s System of Profound Knowledge and his radical ideas about workplace culture as the key to quality became firmly embedded in Japanese manufacturing culture, around the same time as the innovations of pioneers such as Soichiro Honda were taking root.

Statistical process control — measuring everything that mattered to understand the real costs of operating a system — was relentlessly applied to create controlled environments which did astonishingly well at mass manufacturing consumer goods like cameras (resulting in great brands like Nikon and Canon) and pushing forward into previously unknown territory with microcomputer manufacturing at scale. You can’t build an artefact as complex as a microcomputer at affordable prices without essentially perfect components coming into the production line — any single fault will render the machine inoperable — and microcomputers are too complicated to take broken ones and rework them into repaired machines at affordable costs. It has to be done right the first time, there is no alternative. The manufacturing network is only as strong as its least reliable manufacturer.

You can’t build the modern, massively interconnected world from shoddy, unreliable parts. Think of the internet: if routers fell over as often as 1950s motorbikes, and had to be soldered by an experienced practitioner before they would work again — in short, if they worked like radios in the age of valve amps — the internet simply would not function. The odds of getting an intercontinental connection with not a single defective machine on the critical path would be approximately zero. The network as a whole would be too expensive to maintain. This is why manufacturing complex artefacts used to be impossible. Something would always be broken.

Statistical Process Control is the steel spine that Deming built on. SPC figured out that people designing production systems have to be smart about what they measure, or the system will just produce what they measure, and they are measuring the wrong thing.

On this foundation, Deming figured out how to get people to understand and most importantly to accept the truths that SPC revealed: how to build a culture of truth inside of an organization so that they could learn from the statistical observations rather than burying them inside a feudal hierarchy rooted in information control and obscurantism to conceal trade secrets from the workers.

Deming’s Key Principles

  • Accountability came from statistical process control.
  • Transparency came from Deming’s emphasis on a culture of openness and clear communication.
  • Trust came from the non-violent, non-destructive correction of systematic errors, leading to goods and services that consumers could trust, because the people working together inside of organizations to produce those goods and services could trust each-other.

These are Mattereum’s values. We hope we have learned from the best.

This is how our civilization was made and is maintained. It was made by teams of people who learned to trust each other enough to admit what is wrong, to correct systemic problems, and to work together in enormous numbers and at vast, unbelievable scale, to manufacture the complex artefacts required to run the operating system of modernity. Without the social transformation, statistical process control alone does not deliver the desired results.

If we could figure out how to get more effective cooperation spanning all aspects of the supply chain — just as we did for all stations on a production line — what might be possible? Why do investment, production, consumption, and waste have to be so split up across organizational boundaries without effective information sharing arrangements? Why can’t we share information all the way down the value network associated with an object, rather than just along the supply chain it was manufactured on? When I buy something, why isn’t all the information about the object — right down to the CAD files it was cut from — transferred to me as a standard consumer right?

This whole process, this machine, the modern industrial supply chain is without a doubt the pinnacle of human civilization, the rich technical mycelium which gave rise to the moonshot and the curing of polio, the green revolution and everything else. It is everything from tractors to GPS satellites. It is the closest thing we have to a replicator. But in its current form it no longer serves our interests well.

So what went wrong?

Except for a very lucky few, most of us feel like things slipped off the rails somewhere in the past. Something went wrong with our culture. Exactly when, and how, and what went wrong depends on your point of view. The diagram which shows worker wages stop rising in line with worker productivity from 1971 is the thing I show people when I want to talk about “what went wrong”, but it’s only a symptom. Others might point to the Treaty of Versailles, or the ban on personal ownership of gold in America in 1933, or any one of fifty dozen points in history. But, Sometimes it seems like it does not matter how many good seeds we sow, what comes out of the ground is spears.

https://wtfhappenedin1971.com/

We have to negotiate with reality in a new way to get traction on the situation.

The mechanisms we have been using to get good outcomes are not working any more: the mechanisms we expect to help the system self-correct all seem to point the wrong way at the wrong time, and the levers of change appear to be connected to the 8-track player in the dashboard, not to the wheels. We can change the music, but we can’t seem to change the direction. We are skidding.

Big Tech was supposed to help us with this, but it wound up largely as an outgrowth of the surveillance state. The blockchain was meant to fix this, but that political ambition has been almost entirely diluted out by the dreams of taking over the financial services industry (which might be a worthy short term goal, but let us not confuse it with saving the world). And saving the world is table stakes these days, as you might have noticed in the news.

https://www.gq-magazine.co.uk/men-of-the-year/article/greta-thunberg-interview

We manufactured the wrong things

Capitalism is pretty dumb. Price signalling is a very limited stream of data between buyers and sellers, and learning in capitalism is notoriously slow. If I see a bike on sale for $400 and buy it, is that because I’m desperate for a bike RIGHT NOW, or because I look at the price, say “well, that’s a pretty good deal”, and then get rid of my perfectly functional old one? Would I have paid $500 for a model with a slightly better seat and a better color than grey? Would I have paid $3000 for a carbon fibre model, were one available?

If all we’ve got is price signalling, the only way to move forward is to manufacture all the alternatives, advertise them widely and see what people buy. This is an evolutionary “bloom and prune” approach, and fields like market research which attempt to divine or anticipate buyers’ needs are often very inaccurate because people’s self-reporting about what they want is often very inaccurate. Just asking people what they want doesn’t cut it either. It is hard to fine-tune capitalism to manufacture what people want, and it is even harder when the advertising loop starts to take control of the process, not just telling people what is available, but actively trying to make them want things they did not previously want. Do you really want this thing, or do you want this thing because we made you want it? If we created the demand we are serving, are we helping anybody at all by meeting these imaginary needs?

That vortex, that infinite regress, has distorted the feedback systems inside of capitalism to the point where nobody knows what they want any more in any kind of solid, consistent, clear way. It’s created a huge and poisonous semantic fog which has taken away our ability to know ourselves, because the human mind was not made to reason clearly when fed 5000 ads per day. We evolved in a relatively slow moving, information poor environment without the written word. In a fast moving environment, dominated by marketing messages and skillfully composed advertising copy, and images produced by some of the most technically competent artists in the world, is it a wonder that we can’t think straight about what we want?

In fact, ironically, the only place statistical process control is applied to consumer behavior is targeted advertising, in which some of the best minds of our generation collude to gather vast portfolios of data about our personal lives, and use it to try and drive buying behavior without any fundamental model of people’s needs or wants, only their expressed preferences.

Is it a wonder that we’ve soaked up the entire capacity of the wish fulfilling tree of industrial mass production making fashionable junk that nobody needs, and still can’t seem to find a way to get everybody access to the drugs they need to stay alive, even basics like antidepressants or insulin?

We are squandering this plenty that statistical process control and quality control gave us, and it is wasting our lives, and killing the world.

Slow progress on optimizing the waste streams

Massive process engineering work has been done on the Production function of our society, over centuries. We have a name for it: the Industrial Revolution. Because it was a revolution. Quality control was also a revolution, but a quieter one.

In finance, something similar happened. The investment function we discussed earlier also absorbed statistical concepts to manage how money moves around. Over time this became known as Quantitative Finance, and started to hoover up a disproportionate and frightening number of the brightest minds in physics and math. Enormous efforts have gone into building these systems, and they are uniformly amazing. They’re competing head to head, so there are winners and losers, but the actual quality of the work in quantitative finance is phenomenal.

But what about the consumption and waste functions?

Start with waste. Has landfill gotten radically brighter and more efficient over the past 40 years? Maybe a little. But compare it to what has happened in manufacturing over the same period, and essentially our waste management is unchanged. There’s a lot of talk about recycling, and bins everywhere, but the actual reuse of that material in ways which prevent further raw materials being pulled out of the ground is a lot more complicated than people hoped when the recycling movement got started. It’s too early to call post-consumer recycling a failure, but all too often it just means dumping in poorer countries.

Recyclers simply do not have the tools or resources to combat the sheer scale and complexity of the waste. Although we have lots of incremental progress on pulling value out of industrial and post-consumer waste, are these systems really massively more efficient than what we had before? Do we measure, weed out variation, and make maps? Only here and there, and only in certain industries. Steel is pretty well recycled, plastic not so much.

But compared to the sophistication of the industrial processes that produced the plastic bottles we are throwing away, the recycling side is standing still in comparison.

Targeted advertising isn’t much help on consumption

Consumption is hardly better. Yes, there has been progress, but most of that progress is on selling people things they don’t need, ever more efficiently. The level of consumption has outstripped all imaginable process improvements in making that consumption efficient. Let’s talk about some of the measures taken, and the impact they have (not) had.

So the first question is how do we know what we want. You know the general theme: your entire click stream is used to model who you are, so that advertisers can compete at auction to show you signals to control your behavior. And because we are not evolved to deal with these kinds of cognitive attacks (yet!) they are partially effective, enough to pay for progress and improvement in the fundamental techniques. They are still getting better at this.

And let’s not forget, targeted advertising is something like 90% of the profit at Google and Facebook, and also a contributor to Amazon’s revenue stream. How do they know what to recommend to you? A huge part of the economy of the internet is using statistical methods to understand and change consumption patterns, but in the crudest and least-effective possible way. “You get what you measure” remains the dominant fact of life, and measuring click streams and credit card purchases only measures one step of a four step process.

We have optimized only half way through investment, production, consumption, and waste, and no further. We should not be surprised that there are problems. We should be measuring consumer satisfaction and environmental impact, too. Then we would get a better world.

But, ugly and adversarial as the advertising attention-parasitism game is, and dangerous as these information dossiers on us all are, it is still an attempt to apply statistical process control to the consumption system, and it is happening on a truly enormous scale. It takes exactly the same kind of reasoning which was used to make the manufacturing system efficient, and applies it to the consumption system, it just doesn’t push far enough into the consumption system to measure “consumer satisfaction per unit of environmental impact” which is really the sort of thing we ought to be using all this fabulous machinery to measure. And it does not push into the waste system at all. Odious as it is, it may turn out to be the right approach, just applied with too-shallow insight into the problem domain.

The targeted advertising system does what it does with a very partial model of the system it is trying to optimise, and only the most crude and short-term definition of its goals (evolutionary in the worst possible way, non-cognitive at the lowest level). But it does sell product, and it’s paying for the creation of enormous datasets about people and what we think they might want. It just doesn’t ask those all important questions: “Are you still using that thing you bought? Do you like it?” Nor does it know what the thing is made of.

Of course the problem is that it measures the wrong thing: measure spending, get spending. Measure satisfaction, measure progress towards our stated life goals, and maybe get those things instead. Price in environmental damage, and get another thing again, a green economy. The problem is that spending is irrational, and behavioral economics factors thwart the eudaemonic potential to turn large scale datasets about people into the common welfare. We have made computers just smart enough to feed on us like attention parasites, but not smart enough to be good and faithful companions like dogs or horses. I cannot yet meaningfully say to Google “find me a good book to read, but make it a little outside of my norm, the last few suggestions have been a trifle timid” and get any useful intelligible response, but the damn thing won’t stop showing me adverts for books it wants me to read. The stupid system is incapable of partnership, it only knows how to hustle and distract us. We have not gone far enough to get positive results, only negative ones. The system is building momentum, but it is still below the threshold of revolutionary change.

This ad targeting system is a relic: it’s 1960s Mainframe era ideas about production and consumption, running in the 21st century.

Let’s look at other areas where we have large-scale successful attempts to use statistical process control to optimise consumption. Uber tries to put cars where it anticipates there will be riders. It uses price signalling to encourage drivers to turn up during periods when it expects things to be busy. Uber plays the game. Still they seem to have wound up in the same trap as (for example) Apple hardware manufacturing: a truly great service for some people, at the expense of the labour rights of others. The algorithms optimized resource allocation in the pursuit of everyday low prices (or good quality goods which defy our expectations about what phones and tablets can be like, year after year after year). But these systems still wind up treating human beings like machines at every level, and this trend has to be identified and banished before we wind up in Marshall Brain’s vision of dystopia.

Amazon is optimizing what is in the warehouses closest to you, based on the ability to get most of what you order to you same day or next day. This is a fantastic example of using statistical process control to optimize outcomes and serve people’s needs: whatever it is you bought, it’s more valuable to you if it arrives quickly. This is an unmitigated good, again extracted at a very heavy social cost: Amazon warehouses really have picked up the reputation of treating people like a cheaper version of machines. Amazon would certainly automate all the way, if they could.

And that’s basically it for using statistical process control and quality control to optimise consumption. We haven’t made any strides on the scale of the industrial revolution in the consumption system. Not even close. We need this to happen.

Production is already pretty lean, at least for many of our bigger systems. But making consumption lean? The work has not even begun yet.

EAP: Enterprise Resource Planning for the People

How might we begin to make consumption processes as efficient as production processes?

Consider the quantity of stuff we buy in the course of our lives for what amounts to experimenting with our identity — goods we purchase to understand ourselves better, or to foment personal growth. We may want to take up the guitar, try your hand at fly fishing, or start learning Tae Kwon Do. As beginners, we often have no idea what the best kit is to start off. So we end up spending large amounts of time tracking down reviews and recommendations, many of which are contradictory. We wind up either getting the most expensive equipment presuming that price equals quality, or the cheapest with the best overall reviews, knowing the goods will have to be upgraded it if the hobby sticks and becomes part of our identity over the long run. All we want to do the experiment. What we don’t want is to be left lugging around the gear if we don’t like the results.

This process of experimenting with identity is entirely natural. But make no mistake, this process leaves residue: each of us has that stash of stuff sitting unused in closets, attics, and garages; the remnants of hobbies and identities which didn’t quite fit, and were set aside. We may manage to move some of it along by selling it online or at a yard sale, but for the most part it all just sits there. Old clothes. Ice skates. A helmet or two. Silk paints. Did I ever really wear this much tweed? Apparently so.

The transaction costs of getting rid of this stuff are too large for us to take action: the stress of making the decision to sacrifice a slab of our investment and just sell the damn clutter, the time it takes to list on an auction site, the physical logistics of posting it to a new owner, and managing the customer service overheads involved in the entire process often leave the goods (and therefore the capital they represent) stranded.

The other approach, just writing them off and throwing them in the dump, seems wrong — both a waste of money, and of the materials embedded in the objects. So instead of doing the rational thing and getting rid of it, we wind up using nearly every square inch of the Boomer generation’s basements, attics, and closets as a sort of informally specified, unsearchable, distributed warehousing solution as the massive superabundant flow of goods from our hyper-optimised production system hits the analogue slackness of our consumption systems, and simply pools in a huge lake of underutilized or obsolete things. There are tens of millions of metric tons of this kind of waste in America, and it all has value — if only we can find it.

The production systems of the world run on Enterprise Resource Planning (ERP) systems, of which SAP and Oracle are probably the two best-known examples. Similar systems exist in the world of finance to manage capital inside of banks, and to allocate resources in private equity firms. This is the software which runs civilization’s arteries and veins, its digestive system and its lungs. It’s the nervous system of industrial capitalism, and without it, we would almost all be destitute.

But these systems are corporate, intimately tied to the investment and production phases of society, but only very weakly tied to consumption and waste management. They are, essentially, direct descendants of the mainframe paradigm: one big computer that rules the whole organization.

And these systems interoperate only with great reluctance; the world is not run by a big, interwoven, interoperable mesh of big ERP systems seamlessly talking to each-other to make optimal decisions. It’s all still largely stuck in the mainframe phase, on arcane standards that are impossible to parse, and worse to debug. In short, these systems are due for an upgrade.

What we need is ERP for the People.

We need smaller, more flexible software systems to help individuals manage the same kinds of tasks that ERP systems handle: physical assets, time, money, commitments and more, as integrated systems. We would all really benefit from having tools that bring the power of knowing what you’ve got, where you have it, what you paid for it, and what it’s worth to somebody else right now. Imagine how much it would change if it was all at our fingertips in a series of dapps which help us optimize our personal relationship with matter itself, mediated by the marketplaces we all participate in, plus new marketplaces for information about the quality, provenance and value of physical objects.

Our working title for this model is Effective Abundance Platforms: platforms which help us manage our relationship to the abundance that industrial capitalism produces, while optimising the hell out of the inefficient capital allocation mechanisms which are represented by error-prone purchasing and reselling behavior among consumers. It’s clean, it’s green, and we think, with Mattereum in the lead, it could be extremely profitable as a new class of businesses.

The global challenges posed by climate change and resource scarcity are driven by many factors including imprecise capital allocation, a financial system with poorly defined boundaries, dependence on polluting energy, and outdated methods of industrial production. Mattereum is working to solve these problems by creating digital twins of material objects and using blockchain smart contracts to automate all aspects of how material things are traded, owned, and combined. We aim to squeeze out these systemic inefficiencies and more accurately allocate capital to activities which promote wellbeing.

Our universal naming system for physical objects enables the creation of efficient markets for information about the composition and qualities of physical things. This accurate information will let society use the same statistical process control techniques which revolutionized manufacturing and investment over the past two centuries to completely transform consumption and waste management globally.

Over the last few decades, the revolution in production efficiency has changed the world. We know a similar revolution in consumption efficiency will follow suit, if we set out on the right path forward now.

And we need to squeeze every last grain of efficiency that we can out of the global economy, because people are still hungry, and structural waste on a finite planet is the enemy of everything that lives. If the internet has a purpose, if the blockchain has a purpose, this has to be part of it: we aren’t just fighting against authoritarianism, we are also fighting against entropy.

Food rotting in the back of the warehouses does not have to happen. We just need efficient systems to connect hunger to food, and at least half of that problem is just bad software which harms the sellers as much as the hungry buyers.

We are all on the same side against waste, and bad software. It’s all of us, against entropy.

Be sure to read the other two instalments of the Mattereum Manifesto: “Knowledge is Power” and “Introducing the Mattereum Asset Passport”.

If you would like read more about Mattereum’s near-term plans, please read about our upcoming project with William Shatner to do authentication and provenance for the collectibles market.

Thank you for reading, and we hope you enjoyed the ride! Want to know more? Here are our whitepaper and our Telegram group.

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James Hester

James Hester

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Curator, antiquary, and specialist in arms & armour, historical combat, and martial philosophy. http://patreon.com/schoolofmars for my work.