There is a war going on. Humanity and nature are on one side and Big Tech is on the other. The two sides are not opposed. They are orthogonal. The human side is horizontal and the Big Tech side is vertical.*

The human side is personal, social, self-governed, heterarchical, open, and grounded in the physical world. Its model is nature, and the cooperative contexts in which competition, creation, and destruction happen in the natural world.

The Big Tech side is corporate, industrial, hierarchical, competitive, mechanistic, extractive, and closed, even though it produces many positive-sum products and services that are good for people and good for nature. It is also, being competitive and rewarding toward winner-take-most outcomes, dominated by giants.

This war has been fought over many other things in the past, especially in tech. But AI is the big one right now—and perhaps the biggest one of all time.

Over the long run, both sides will win, because we need the best of what both bring to the world’s big round table. In the past, this has happened in countless markets, countries, polities, societies, and other contexts. In tech it happened with the conflict between PCs and mainframes, between the open and public Internet and closed private networks, and between open operating systems and closed ones. None of those conflicts have final outcomes, but they have arrived at stable and cooperative states. They arrived at those states because everyone recognized that people can do more with their own tools than those big entities can do on theirs, or by using people as extracted resources.

This conflict is perhaps best represented by the ironies that have been at work within OpenAI and Sam Altman, its prior and current CEO. We can also see it with sparkling clarity in something amazing that Sam said near the close of his keynote at the first OpenAI Developer Conference, in November of last year:

Lets unpack that.

Individual empowerment means exactly what it says. Agency is the ability to take action or to choose what action to take (Cambridge Dictionary). Scale means growth toward ubiquity.

You have individual empowerment and agency when you ride your bike, drive your car, mow your lawn, or button your shirt. You also have it when you work on your laptop, phone, and other smart rectangles. Scale means your power and agency will grow to the max while the same will happen for nearly everyone else. Scale for you alone won’t cut it. When most of us have it, that’s scale.

Technologies extend us. They enlarge our capacities in the world. Intelligence is one of those capacities. So is memory. Your rectangles help a lot with both. That’s why those have already scaled to ubiquity, or close enough.

AI will do the same, but only if it’s personal. Not if it’s  just “personalized.”

As Jamie Smith made clear here a year ago, “your personal AI isn’t really personal.”

ChatGPT4 doesn’t know that yet. I can tell because I just asked:

Me:
What is “personal AI”?

ChatGPT4:
“Personal AI” refers to artificial intelligence systems that are designed to serve individual users through personalized interactions and services. These AI systems learn from the specific preferences, behaviors, and needs of their users to offer more tailored assistance, recommendations, or information. The goal of personal AI is to enhance user experience by adapting to unique personal profiles.

There are various applications of personal AI, including:

1. Virtual Assistants: Like Siri, Google Assistant, and others, these AIs help with daily tasks such as scheduling, reminders, searching information, and controlling smart home devices, all customized to the user’s habits and preferences.
2. Recommendation Systems: Used in streaming services like Netflix or shopping platforms like Amazon, these AI systems analyze your past behavior to recommend movies, shows, or products you might like.
3. Healthcare: Personal AI can track individual health metrics and provide personalized advice, monitor conditions, or even alert medical professionals if needed.
4. Learning and Development: In education, personal AI can adapt learning materials to the pace and learning style of the student, offering a customized educational experience.
5. Gaming: AI in gaming can adapt to provide a challenge tailored to the player’s skill level or alter the game narrative based on choices made by the player.

The development of personal AI focuses on enhancing user engagement by creating more intuitive and responsive interactions that feel uniquely suited to each individual.

For a second opinion, I asked Google’s Gemini the same question.

Me:
What is “personal AI”?

Gemini:
Personal AI refers to artificial intelligence designed to assist and interact with a specific individual. It’s essentially your own AI companion that learns about you and tailors its services to your needs. Here are some key aspects of personal AI:

1. Personalization: Unlike general-purpose AI assistants, personal AI focuses on you. It learns from your interactions, preferences, and habits to become more helpful over time.
2. Focus on Memory and Knowledge: Some personal AI systems aim to become an extension of your mind. They can store your memories, knowledge, and experiences, making them readily accessible and even usable to help with tasks like writing or communication.
3. Varied Applications: Personal AI can have a wide range of applications. Imagine an AI that helps you:
4. Organize your life: Manage schedules, appointments, and reminders.
5. Boost productivity: Assist with tasks, research, and communication.
6. Enhance creativity: Help you brainstorm ideas or generate creative content.
7. Improve learning: Personalize learning experiences and provide targeted information.

Privacy Concerns: Storing personal data on an AI raises concerns about privacy and security.

Overall, personal AI holds promise for a future where technology seamlessly integrates with our lives, offering personalized assistance and support.

To both companies, personal AI is a personalized service—from them. It’s not something you own and control. It’s not about individual empowerment and agency. It doesn’t extend you. It’s hired prosthesis: an extension of them rather than of you.

Let’s name the category: AIaaS—AI as a Service.

But hats off. OpenAI and Gemini do amazing jobs. So do lots of other AI services by wannabe giants. I use AI every day to improve what I write, to generate and fix images, to answer questions that old-fashioned search engines can’t answer or suck at. I even pay extra for some of it (such as ChatGPT4 and Adobe’s Creative Suite). And it seems they all get better, all the time, at everything. Won’t personalization be good enough, eventually?

No. Because they aren’t you. They also aren’t yours, so they can’t extend you. They can seem to. But they don’t. They also don’t have access to most of your private life. Nor should they.

But your private life could use some AI help. For example—

• Schedules, including on your calendars, past and future
• Health data, including all your medical reports, prescriptions, appointments, insurance information, past and present providers, plus what your watch, phone, and other devices record about you
• Financial records, including bills, receipts, taxes, and anything called an account that involves money
• Travel, including all the movements your phone (and phone company), watch, and car record about where you go and where you’ve been
• Work—past and present, including whatever HR systems know or knew about you
• Contacts—all the people, businesses, and other entities you know
• Business relationships, with brokers, retailers, service providers, whatever
• Subscriptions, including all those “just $1 for the first four weeks” offers you’ve accepted, plus other forms of screwage that are stock-in-trade for companies selling subscription systems to businesses.
• Property, including all the stuff on your shelves, floors, closets, garages, and storage spaces—plus your stocks and real estate.

It’s not easy to visualize what a personal AI might do for those, but let’s try. Here’s how Microsoft’s Copilot (or whatever it’s called this week) did it for me before I got rid of all its misspellings and added my own hunks of text:

All that stuff is data. But most of it is scattered between apps and clouds belonging to Apple, Google, Microsoft, Amazon, Meta, phone companies, cable companies, car makers, health care systems, insurance companies, banks, credit card companies, retailers, and other systems that are not yours. And most of them also think that data is theirs and not yours.

To collect and manage all that stuff, you need tools that don’t yet exist: tools that are yours and not theirs. We could hardly begin to imagine those tools before AI came along. Now we can.

For example, you should be able to take a picture of the books on your shelves and have a complete record of what those books are and where you got them. You’ll know where you got them because you have a complete history of what you bought, where and from whom. You should be able to point your camera in your closets, at the rugs on your floors, at your furniture, at the VIN number of your car that’s visible under your windshield, at your appliances and plumbing fixtures, and have your AI tell you what those are, or at least make far more educated guesses than you can make on your own.

Yes, your AI should be able to tap into external databases and AI systems for help, but without divulging identity information or other private data. Those services should be dependent variables, not independent ones. For full individual empowerment and agency, you need to be independent. So does everyone else with personal AI.

Now imagine having a scanner that you can feed every bill, every receipt, every subscription renewal notice, and have AI software that tells you what’s what with each of them, and sorts records into the places they belong.

Ever notice that the Amazon line items on your credit card bill not only aren’t itemized, but don’t match Amazon’s online record of what you ordered? Your personal AI can sort that out. It can help say which are business and personal expenses, which are suspicious in some way, what doesn’t add up, and much more.

Your personal AI should be able to answer questions like, How many times have I had lunch at this place? Who was I with? When was it we drove to see so-and-so in Wisconsin? What route did we take? What was that one car we rented that we actually liked?

Way back in 1995, when our family first got on the Internet over dial-up, using the first graphical browsers on our PC, and e-commerce began to take off with Amazon, eBay, and other online retailers, my wife asked an essential question: Why can’t I have my own shopping cart that I take from site to site?

Twenty-nine years later, we still don’t have the answer, because every retailer wants you to use its own. And we’re stuck in that system. It’s the same system that has us separately consenting to what sites ironically call “your privacy choices.” And aren’t.

There are countless nice things we can’t have in the digital world today because we aren’t people there. We are accounts. And we are reduced to accounts by every entity that requires a login and password.

This system is a legacy of client-server, a euphemism for slave-master. We might also call it calf-cow, because that’s how we relate to businesses with which we have accounts. And that model is leveraged on the Web like this:

We go to sites for the milk of content and free cookies, whether we want them or not. We are also just “users.”

In the client-server world, servers get scale. Clients have no more scale than what each account—each cow—separately allows. Sure, users get lots of benefits, but scale across many cows is not one of them. And no, “login with Google” and “login with Facebook” are just passes that let calves of ruling cows wander into vassal pastures.

For individual empowerment and scale to happen, we need to be self-sovereign and independent. Personal AI can give that to us. It can do that by solving problems such as the ones listed above, and by working as agents that represent us as human beings—rather than mere users—when we engage with Big Tech’s cows.

This will be a fight at first, because the cows think they run all of nature and not just their own farms. And $trillions are being invested in the same old cattle industry, with AI painted all over the new barns. Comparatively speaking, close to nothing is going toward giving independent and self-sovereign individuals the kind of power and scale Sam Altman says he wants to give us but can’t because he’s on the big cow side of this thing.

So where do we start?

First, with open source code and open standards. We have some already. Llama 3, from Meta AI, is “your own intelligent assistant,” and positions Meta as a more open and user-friendly cow than OpenAI. Meta is still on the top-down Big Tech side of the war we’re in. But hell, we can use what they’ve got. So let’s play with it.

Here on the ground there are all these (with quotage lifted from their sites or reviews such as this one)—

• MindsDB: “an open-source AutoML framework”
• Alt.ai: “It’s an A.I. which aims to digitize users’ intentions and place it on the cloud to let our clones deal with all digital operations.”
• Keras: “a multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch”
• PyTorch: “Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration, and Deep neural networks built on a tape-based autograd system
• Tensor Flow: “open-source framework for machine learning”
• CoreNet: a deep neural network toolkit for small and large-scale models, from Apple
• Haystack: an “open source Python framework by deepset for building custom apps with large language models (LLMs).”
• Image Super-Resolution (ISR): “(an) open source tool employs a machine learning model that you can train to guess at the details in a low-resolution image:
• Blender: “A rich interface and numerous plugins make it possible to create complex motion graphics or cinematic vistas”
• DeepFaceLab: “open source deepfake technology that runs on Python”
• tflearn: “an advanced deep learning library”
• PYTensor: “a Python library that allows you to define, optimize/rewrite, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.” (Was Theano)
• LM Studio: “Discover, download, and run local LLMs”
• HuggingFace Transformers: “a popular open-source library for Natural Language Processing (NLP) tasks”
• Fast.ai: “a library for working with deep learning tasks”
• OpenCV: “a popular Computer Vision and Image Processing library developed by Intel”
• Detectron2: “a next-generation library that provides advanced detection and segmentation algorithm” and “a PyTorch-based modular object detection library”
• Ivy.ai: “an open-source deep learning library in Python focusing on research and development”
• OpenAssistant: “a project aimed at giving everyone access to a great chat-based large language model”
• PaddleNLP: “a popular open source NLP library that you can use to glean search sentiment and flag important entities”
• Delphi.AI: “Clone yourself. Build the digital version of you to scale your expertise and availability, infinitely.”
• Fauxpilot: “This is an attempt to build a locally hosted alternative to GitHub Copilot. It uses the SalesForce CodeGen models inside NVIDIA’s Triton Inference Server with the FasterTransformer backend.”
• Ray: “An open source framework to build and scale your ML and Python applications easily”
• Solid: “Solid is a specification that lets individuals and groups store their data securely in decentralized data stores called Pods. Pods are like secure web servers for data. When data is stored in a Pod, its owners control which people and applications can access it.”
• Sagen.ai: “Your very own AI Personal Assistant to manage your digital life.”
• YOLOv7: “is one of the fastest and most accurate open source object detection tools. Just provide the tool with a collection of images full of objects and see what happens next.”

—and lots of others that readers can tell me about. Do that and I will add links later. This is a work in progress.

Below all of those we still need something Linux-like that will become the open base on which lots of other stuff runs. The closest I’ve seen so far is pAI-OS, by Kwaai.ai, a nonprofit I now serve as Chief Intention Officer. I got recruited by Reza Rassool, Kwaai’s founder and chair, because he believes personal AI is required to make The Intention Economy finally happen. So that was a hard offer to refuse. Kwaai also has a large, growing, and active community, which I believe is necessary, cool, and very encouraging.

As with most (maybe all) of the projects listed above, Kwaai is a grass-roots effort by human beings on the natural, human, and horizontal side of a battle with giants who would rather give us personalized AI than have us meet them in a middle to which we will bring personal AI powers of our own. In the long run, we will meet in that middle, because personal AI will be better for everyone than personalized AI alone.

Watch us prove it. Better yet, join the effort.

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*I am indebted to Lavonne Reimer for introducing and co-thinking the horizontal vs. vertical frame, and look forward eagerly to her own writings and lecturings on the topic.

This post is for the benefit of anyone wondering about, researching, or going into business on the proposition that selling one’s own personal data is a good idea. Here are some of my learnings from having studied this proposition myself for the last twenty years or more.

1. The business does exist. See eleven companies in Markets for personal data listed among many other VRM-ish businesses on the ProjectVRM wiki.
2. The business category harvesting the most personal data is adtech (aka ad tech and “programmatic”) advertising, which is the surveillance-based side of the advertising business. It is at the heart of what Shoshana Zuboff calls surveillance capitalism, and is now most of what advertising has become online. It’s roughly a trillion-dollar business. It is also nothing like advertising of the Mad Men kind. (Credit where due: old-fashioned advertising, aimed at whole populations, gave us nearly all the brand names known to the world). As I put it in Separating Advertising’s Wheat and Chaff, Madison Avenue fell asleep, direct response marketing ate its brain, and it woke up as an alien replica of itself.
3. Adtech pays nothing to people for their data or data about them. Not personally. Google may pay carriers for traffic data harvested from phones, and corporate customers of auctioned personal data may pay publishers for moments in which ads can be placed in front of tracked individuals’ ears or eyeballs. Still, none of that money has ever gone to individuals for any reason, including compensation for the insults and inconveniences the system requires. So there is little if any existing infrastructure on which paying people for personal data can be scaffolded up. Nor are there any policy motivations. In fact,
4. Regulations have done nothing to slow down the juggernaut of growth in the adtech industry. For Google, Facebook, and other adtech giants, paying huge fines for violations (of the GDPR, the CCPA, the DMA, or whatever) is just the cost of doing business. The GDPR compliance services business is also in the multi-$billion range, and growing fast. In fact,
5. Regulations have made the experience of using the Web worse for everyone. Thank the GDPR for all the consent notices subtracting value from every website you visit while adding cognitive overhead and other costs to site visitors and operators. In nearly every case, these notices are ways for site operators to obey the letter of the GDPR while violating its spirit. And, although all these agreements are contracts, you have no record of what you’ve agreed to. So they are worse than worthless.
6. Tracking people without their clear and conscious invitation or a court order is wrong on its face. Period. Full stop. That tracking is The Way Things Are Done online does not make it right, any more than driving drunk or smoking in crowded elevators was just fine in the 1950s. When the Digital Age matures, decades from now, we will look back on our current time as one thick with extreme moral compromises that were finally corrected after the downsides became clear and more ethically sound technologies and economies came along. One of those corrections will be increasing personal agency rather than just corporate capacities. In fact,
7. Increasing personal independence and agency will be good for markets, because free customers are more valuable than captive ones. Having ways to gather, keep, and make use of personal data is an essential first step toward that goal. We have made very little progress in that direction so far. (Yes, there are lots of good projects listed here, but there we still a long way to go.)
8. Businesses being “user-centric” will do nothing to increase customers’ value to themselves and the marketplace. First, as long as we remain mere “users” of others’ systems, we will be in a subordinate and dependent role. While there are lots of things we can do in that role, we will be able to do far more if we are free and independent agents. Because of that,
9. We need technologies that create and increase personal independence and agency. Personal data stores (aka warehouses, vaults, clouds, life management platforms, lockers, and pods) are one step toward doing that. Many have been around for a long time: ProjectVRM currently lists thirty-three under the Personal Data Stores heading. Some have been there a long time. The problem with all of them is that they are still too focused on what people do as social beings in the Web 2.0 world, rather than on what they can do for themselves, both to become more well-adjusted human beings and more valuable customers in the marketplace. For that,
10. It will help to have independent personal AIs. These are AI systems that work for us, exclusively. None exist yet. When they do, they  will help us manage the personal data that fully matters:
    • Contacts—records and relationships
    • Calendars—where we’ve been, what we’ve done, with whom, where, and when
    • Health records and relationships with providers, going back all the way
    • Financial records and relationships, including past and present obligations
    • Property we have and where it is, including all the small stuff
    • Shopping—what we’ve bought, plan to buy, or might be thinking about,
    • Subscriptions—what we’re paying for, when they end or renew, what kind of deal we’re locked into, and what better ones might be out there.
    • Travel—Where we’ve been, what we’ve done, with whom, and when

Personal AIs are today where personal computers were fifty years ago. Nearly all the AI news today is about modern mainframe businesses: giants with massive data centers churning away on ingested data of all kinds. But some of these models are open sourced and can be made available to any of us for our own purposes, such as dealing with the abundance of data in our own lives that is mostly out of control. Some of it has never been digitized. With AI help it could be.

I’m in a time crunch right now. So, if you’re with me this far, read We can do better than selling our data, which I wrote in 2018 and remains as valid as ever. Or dig The Intention Economy: When Customers Take Charge (Harvard Business Review Press, 2012), which Tim Berners Lee says inspired Solid. I’m thinking about following it up. If you’re interested in seeing that happen, let me know.

Most AI news is about what the giants (OpenAI/Microsoft, Meta, Google/Apple, Amazon, Adobe, Nvidia) are doing (seven $trillion, anyone?), or what AI is doing for business (all of Forbes’ AI 50). Against all that, personal AI appears to be about where personal computing was in 1974: no longer an oxymoron but discussed more than delivered.

For evidence, look up “personal AI.” All the results will be about business (see here and here) or “assistants” that are just suction cups on the tentacles of giants (Siri, Google Assistant, Alexa, Bixby), or wannabes that do the same kind of thing (Lindy, Hound, DataBot).

There may be others, but three exceptions I know are Kin, Personal AI and Pi.

Personal AI is finding its most promoted early uses on the side of business more than the side of customers. Zapier, for example, explains that Personal AI “can be used as a productivity or business tool.”

Kin and Pi are personal assistants that help you with your life by surveilling your activities for your own benefit. I’ve signed up for both, but have only experienced Pit,” or “just vent,” when I ask it to help me with the stuff outlined in (and under) the AI-generated image above, it wants to hook me up with a bunch of siloed platforms that cost money, or to do geeky things (PostgreSQL, MongoDB, Python on my own computer. Provisional conclusion: Pi means well, but the tools aren’t there yet. [Later… Looks like it’s going to morph into some kind of B2B thing, or be abandoned outright, now that Inflection AI’s CEO, Mustafa Suleyman is gone to Microsoft. Hmm… will Microsoft do what we’d like in this space?]

Open source approaches are out there: OpenDAN, Khoj, Kwaai , and Llama are four, and I know at least one will be at VRM Day and IIW.

So, since personal AI may finally be what pushes VRM into becoming a Real Thing, we’ll make it the focus of our next VRM Day.

As always, VRM Day will precede IIW in the same location: the Boole Room of the Computer History Museum in Mountain View, just off Highway 101 in the heart of Silicon Valley. It’ll be on Monday, 15 April, and start at 9am. There’s a Starbucks across the street and ample parking because the museum is officially closed on Mondays, but the door is open. We lunch outdoors (it’s always clear) at the sports bar on the other corner.

Registration is open now at this Eventbrite link:

https://vrmday2024a.eventbrite.com

You can also just show up, but registering gives us a rough headcount, which is helpful for bringing in the right number of chairs and stuff like that.

See you there!

 

I’m in a discussion of business constituencies. On the list (sourced from the writings of Doug Shapiro) are investors, employees, suppliers, customers, and regulators.

The first three are aware of their membership, but the last two? Not so sure.

Since ProjectVRM works for customers, let’s spin the question around. Do customers have a business constituency? If so, businesses are members by the customer’s grace. She can favor, ignore, or more deeply engage with any of those businesses at her pleasure. She does not “belong” to any of them, even though any or all of them may refer to her, or their many other customers, with possessive pronouns.

Take membership (e.g. Costco, Sam’s Club) and loyalty (CVS, Kroger) programs off the table. Membership systems are private markets, and loyalty programs are misnomered. (For more about that, read the “Dysloyalty” chapter of The Intention Economy.)

Let’s look instead at businesses that customers engage as a matter of course: contractors, medical doctors, auto mechanics, retail stores, restaurants, clubs, farmers’ markets, whatever. Some may be on speed dial, but most are not. What matters in all cases is that these businesses are responsible to their customers. “The real and effectual discipline which is exercised over a workman is that of his customers,” Adam Smith writes. “It is the fear of losing their employment which restrains his frauds and corrects his negligence.” That’s what it means to be a customer’s constituent.

An early promise of the Internet was supporting that “effectual discipline.” For the most part, that hasn’t happened. The “one clue” in The Cluetrain Manifesto said “we are not seats or eyeballs or end users or consumers. we are human beings and our reach exceeds your grasp. deal with it.” Thanks to ubiquitous surveillance and capture by corporate giants and unavoidable platforms, corporate grasp far outreaches customer agency.

That’s one reason ProjectVRM has been working against corporate grasp since 2006, and just as long for customer reach. Our case from the start has been that customer independence and agency are good for business. We just need to prove it.

Eigth in the News Commons series.

Back when I was on the board of my regional Red Cross chapter (this one), I learned four lessons about fund raising:

1. People are glad to pay value for value.
2. People are most willing to pay when they perceive and appreciate the value they get from a product or service.
3. People are most willing to pay full value when the need or opportunity to pay is immediate, and the amount they pay is up to them.
4. People are willing to pay more when they have a relationship with the other party (seller, service provider, philanthropy, cause, whatever)

Here’s something I wrote in The Cluetrain Manifesto (10th anniversary edition) about all four lessons at work:

Not long after Cluetrain came out in early 2000, I found myself on a cross-country flight, sitting beside a Nigerian pastor named Sayo Ajiboye. After we began to talk, it became clear to me that Sayo (pronounced “Shaiyo”) was a deeply wise man. Among his accomplishments was translating the highly annotated Thompson Bible into his native Yoruba language: a project that took eight of his thirty-nine years.

I told him that I had been involved in a far more modest book project—The Cluetrain Manifesto—and was traveling the speaking circuit, promoting it. When Sayo asked me what the book was about, I explained how “markets are conversations” was the first of our ninety-five theses, and how we had unpacked it in a chapter by that title. Sayo listened thoughtfully, then came back with the same response I had heard from other readers in what back then was still called the Third World: “Markets are conversations” is a pretty smart thing for well-off guys from the First World to be talking about. But it doesn’t go far enough.

When I asked him why, he told me to imagine we were in a “natural” marketplace—a real one in, say, an African village where one’s “brand” was a matter personal reputation, and where nobody ruled customer choices with a pricing gun. Then he picked up one of those blue airline pillows and told me to imagine it was a garment, such as a coat, and that I was interested in buying it. “What’s the first thing you would say to the seller?” he asked.

“What does it cost?”

“Yes, you would say that,” he replied, meaning that this was typical of a First World shopper for whom price is the primary concern. Then he asked me to imagine that a conversation follows between the seller and me—that the two of us get to know each other a bit and learn from each other. “Now,” he asked, “What happens to the price?”
I said maybe now I’m willing to pay more while the seller is willing to charge less.

“Why?” Sayo asked.

I didn’t have an answer.

“Because you now have a relationship,” he said.

As we continued talking, it became clear to me that everything that happens in a marketplace falls into just three categories: transaction, conversation, and relationship. In our First World business culture, transaction matters most, conversation less, and relationship least. Worse, we conceive and justify everything in transactional terms. Nothing matters more than price and “the bottom line.” By looking at markets through the prism of transaction or even conversation, we miss the importance of relationship. We also don’t see how relationship has a value all its own: one that transcends, even as it improves, the other two.

Consider your relationship with friends and family, Sayo said. The value system there is based on caring and generosity, not on price. Balance and reciprocity may play in a relationship, but are not the basis of it. One does not make deals for love. There are other words for that.

Back in the industrialized world, few of our market relationships run so deep, nor should they. By necessity much of our relating is shallow and temporary. We don’t want to get personal with an ATM machine or even with real bank tellers. Friendly is nice, but in most business situations that’s about as far as we want to go.

But relationship is a broad category: broad enough to contain all forms of relating—the shallow as well as the deep, the temporary as well as the enduring. In the business culture of the industrialized world, Sayo said, we barely understand relationship’s full meaning or potential. And we should. Doing so would be good for business.

So he told me our next assignment was to unpack and study another thesis: Markets are relationships.

That is why, six years after the first edition of Cluetrain came out, I started ProjectVRM (the R means Relationship) at the Berkman Klein Center, wrote The Intention Economy: When Customers Take Charge, (Harvard Business Review Press, 2012), co-founded Customer Commons (in 2013), and am now a visiting scholar with the Ostrom Workshop at Indiana University, thinking out loud about how a news commons might thrive as a market of relationships—starting here in Bloomington, IU’s home town.

In The News Business (which precedes this post), I said the three current business models for local news were advertising, subscription, and philanthropy, and promised a fourth. This is it: emancipayments.

We* came up with this idea in 2009. Here is how the EmanciPay page on the ProjectVRM wiki puts it:

Overview

Simply put, Emancipay makes it easy for anybody to pay (or offer to pay) —

1. as much as they like
2. however they like
3. for whatever they like
4. on their own terms

— or at least to start with that full set of options, and to work out differences with sellers easily and with minimal friction.

Emancipay turns consumers (aka users) into customers by giving them a pricing gun (something which in the past only sellers used) and their own means to make offers, to pay outright, and to escrow the intention to pay when price and other requirements are met. And to be able to do this at scale across all sellers, much as cash, browsers, credit cards and email clients do the same. Payments themselves can also be escrowed.

In slightly more technical terms, EmanciPay is a payment framework for customers operating with full agency in the open marketplace, and at scale. It operates on open protocols and standards, so it can be used by any buyer, seller or intermediary.

It was conceived as a way to pay for music, journalism, or what any artist brings into the world. But it can apply to anything. For example, [subscriptions], which have become by 2021 a giant fecosystem in which every seller has separate and non-substitutable scale across all subscribers, while subscribers have zero scale across all sellers, with the highly conditional exceptions of silo’d commercial intermediaries. As [Customer Commons] puts it,

There’s also not much help coming from the subscription management services we have on our side: Truebill, Bobby, Money Dashboard, Mint, Subscript Me, BillTracker Pro, Trim, Subby, Card Due, Sift, SubMan, and Subscript Me. Nor from the subscription management systems offered by Paypal, Amazon, Apple or Google (e.g. with Google Sheets and Google Doc templates). All of them are too narrow, too closed and exclusive, too exposed to the surveillance imperatives of corporate giants, and too vested in the status quo.

That status quo sucks (see here, or just look up “subscription hell”), and it’s way past time to unscrew it.) But how?

The better question is where?

The answer to that is on our side: the customer’s side.

While EmanciPay was first conceived by ProjectVRM as a way to make live payments to nonprofits and to provide a new monetization method for publishers. it also works as a counterpart to sellers’ subscription systems in what Zuora (a supplier of subscription management systems to the publishing industry, including The Guardian and Financial Times) calls the “subscription economy“, which it says “is built on ever changing relationships with your customers”. Since relationships are two-way by nature, EmanciPay is one way that customers can manage their end, while publisher-side systems such as Zuora’s manage the other.

EmanciPay economic case

EmanciPay provides a new form of economic signaling not available to individuals, either on the Net or before the Net became available as a communications medium. EmanciPay will use open standards and be comprised of open source code. While any commercial [Fourth party] can use EmanciPay (or its principles, or any parts of it they like), EmanciPay’s open and standard framework will support fourth parties by making them substitutable, much as the open standards of email (smtp, pop3, imap) make email systems substitutable. (Each has what Joe Andrieu calls service endpoint portability.)

EmanciPay is an instrument of customer independence from all of the billion (or so) commercial entities on the Net, each with its own arcane and silo’d systems for engaging and managing customer relations, as well as receipt, acknowledgement and accounting for payments from customers.

Use Case Background

EmanciPay was conceived originally as a way to provide a customers with the means to signal interest and ability to pay for media and creative works (most of which are freely available on the Web, if not always free of charge). Through EmanciPay, demand and supply can relate, converse and transact business on mutually beneficial terms, rather than only on terms provided by the countless different silo’d systems we have today, each serving to hold the customer captive, and causing much inconvenience and friction in the process.

Media goods were chosen for five reasons:

1. because most are available for free, even if they cost money, or are behind paywalls
2. paywalls, which are cookie-based, cannot relate to individuals as anything other than submissive and dependent parties (and each browser a users employs carries a different set of cookies)
3. both media companies and non-profits are constantly looking for new sources of revenue
4. the subscription model, while it creates steady income and other conveniences for sellers, is often a bad deal for customers, and is now so overused (see Subscriptification) that the world is approaching a peak subscription crisis, and unscrewing it can only happen from the customer’s side (because the business is incapable of unscrewing the problem itself
5. all methods of intermediating payment choices are either silo’d by the seller or silo’d by intermediators, discouraging participation by individuals.

What the marketplace requires are new business and social contracts that ease payment and stigmatize non-payment for creative goods. The friction involved in voluntary payment is still high, even on the Web, where one must go through complex ceremonies even to make simple payments.  There is no common and easy way either to keep track of what media (free or otherwise) we use (see Media Logging), to determine what it might be worth, and to pay for it easily and in standard ways — to many different suppliers. (Again, each supplier has its own system for accepting payments.)

EmanciPay differs from other payment models (subscriptions, newsstand, tip jars) by providing customers with the ability to choose what they wish to pay and how they’ll pay it, with minimum friction — and with full choice about what they disclose about themselves.

EmanciPay will also support credit for referrals, requests for service, feedback and other relationship support mechanisms, all at the control of the user. For example, EmanciPay can provide quick and easy ways for listeners to pay for public radio broadcasts or podcasts, for readers to pay for otherwise “free” papers or blogs, for listeners to pay to hear music and support artists, for users to issue promises of payment for for stories or programs — all without requiring the individual to disclose unnecessary private information, or to become a “member” — although these options are kept open.

This will scaffold genuine relationships between buyers and sellers in the media marketplace. It will also give deeper meaning to “membership” in non-profits. (Under the current system, “membership” generally means putting one’s name on a pitch list for future contributions, and not much more than that.)

EmanciPay will also connect the sellers’ CRM (Customer Relationship Management) systems with customers’ VRM (Vendor Relationship Management) systems, supporting rich and participatory two-way relationships. In fact, EmanciPay will by definition be a VRM system.

Micro-accounting and Macro-distribution

The idea of “micro-payments” for goods on the Net has been around for a long time, and is often brought up as a potential business model for journalism. For example in this article by Walter Isaacson in Time Magazine. It hasn’t happened, at least not globally, because it’s too complicated, and in prototype only works inside private silos.

What ProjectVRM suggests instead is something we don’t yet have, but very much need:

1. micro-accounting for actual uses. Think of this simply as “keeping track of” the news, podcasts, newsletters, or music we consume.
2. macro-distribution of payments for accumulated use (that’s no longer “micro”).

Much — maybe most — of the digital goods we consume are both free for the taking and worth more than $zero. How much more? We need to be able to say. In economic terms, demand needs to have a much wider range of signals it can give to supply. And give to each other, to better gauge what we should be willing to pay for free stuff that has real value but not a hard price.

As currently planned, EmanciPay would –

1. Provide a single and easy way for consumers of “content” to become customers of it. In the current system — which isn’t one — every artist, every musical group, and every public radio and TV station has his, her or its own way of taking in contributions from those who appreciate the work. This can be arduous and time-consuming for everybody involved. (Imagine trying to pay separately every musical artist you like, for all your enjoyment of each artist’s work.) What EmanciPay proposes, however, is not a replacement for existing systems, but a new system that can supplement existing fund-raising systems — one that can soak up much of today’s MLOTT: Money Left On The Table.
2. Provide ways for individuals to look back through their media usage histories, inform themselves about what they have been enjoying, and determine how much it is worth to them. The Copyright Arbitration Royalty Panel (CARP), and later the Copyright Royalty Board (CRB), both came up with “rates and terms that would have been negotiated in the marketplace between a willing buyer and a willing seller.” This almost absurd language first appeared in the 1995 Digital Performance Royalty Act (DPRA) and was tweaked in 1998 by the Digital Millennium Copyright Act (DMCA), under which both the CARP and the CRB operated. The rates they came up with peaked at $.0001 per “performance” (a song or recording), per listener. EmanciPay creates the “willing buyer” that the DPRA thought wouldn’t exist.
3. Stigmatize non-payment for worthwhile media goods. This is where “social” will finally come to be something more than yet another tech buzzmodifier.

All these require micro-accounting, not micro-payments. Micro-accounting can inform ordinary payments that can be made in clever new ways that should satisfy everybody with an interest in seeing artists compensated fairly for their work. An individual listener, for example, can say “I want to pay 1¢ for every song I hear,” and “I’ll send SoundExchange a lump sum of all the pennies wish to pay for songs I have heard over a year, along with an accounting of what artists and songs I’ve listened to” — and leave dispersal of those totaled pennies up to the kind of agency that likes, and can be trusted, to do that kind of thing. That’s the macro-distribution part of the system.

Similar systems can also be put in place for readers of newspapers, blogs, and other journals. What’s important is that the control is in the hands of the individual and that the accounting and dispersal systems work the same way for everybody.

I visited EmanciPay use cases twice in Linux Journal:

• An Easy Way to Pay for Journalism, Music and Everything Else We Like (May 2015)
• An Immodest Proposal for the Music Industry (November 2018)

There are two differences in the world today that should make it easier to code up something like EmanciPay:

1. Smartphones and apps on them have become extensions of ourselves.
2. AI.

For the latter, I am not talking about the kind of centralized AI we get from Amazon, Microsoft/OpenAI, Adobe, and the rest. I’m talking about AI that’s as personal as our own underwear and gives us what Sam Altman calls “individual empowerment and agency on a scale we’ve never seen before.” That quote became the title of the post I wrote at that link. I will unpack it further in an upcoming News Commons post.

But first I’ll dig deeper into what we need to develop EmanciPay, and how we can use it to scaffold up the kind of markets first imagined by The Cluetrain Manifesto, a quarter century ago.

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*Big hat tip to Keith Hopper for his thinking and work on this, especially toward ListenLog, which is now fourteen years ahead of its time. And that time will come. Also to Joe Andrieu, whose The User as a Point of Integration (published in 2007) is a founding document in the VRM canon. He reported on progress here in 2017. All hail writers who keep their archives alive on the Web.

After 17 years and 761 episodes, FLOSS Weekly ended its run on the TWiT network yesterday. I hosted the last 179 of those shows. My career as a professional (meaning paid) advocate of open source also ended with that show. The full span ran from 1996, when I first appeared on the Linux Journal masthead, until yesterday: about 27 years.

I still participate in market conversations around the many topics I covered in that span, but I’m mostly working on other stuff now. For example, in random-ish order:

• a commons-based approach to news,
• a history (and archive) based approach to journalism in the Digital Age,
• the Digital Age itself (such as what we make of digital tech and the Internet, and what they make of us),
• blogging, such as in my three blogs here (this one, plus ProjectVRM and Trunkline), and supporting what Dave Winer is doing with Feedland and allied work,
• freeing e-commerce from the grips of giants like Amazon,
• fighting surveillance online and off,
• self-sovereign identity,
• infrastructure old, new, and in the future,
• saving the Internet, and
• making an intention economy happen.

All of those are cars in a cluetrain, about which more below.

They are also featured now and then on Reality 2.o, the podcast Katherine Druckman and I have been doing since our Linux Journal days.

For many decades now, I’ve been spoiled by success. For example, open source, an expression whose current meaning was born in 1998, is now beyond huge. Here’s VentureBeat:

Today, open-source software underpins almost everything: A whopping 97% of applications leverage open-source code, and 90% of companies are applying or using it in some way.

GitHub alone had 413 million open-source software (OSS) contributions in 2022.

“Open-source software is the foundation of 99% of the world’s software,” said Martin Woodward, VP of developer relations at GitHub.

By covering open source for Linux Journal from the start, I helped make that happen.

Same with The Cluetrain Manifesto. “Markets are conversations,” a one-liner of mine that became the first thesis in the manifesto, grew to become a meme that hasn’t gone away. The word cluetrain also appears almost daily in tweets on X, almost a quarter century after it was coined. (When Twitter was still itself, cluetrain was mentioned in tweets several times daily. The decline in cluetrain mentions is one small measure of how lame X has become.)

Also blogging!

Hmmm… I don’t think I ever blogged about my only encounter with Robin Williams. It was at some trade show in the early aughts. There was a scrum of attendees gathered around something or someone unseen in the middle. On the periphery was my old friend Tom Rielly, who quickly grabbed me and pulled me into the middle of the crowd, where stood Robin Williams, with two bags of swag. I almost said, “Hey, you look like Robin Williams, only shorter.” Then Tom introduced me, saying “This is Doc. He’s one of the top five bloggers in the world.” I said, “More like one of the top sixteen, but most of the others are duplicates.” Robin then said something funny, and I responded with something funny of my own, and an all-funny exchange ensued during which my separate self said, “Holy shit! I’m doing humor schtick with Robin Williams and holding my own!” After maybe half a minute of this, I excused myself, saying something like, “I’ll leave you to your private audience here,” and exited the crowd.

Oh, and photography. As of this moment, my photos have had 16,855,107 views on one Flickr account, and 1,470,281 on the other. Visits to those run from the hundreds to thousands per day. A search for my name on Wikimedia Commons also brings up 1850 results, nearly all of which are photos I’ve shared using Creative Commons licensing that encourages use and re-use, which is why many (or most) of them find their way into Wikipedia articles.

I’ve had less luck with the other missions I’ve listed above. But I believe in all of them, and in faith, I truck onward.

By the way, FLOSS Weekly has not slipped below the waves. I expect it will be picked up somewhere else on the Web, and wherever you get your podcasts. (I love that expression because it means podcasting isn’t walled into some giant’s garden.) When FLOSS Weekly becomes re-manifest, I’ll point to it here.

From 2007 until about a month ago, I wrote on three blogs that lived at blogs.harvard.edu. There was my personal blog (this one here, which I started after retiring my original blog), ProjectVRM‘s blog (also its home page), and Trunkline, a blog about infrastructure that was started by Christian Sandvig when he and I were both fellows at Harvard’s Berkman Center for Internet and Society (and which I kept alive with very occasional posts since then). The image above is from those blogs’ header images.

All three are now re-homed. This one is at doc.searls.com (a URL that had redirected to blogs/harvard.edu/doc for many years), ProjectVRM’s is at ProjectVRM.org (a URL that had redirected to blogs.harvard.edu/vrm address) and Trunkline’s (which had been at blogs.harvard.edu/trunk) is now at trunkli.org.

Their hosting service is Pressable.com, a WordPress subsidiary that worked with the Berkman Klein Center to make sure that every link on the Web pointing to pages at those three Harvard-hosted blogs now goes to those pages’ new locations, without anything being 404’d. Which is just. freaking. awesome.

My thanks and gratitude to all the people who helped, both within those organizations and in my own network of friends. Together they demonstrate that the Web is a living archive and not just a whiteboard.

I’ll be talking shortly to some readers of The Intention Economy who are looking for ways to connect that economy with advertising. (Or so I gather. I’ll know more soon.) What follows is the gist of what I wrote to them in prep for the call.

First,  take a look at People vs. Adtech, and/or Separating Advertising’s Wheat and Chaff.

Both are still valid (IMHO), but don’t yet cover what A.I. will inevitably do to advertising. There are two possible ways that can go.

One is toward hyper-personalized advertising based on even more pernicious uninvited tracking than we already have, with A.I.s rather than lawyers and hired intermediaries finding loopholes in privacy law that will automate specious forms of “consent” far more efficiently than possible without it.

The other is toward finding the best vectors for targeting the right audiences rather than the most-tracked individuals—and to find those amidst the millions of podcasts, newsletters, blogs, mainstream media, and other online outlets into the ever-widening world of thought, opinion, news, scholarship, journalism, sports, and the rest of it.

The former will make tracking and personalized targeting far worse, and the latter will make advertising targeted at audiences far better. It will also do a much better job of supporting journalism in the process because more money can get through to publishers and reporters who won’t be fed by an evil hand they avoid biting.

Those two directions are the chaff-vs-wheat choices for A.I.’s future in advertising. For now, there is surely far more action happening with the former than with the latter, given the sizes of today’s spinning adtech flywheels. But this also means there will be bigger opportunities with the latter: a blue ocean away from the red one.

What makes the intention economy ocean blue is that it will exist almost entirely outside both those advertising systems—and inside horizons that are far more expansive than can be seen through the lens of advertising and marketing as we’ve known them.

Here the opportunities will be in creating better signaling from demand to supply, and better intermediation between them: forms that will safeguard the privacy needs of individuals and the legitimate needs of businesses. In some cases there will be no intermediation at all—just forms of agency on both sides that are friendly to each other and can interact directly. And, where intermediations are required, they will find a wide-open space for what we’ve long called fourth parties.

To visualize the opportunities here, think of every customer as a boat afloat on a sea of goods and services, and friendly to the ecosystems where demand encourages supply at least as well as supply satisfies demand.

If you’re looking for market opportunities in this vast new ocean, here are thirteen of them.

Maybe history will defeat itself.

Remember FreePC? It was a thing, briefly, at the end of the last millennium, right before Y2K pooped the biggest excuse for a party in a thousand years. This may help. The idea was to put ads in the corner of your PC’s screen. The market gave it zero stars, and it failed.

And now comes Telly, hawking free TVs with ads in a corner, and a promise to “optimize your ad experience.” As if anybody wants an ad experience other than no advertising at all.

Negative demand for advertising has been well advertised by both ad blocking (the biggest boycott in human history) and ad-free “prestige” TV, (or SVOD, for subscription video on demand). With those we gladly pay—a lot— not to see advertising. (See numbers here.)

But the advertising business (in the mines of which I toiled for too much of my adult life) has always smoked its own exhaust and excels best at getting high with generous funders. (Yeah, some advertising works, but on the whole people still hate it on the receiving end.)

The fun will come when our own personal AI bots, working for our own asses, do battle with the robot Nazgûls of marketing — and win, because we’re on the Demand side of the marketplace, and we’ll do a better job of knowing what we want and don’t want to buy than marketing’s surveillant AI robots can guess at. Supply will survive, of course. But markets will defeat marketing by taking out the middle creep.

The end state will be one Cluetrain forecast in 1999, Linux Journal named in 2006, the VRM community started working on that same year, and The Intention Economy detailed in 2012. The only thing all of them missed was how customer intentions might be helped by personal AI.

Personal.* Not personalized.

Markets will become new and better dances between Demand and Supply, simply because Demand will have better ways to take the lead, and not just follow all the time. Simple as that.

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*For more on how this will work, see Individual Empowerment and Agency on a Scale We’ve Never Seen Before.

I’ve owned a lot of bad cars in my decades.  But some I’ve loved, at least when they were on the road. One was the 1965 Peugeot 404 wagon whose interior you see above, occupied by family dog Christy, guarding the infant seat next to her. You’ll note that the hood is open, because I was working on it at the time, which was constantly while I owned it.

I shot that photo in early 1974, not long after arriving at our new home in Graham, North Carolina. The trip down from our old home in far northern New Jersey was one of the most arduous I’ve ever taken, with frequent stops to fix whatever went wrong along the way, which was plenty.

Trouble started when a big hunk of rusted floor fell away beneath my feet, so I could see the New Jersey Turnpike whizzing by down there, while worrying that the driver’s seat itself might fall to the moving pavement, and my ass with it.

The floor had rusted because rainwater would gather in the air vents between the far side of the windshield and the dashboard, and suddenly splat down on one’s feet, and the floor, soon as the car began to move.  (The floor was prepared for this with a drainage system of tubes laminated between layers of metal, meant to carry downward whatever water fell on top. Great foresight, I suppose. But less prepared was the metal itself, which was determined to rust.)

Later a can attached to the exhaust manifold blew to pieces so sound and exhaust straight from the engine sounded like a machine gun and could be heard to the horizons in all directions, and echoed into the cabin off the pavement through the new hole in the floor. I am sure that the hearing loss I have now began right then.

I replaced the lost metal with an emptied V8 juice can that I filled with steel wool for percussive exhaust damping, and fastened into place with baling wire that I carried just in case of, well, anything. I also always carried a large toolbox, because you never know. If you owned a cheap used car back in those days, you had to be ready for anything.

The car did have its appeals, some of which were detailed by coincidence a month ago by Raphael Orlove in Jalopnik, calling this very model the best wagon he’s ever driven. His reasons were correct—for a working car. The best feature was a cargo area was so far beyond capacious that I once loaded a large office desk into it with room to spare. It also had double shocks on the rear axle, to help handle the load, plus other arcane graces meant for heavy use, such as a device in the brake fluid line to the rear axle that kept the brakes from locking up when both rear wheels were spinning but off the ground. This, I was told, was for drivers on rough dirt roads in Africa.

While the Peugeot 404 was not as weird in its time as the Citroën DS or 2CV (both of which my friend Julius called “triumphs of French genius over French engineering”), it was still weird as shit in some remarkably impractical ways.

For example, screw-on hubcaps. These meant no tire machine could handle changing a tire, and you had to do the job by hand with tire irons and a sledgehammer. I carried those too. For unknown reasons, Peugeot also also hid spark plugs way down inside the valve cover, and fed them electricity through a spring inside a bakelite sleeve that was easy to break and would malfunction even if they weren’t broken.

I could go on, but all that stuff is beside my point, which is that this car was, while I had it, mine. I could fix it myself, or take it to a mechanic friendly to the car’s oddities. While some design features were odd or crazy, there were no mysteries about how the car worked, or how to fix or replace its parts. More importantly, it contained no means for reporting its behavior or use back to Peugeot, or to anybody.

It’s very different today. That difference is nicely unpacked in A Fight Over the Right to Repair Cars Turns Ugly, by @Aarian Marshall in Wired. At issue are right-to-repair laws, such as the one currently raising a fuss in Massachusetts.

See, all of us and our mechanics had a right to repair our own cars for most of the time since automobiles first hit the road. But cars in recent years have become digital as well as mechanical beings. One good thing about this is that lots of helpful diagnostics can be revealed. One bad thing is that many of those diagnostics are highly proprietary to the carmakers, as the cars themselves become so vertically integrated that only dealers can repair them.

But there is hope. Reports Aarian,

…today anyone can buy a tool that will plug into a car’s port, accessing diagnostic codes that clue them in to what’s wrong. Mechanics are able to purchase tools and subscriptions to manuals that guide them through repairs.

So for years, the right-to-repair movement has held up the automotive industry as the rare place where things were going right. Independent mechanics remain competitive: 70 percent of auto repairs happen at independent shops, according to the US trade association that represents them. Backyard tinkerers abound.

But new vehicles are now computers on wheels, gathering an estimated 25 gigabytes per hour of driving data—the equivalent of five HD movies. Automakers say that lots of this information isn’t useful to them and is discarded. But some—a vehicle’s location, how specific components are operating at a given moment—is anonymized and sent to the manufacturers; sensitive, personally identifying information like vehicle identification numbers are handled, automakers say, according to strict privacy principles.

These days, much of the data is transmitted wirelessly. So independent mechanics and right-to-repair proponents worry that automakers will stop sending vital repair information to the diagnostic ports. That would hamper the independents and lock customers into relationships with dealerships. Independent mechanics fear that automakers could potentially “block what they want” when an independent repairer tries to access a car’s technified guts, Glenn Wilder, the owner of an auto and tire repair shop in Scituate, Massachusetts, told lawmakers in 2020.

The fight could have national implications for not only the automotive industry but any gadget that transmits data to its manufacturer after a customer has paid money and walked away from the sales desk. “I think of it as ‘right to repair 2.0,’” says Kyle Wiens, a longtime right-to-repair advocate and the founder of iFixit, a website that offers tools and repair guides. “The auto world is farther along than the rest of the world is,” Wiens says. Independents “already have access to information and parts. Now they’re talking about data streams. But that doesn’t make the fight any less important.”

As Cory Doctorow put it two days ago in Agricultural right to repair law is a no-brainer, this issue is an extremely broad one that basically puts Big Car and Big Tech on one side and all the world’s gear owners and fixers on the other:

Now, there’s new federal agricultural Right to Repair bill, courtesy of Montana Senator Jon Tester, which will require Big Ag to supply manuals, spare parts and software access codes:

https://s3.documentcloud.org/documents/21194562/tester-bill.pdf

The legislation is very similar to the Massachusetts automotive Right to Repair ballot initiative that passed with a huge margin in 2020:

https://pluralistic.net/2020/09/03/rip-david-graeber/#rolling-surveillance-platforms

Both initiatives try to break the otherwise indomitable coalition of anti-repair companies, led by Apple, which destroyed dozens of R2R initiatives at the state level in 2018:

https://pluralistic.net/2021/02/02/euthanize-rentiers/#r2r

It’s a bet that there is more solidarity among tinkerers, fixers, makers and users of gadgets than there is among the different industries who depend on repair price-gouging. That is, it’s a bet that drivers will back farmers’ right to repair and vice-versa, but that Big Car won’t defend Big Ag.

The opposing side in the repair wars is on the ropes. Their position is getting harder and harder to maintain with a straight face. It helps that the Biden administration is incredibly hostile to that position:

https://pluralistic.net/2021/07/07/instrumentalism/#r2r

It’s no coincidence that this legislation dropped the same week as Aaron Perzanowski’s outstanding book “The Right to Repair” — R2R is an idea whose time has come to pass.

https://pluralistic.net/2022/01/29/planned-obsolescence/#r2r

[The next day…]

Cory just added this in a follow-up newsletter and post:

…remember computers are intrinsically universal. Even if manufacturers don’t cooperate with interop, we can still make new services and products that plug into their existing ones. We can do it with reverse-engineering, scraping, bots – a suite of tactics we call Adversarial Interoperability or Competitive Compatibility (AKA “comcom”):

https://www.eff.org/deeplinks/2019/10/adversarial-interoperability

These tactics have a long and honorable history, and have been a part of every tech giant’s own growth…

Read all three of those pieces. There is much to be optimistic about, especially once the fighting is mostly done, and companies have proven knowledge that free customers—and truly free markets—are more valuable than captive ones. That has been our position at ProjectVRM from the start. Perhaps, once #R2R and #comcom start paying off, we’ll finally have one of the proofs we’ve wanted all along.

Robin Chase, co-founder and original CEO of Zipcar and author of Peers Inc: How People and Platforms are Inventing the Collaborative Economy and Reinventing Capitalism, will speak at the Ostrom Workshop s Beyond the Web Salon Series at Indiana University at 2:00 PM Eastern this coming Monday, February 7, 2022. The event link is here, where you’ll also find the Zoom link.

The full theme of the salon series is Beyond the Web: Making a platform-free online marketplace for goods, ideas and everything else, about which you can read more here.

Robin’s work with transportation and peer production has been VRooMy from the start, and especially consistent with our work with the Ostrom Workshop on the Intention Byway in Bloomington, Indiana.

Upcoming speakers in the Salon Series (mark your calendars) are Ethan Zuckerman and Shoshana Zuboff. Both are BKC veterans and, like Robin, devoted to moving beyond status quos that vex us all. Ethan will be with us on March 7 and Shoshana on April 11. Days and times for both are Mondays at 2:00 PM Eastern. Details at those links.<

These events are all participatory, informative, challenging and fun. Please join us.