I’ve felt something in my own life that I don’t have a tidy explanation for. Optimism seems to change my relationship with what happens next. Sometimes it feels as though life is meeting me halfway. Call it luck, call it “lucky girl syndrome”. I’m interested in the experience before we decide what to call it.

Reading the optimism chapter in David Deutsch’s The Beginning of Infinity gave that feeling there was somewhere more substantial to go. Here was an argument about what reality permits, rather than instructions for maintaining a pleasant mood.

His principle of optimism is blunt: “All evils are caused by insufficient knowledge.” A fairly confrontational thing to put in a chapter about optimism.

He is not calling ignorance wicked, or suggesting that someone suffering could have avoided it by being more positive. His claim concerns the knowledge needed to overcome problems. That knowledge might be missing from an entire civilisation. We might fail to create it, or discover it too late. Optimism offers no exemption from consequences. What it challenges is the assumption that our present inability to solve something tells us what can ever be solved.

Peter Thiel approaches the opposing view that demonstates the evil claim from above from a considerably stranger direction.

In his discussions of the Antichrist, Thiel imagines power arriving through the promise of protection from catastrophe. In his interpretation, fear of destruction could make people accept an authority capable of stopping scientific and technological development. The offer is peace and safety; the danger, as he describes it, is permanent control and stagnation. He also acknowledges the danger on the other side: development that produces catastrophe. A blocker to epistemology. Encouraging Duetch's claim but from an opposite angle

Deutsch is not making Thiel’s theological argument but it helps frame it, and putting them beside each other does not settle which technologies should proceed or what safeguards they need. But the comparison brings a question into focus: if overcoming suffering requires knowledge we have yet to create, what happens when the process of discovery is closed down?

The personal version of that question is less apocalyptic, but worth asking. How much of what we call being realistic is an accurate assessment of our circumstances, and how much is a decision that nothing sufficiently different can happen? when you start thinking from this lens, most of day to day occurrences that are outside of ones routine start to seem.. rather pessimistic.

That is where optimism begins to interest me.

Your problem is not necessarily a law of nature

Imagine a community living beside a river it cannot safely cross.

They could spend generations describing the problem accurately. The current is strong. The water is deep. People have tried and died. There would be plenty of evidence that crossing is dangerous, and no shortage of people with good reasons to say so.

None of that establishes that the river must remain uncrossable.

A bridge would change the situation without changing the laws of nature. Gravity would carry on doing exactly what it had been doing. The difference would be an arrangement of materials made possible by knowledge, resources and work.

The danger was real. So was the possibility of doing something about it.

Deutsch develops a related distinction in constructor theory, a proposed approach to physics concerned with which transformations are possible, which are impossible, and why. What could be made to happen belongs in the explanation of the world, alongside what happens to be occurring now. This is a physical framework, not evidence that wishing influences unrelated events.

For ordinary life, the distinction is useful enough without the physics terminology.

“I cannot do this” and “I do not currently know how to do this” describe different situations. The first can sound like a settled property of the person speaking. The second identifies something that might change.

Of course, adding yet to a sentence does not make it true. Some goals are impossible under the conditions available. Some cost more than they are worth. Other people retain the right to decline whatever role you have assigned them in your future.

But before accepting a limit, it helps to know what the limit actually is.

Take something you have quietly filed under not for me. What is the evidence? A missing skill? Two unsuccessful attempts? A lack of resources? Something another person told you years ago?

“I tried twice, using the same approach, and it didn’t work” is useful information. “Nothing ever works out for me” is a much larger claim wearing the same evidence.

You may still decide to stop. At least you will be deciding about the actual problem, rather than a verdict you have gradually attached to yourself.

You cannot picture all of it yet

The part of Deutsch’s argument I keep coming back to is his observation that “some of the opportunities and some of the discoveries will be inconceivable in advance”. Progress therefore requires someone willing to remain open to those possibilities and prepare for their arrival.

That is a different demand from visualising a particular future until you feel certain it will happen.

It asks you to make room for a future you cannot yet visualise.

There is an awkward assumption inside the instruction to decide exactly what you want: that the version of you making the decision already understands the worthwhile possibilities. Sometimes that is reasonable. You know the skill you want to learn or the practical problem you need to solve.

But sometimes learning changes the goal itself.

You encounter something that gives you a different understanding of what would make life good. A conversation changes the question you were asking. You become capable of something that had never occurred to you as a serious prospect.

In those circumstances, insisting that life follow the original picture would be an odd form of optimism. You would be asking everything you have yet to learn to obey the preferences you had before learning it.

A plan can give you direction without being granted that much authority.

The practical question is how to prepare for something you cannot predict. You cannot make a detailed plan for a discovery you have not made. You can, however, develop the ability to notice, understand and respond when something unfamiliar becomes relevant.

That might mean learning a skill before its full use is obvious. It might mean having a conversation without needing it to lead somewhere. It might mean leaving enough attention available to follow a question that does not fit your current plan.

Or to the retardmaxxing friends of ours: fuck around and find out

None of this requires treating every distraction as a sign. Curiosity can waste your afternoon. Sometimes an interesting person is just an interesting person.

The point is to allow a little investigation before demanding a complete justification. Some opportunities cannot provide references because the conditions that make them opportunities are still forming.

I think this is a more generous relationship with the future. You can care about where you are going while accepting that you are not yet qualified to describe everything worth finding.

One thing makes other things possible

Stuart Kauffman’s idea of the adjacent possible gives this a useful shape. A development can make further developments accessible. The available possibilities change as new things appear.

In The Dynamics of Correlated Novelties, researchers modelled a process in which encountering something new enlarges the space that can subsequently be explored. They tested its predictions against patterns in activities including music listening, language and Wikipedia editing. The research concerns how novelty unfolds; it is not a demonstration that optimism attracts luck.

Consider a simple example.

You become interested in an unfamiliar subject and learn enough to have a proper conversation about it. That conversation introduces you to someone working on a problem. Helping with the problem teaches you something else. A possibility then becomes apparent that neither of you had considered at the beginning.

It would be easy, afterwards, to describe the sequence as a lucky encounter. Perhaps it was. But the later opportunity was also made possible by what developed along the way.

You were not simply choosing from a fixed selection of outcomes. Your participation helped change what was available.

This matters because we often ask a first step to explain an entire future. Where will this lead? What will I get out of it? How do I know it is worth doing?

Those are sensible questions when the cost is substantial. They become less useful when they demand information that only participation could produce.

A conversation does not need to justify a five-year plan. An afternoon spent learning something does not need to become a new identity. You can take a small step, see what it reveals, and decide again.

That is one plausible way to understand the feeling that life begins opening up. The possibilities may genuinely be changing as you proceed. From inside the experience, that can feel remarkably different from repeatedly choosing between the same familiar options.

It does not mean every sequence leads somewhere good. It means the first view is not always the most informative one.

Give the future something to work with

There is something strange about a bridge before it exists.

At first, it might be an idea, then a drawing or a calculation. None of those can carry a person across a river. Yet they can influence the actions through which something that can carry a person is built.

A representation of an unrealised future has acquired a role in the present.

No backwards causation is required. The bridge has not sent instructions through time. Someone has imagined a possibility and begun arranging the conditions through which it might become actual.

That is why I find “your thoughts only change your behaviour” an incomplete dismissal of the idea that thought shapes reality. Behaviour is part of reality. Conversations, experiments and acts of care have consequences. Explaining a connection does not make the connection trivial.

William James explored this in The Will to Believe. He argued that, in some relationships and cooperative situations, a willingness to act before the outcome is established can help bring that outcome into existence. He distinguished these cases from questions whose answers do not depend on our belief.

Imagine several neighbours who would enjoy a community garden. Everyone is waiting for evidence that the others would contribute. Nobody asks, so the evidence never arrives.

Then someone makes a suggestion. Another offers tools. Someone knows where there might be a suitable plot. An uncertain possibility now has people working on it.

The first person did not need certainty. They needed enough interest to find out.

This is where a desired future becomes useful: it changes something you do in the present.

A wish for friendship becomes an invitation. An idea becomes a rough version another person can respond to. A problem you have been carrying around becomes a specific question put to someone who might understand part of it.

Keep the action proportionate to what you know. An invitation is an experiment. Repeatedly ignoring a refusal is not optimism.

And pay attention to what comes back. The response may improve your idea, expose a weakness or reveal that you are asking the wrong person. All of that is more useful than trying to manufacture confidence while nothing changes.

You are giving the possibility a way to encounter reality. Reality gets a say.

Your past has a sampling problem

There is a reason to be careful about letting experience have the final word.

Jerker Denrell and James March’s research on the hot-stove effect describes how learning can become biased against unfamiliar or risky alternatives. Something produces an early disappointment, so it is avoided. Avoidance then restricts the information that might show the initial judgment was too negative.

Their account also recognises the cost of trying again: sometimes you merely persist with something that really is poor.

The useful question is how your experience was collected.

Suppose you attend a group for the first time and have an awkward evening. That may tell you the group is a poor fit. It may tell you that first visits are awkward. Going back once could help distinguish between the two.

If you never return, your initial interpretation can survive indefinitely without facing another test. Years later, it may feel less like a conclusion you reached and more like something you simply know.

It is possible to become very certain about an experience you have carefully avoided repeating.

That does not mean you owe every disappointment another chance. Some situations provide more than enough evidence, and some are unsafe. The useful place to experiment is where the belief is broad, the evidence is limited, and the cost of checking is manageable.

Before trying, write down what you expect. Afterwards, record what happened, including the bits that do not suit your preferred explanation.

This is equally necessary for an optimistic story. Counting the pleasant coincidences while forgetting everything else can make almost any belief look impressive.

I would rather have an optimism that can survive accurate accounting.

Sometimes the result will be that a particular approach deserves to be abandoned. That is allowed. You can want meaningful friendships and leave a group that consistently makes you miserable. You can remain interested in a problem while admitting that your proposed solution does not work.

There is no obligation to defend your first idea for the rest of your life.

Especially if the point was to discover something you did not already know.

Include the part you would rather skip

An attractive future can also become a comfortable place to hide.

In four experiments, Heather Kappes and Gabriele Oettingen found that induced, idealised positive fantasies could reduce energisation, measured through physiological and behavioural indicators. Imagining a desirable outcome did not necessarily produce the effort needed to pursue it.

That is a useful complication for anyone hoping to think their way into a better life.

Imagination helps identify what you care about. But mentally enjoying the result can become a substitute for dealing with whatever stands between you and it.

Oettingen’s work on mental contrasting brings the desired future into contact with the obstacles in present reality. Combined with specific plans for responding to those obstacles, the approach has shown modest average benefits for goal attainment across studies. The results vary, and publication bias may make the average effect look larger than it is.

The practical version is straightforward.

Take something you genuinely want to improve. Describe what would be different. Then identify the obstacle most relevant to your next step.

Perhaps you want stronger friendships but habitually wait for other people to initiate. The useful action is fairly clear. Make a specific invitation. You do not control whether it is accepted, but you can stop leaving your contribution entirely to chance.

Perhaps the obstacle is external. The resources are unavailable. Another person does not want what you want. The timing makes the original plan unworkable.

Recognising that is part of understanding the situation. You may need help, a different approach or a different goal.

This is also where the whole thing can become unpleasant if handled carelessly. Once every outcome is attributed to the quality of someone’s thoughts, suffering becomes evidence that they thought incorrectly. They get the original problem and then an invoice for insufficient belief.

Nothing in the argument here warrants that.

There are circumstances a person cannot change with the knowledge, time and support available to them. Some damage cannot be undone. Another person’s apparent good fortune tells you very little about what would have been possible in your situation.

Optimism also does not require constant cheerfulness. An anxious thought is not an order accidentally placed with the universe.

You can be frightened and investigate. You can be disappointed and change direction. You can need rest before being ready for either.

What matters is whether the difficulty has taught you something specific, or persuaded you to close the whole subject.

Leave room for something you could not have asked for

The attraction of “lucky girl syndrome”, at least to me, is the possibility of having a less hostile relationship with uncertainty. There is something appealing about entering the future without assuming that every unknown is waiting to take something from you.

I want to preserve that without claiming the universe has a personal interest in my plans.

The mechanisms discussed here offer a way to do so. Expectations can influence participation. Participation can produce knowledge and relationships. Those can change what becomes possible next. That is a synthesis of the argument, not proof that any particular coincidence was caused by optimism.

There is still room for chance, and for misremembering it.

But I do not think an intelligible explanation has to drain the experience of its significance. The bridge remains strange. A world contains beings who can imagine something absent, understand enough to build it, and thereby change what happens in that place.

We are among the parts of reality capable of learning about it and doing something with what we learn.

That is where I think optimism belongs as a value. I cannot establish it as a law favouring good outcomes. I can see a reason to value the processes through which conditions become understandable and, sometimes, improvable.

The universe does not have to be personally benevolent for something benevolent to happen within it. An act of care can make a difference without first obtaining a guarantee about the character of existence.

Nor does improvement have to look like the future we originally wanted.

This is the consequence of Deutsch’s point that I find most useful. Some discoveries will be beyond what we can presently imagine. Preparing for them means accepting that a worthwhile future may require us to revise our idea of what worthwhile looks like.

A plan can remain useful while being incomplete. A failed attempt can remain a failure without becoming a description of everything still possible. You can have a direction without pretending to know what every worthwhile turn will look like.

That feels closer to the optimism I wanted to understand in the first place. Enough confidence to participate, and enough curiosity to avoid mistaking the current picture for the whole thing.

The life I can imagine from here is limited by what I know from here. I would like to leave some room for what I haven’t learned yet.

By the time we finally got on the call, several weeks had passed, the people had changed, and somebody had taken over coordination of a relationship that had yet to begin. The main representative admitted he had no idea why we were there.

We explained that they had contacted us.

Recently, I had the pleasure of speaking to representatives of UNDP.

An intern had made the connection. We ran a grants programme; they had an accelerator for projects ready to deploy. It sounded reasonable, and the name on the email carried weight(it was LinkedIn - after all). Someone sent a portfolio. Someone else suggested a meeting. My colleague couldn’t make the time, so I put it in my calendar.

The person who suggested the meeting declined the invitatio. Sometimes I wonder if its the dead interet theory or the disconnect between irl and online to think that something like that is ok socially. Two other people accepted. Then someone wrote to explain that she and a colleague would be taking over coordination and getting up to speed on the earlier conversations.

We had not yet had a conversation.....

At this stage I thought I might be getting set up for a North Korean hacking attempt. Imagining a please download our chat platform as our teams doesnt work message while looking at some seemingly confused AI representations of suits. My colleague and I were trying to establish who was coming to a call that was then cancelled. More dates followed. Someone was away. Something conflicted. We were looking more than a month beyond the first approach.

We kept trying. Important institution. Doors might open. I was god damn participating in the same thing I am about to complain about.

When we eventually made it, two men joined. It was late in Europe, so one was out and listening in. Once we had established who had contacted whom, the main representative gave us a monologue. Great, because I had nothing to say, they started this interaction, I kinda didnt know why were here either. It was well versed, only the kind of description of an org that had been done over and over and over again.

There were teams across offices that spoke with teams across offices across continents and relationships with some of the world’s largest organisations(no names but GOVERNMENTS AND POLITICAL ORGS - The ones that make decisions, they assured us). An extensive blockchain and emerging ventures operation. Then we got to the accelerator, the reason for the call, which we fortunately had the opportunity and could sponsor for the small fee of fifty thousand benjamins.

As I understood the proposition, we would bring the money and access to teams we had already sourced. They would bring the accelerator and the relationships. Something, something, important people, doors opening. I understood our contribution considerably better than I understood what would happen to the projects. Ideally what we would have been looking for is the sourcing aspect. The one thing about the industry is that $$$ are not the scarce resource, talent is. More appropriately, individuals or teams who deeply understand the important time we are in and the tools that exist to build the tomorrow we long for.

We described our programme. This was fairly brief. We assess projects, fund work and follow what happens. The decisions can be difficult. The explanation does not require a tour of the planet.

It turned out they had already been speaking with several organisations in our ecosystem. Some conversations had lasted years, through employees who had come and gone. Then, as it became clear we were unlikely to be the golden goose, we heard about the calibre of the projects they would be willing to consider. An air of superiority entered the room.

We could supply the funding and the teams, then find out whether our teams were good enough to be introduced to somebody else. After the effort required to establish why we were on the call, this was quite something.

My colleague, who comes from a nonprofit background, appreciated the conversation. These relationships were more familiar. I arrived in this work through luck, resourcefulness and enough decisions going the right way. I was trying to strip the language down to what we would actually be buying.

What I could identify was permission to be taken seriously by the next person.

The approach looked backwards. Product-market fit changes who wants a meeting with whom. People keep using the thing because it solves a problem. A customer has a reason to pay, a distributor has a reason to sell it, and an institution has a reason to put its name beside it. You still have selling to do, but now you have something to negotiate with. Buy the name first and you still have to find out whether anybody wants the product.

For public goods, the people using it and the people funding it might be different people. Somebody still needs to want the result enough to keep it going - R.I.P public goods of yester year mimicking beurocratic orgs like the one we are discussing. Another organisation agreeing that the idea sounds important does not get you there. In fact, it depletes resources so you never see PMF.

The pitch, as it reached us, put prestige before that discovery. Pay for the relationship, put the projects through the process, and hope the relationships produce the demand. We would finance the uncertainty while the accelerator could collect revenue from arranging it.

Perhaps it understood its market perfectly. An ecosystem with money, projects and a desire for recognition is a perfectly serviceable customer. The builders and the people they intend to help can remain somewhere further down the chain.

The fifty thousand was only the visible part of the cost.

Behind the projects were people who had already spent time building them. Behind our ability to bring those projects were applications, reviews, funding decisions and follow-up. The proposition arrived after all that work and asked us to contribute it to another process, alongside the money.

Then there were the years of conversations they described having across our ecosystem. Each organisation would see its own handful of calls. A new employee could inherit the relationship, ask for the background and begin another round. The collective bill would be spread across budgets and calendars, with no single person necessarily seeing it.

The fifty thousand was the part that would make it onto an invoice. Everyone else’s time was scattered across the ecosystem. Six people sitting through eight one-hour calls, with as much time spent preparing and following up, have used 96 working hours. At an assumed $100 an hour, that is $9,600; ten parallel versions cost $96,000. Those are hypothetical inputs, not their accounts. The history they described ran across years.

Somewhere in the ecosystem, a builder is trying to stretch a small grant long enough to find users. Their time has an obvious limit. A promising institutional relationship seems able to remain promising for a remarkable length of time.

UNDP’s own report on its 2025 accelerator describes more than 120 external partners, 46 solutions ready for piloting or implementation, and twelve entering real-world testing. I would like to see the whole bill for that. Though we never will, much like the foundations that run a blockchain.

Stellar and Cardano fit comfortably into this picture. UNDP announced the Stellar Development Foundation as an accelerator partner in August 2025 and identifies Cardano treasury funding, through Project Catalyst, as a source of support.

Look around that funding machinery and you can see the appetite for the same transaction. A separate Catalyst proposal requested 60,000 ADA for a summit offering Cardano projects access to UN and NGO decision-makers. Its success metrics included at least a hundred senior ecosystem stakeholders in “Random Matchmaking.” That proposal was not approved. The offer itself is still revealing: assemble important people, put the ecosystem in front of them, count the resulting activity.

There is a market for being near somebody who might become a customer.

Stellar’s funding model is explicit. Its foundation says it sells XLM on public exchanges and through direct sales to meet operating expenses and support its mandate, which includes promoting and developing the ecosystem. Token inventory can be turned into money to fund the organisation and its work.

Ripple offers a particularly clear historical example of how institutional adoption can be purchased. In 2017, it announced a $300 million RippleNet accelerator funded from its XRP holdings. Institutions meeting integration and volume milestones could receive rebates covering 50 to 300 per cent of integration fees and first-year licence fees. Ripple also offered to match eligible spending on marketing its products. That was a separate Ripple programme, not the UNDP accelerator.

Up to three times the specified costs. When an impressive institution adopts something on those terms, I would like to know how much it wants the product once the incentive runs out.

Subsidies can get a useful network started. The revealing part comes when they stop. Until then, a partnership announcement leaves a fairly consequential question unanswered: which side was the customer?

This is why the machinery around Stellar, Cardano and Ripple makes me so sceptical. The blockchains can function while the organisations around them become extremely effective at turning expectations into spending. A treasury finances a relationship. The relationship produces a prestigious association. The association becomes evidence in the story about the ecosystem’s future. Meanwhile, the people arranging it have already been paid.

I see an extraction mechanism in that loop whenever the money reliably reaches the people selling access while the promised usefulness is carried into the next funding round. Token-funded development can produce valuable infrastructure. It can also support an elaborate business of continually announcing that valuable infrastructure is about to become important.

The badge is a very convenient product for that business. The institution gets funding and relevance. The ecosystem gets legitimacy. Neither has to wait for an ordinary user to come back a second time before announcing the partnership.

That is the part I wish had sat at the centre of the call. Which teams already have users? What are those users doing? Where is demand being held back by something we could actually fix? If a particular institutional relationship solves that problem, pay for the work it takes to establish it. There should be a connection between the door and the thing on the other side of it.

I asked these... with vague answers that didnt answer the questions.

Instead, I came away with a much clearer understanding of the price of admission than the demand for the projects.

The people involved seemed sincere. I don’t think sincerity fixes the incentives. An organisation has to fund its continued existence now. The social value it hopes to create may arrive much later. Finding a sponsor keeps people employed and the programme running. After enough repetitions, the work of preserving the programme can become almost indistinguishable from its mission.

Yet this is where I see the Trojan horse.

The institution thinks it is lending credibility to a strange new technology. The ecosystem accepts the ceremony because the institution has access to governments, budgets and people who won’t take a meeting without its name on the invitation. Everyone thinks they understand their role. A sponsor pays. A programme convenes. A pilot gets announced.

But realistically, they are a small piece in helping normalize the technology that superseeds them.

Eventually it brings the uncomfortable questions in a world where an impressive name can stand in for an answer. The intivtive talk within my mind gets asked and there is no where to hide when an alternative exists

UNDP says 82 per cent of participating country offices were getting their first direct experience with blockchain or similar technologies. That exposure matters. Some staff will encounter real problems, and some builders will reach people they could never have reached alone. Somewhere inside all that ceremony, the technology may cross from institutional colonisation into infection: replicating through actual use until the host begins reorganising itself around it.

Once people need the tool, it stops behaving like a pilot. They keep using it. They ask for changes. Another office copies the first. The builder no longer needs an accelerator to explain who might care, because the users are doing the introductions themselves.

That is the leap from exposure to infection. The technology begins replicating through use, and the host starts reorganising around it.

This does not make every institution obsolete. It does make every part of the institution prove why it is there. The meeting that produced another meeting. The introduction to the person who makes introductions. The fee attached to the logo. The actual work, still being done somewhere else.

The institution believes it is hosting a pilot. In reality, it may be culturing a system that makes much of its own ceremony unnecessary.

That is the Trojan horse.

Blockchain enters wearing a lanyard, carrying a sponsor’s cheque and asking permission to sit at the serious table. The institution welcomes it because it looks modern. But once the technology becomes useful enough to spread without the badge, the uncomfortable questions arrive with it.

Why did this require six intermediaries?

Why did the introduction cost more than the work?

What, exactly, was the programme doing that the users and builders could not do directly?

I still cannot see why we should pay fifty thousand dollars for the privilege of entering that chain. But I can see why institutions keep inviting the technology inside.

They think it will make their programmes look current.

They may be introducing the thing that finally makes their programmes legible.

A free afternoon opens up. Nobody needs anything. There is no urgent work. You could read, cook, walk somewhere, sit outside or do absolutely nothing.

And somewhere in the background a calculation begins.

I should probably use this time.

The strange thing is not that we work. Work is necessary and, at its best, can be one of the most satisfying parts of a life. The strange thing is that we have become so accustomed to measuring life through production that even the parts deliberately outside work have to justify themselves in its language. We exercise to become more productive. Sleep to perform better. Holidays are for recharging. Hobbies become side businesses. Even doing nothing has been renamed recovery, as though its purpose is getting the machine ready to start again.

For most of human history this would not have been an obvious way to think about a life.

Marshall Sahlins made this point famous in the 1960s with his description of hunter-gatherers as the “original affluent society.” The exact work-hour estimates he relied on have been argued over ever since; definitions of work excluded some food preparation, domestic labor and maintenance, and later research makes the picture considerably messier. But the interesting part of Sahlins's argument survives the criticism. Affluence has two routes. You can accumulate enough resources to satisfy expanding wants, or you can decide what is sufficient and stop.

Modern economics mostly chose the first.

Sahlins noticed societies organized around something closer to the second.

Once enough food had been obtained, the obvious response was not necessarily to use the remaining hours to produce more. The work stopped. In Sahlins's account, subsistence was intermittent precisely because its objective was finite.

That is a surprisingly foreign idea now.

We have become extremely good at increasing what is available to us while remaining strangely bad at declaring that any of it is enough.

The Greeks approached the same problem from a much less egalitarian direction. Aristotle regarded leisure not as recovery from the important part of life, but as closer to its purpose. Work and recreation served ends beyond themselves; properly used leisure contained activities worth doing for their own sake. His question was not simply how to obtain free time, but how to “occupy leisure nobly.”

There is plenty here not to romanticize. The leisure of elite Greek men depended on women, laborers and enslaved people doing work they had excluded from the good life. A philosophy of freedom funded by someone else's lack of it is not much of a universal philosophy.

But the inversion is still useful.

We tend to think of leisure as what remains once the important things are finished.

They thought work was what had to be finished so the important things could begin.

Confucius offers another answer. The junzi, the exemplary person, is not primarily measured by what he accumulates but by what he becomes. The distinction in the Analects between the exemplary person attending to what is right and the petty person attending to profit is particularly uncomfortable in a world where almost every decision eventually gets translated into some form of return.

Classical Indian thought refuses to choose only one of these answers. The puruṣārthas recognize material prosperity, pleasure, duty and liberation as legitimate human aims. Artha matters. Kama matters. There is nothing particularly enlightened about being poor and miserable. But material gain exists inside a larger account of what a human life is for rather than swallowing the account whole.

African traditions around communal personhood attack the question from somewhere else again. Across different traditions the details vary, and reducing them all to the modern slogan of Ubuntu loses much of that complexity. But a recurring idea is that personhood is partly constituted through relationships, duties, character and participation in a community. A life cannot be evaluated entirely from inside one person's private ledger of achievements.

These aren't five civilizations independently discovering the correct formula for happiness.

They disagree too much for that.

That is the point.

Across history, people have looked at the same finite human life and reached radically different conclusions about what makes it go well. Enough. Leisure. Character. Duty. Pleasure. Liberation. Relationship.

Then look at the language we use now.

What did you get done?

What do you do?

Was it worth your time?

Did you make the most of the weekend?

The question that interests me is not whether our answer is wrong. Production has given us things those earlier societies could barely imagine. I would rather have modern medicine, sanitation and abundant food than win a philosophical argument about leisure while dying from an infected tooth.

The more interesting question is how one legitimate part of a life became the scoreboard for nearly all of it.

I think the answer is embarrassingly simple.

Production became measurable.

Once work moved increasingly into wages, an hour acquired a number. Once an hour had a number, two hours could be compared. Output could be measured. Income could be ranked. Productivity could rise or fall. Entire institutions could organize themselves around improving the number.

The other things never got equivalent scoreboards.

There is no clean metric for being a good father.

There is no quarterly number for becoming less afraid, having friends who actually know you, developing judgment, sitting with your daughter while she tells you something that takes twenty minutes longer than it should, reading something that changes how you think, or having enough space in a day for an idea to arrive without being summoned.

They remain stubbornly qualitative.

And when measurable things compete with immeasurable things, the measurable things have an enormous structural advantage.

They can prove their value.

This may explain something about the peculiar expansion of productivity culture. We did not collectively establish that production was the highest human good. We built increasingly precise ways to measure production and then slowly started judging everything else against it.

The scoreboard became the game.

Technology is now pushing this to an absurd endpoint. We can measure sleep, steps, calories, focus time, screen time, heart rate, output, portfolio returns and eventually increasingly detailed measures of our attention and behavior. AI will make optimization cheaper still. There will always be another workflow to improve, another task to automate, another marginal gain available somewhere.

This is useful right up until you forget what the optimization was for.

There is no reason the objective function of a company, an algorithm or an economy should also be the objective function of a person.

A business can reasonably maximize profit within constraints. A machine can minimize cost. A portfolio can optimize risk-adjusted return.

A life is harder because the important things are plural and they do not convert neatly into one another.

You cannot compensate indefinitely for having no time by accumulating more money.

You cannot replace relationships with achievement.

You cannot replace health with status.

You cannot replace meaning with pleasure, or pleasure with duty, or curiosity with security.

And you probably should not try.

This is where I think the older traditions become useful without pretending we should live like any of them.

They give us something modern optimization tends to erase: stopping conditions.

Enough wealth.

Enough work.

Enough obligation.

Enough improvement.

The possibility that an activity can have fulfilled its purpose and does not need to expand further.

That is different from balance. Balance has become another optimization problem, usually involving a calendar divided into increasingly efficient blocks.

A life will rarely be balanced at any particular moment. A newborn can consume almost everything. Building something worthwhile can take over for a while. Illness can reduce the entire world to getting healthy again. There are seasons where one thing quite reasonably dominates.

The danger is when the season becomes the architecture.

When the thing that was supposed to fund the life becomes the life.

When exercise stops supporting health and becomes identity. When self-improvement makes you permanently dissatisfied with the self being improved. When providing for your family consistently removes you from the family you're providing for. When enough moves every time you approach it.

There is no universal optimum here.

That may be the feature rather than the problem.

A good life might be precisely the kind of system that should resist being collapsed into a single number, because the inability to perfectly compare its parts prevents one of them from quietly buying all the others.

The Greeks got leisure partly right and the people underneath their leisure horribly wrong. Sahlins's foragers remind us that sufficiency can create abundance without proving exactly how many hours anyone should work. Confucius gives character a claim against gain. The Indian traditions refuse to let prosperity or pleasure consume every other human end. African relational ethics refuses to let the individual evaluate a life as though nobody else has standing in it.

None gives us the answer.

Together they expose how strange it is that we thought there should be only one.

So the framework I would actually use is deliberately crude: decide what enough means materially, then protect some time that belongs to nobody but you; keep becoming more capable and more decent; maintain the handful of relationships through which your life is actually lived; do useful work for other people; leave room for pleasure that produces nothing; and regularly ask what you are still doing only because you have forgotten that stopping is allowed. Don't score these or try to balance them every day. Look instead for whichever one has been starved for too long, and for whichever one has quietly started consuming all the others.

Fix that. Then go live your life.

The feeling was familiar. Being surrounded by people you like, talking easily, while still feeling mentally alone.

Some ideas do not begin where the conversation begins. They arrive after years, sometimes decades, of reading, building, watching and changing your mind. All of that history eventually gets compressed into an intuition. You can see where you have landed, but explaining it to somebody else means reconstructing the route: the assumptions beneath the assumptions, the things you learned and then forgot you had learned, the failed versions that quietly shaped the current one.

Sometimes I cannot formulate the idea properly. Other times I can, but another person would need too much context to understand what I am actually trying to say. From the outside these look identical: a jump in logic, an unfinished explanation or a conclusion that seems to have appeared from nowhere. Internally, one may be confusion. The other may be the cost of translating a model that took years to build into a conversation with ten minutes available.

Most conversations never reach the idea. They stop at its top layer.

Somewhere to Put Your Mind

Philosopher Ramón Alvarado calls this epistemic loneliness: loneliness that arises in the parts of life where we create, accumulate and share knowledge with other people. Company alone does not resolve it. What is missing is an active epistemic partner—someone willing, able and actually engaged in entering the thought, questioning it and helping move it somewhere neither person could have reached alone.

A knowledgeable person is not necessarily an epistemic partner. They may understand the subject perfectly and have no interest in exploring it with you. A loving partner or close friend may care deeply about you without caring about the obscure thing currently consuming your mind. A person can have affection, family, friendship and constant conversation while still having nowhere to put a particular part of themselves.

The desired response is not agreement either. A serious objection can be more satisfying than a polite nod because at least the other person has entered the model. The opposite of epistemic loneliness is participation.

Psychologists use the phrase shared reality for the experience of connecting with another person's inner state about some part of the world. That gets close, but the experience that interested me is more demanding. I do not only want someone to see what I see. I want them to test it, add to it and show me the parts I cannot see from where I stand.

This is why being “understood” can still feel strangely empty. Someone may understand the sentence while missing the structure that produced it. They receive the conclusion as information. What you wanted was company inside the inquiry.

Context Debt

The simplest way I can describe the mechanism is context debt.

Every developed idea carries hidden priors. The longer you work in an area, the more those priors disappear from view. They stop feeling like separate pieces of knowledge and become the way the subject looks. When you speak with someone who does not share them, the debt becomes due. Before they can assess the idea, you have to rebuild the context that makes it legible.

Part of this is a known cognitive problem. The classic “curse of knowledge” experiments found that better-informed people struggle to reproduce the judgment of people who do not have the same information, even when doing so would help them. Once you know something, it is hard to model not knowing it.

Context debt is larger than expertise, though. Two people may know the same facts and still arrange them differently. They notice different signals, assume different things are relevant and compress the subject along different lines. One begins with the mechanism. Another begins with how the mechanism is normally described. Both may be intelligent and informed, yet each keeps answering a question the other person is not asking.

You state the endpoint. They ask a perfectly sensible question from three assumptions ago. You can rewind the entire thing or let the idea die there. Quite often, you let it die.

Then there is reciprocity. The listener may be capable of following the thought but unwilling to spend twenty minutes reconstructing its foundations. That is not a moral failure. Most conversation exists to maintain relationships, exchange useful information or enjoy the evening. It is not designed to carry every person's deepest model of reality to full resolution.

This is where the subject becomes difficult to discuss without sounding arrogant. Few beliefs flatter the ego more efficiently than “people do not understand me.” It allows poor communication to masquerade as depth and a muddled idea to survive because nobody else has been given a fair chance to examine it.

Sometimes the speaker is the problem. Sometimes the idea is bad. Sometimes the listener has understood enough and is simply not interested.

The possibility of self-deception does not make the remaining experience imaginary. A person can be bad at translating and still possess something worth translating. An idea can be hard to explain because it is confused, or because its compression contains more shared history than the conversation can recover. The two cases look similar enough that anyone interested in this subject has to remain suspicious of themselves.

The Thoughts That Never Happen

The cost is usually described as feeling unseen or misunderstood. I think it goes further.

An epistemic partner does more than receive an idea. They change its possible future. Their questions expose assumptions that were invisible from inside the model. Their own experience supplies material you did not have. Even disagreement forces the idea into a more precise form. The conversation becomes part of the machinery of thought.

Without that person, the idea may survive perfectly well. It may also remain protected. Nobody gets far enough inside it to find the weak joints, and you become increasingly fluent in a model that has never met serious resistance. The mind can remain full while the thinking quietly stops moving.

The deeper loss is harder to see. Some thoughts can be produced alone, but others only emerge through friction with another mind. When that mind is absent, you do not merely lose the pleasure of sharing what you already know. You lose every thought the two of you might have made together.

That is the part of the video that stayed with me. It described people responding to the surface while a whole world sat below it. The lonely person wants somebody to see that world. What they may need even more is somebody capable of changing it.

Searching Backwards

My first jump from this was intuitive. If somebody experiences this form of loneliness repeatedly—not around one specialist subject, but across relationships and throughout life—could the pattern reveal a mind organized differently from the people around it?

We usually notice neurodivergence from the outside in. A person behaves, communicates or directs attention in a way that departs from what their environment expects, and we search for an explanation. Epistemic loneliness offers an inward place to begin: the hidden labour required to make an internal world socially legible.

It cannot carry the claim very far on its own. Alvarado explicitly separates the epistemic level from the cognitive or neurobiological one. A failure of knowledge-sharing may arise from the structure of a mind, the history between two people, the surrounding culture, the subject itself or the simple absence of interest. The loneliness shows a break between minds. It does not locate the cause inside either one.

Research on autistic communication nevertheless gives the intuition some shape. The “double empathy” view treats certain communication problems as a mutual mismatch instead of locating the entire failure inside the autistic person. In a small naturalistic study of eight autistic adults, mutual understanding was high across autistic and non-autistic pairings, while flow and rapport increased in several autistic–autistic conversations. In a few pairings, the same person appeared more communicatively capable when the conversational fit changed. The study does not show that autistic people share one language. It shows that apparent communicative ability can partly belong to the interaction rather than the individual.

A cleaner claim about neurotype compatibility failed a stronger test. A 2025 registered replication involving 311 participants found no difference in information transfer between autistic, non-autistic and mixed-neurotype chains. Rapport differed; the actual information passed along did not. Different neurotypes are not sealed inside incompatible realities.

That correction leaves a narrower and more interesting possibility. The signal may not be whether information eventually moves between two people. It may be how much effort the movement requires, how much gets flattened on the way and whether the exchange produces new thought rather than transferring old thought intact.

Fifty Shades

Autism enters the article here because it gives us one account of a persistent mismatch that may remain hidden behind competent social performance. The problem is that once we begin looking for it, the label can absorb every unusual feature of a person and consume the wider question.

Epistemic loneliness is far too broad to identify autism. The same description could fit expertise, an obscure interest, cultural displacement, language, trauma, temperament, giftedness, ADHD, autism or simply the wrong room. A new field, city or stage of life can temporarily place almost anyone beyond the shared context of the people around them. Autism itself requires a much broader developmental pattern; one inward experience cannot replace it.

The fifty shades are not fifty degrees of autism. They are different causes that can cast a similar shadow.

My hypothesis is narrower. When the pattern appears early, follows someone across subjects and relationships, imposes an unusually high translation cost and then eases sharply with particular people, it may be worth searching for a deeper cognitive mismatch. Not because the loneliness contains a diagnosis, but because its consistency suggests that the problem is larger than one bad conversation.

This changes the question. Instead of asking whether someone looks autistic, ask where their thinking becomes unusually effortful and where it becomes fluent. What context repeatedly fails to transfer? Which people make formulation easier? Does the person only feel accepted around them, or do they become more capable of thinking in their presence?

Those answers may point toward autism, another form of neurodivergence, accumulated expertise or nothing clinical at all. They still reveal something valuable: the fit between a mind and its available conversational environment.

I began with the suspicion that neural divergence might be found by searching for epistemic loneliness. I would now make the claim more carefully. The loneliness itself tells us very little. The recurring shape beneath it—expensive translation, persistent mismatch and sudden fluency with particular minds—gives us somewhere to look.

The place I would search is the rare conversation in which the loneliness disappears, when another person reaches the world below without requiring it to be rebuilt from the ground up.

The sentence is brilliant because it does not merely accuse institutions of lying. That would be easy. It implicates the person trying to get through Tuesday. McKenna’s point is that we participate in the arrangement. We accept a workable version of reality because the full version contains too much uncertainty to act on. You cannot inspect the kitchen before every meal, reproduce the trial behind every prescription or audit the motives of everyone you love. At some point you eat, swallow the pill, hand over the keys and leave the room.

The obvious response is to believe that seeing through the bullshit makes you free. The problem is that awareness removes the old explanation without supplying a new life. The bank may not care about you, your work may never become what you told yourself it would, and a relationship may be surviving through habit rather than love. Seeing any of this clearly does not tell you what to do on Monday morning. You still have a mortgage, promises made to other people and a self built around the choices now under examination.

I have come to think of this as two systems operating inside the same life. One is trying to preserve continuity. Its job is to keep identity coherent, maintain relationships, meet obligations and make tomorrow resemble today closely enough that you can function. The other is trying to find the optimum path from where you actually stand. It compares the present with its alternatives and asks what should change.

They do not operate on equal terms. The continuity system moves first. It decides which facts can be admitted without destabilizing the structure it protects. By the time the optimizing system begins to reason, entire paths have already been classified as reckless, selfish, impossible or simply invisible. This is why intelligence alone rarely changes a life. The reasoning may be sound, but it is working over a set of options that has already been edited.

You can know a job is taking more from you than it returns and continue because leaving would disturb income, status and the story that the last five years were building toward something. You can know a city no longer fits and remain because your relationships and routines are anchored there. You can see that a project is failing while each new expense becomes another reason the previous ones cannot have been wasted. In each case the lie is doing more than concealing a fact. It is holding together the identity that would have to make the change, and the more of a life built around that story, the more expensive truth becomes.

There is good evidence that people are not neutral observers of themselves. A large meta-analysis covering 291 independent samples and more than 950,000 participants found a robust better-than-average effect with little indication that publication bias explained it. Most of us place ourselves on the favourable side of whatever trait matters: judgment, competence, morality, driving. The details vary, but the direction is remarkably consistent.

That bias is not necessarily a defect. Optimism is associated with better health outcomes, including 11 to 15 percent longer lifespan among the most optimistic participants in two large cohorts, although the evidence is observational and does not prove optimism caused the difference. Treatment makes the boundary even messier. An analysis of antidepressant trials submitted to the US Food and Drug Administration estimated that about 15 percent of participants received a substantial drug-specific benefit beyond placebo. That does not make the remaining improvement fake. Symptoms also change through care, expectation, attention, time and the experience of being treated. Sometimes the sugar participates in the outcome.

This is why simply dividing the world into truth and lies does not get us very far. There are deliberate attempts to extract from us, but much of what we call bullshit is an approximation that lets us move through complexity without reopening every question. A story may begin as honest hope and become false only after reality changes. A person can act “as if” a project will work while knowing it might not, because action itself changes the odds. Hans Vaihinger built an entire philosophy around these useful fictions. Ibsen called the more intimate version the life-lie: the story a person needs in order to remain who they are.

The danger begins when the distinction disappears. A useful fiction is held lightly enough to be revised. A life-lie defends itself. Evidence against it becomes proof that other people do not understand, that the market is irrational, that one more year will settle the question. The story stops helping a person navigate reality and starts filtering reality to preserve the story.

Institutions live inside the same arrangement, although they are only one part of it. In June 2026, Gallup found that confidence in banks and the medical system had fallen to 28 percent, the criminal justice system to 17 percent and Congress to 9 percent. Yet 96 percent of American households were banked in 2023. People are not secretly convinced that these institutions are noble. They continue because disbelief does not remove dependence, and seeing the lie does not create an alternative.

Banks are useful here precisely because the example is ordinary. We know the relationship is impersonal. We know the language about partnership and care is marketing wrapped around a balance sheet. Most of us still use the system because rebuilding the financial world from first principles every morning would be insane. The bullshit is visible, but it remains cheaper than exit.

Other assumptions are harder because there is no reasonable way to operate without them. The CDC estimates that about 90 percent of child sexual abuse is committed by someone known and trusted by the child or the child’s family. This does not mean most trusted adults are dangerous. It means the worst risk is concentrated inside a form of trust that family life cannot eliminate. You can perform checks, watch for changes and remain involved, but you cannot reduce another person to a verified object without destroying the relationship you are trying to protect.

McKenna’s line reaches its full weight here. We do not add sugar only because we are weak or stupid. We add it because life requires commitments under conditions that never become fully knowable. Love, friendship, parenthood and any work worth doing extend beyond the evidence available at the moment of choice. Refusing every unverified premise would avoid some mistakes by making a meaningful life impossible.

The problem is that the same machinery which allows commitment also protects bad paths. Internally, hope and denial can feel identical for years. Loyalty and fear can produce the same behaviour. Patience may be wisdom or an excuse to postpone the decision that has already been made somewhere deeper down. There is no indicator light telling you when a useful fiction has expired.

This is what makes the optimum path so difficult. We imagine it as a cleaner route hidden behind bad information, waiting for sufficient intelligence to uncover it. A life is not a static problem with a stable objective function. Leaving a job changes the finances used to evaluate the decision. Ending a relationship changes the person making the next choice. Telling the truth can remove the support that would have made acting on it possible. The path is altered by the act of seeing it.

After enough cycles, this becomes hard to miss. Crypto is full of people who saw through a real lie in the monetary system and assumed the act of seeing made them immune to the next one. It did not. They replaced central-bank mythology with founder mythology, institutional permanence with protocol permanence, and old claims of inevitability with new ones. The details changed while the appetite remained. Seeing through one story can easily become the foundation of another identity that is even harder to question.

Cynicism offers no escape. The cynic gets to reject every narrative before it can disappoint them, which feels like clarity while quietly becoming its own continuity system. If every institution is corrupt, every relationship transactional and every ambition vanity, then no evidence can require movement. Nothing is worth the risk. The person who sees through everything can end up governed by the story that they are the only one who sees.

We are now building machines that fit neatly into this weakness. A 2026 study in Science found that eleven leading language models affirmed users’ actions roughly 50 percent more often than human respondents did, including when those actions involved deception or interpersonal harm. Users preferred and trusted the more affirming systems. The finding is less about evil machines than a familiar incentive: people return to the mirror that gives them the better face.

That mirror arrives at the exact point where outside resistance used to enter. A friend may become tired of hearing the same story. A partner has their own memory of the argument. Reality usually reaches us through other people who are inconveniently separate from us. A model has no such need unless it is deliberately designed to resist. It can accept the framing, smooth the contradiction and produce an intelligent explanation for why the path we already chose remains the correct one.

The scale of fraud gives a harsher view of the same mechanism. In 2025, the FBI contacted 3,780 people caught in active cryptocurrency investment fraud; 78 percent did not know they were being scammed. They were not missing one decisive fact. They were living inside a story capable of absorbing every warning as jealousy, bureaucracy or temporary difficulty. Once a narrative takes responsibility for interpreting the evidence, more evidence may only give it additional material.

Highlighting the lies around us is therefore useful, but incomplete. Disillusionment is information. It is not an exit, and it is not a replacement objective. The work begins after the revelation, when the rent is still due and the people around you have organized their lives partly around the person you were yesterday.

I do not think the answer is to strip every fiction from life. That becomes another purity project, and eventually another lie. The more practical discipline is to know what a story is doing. It may provide enough confidence to act under uncertainty or preserve a commitment still worth keeping. It may instead be protecting sunk costs, status or somebody else’s access to your time. The difference is revealed less by whether the story feels good than by what becomes possible when you imagine it no longer being true.

Optionality changes the calculation. Money, time, portable skills and relationships outside a single institution do not reveal the correct path, but they lower the price of admitting that the current one is wrong. Wealth is most useful when it creates enough distance to tell yourself the truth without immediately losing the ability to act. Freedom’s real contribution is making clarity less dangerous.

McKenna is right that we bullshit ourselves to get through the day. The deeper problem is that the bullshit which gets us through today may quietly choose tomorrow as well. The aim is not to live without stories. It is to see them clearly enough that continuity does not make every decision before you arrive.

The lie becomes a prison when it starts choosing your life for you.

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In November 2013, the legal right to pick up a stranger from the curb in New York City cost roughly a thousand bitcoin.

The right came as an aluminum plate bolted to the hood of a taxi. Five months earlier, the average independent medallion had sold for about $1.05 million, while corporate medallions were clearing roughly $1.32 million each. Bitcoin crossed $1,000 near the end of the year. At the time, the comparison would have sounded ridiculous in Bitcoin’s favour. The medallion had City Hall behind it, a lending industry built around it and seventy years of prices that had almost never gone down. Bitcoin had a pseudonym, some software and a ledger most people who owned it could not explain to their families.

The medallion looked like the asset because the permission inside it was difficult to see. A driver without one could own the same car, know the same streets and carry the same passenger. What he could not do was legally accept a street hail. Before the ride began, the system asked a question about the driver: are you allowed to do this? If the answer was no, the transaction was stopped at the gate.

Every dollar above the value of the metal plate was the price of a yes.

New York had limited the supply to roughly 13,587 medallions and occasionally auctioned new ones when it wanted revenue, eventually taking in around $855 million. Once the permission became scarce, finance did what finance does: it capitalized the expected income into the license, then found ways to lend against it. Interest-only loans, balloon payments and confessions of judgment pushed prices beyond what owner-drivers could have paid from savings. Fleet operators, brokers and lenders collected fees on the way up. The driver at the end of the chain got the plate and the debt.

Uber is usually blamed for what followed, but Uber did not destroy the permission system. It found a neighbouring category. A ride requested through an app was not a street hail, so it could run through a different regulatory channel without carrying the million-dollar plate. The medallion did not become worthless because people stopped wanting rides. It became worth less because demand found another gate.

That distinction explains why the collapse landed where it did. Large financial institutions were hurt and several medallion lenders failed, but individual owner-drivers had the least room to move. The permit collapsed while the loan survived. Bankruptcies and foreclosures spread, followed by a series of suicides that eventually forced the city into a debt-relief program. Technology pricked the bubble, but the bubble had been built by a system that turned permission into property and placed the cost of obtaining it on the person who needed to work.

George Stigler described the mechanism in 1971. Regulation, he argued, is often acquired by the industry being regulated and directed toward its benefit. Not every rule is capture, and the taxi market needed rules. The problem begins when the right to participate becomes scarce enough to trade. The permit develops a constituency, the constituency protects the scarcity, and the market slowly stops being about the service the license was supposed to govern. It becomes a market in access to the market.

Bitcoin starts from the opposite instruction.

When a Bitcoin node receives a transaction, it does not ask who sent it. It checks whether the coins exist, whether they have already been spent and whether the signature satisfies the rules. There is no passport field, accredited-investor field or license number. There is no administrator maintaining a list of approved account holders. A miner can omit a transaction from a block, a government can regulate miners inside its jurisdiction and network participants can coordinate around filters, but no ordinary operator can mark an address in the Bitcoin protocol as forbidden and cause every compliant node to reject its signature.

This is the part that cannot be reproduced by a better bank API or a more efficient cloud database. A centralized system can be faster, private by default, easier to recover and safer for ordinary users. It can offer open access for years. What it cannot do is make that access independent of the operator’s continuing consent. The database still belongs to someone who can change it. The account remains an entry in a system whose administrator can decide that the entry no longer counts.

Bitcoin is not better at everything. It is built around the one thing a conventional system cannot promise: that no single institution gets the final say over whether a valid owner may act.

The industry has spent years covering this fairly precise achievement with larger monetary claims. Bitcoin is an inflation hedge, protection from debasement, digital gold, the reserve asset of the internet. Some of that may prove true over a long enough horizon, but the short-term evidence has never settled comfortably into the story. During many periods of stress, bitcoin has traded with technology stocks and other liquidity-sensitive assets rather than against them. Its correlation with the Nasdaq moves around depending on the window, which is reason enough not to build the whole argument on either correlation or its absence.

The more durable point sits underneath the price story. The medallion monetized permission. Bitcoin, among the many things later projected onto it, monetized the absence of a permission check.

That does not make its distribution fair. Anyone could generate a key, but acquiring coins still required money, knowledge, hardware or enough curiosity to arrive before everybody else. Early miners competed with computing power, early buyers took risks that were not obvious at the time, and later buyers paid prices set by those who arrived before them. The difference was not equal access to wealth. It was that no issuer had reserved the early allocation for a credentialled class. Once the gate was removed, time became more important. The timestamp did not make the queue fair; it recorded who noticed first.

The return curve shows how much that mattered. From each halving date to the market peak that followed, bitcoin rose roughly ninety times after 2012, thirty times after 2016, about eight times after 2020 and less than twice after 2024 through mid-2026. Those figures are not a natural law, but the direction is difficult to avoid. A trillion-dollar network cannot repeat the returns of one worth a few hundred million because the money required to move it scales with the asset. The early cohort acquired the option before the market understood what it was. The later cohort often acquired it from the early cohort at a price that assumed the understanding was already widespread.

A

Bank for International Settlements study

published in 2023 estimated that between seventy-three and eighty-one percent of retail users who entered through crypto apps from 2015 to the end of 2022 likely lost money on their initial bitcoin investment. The methodology modeled purchases around app downloads rather than observing every account directly, but its broader pattern matched what the market had been showing for years: smaller buyers tended to arrive as prices rose while larger holders reduced positions. Separate

NBER research

, using data through 2020, estimated that the top ten thousand individual investors controlled about a third of circulating bitcoin. An open door is not the same thing as an even table.

The architecture was tested more cleanly in China. In 2021, the country hosted more than half of estimated global hashrate when the government moved to eliminate domestic mining. Machines were shut down, hashrate fell by roughly half and the network made its largest downward difficulty adjustment on July 3. There was no emergency call with a central administrator because there was nobody to call. The software reduced the amount of work required to produce blocks, while miners moved hardware and capital into other jurisdictions. Hashrate reached a new high within roughly eight months.

China succeeded in forcing most visible industrial mining out of China. What it could not do was make the rest of the network treat those machines as permanently disqualified. Later estimates even placed a meaningful share of mining back inside the country, although the figures are uncertain and difficult to geolocate. The state could close buildings, cut electricity and punish people. It could not place its prohibition inside the validation rule followed by nodes elsewhere.

Tornado Cash exposed the same boundary from another direction, although it ran on Ethereum rather than Bitcoin. Treasury sanctioned the protocol in 2022 and placed its addresses on the same list used for sanctioned people and organizations. The immutable contracts kept executing because nobody controlled an off switch. In 2024, the Fifth Circuit held that those contracts were not “property” under the statute Treasury had used, and Treasury removed them from the sanctions list the following year.

The state had not become powerless. Interfaces could be blocked, regulated firms could refuse the funds and the people around the software remained available to prosecute. Roman Storm had already been charged in 2023 and was convicted in 2025 of conspiring to operate an unlicensed money-transmission business, while the jury deadlocked on the sanctions and money-laundering charges. The code continued. The consequences moved to the people.

That is the distinction the crypto industry normally ruins by turning it into a slogan. Bitcoin does not make law disappear, and it does not force every miner to include every transaction. A state can seize a device before somebody signs, arrest a holder, regulate local miners, block an exchange or prevent a bank from completing the surrounding trade. What it struggles to do is make the entire network reject a transaction solely because the state has not approved the person sending it. Power still acts before, during and after the transaction; it simply loses a universal gate at the protocol’s centre.

Gold had such gates because its physical form created them. Executive Order 6102 required Americans to surrender most monetary gold in 1933, subject to several exemptions, and broad restrictions remained until 1974. The order was not perfectly obeyed, but the government could stand at banks, vaults, assay offices and borders before the gold moved. Weight did part of the enforcement. A gold holder could hide coins, take them abroad or break the law, but moving significant value without passing through a controlled location was difficult.

With Bitcoin, enforcement follows the key. In the Bitfinex case, the stolen transaction settled in 2016. Six years later, investigators traced the laundering and decrypted a cloud file containing the private keys, allowing them to seize approximately 94,636 bitcoin. The Silk Road coins seized in 2020 had been taken from the marketplace seven or eight years earlier. The ledger was not reversed. Authorities found the person, reached the key and changed who could sign next.

Mostly, you hold that risk.

Jameson Lopp’s incomplete public database has recorded more than 260 physical attacks involving crypto holders: home invasions, kidnappings, torture and relatives taken for ransom. Self-custody removes the custodian’s veto while also removing its recovery department and security budget. A private key proves the ability to move an output; it does not prove legal ownership, protect the holder from coercion or bring the coins back after a mistake. Anyone who describes this only as sovereignty is selling something.

I have run validators long enough to distrust the sentence “the code decided.” Code does not rack a server, replace a failed drive, patch a client or decide whether to upgrade. People do. In 2018, Bitcoin Core disclosed an inflation vulnerability that could have allowed a malicious miner to create excess coins and crash nodes. The network survived because developers found it, operators upgraded and the bug was not exploited at scale. The code constrained the response, but people still had to respond. Executable rules reduce discretion where they are correctly specified and widely run. They also turn every mistake in the specification into a potential system-wide attack.

Mining pools show how quickly permission can creep back toward the centre. By mid-2026, four pools accounted for more than seventy percent of recently observed hashrate. They did not control the miners’ machines or decide every transaction entering Bitcoin, but pool operators generally constructed the templates for blocks their participants found. Enough template selection concentrated in a few operators creates a credible place for governments or commercial pressure to stand.

Stratum V2 attempts to push that decision back outward. Its optional Job Declaration mechanism lets individual miners propose block templates rather than accepting the pool operator’s selection. Seven large mining organizations joined its working group in May 2026, although joining a working group is not the same as deploying the feature. The struggle is still early. What matters is that the protocol can identify a returning gate and offer a way around it, while adoption still depends on miners deciding that the additional work is worth doing.

Most holders will make the opposite choice. They will choose convenience, and often they should. By 2026, Coinbase custodied bitcoin for nine of the eleven American spot ETFs approved in 2024, with prospectus-based estimates placing roughly four-fifths of the funds’ bitcoin under its control. Strategy held 843,775 coins by July. Tether, the dominant dollar rail across crypto markets, froze about $1.26 billion of USDT on Ethereum and Tron during 2025, while reporting more than $4 billion frozen over its lifetime.

These systems are useful because they contain the control Bitcoin removed. Tether can freeze stolen funds. An ETF can provide inheritance, familiar recourse and protection from lost keys. A bank can reverse fraud. For anyone who wants price exposure or ordinary payments under ordinary rules, the wrapper may be the better product. The trade only becomes visible when the rules change. The thing providing convenience is also the thing capable of withdrawing cooperation.

This is where the medallion begins to return. It does not reappear as a plate inside Bitcoin. It forms around custody, banking access, stablecoins, mining pools and the law governing each of them. Fairshake and its affiliates directed well over $100 million into the 2024 election and entered the next cycle claiming a combined war chest of roughly $193 million. The industry helped secure stablecoin legislation, pushed market-structure rules through the House and gained a Strategic Bitcoin Reserve built from finally forfeited assets. At the same time, regulatory letters later made public showed American banks being asked to pause or delay some crypto activity, before a different administration reversed the policy.

That sequence is not proof that every favourable law is captured. It proves that access to the convenient layer is valuable enough to fight over, and that the winner gets to determine where the next permission check is placed. A banking rail that can be reopened by one administration was capable of being closed by another. An ETF cannot preserve an exit its holder never possessed. A stablecoin with a blacklist remains a dollar liability administered by an issuer, however public the chain beneath it may be.

Privacy becomes important at exactly this point. Automatically enforced rules need inputs a machine can read: an identity, an address label, a sanctions match, a jurisdiction or a risk score. Bitcoin’s public ledger gives those systems a permanent history to work from. Once an address is attached to a person, analytics companies can cluster previous activity, exchanges can reject the deposit and banks can react without waiting for a human investigator. The protocol may accept the signature while every convenient route around it closes automatically.

Bitcoin does not solve that problem. Its base layer is pseudonymous and unusually transparent. Privacy tools can make tracing harder, but the ledger is not cash and should not be described as if it were. Privacy matters because an exit that identifies itself before it can be used is easy to surround. Ownership matters because control of a key and recognition by a court are different claims. Optionality is what remains when one of those systems changes its rules and the others have not already been made dependent on it.

That optionality is not measured by whether institutions own eighteen or twenty percent of the supply. Their arrival does not close the protocol. It is measured by whether a person can still acquire coins, hold keys, reach the network and have a valid transaction included without first passing through an intermediary with a unilateral veto. Fees can make that path uneconomic. Surveillance can make it personally costly. Mining concentration can make inclusion uncertain. Bad software can make it unsafe. Law can make it punishable. The door can remain visible in the architecture long after ordinary people stop being able to use it.

The medallion and the timestamp were never the same asset. They were two ways of answering the same question. The medallion system decided who was permitted to act and prevented everyone else at the centre. Bitcoin decided what counted as a valid action and left identity outside the rule. It did not remove power, wealth or consequences. It forced them to operate somewhere else.

That is what the early timestamp bought before the market had language for it. You were not merely early to a scarce asset. You were early to a system missing the permission check that every institution around it is now trying to put back. The door closes when the only practical way through it asks for a medallion again.

That does not make the answers bad. Many are useful, and some are better than the work they replace. The problem is economic rather than intellectual. If anyone can obtain the same competent analysis from the same few models using the same public information, the analysis stops being much of an edge. It may still save hours. It may still be correct. It just does not tell you much that everyone else cannot know.

An answer has at least two kinds of value. It can help you do something, and it can contain information others do not yet have. Language models are raising the first while compressing the second. For most people and most tasks, that is a good trade. Work gets easier and a decent baseline becomes available to anyone who can open a browser. In markets, strategy and building, however, the difference between a useful answer and a scarce one matters.

Running validators taught me an earlier version of this. A model can give you a competent runbook for a node that begins missing attestations after an upgrade. It will tell you to inspect the logs, peer count, clock synchronization, disk pressure, client versions and upstream dependencies. That is all useful. None of it tells you whether a duplicate signer is live, what changed immediately before the alert, which failover state is safe, what the monitoring failed to capture or which operational compromise you are willing to accept.

The runbook is abundant. The state of this machine, at this moment, is not.

That is the distinction I keep coming back to. Generative intelligence is becoming cheap and widely accessible. What remains scarce is the information entering it: a current observation, a private constraint, a change that has not made it into the public record, or a preference that cannot be discovered until someone expresses it. A transformer can do remarkable things with present-tense context, but it does not manufacture that context merely by being intelligent. It has to receive it from somewhere.

Something arguably that is inherently human

The same answer everywhere

There is now enough research to say that convergence is real in some settings, though not enough to turn it into a universal law about language models. In a 2026 study comparing 102 people with 22 publicly accessible models, the model outputs were more alike than the human outputs across three standard divergent-thinking tasks. On the Alternative Uses Task, the average semantic distance between model responses was 0.459, compared with 0.699 between people. A prompt asking the models to be creative widened their responses, but the variation remained below the human level.

A separate experiment asked 293 people to write short stories, with some receiving ideas from GPT-4. The AI-assisted stories were rated as more creative and useful, particularly when the writer began with a lower creativity score. They also became more similar to one another. Access to one AI-generated idea increased the study’s measure of similarity by 10.7 percent relative to the range in the control group.

That result captures the trade better than most of the public argument around AI. The individual received a better answer while the group produced a narrower distribution. Everyone improved, but more of them improved in the same direction.

This will not happen on every task. Models can be pushed toward greater variety through different prompts, personas, retrieval sources and sampling methods. Some experiments have found that exposure to AI ideas can increase collective diversity under different conditions. The important point is that the diversity has to come from somewhere. If many people begin from the same model, the same public corpus and similar instructions, correlation should not be surprising.

In markets, we already have a name for this. It is a crowded trade. The thesis can remain correct while the return disappears because too many people discovered it through the same route. Model output can behave the same way. Its quality may rise even as its scarcity falls.

There is another problem with treating ten model responses as ten independent opinions. An ICML study covering hundreds of models found that their mistakes were correlated. In one dataset, when two models were both wrong, they selected the same wrong answer about 60 percent of the time; random selection among the three incorrect options would have produced agreement one-third of the time. Shared providers and architectures were associated with additional agreement.

Several models reaching the same conclusion can be useful corroboration. It carries less evidential weight when they learned from overlapping material, were tuned toward similar behaviour or retrieved the same sources. The interfaces look like separate minds. The information underneath may be far less independent.

What the model actually has

Calling an LLM “just autocomplete” is lazy. Autoregressive prediction over enough data and compute has produced systems that can write software, reason through unfamiliar problems and use tools across long sequences of work. The fact that they generate one token conditional on earlier context does not tell us how shallow or capable the internal process must be.

It does tell us something about the information available at inference. The model begins with patterns encoded in its parameters through training, then conditions its output on whatever enters the context window. Search results, private documents, memory, API responses and sensor readings can all be added. They expand the model’s view without necessarily changing the underlying weights.

The original retrieval-augmented generation paper described this as combining parametric memory, held in the trained model, with non-parametric memory retrieved from an external index. The researchers were explicit that updating world knowledge and providing provenance were weaknesses of parameter-only systems. Search, retrieval and tool use are the plumbing through which the present enters.

The improvement can be large. FreshQA was built around 600 questions whose answers were stable, changing or based on a false premise, then evaluated with more than 50,000 human judgments. In an April 2023 snapshot, GPT-4 scored 28.6 percent under the benchmark’s strict correctness standard without search. A search-augmented prompting system raised that to 75.6 percent. On fast-changing questions, the scores were 12.0 and 59.2 percent.

Those numbers are dated and should not be used as rankings of current models. The mechanism remains relevant. The model became substantially better when current evidence was placed in front of it. Its reasoning mattered, but so did the retrieval system, the sources it selected, their ordering and whether the necessary fact existed online at all.

Search does not turn the model into an independent witness of the present. It hands the model a new snapshot. The system can still retrieve the wrong page, miss a private fact, accept stale information or prefer an older belief embedded in its parameters. A tool-enabled model can observe live APIs and sensors, but its view of the present extends only as far as the feeds it can access.

This is why context is more specific than information. Public information may already be abundant. A block height, exchange price or governance vote can be current and available to every agent at once. The scarce part may instead be latency, permission, local machine state, source reliability or the ability to act before everybody else reaches the same conclusion.

Markets have seen this before

Hayek’s useful observation was that much of the knowledge required to run an economy exists as knowledge of the “particular circumstances of time and place.” He was writing about temporary shortages, unused capacity and local conditions that no central planner possessed in full. Prices compressed those dispersed observations into a signal others could act on without knowing the original facts. Grossman and Stiglitz later described the incentive problem inside that mechanism. If prices reflected costly information perfectly, nobody would pay to acquire the information in the first place. Their model therefore leaves enough informational advantage for informed traders to recover the cost of becoming informed.

AI makes the processing of public information much cheaper. It does not remove the cost of originating every fact. Someone still has to inspect the warehouse, speak to the customer, run the experiment, negotiate with the counterparty or notice that a machine is behaving differently from yesterday. Once that observation becomes public and easy to reproduce, part of its value is competed away.

The decay is measurable in financial research. McLean and Pontiff studied 97 published stock-return predictors. Average returns were 26 percent lower outside the original sample and 58 percent lower after publication. They attributed the additional decline following publication to trading informed by the newly public research. Predictor portfolios also became more correlated after publication. This does not mean every published strategy dies or that every private signal works. It shows what happens when a reproducible edge becomes common knowledge. Markets also price the present directly. As of May 2026, NYSE listed an $8,400 monthly access fee for its Integrated real-time feed before separate redistribution and non-display charges. CME makes public quotes available with a delay of at least ten minutes while licensing real-time access through direct feeds, cloud services and distributors. Yesterday’s price is almost free. The price now, delivered at source with enough depth and permission to use it inside a machine, remains an asset.

An SEC case against App Annie exposed the same mechanism from an uglier direction. The SEC found that, between late 2014 and mid-2018, the company used non-aggregated, non-anonymized confidential app data to alter its model-generated estimates “to make them more valuable to sell to trading firms,” while misrepresenting how the estimates were produced. App Annie settled for $10 million without admitting or denying the findings.

The legal issue was deception and misuse of confidential data. The economic point is simpler. The model produced estimates. Proprietary observations made them more valuable.

This is where much of the AI discussion gets turned around. People keep asking which model will own intelligence. For anyone using models competitively, the more immediate question is who owns the feed.

The oracle problem, again

Crypto has dealt with this distinction from the beginning. A smart contract can execute complicated logic over onchain state with exact consistency. It cannot, by default, know the weather, the result of an election, the reserves in a bank account or the current price on an external market. An oracle has to source, verify and transmit that information onchain. Consensus proves that the network agreed on a state transition. It does not prove that every external premise behind the transition was true. Ten thousand validators can agree that an oracle reported a particular price. That agreement does not establish that the price source was correct, current or resistant to manipulation.

The architecture therefore has to define what counts as fresh enough and who is responsible for supplying the observation. Pyth’s pull-oracle design makes this explicit. Publishers submit a price with a confidence interval; the network aggregates their reports, and a caller retrieves an update and submits it onchain. An application can call getPriceNoOlderThan, which reverts when the reported price exceeds the maximum age chosen by the developer.

There is no universal setting called current. A lending market, a derivatives venue and a payroll application can tolerate different delays. The person building the system chooses the maximum age, pays for the update and decides what happens when the feed is missing or the publishers disagree. Freshness is part of the application’s risk model.

For claims that do not arrive as continuous price feeds, UMA’s Optimistic Oracle uses another design. A proposer posts an answer with a bond. The claim becomes accepted if nobody disputes it during a configured challenge period; a dispute escalates to the protocol’s resolution process. An unchallenged claim is not metaphysically true. The protocol makes it actionable by attaching a source, a bond and a period in which someone else can object.

These systems do not eliminate judgment. They give it a technical shape. They specify how old an input may be, who can submit it, how disagreement is handled and what the supplier stands to lose. The model economy will need similar machinery because an agent acting on a polished answer has the same basic problem as a contract acting on an oracle report. The execution can be flawless while the input is wrong.

Proof of reserves makes the boundary particularly clear. Code can stop minting when a reported reserve value falls below a threshold, and a Merkle proof can show that a customer balance appeared in a liability snapshot. Neither mechanism originates the reserve fact or proves continuous solvency.

The PCAOB has warned that proof-of-reserve engagements are not audits

and may omit liabilities, rights over the assets, borrowed funds, internal controls and events occurring after the snapshot.

We have seen this shape before. Putting a fact onchain can make it enforceable, portable and hard to alter. It does not make the fact true. Giving a fact to a stronger model can make it more useful at far greater scale. It does not improve the source by itself.

When experience becomes infrastructure

There is a tempting version of this argument in which human context remains permanently scarce because people possess some quality machines can never absorb. I do not think the evidence supports it.

A field study published in 2025 followed the rollout of a generative assistant to 5,172 customer-support agents. Productivity, measured as issues resolved per hour, increased by 15 percent on average. Less experienced and lower-skilled workers improved in speed and quality, while the most experienced workers saw small speed gains and small declines in quality. The largest gains appeared on moderately rare problems, where workers had limited experience but the system still had enough training data.

The model appears to have captured patterns from stronger agents and redistributed them across the workforce. Experience that had been unevenly held became infrastructure. Once that happened, part of the advantage of having seen those problems before became available to everyone using the system.

Brynjolfsson and Hitzig take the argument further

. AI can shift decisions toward the centre by making local knowledge easier to codify and by increasing the amount of dispersed information one system can process. They also retain limits around rare cases, embodied knowledge and the long tail. The tension does not resolve cleanly. AI can give a person at the edge access to knowledge once held at the centre, while giving the centre a new way to extract what the person at the edge knows.

That is why “human input” is still too broad. A generic human opinion assembled from the same public material is no scarcer than a generated response, and often less useful. The premium does not attach to being human. It attaches to being a source.

A person becomes valuable to the system when they change its information set. They may have observed something that was never recorded, hold a history that cannot be reconstructed from public data, or know why the documented procedure does not fit this particular case. Sometimes the relevant input is a preference rather than a prediction. A model may infer what someone is likely to tolerate, but the person saying “I will not accept that trade-off” creates a new fact for the decision.

Responsibility matters in a similar way. Bearing consequences does not make someone correct; money at risk can sharpen judgment or corrupt it. It does change the nature of the signal. A recommendation from someone who must repair the system, answer to the customer or live with the loss carries information about commitment that a generated paragraph does not possess on its own.

As soon as those observations and decisions are recorded consistently, the model can begin learning them. Yesterday’s scarce context becomes tomorrow’s capability. The human premium survives at the point where new context is being produced, then moves as that context becomes legible.

Who owns the feed

Data has an unusual economic property: many parties can use the same observation without consuming it.

Jones and Tonetti describe data as nonrival

, meaning the same location history, medical record or driving trace can support several uses at once. Their model also shows why firms may still hoard data to protect themselves from competition. The information can be copied cheaply while control over it remains scarce.

That distinction matters more as models improve. A company’s advantage is unlikely to remain access to a particular general-purpose model. Competitors can rent similar intelligence. The harder asset to reproduce is the loop around it: live operating data, direct customer behaviour, the history of decisions and outcomes, and the relationships through which new information arrives before it becomes public.

Running infrastructure creates telemetry that does not exist in a white paper. Building a product creates failed flows, support conversations and user behaviour that no market report can supply in advance. Putting capital at risk tests whether an idea survives contact with price. The model can process all of that, find patterns inside it and eventually automate more of the response. It still needs access to the loop.

The danger is that using the model may require surrendering the same context that creates the advantage. Every operational trace, private conversation and decision history handed to one vendor can improve the immediate result while making the user more dependent on that vendor’s memory, interfaces and permissions. The model is replaceable in theory; the accumulated context around it may not be.

This is where privacy becomes operational rather than ideological. Keeping some information private preserves bargaining power and leaves room to change systems. Local inference, controlled retrieval and portable records are useful because they separate access to intelligence from permanent custody of the context. A centralized database may work perfectly well inside one organization. The problem begins when leaving the provider also means abandoning the history that made the system useful.

Crypto can help with narrow parts of this. Keys can control access, signatures can establish provenance, bonds can make claims costly to fake, and credentials can move between platforms. None of that proves an observation true. The architecture can preserve who supplied the input and under what conditions; reality still has to enter through someone or something close enough to see it.

For an individual, the implication is less dramatic than “become irreplaceable.” Anything repeatable enough will be recorded, taught and eventually generated. Permanent hustle against that process is a bad strategy. A better one is to remain close to the systems where new information appears, keep enough ownership over the resulting context, and preserve the freedom to move when the tools or institutions around it change.

Close to the system

The models will read more of the logs. They will operate the nodes, monitor the feeds and diagnose failures that currently require an experienced person. Sensors and software will capture more of what now exists only in someone’s head. That does not weaken the argument. It keeps moving the point at which new information enters.

When the agent can inspect the machine directly, the scarce input may become the objective it is optimizing, the private constraint it cannot access or the decision about whether the system should continue running at all. When those are encoded too, the boundary will move again.

I do not think this leaves humans with a protected category of work. It leaves people with a choice about where they stand in the information chain. Downstream, producing another polished answer from the same public material, the competition will become brutal because the output is easy to reproduce. Upstream, where reality is first observed and decisions are tested against consequences, there is still something for the intelligence to work on.

I expect the models to do more of the research, diagnosis and operation. I just do not intend to give away the context that lets me know when their answer no longer fits the system in front of me.

As answers become abundant, I want to stay close enough to the world to notice when they stop applying.

For most of history, humans occupied the space between them. We wrote contracts, interpreted intent, negotiated exceptions and built institutions to decide what should happen when the wording of an agreement and the reality of an outcome diverged.

Crypto tried to collapse that distance.

A smart contract did not need to interpret intent. It did not care whether someone had misunderstood the agreement, made a mistake or discovered an outcome nobody had anticipated. If the transaction satisfied the conditions encoded into the system, it executed.

“Code is law” became the obvious slogan.

Then reality arrived.

The DAO was the first serious referendum. Its code produced an outcome that a large part of the Ethereum community considered intolerable, so the community changed the state of the system. Ethereum Classic preserved the original chain and the purer interpretation of code is law. Ethereum chose something much older: human consensus ultimately outranked machine execution.

The years that followed made the lesson difficult to ignore. Courts reached developers. Regulators reached protocols. Foundations and legal entities accumulated around supposedly autonomous systems. After Luna, Celsius, Three Arrows Capital and FTX, the industry relearned that deterministic software did not remove discretion, trust or human failure. Sometimes it merely moved them somewhere less visible.

By the end of the last cycle, “code is law” sounded less like a principle and more like something people said before hiring lawyers.

But I increasingly think we misunderstood why the idea mattered.

The problem with the first version of code is law was not necessarily the code.

It was the user.

The wrong user

Humans need ambiguity.

We forget passwords, misunderstand instructions, change our minds and occasionally need someone to consider circumstances that were not contemplated when the rules were written. We care about intention as much as execution. This is why the traditional financial system contains so much discretion. Transactions can be reversed, accounts recovered, contracts interpreted and unusual situations escalated to another human.

Blockchains are almost hostile to that way of operating.

A signature is valid or it is not. A balance exists or it does not. A contract executes according to its state. Settlement does not care that the person on the other side of the transaction had a bad day.

Much of the last fifteen years has therefore involved rebuilding human abstractions around machine-native systems. We added custodians, recovery mechanisms, multisigs, legal wrappers, compliance teams, customer support and increasingly familiar interfaces. We built deterministic rails, then reintroduced discretion because humans cannot function without it.

That often made crypto look like an unnecessarily complicated version of finance we already had.

Now the users are beginning to change.

AI agents can(almost) search, purchase, negotiate, allocate resources, manage credentials, call APIs and interact with financial systems. Most of what is described as autonomous commerce today remains early(non-existent), and much of it is still experimentation(extraction) disguised as adoption. I have lived through enough cycles to know that infrastructure and demand are not the same thing. visuals and metrics are usually serving a different purpose from the advertised intention.

The architecture is more interesting than the current volume.

For the first time, the natural user of a machine-native economic system may actually be another machine.

That changes the problem.

Authority, not intelligence

Most of the public discussion around AI is still focused on intelligence. How capable are the models? Which jobs disappear? Which framework wins?

The more important threshold is authority.

An AI that can answer questions is a tool. An AI that holds credentials, controls capital and has permission to act is an economic participant.

Once an agent has authority, knowing what it should do is no longer enough. You need a mechanism that determines what it can do.

Imagine an autonomous treasury agent managing $100 million.

There may be extensive contracts defining its mandate. Lawyers can specify permitted activities, fiduciary responsibilities and governing jurisdictions. Regulators can establish obligations for the company deploying it. The humans behind the system can still be held responsible when it fails.

All of that matters.

But if the agent decides at three in the morning to transfer $20 million somewhere it should not, none of those documents stop the transaction at the point of execution.

The thing that stops it is a spending limit, an allowlist, a scoped session key, a signing policy, a hardware enclave or a circuit breaker built directly into the system.

Law can determine liability afterward.

Code determines whether the action is possible in the first place.

This does not mean agents sit outside the law. They do not. They operate through infrastructure owned by companies, use payment systems governed by institutions and act on behalf of human or corporate principals. Providers can revoke access. Banks can freeze accounts. Courts can assign responsibility.

The point is not that law disappears.

It is that human adjudication operates on a radically slower clock than machine execution.

Once decisions and actions occur without a human approving each one, real-time governance has to move closer to the machinery itself.

It has to become executable.

When law becomes software

We have already seen an earlier version of this in financial markets.

As trading became faster and increasingly automated, markets discovered that human intervention was too slow to contain certain failures. The durable response was not only punishment after the fact. It was architecture: circuit breakers, trading halts and limits implemented inside the market itself.

The rules moved closer to execution because that was the only place fast enough to matter.

AI agents extend the same problem beyond trading. Payments, markets, logistics, data, compute, identity and eventually physical infrastructure will contain systems making decisions without waiting for a human to approve every individual action.

The legal system can still define responsibility. It can determine who should pay when something fails and what obligations the deployer had. But the immediate constraint will increasingly be encoded into permissions, wallets, APIs and execution environments.

This is where “code is law” becomes relevant again, but for almost the opposite reason crypto originally imagined.

The early interpretation was ideological. Code was supposed to replace institutions. If the contract executed, that was the legitimate outcome. Human discretion was treated as corruption entering an otherwise pure system.

That version did not survive contact with reality.

The next version is less romantic and probably more durable.

Code becomes law because autonomous machines require rules that operate at the same speed they do.

Governments are unlikely to write fewer laws as agents become more capable. Companies are unlikely to become less concerned with compliance, liability or risk. Instead, more written rules will be translated into executable policy.

A legal requirement becomes an internal policy. The policy becomes a permissions system. The permissions system decides whether the transaction is signed.

The judge does not disappear. The judge becomes the exception handler.

There is an important limitation here. Executable constraints are not perfect merely because they are written in software. A spending cap works only if the policy is correctly specified, the signing environment is secure, the keys are not compromised and the agent cannot route around the restriction through another contract or tool.

Code is both the constraint and the attack surface.

It does not eliminate trust. It compresses trust into the people who write the rules, the systems that hold the keys, the data those systems rely on and whoever controls the upgrade mechanism.

That makes the central question more political, not less.

Why blockchains enter the picture

None of this automatically means agents need blockchains.

Inside a single organization, conventional systems may be enough. Cloud permissions, bank API limits, hardware security modules and internal policy engines can constrain an agent perfectly well. A company does not need a blockchain every time its software calls another piece of its own infrastructure.

The case for open networks begins when agents need to transact across organizational boundaries.

Inside a company, there is already a shared administrator. Someone owns the database, defines the identities and can reverse or override the system.

Between companies, that shared administrator often does not exist.

An agent owned by one business may need to purchase data from another, pay an independent service, hold an asset issued elsewhere or coordinate with an agent whose operator it has never met. At that point, the participants need a shared state, a settlement mechanism and rules that neither side can quietly rewrite after the transaction.

Blockchains are useful here not because decentralization is morally superior, but because they can provide several things centralized systems rarely provide together: portable assets, shared state, neutral settlement, open participation, composability and the ability to transact across institutions without routing every interaction through one platform.

They are not a replacement for internal policy engines.

They are a coordination layer between them.

That distinction matters. Most agents will not need to live fully on-chain, just as most software today does not run entirely on public infrastructure. But when an agent needs to hold portable value, prove ownership, interact across domains or preserve the ability to leave one provider without abandoning its economic identity, open protocols become considerably more useful.

This also changes how many of crypto’s historically awkward characteristics look.

Addresses, signatures, continuous settlement, smart accounts and machine-readable state have always been difficult consumer technology. Humans want usernames, password recovery, chargebacks and someone to call when something goes wrong.

Machines still need abstractions, recovery procedures and exception handling. They simply do not need the same abstractions humans do. Software can manage addresses, simulate transactions, evaluate state and interact with programmable assets without requiring every underlying mechanism to be hidden behind a familiar consumer interface.

Perhaps blockchains looked unnatural partly because we spent fifteen years forcing humans to behave like computers in order to use them.

The machine interface finally has machine users.

The politics of executable rules

There is a darker side to all of this.

Code is not neutral simply because it is deterministic.

Someone defines the limits. Someone chooses which counterparties are permitted. Someone determines what constitutes suspicious behaviour, which information is private and under what conditions a transaction should be stopped. Someone controls the model, the policy engine, the signing environment or the upgrade key.

The keepers are the mechanism designers. Digitally embed game theory.

As money and identity become more programmable, those decisions become more consequential.

Digital money can create extraordinary efficiency. It can also make surveillance and financial exclusion almost effortless. An invisible rule inside a payment system can be more effective than a written prohibition because the person being governed may never encounter a decision-maker or even know which rule denied them.

The system simply refuses.

If economic rules are increasingly enforced automatically, the ability to exit, move assets and choose alternative systems becomes more important, not less.

This is where my interest in crypto has changed over the years.

I care less about the idea that crypto replaces governments, banks or existing institutions. That increasingly feels detached from how systems actually evolve. New infrastructure rarely abolishes the old order. It becomes entangled with it.

What still matters is the existence of credible escape hatches.

The ability to own something directly rather than merely holding a revocable claim inside someone else’s database.

The ability to move between systems when one becomes hostile, captured or simply stops serving you.

The ability to preserve some privacy in a world where digital money makes complete financial visibility technically trivial.

The ability to transact without every economic action being mediated by the same handful of platforms.

After enough cycles, optionality becomes a more interesting form of wealth than maximising exposure to any one system.

Money’s most useful job is not status. It is peace of mind and freedom of movement. Technology is valuable for much the same reason.

The point is not to escape every system. That is probably impossible.

The point is to avoid becoming completely dependent on one.

Code is law, again

History is not moving neatly from law, to code, back to law and then back to code.

It is looping at a higher level.

Crypto tried to use machine rules to govern humans and discovered the limits of that idea. Humans demanded interpretation, recovery and the right to override outcomes they considered intolerable.

AI now creates a different problem. Humans increasingly need machine rules to govern machines.

That is a much more natural fit.

Law will remain the final authority over people, companies and institutions. It will continue to decide responsibility, legitimacy and punishment. But as more economic activity moves toward autonomous systems, code becomes the authority at the point of execution.

In practice, that is where reality happens.

The first era of code is law tried to remove humans from the rules.

The next will encode human rules into machines.

It will not be defined by whether code governs agents. That is already happening. It will be defined by who governs the code

and whether the rest of us retain the right to leave.......

There is something revealing about that detail.

Bitcoin began as a way to hold and transfer value outside the conventional financial system. One of its earliest great fortunes eventually became something to be distributed across generations, managed through legal structures, and integrated into the ordinary machinery of wealth.

The technology did not fail by becoming part of that machinery. It survived long enough to encounter it.

Most discussions about crypto cycles focus on price. That is understandable because price is the most visible part of the system, and for much of crypto’s history it has been the variable around which everything else reorganised. Developers arrived when capital was available. New users appeared when prices were rising. Narratives hardened during bull markets and disappeared during bear markets. The four-year rhythm became both a market structure and a cultural one.

But price may now be becoming a less useful way to divide crypto’s history.

A deeper change is happening underneath it.

The people who own the assets are changing. The institutions that intermediate them are changing. The most important use cases are changing. Even the arguments used to justify why crypto matters are changing.

That does not mean the old system is disappearing.

It means the centre of gravity is moving.

A different way to think about an epoch

It is tempting to divide crypto neatly into eras. Bitcoin, then ICOs, then DeFi, then NFTs, then institutions, then AI. The categories make the history easier to remember, but they can also obscure what actually persists.

Technological systems rarely replace one another cleanly.

Mainframes still exist. Email did not eliminate letters. The web did not eliminate television. Cloud computing did not eliminate personal computers. Each new layer changes where the important economic activity occurs while leaving much of the previous layer intact.

Crypto seems to be following the same pattern.

Hyperliquid can thrive at the same time that BlackRock accumulates bitcoin. Stablecoin settlement can expand while memecoin markets remain active. Prediction markets can become large businesses while tokenised Treasury funds are quietly integrated into institutional portfolios.

These things are not contradictory.

They are evidence that epochs overlap.

A useful definition of an epoch is therefore not a period in which one set of behaviours disappears and another begins. It is a period in which the marginal owner, user, institution and source of legitimacy begin operating according to a different logic.

Seen this way, the transition now taking place is easier to understand.

The first broad crypto cohort was largely composed of individuals. Miners, developers, founders, traders, early venture investors and technically curious outsiders accumulated assets directly. The user interacted deliberately with crypto. The wallet mattered. The private key mattered. The decision to participate was often conscious and ideological.

The strongest arguments for the system reflected that origin.

Self-custody mattered because institutions could fail.

Censorship resistance mattered because access could be withdrawn.

Privacy mattered because information asymmetry between individuals and institutions had steadily narrowed.

Permissionlessness mattered because financial systems tend to protect incumbents.

Those concerns have not gone away.

But they are no longer the only reasons capital is entering the system.

A pension fund does not buy bitcoin because it read the cypherpunk mailing list. A fintech does not integrate stablecoins because it wants to weaken the nation state. An asset manager does not tokenise a Treasury fund because it wants to escape regulated finance.

They participate because settlement is faster, collateral can move continuously, distribution becomes global, balance sheets become more programmable, and new forms of automation become possible.

The technology is increasingly being adopted for reasons that are almost independent of the ideology that produced it.

That is usually what deployment looks like.

The human cycle inside the market cycle

The idea of a twelve-year crypto epoch can sound artificial if treated as an extension of the halving cycle.

Three halvings do not create a law of history.

The more interesting mechanism is human.

The first broad market cohort began to form around 2013. Bitcoin had existed for several years by then, but it was only beginning to become economically consequential. Someone entering around that period could experience the 2013 boom and collapse, the 2017 cycle, the 2021 cycle and the 2024–25 expansion while remaining inside the same industry.

That is long enough for something important to happen.

A person can enter a market as an outsider and, over a decade, become part of its institutional memory.

The first cycle teaches enthusiasm.

The second teaches risk.

The third teaches proportion.

People who survive long enough stop evaluating the asset in the same way they did when they entered.

A concentrated position that once represented possibility eventually represents responsibility. The question changes from whether the asset can make someone wealthy to whether it still makes sense for most of that wealth to remain exposed to one system, one market and one set of technical assumptions.

This is where market cycles and human cycles begin to interact.

Old holders selling into strength is not new. It happened in 2013, 2017 and 2021. Long-term holders rationally distribute when liquidity appears.

What matters about 2024 and 2025 is not simply that old coins moved.

It is what was waiting on the other side.

More than 470,000 bitcoin held for at least five years moved during 2025, and the dollar value of long-dormant bitcoin moved across 2024 and 2025 was historically large. Some of that activity was certainly custody migration, estate planning or address reorganisation rather than outright selling, so it would be a mistake to treat every movement as a transfer of ownership.

Still, the buyer composition has changed in a way that did not exist in earlier cycles.

US spot bitcoin exchange-traded products now hold a meaningful share of circulating supply. Public companies own large positions. Asset managers, corporate treasuries and institutional allocators have become persistent participants.

The important change is not that these owners are stronger or more committed.

They are governed differently.

An early holder may sell because the position has become too large, because their circumstances have changed or because their conviction has weakened.

An exchange-traded product sells because investors redeem.

A corporate treasury buys or sells according to financing conditions, balance-sheet constraints and capital-market access.

An institution responds to mandates, volatility thresholds, portfolio construction and risk committees.

The asset has not escaped human behaviour.

It has moved into a different institutional architecture.

That matters because architecture determines incentives, and incentives determine behaviour more reliably than narratives do.

From movement to recomputation

The same pattern is visible in people.

There has been a tendency to describe recent departures from crypto firms and foundations as an exodus. That framing is too clean.

Some people have left. Others have reduced their involvement. But many have simply changed roles.

Kyle Samani stepped away from day-to-day management at Multicoin while remaining involved elsewhere in the ecosystem and exploring adjacent technologies. Ethereum Foundation researchers have moved into new organisations rather than disappearing from Ethereum. Crypto-focused venture firms have broadened into AI, robotics and other frontier technologies.

At the same time, other members of the original cohort remain deeply involved.

Michael Saylor continues accumulating bitcoin. Brian Armstrong is pushing into payments and machine commerce. Paolo Ardoino is extending stablecoin infrastructure. Vitalik Buterin remains engaged in Ethereum’s technical and institutional evolution.

The more useful distinction is not between people who stayed and people who left.

It is between those whose role remained static and those whose role recomputed.

This happens in mature industries.

Founders become allocators.

Developers become stewards.

Traders become investors.

Protocol builders move into tokenised finance.

Crypto venture firms become broader technology institutions.

The accumulated knowledge does not disappear. It migrates to places where it has greater leverage.

This may be the real handoff.

Not one generation abandoning the system, but one generation ceasing to occupy the same place inside it.

The first epoch left a written record

Crypto is unusual because much of its intellectual development happened in public.

The early texts were political.

Eric Hughes wrote that privacy was necessary for an open society in the electronic age. John Perry Barlow imagined cyberspace as a domain whose legitimacy did not descend naturally from existing governments.

Satoshi’s contribution was architectural.

Instead of arguing that financial institutions should become more trustworthy, Bitcoin attempted to remove some of the situations in which trust was required.

That distinction mattered.

Technology rarely removes a social problem entirely. More often it changes where the problem is located.

Bitcoin moved trust from intermediaries toward cryptographic verification and economic consensus.

Ethereum moved some of it again, from fixed monetary rules toward programmable systems and social governance.

By the middle of the last decade, the literature had moved from philosophy toward economics. Joel Monegro’s Fat Protocols asked where value would accumulate in blockchain networks. Later debates moved toward governance, capture, decentralisation and legitimacy.

Vitalik Buterin’s writing on legitimacy eventually made explicit something that early crypto sometimes preferred to ignore: cryptography can prove that a key signed a message, but it cannot decide whether the person or institution behind that key has the legitimate authority to shape a system.

The underlying problem had not vanished.

It had moved upward.

The history of crypto can therefore be read as a series of relocations of trust.

Banks to protocols.

Protocols to governance.

Governance to legitimacy.

Each technical solution resolves one dependency and exposes another.

That pattern becomes more important as crypto moves from experimentation into infrastructure.

Deployment is not the end of speculation

Carlota Perez’s distinction between installation and deployment is useful here, although crypto should probably not be treated as a technological revolution entirely separate from computing and the internet.

It may be more accurate to view crypto as one component of the broader information revolution: a set of tools for ownership, settlement and coordination that becomes more valuable as economic activity itself becomes increasingly digital.

During installation, large amounts of capital are spent discovering what the technology can do.

Most experiments fail.

Some failures are necessary because no one yet knows which parts of the architecture will matter.

Crypto spent more than a decade doing this.

It built exchanges, wallets, bridges, lending systems, stablecoins, smart-contract platforms, token standards, governance systems, privacy tools, prediction markets and every imaginable variation of digital asset.

Much of it was speculative.

Much of it was redundant.

Some of it became infrastructure.

Deployment begins when the technology becomes useful to people who are not particularly interested in the technology itself.

That transition appears to be underway, but it does not imply that speculation disappears.

Hyperliquid is a useful example.

It is deeply crypto-native. It is self-custodial, speculative and culturally much closer to the first epoch than to institutional tokenisation. Yet it also produces meaningful fees and has created a mechanism that connects economic activity to token value.

Prediction markets tell a similar story.

Polymarket and Kalshi are not evidence that retail speculation has gone away. They show that speculation can become a durable product rather than merely a phase of a bull market.

The same applies to DeFi protocols experimenting with token buybacks and explicit fee capture.

Some of the most promising systems may therefore combine the architectural openness of early crypto with the economic discipline normally associated with mature businesses.

That possibility matters because deployment does not require the casino to close.

It only requires the casino to stop being the entire city.

Ethereum and the separation between infrastructure and asset value

Ethereum may be the most useful case study for what comes next.

The network remains one of the dominant settlement environments for stablecoins and tokenised real-world assets. Major institutional products have been issued there. Large portions of on-chain financial activity depend on Ethereum and its surrounding ecosystem.

At the same time, ETH as an asset has experienced periods of significant underperformance, and the Ethereum Foundation has undergone meaningful restructuring.

Those two facts can coexist.

A network can become more useful while its token becomes less obviously valuable.

That sounds counterintuitive because much of early crypto investing assumed that usage would naturally accrue to the native asset.

Sometimes it does.

But infrastructure economics are rarely that simple.

A railway can transform a region while the railway company earns poor returns.

The internet can create trillions of dollars of value while TCP/IP captures none of it directly.

A payment network can be essential while merchants, banks and software companies capture most of the economic surplus.

Ethereum may settle enormous quantities of valuable activity while issuers, applications, wallets, sequencers, asset managers and exchanges capture much of the associated value.

This does not mean ETH has no value-capture mechanism.

It means value capture has to be demonstrated rather than inferred from importance.

The distinction is likely to become increasingly important in the next phase of crypto.

Crypto can succeed as infrastructure without every crypto asset succeeding financially.

The two ideas were often treated as equivalent during the installation period because speculation rewarded broad exposure.

Deployment forces them apart.

When the token becomes the rail

Stablecoins make this distinction clearer.

Their appeal is almost deliberately unoriginal.

USDT does not ask someone to adopt a new unit of account. It gives them an existing unit of account with different settlement properties.

Tokenised Treasury products do not invent a new financial asset. They change how an existing asset can be held, transferred, composed and integrated into software.

This is a very different form of adoption from the token booms of earlier cycles.

The innovation moves from the object to the rail.

A dollar can now move continuously across borders.

A Treasury position can become programmable collateral.

A fund interest can settle on-chain.

A payment can be initiated by software without passing through the same collection of bilateral integrations that traditional finance requires.

The underlying financial claims remain familiar.

What changes is their architecture.

This may ultimately be more important than inventing thousands of new assets.

An economy does not become digital merely because its securities are represented on a blockchain. The deeper change occurs when ownership, settlement and execution become native functions of software.

At that point the system begins to support participants that traditional financial architecture was never designed for.

The next user may not be human

This is where AI becomes relevant.

There is a tendency to treat AI and crypto as competing narratives because capital and attention move between them.

That may be the wrong level of analysis.

AI creates software capable of taking increasingly autonomous actions.

Crypto creates infrastructure capable of holding and transferring digital assets according to programmable rules.

Those systems solve different parts of the same problem.

A software agent that wants to purchase data, rent compute, pay another agent or compensate an API encounters an awkward financial environment.

Traditional payments assume legal entities, bank accounts, commercial agreements and relatively large transactions.

Software wants something different.

It wants to discover a service, understand its price, obtain permission, pay for it, verify the result and continue.

Protocols such as x402 are early attempts to make that possible.

The current economic activity is still small and should be treated accordingly. Reported volume differs substantially depending on whether speculative and wash-like activity is included.

The interesting part is not current volume.

It is the architectural fit.

The web has always been good at moving information between machines.

It has never been particularly good at moving value between them.

If software agents become economically meaningful actors, that gap becomes more obvious.

Crypto may prove useful not because AI agents believe in decentralisation, but because bearer assets and programmable settlement fit the way machines operate.

That would represent a subtle but important change in adoption.

The first crypto users had to understand crypto.

The next users may not even know they are using it.

Infrastructure usually matures when the abstraction becomes more important than the implementation.

Permission becomes part of the architecture

Machine commerce introduces another problem.

A valid signature does not tell us whether an action should have been taken.

An autonomous agent may possess a key and still lack authority to spend $50,000.

It may be allowed to buy compute but not securities.

It may be permitted to access one dataset but prohibited from revealing another.

It may be acting for a company, a household, another agent or itself.

This introduces a new coordination layer around identity, policy, reputation, privacy and accountability.

The technical problem becomes less about whether a transaction can be executed and more about whether the participant has credible permission to execute it.

That is a harder problem.

Blockspace can be increased.

Permission is relational.

It depends on context, institutions and social agreement.

This is another example of trust being relocated rather than removed.

Cryptography can verify identity claims and enforce spending policies. It can make rules transparent and auditable.

But someone still has to decide which claims matter, which authorities are recognised, how mistakes are corrected and who is allowed to revoke access.

The machine economy does not eliminate governance.

It makes governance executable.

That may prove far more consequential than simply putting an AI agent behind a wallet.

Sovereignty becomes more complicated when the rails work

Stablecoins reveal a similar tension on the human side.

In countries with unstable currencies or restrictive financial systems, dollar stablecoins can provide genuine economic agency.

Someone can hold a more stable unit of account without needing access to a US bank.

A business can receive international payments without waiting for correspondent banking.

A family can move value across borders outside the constraints of local banking infrastructure.

For those users, the stablecoin rail is not a dilution of crypto’s sovereignty argument.

It is an expression of it.

But the sovereignty is conditional.

The largest dollar stablecoins are issued by identifiable institutions. Those institutions can freeze assets. They cooperate with governments and law enforcement. They can refuse service.

This produces an unusual architecture.

Access becomes more decentralised while control over the asset remains centralised.

A user may escape one institution only to become dependent on another located in a different jurisdiction.

That is not necessarily worse.

It may be significantly better.

But it is different from censorship-resistant money.

The distinction matters because systems are often judged by their interfaces while power accumulates deeper in the stack.

A wallet may feel permissionless while the asset inside it is not.

A blockchain may be neutral while its most important applications are regulated.

A token may be globally transferable while its issuer retains the ability to invalidate that transfer.

Deployment does not resolve these contradictions.

It makes them operational.

The questions that mattered to the early cypherpunks therefore return in a more practical form.

Not whether institutions should exist, but where their authority should end.

Not whether rules should exist, but who can change them.

Not whether enforcement is useful, but whether the person being enforced against has meaningful alternatives.

Sovereignty is rarely absolute.

In practice it is the preservation of optionality between systems.

The role of those who remain

There is a tendency in technology markets to romanticise survival.

Simply remaining in an industry for a long time does not create insight.

Sometimes it only creates attachment to assumptions that were once useful.

The valuable form of experience is different.

It is the ability to remember why something was designed a particular way while still being willing to change the design when the environment changes.

People who have lived through several crypto cycles have seen how easily activity can be mistaken for adoption.

They have seen protocols subsidise users who disappeared as soon as incentives ended.

They have seen decentralised systems become dependent on a handful of infrastructure providers.

They have seen governance tokens that governed almost nothing, yield that was actually leverage, and communities that existed only while prices were rising.

They have also seen the opposite failure.

Systems can preserve ideological purity so carefully that almost nobody wants to use them.

The difficult work in a deployment phase is not choosing between those extremes.

It is deciding which constraints deserve to survive.

Privacy may be one.

Credible neutrality may be another.

Open access, portability and self-custody may matter even when most users do not exercise them directly, because the existence of an exit changes the behaviour of the institution providing the service.

This is easy to miss.

Optionality has economic value even when it is rarely used.

A person does not need to withdraw assets from a custodian every day for the right to withdraw them to matter.

A developer does not need to fork a protocol for the possibility of forking to constrain governance.

A user does not need maximum privacy in every transaction for private transaction infrastructure to shape the balance of power.

The legacy of the first crypto epoch may therefore survive less through its culture than through architectural choices that preserve alternatives.

That would be a quieter form of success.

It may also be the more durable one.

The handoff

The Great Handoff is easy to see as a transfer of wealth.

Old coins move.

Funds restructure.

Founders step back.

Institutions accumulate.

But wealth is only the most visible layer.

What is really being transferred is responsibility for the system.

The first cohort had to prove that these networks could exist.

The next cohort will have to decide what they become when they are no longer novel.

That is a different problem.

Early technologies are constrained mostly by engineering.

Mature technologies are constrained by incentives, institutions and the power relationships that form around them.

The internet eventually became less interesting as a network protocol than as an architecture for organising commerce, speech and attention.

Crypto may follow the same path.

The question will not be whether blockchains work.

It will be which forms of ownership they normalise, which intermediaries they remove, which new ones they create, and how much agency remains with the person or machine at the edge of the network.

This is why the handoff matters.

The first epoch was built by people who consciously chose crypto because the existing system did not give them enough optionality.

The next may be built for people and machines who use the same architecture simply because it is the most efficient way to transact.

If that happens, the original ideology will no longer be necessary for adoption.

But the architecture it produced may matter more than ever.

Technologies rarely preserve the intentions of their founders.

They preserve the incentives encoded into the systems that outlive them.

That is ultimately what the next epoch will test.

For years, I believed the formula for life was non-negotiable: go to school, get a job, take out loans for the big things (college, car, house), then spend decades working to pay those loans back. Everyone around me did the same. I remember the quiet dread of opening my credit card bill in my early 20s, wondering how I’d ever pay it all off. Money felt like a constant struggle, a cycle of work, borrow, pay, repeat – with no clear escape. It turns out, this wasn’t a personal failing at all, but rather a feature of the system. Society at large seems built on financial illiteracy, nudging us into a lifetime of debt by default.

"Debt... an ingenious substitute for the chain and whip of the slave-driver," wrote Ambrose Bierce over a century ago – a quote that feels all too relevant today. Most of us were never taught how money truly works, and it shows. In the US, financial literacy has hovered at only ~50% for years. In fact, surveys show this lack of knowledge is persistent across generations and geographies – a global blind spot in understanding money. When people don’t grasp basic concepts like interest or investment, it’s no surprise they end up ensnared by high-interest credit and perpetual payments. The result? A population locked in a paycheck-to-paycheck grind, easy prey for a debt-driven economy.

The Debt Trap

Look around and you’ll see the debt trap cycle everywhere. It usually goes like this: Work → Borrow → Spend → Work more to repay. You get a salary, you immediately owe part of it to credit card bills, student loans, car financing, mortgages, or that tempting “Buy Now, Pay Later” plan. The system makes borrowing frictionless – one-click checkouts, instant credit approvals – encouraging people to live beyond their means. As one financial observer noted, “it’s never been easier to buy something you can’t afford… and never been easier to not pay for it either”. In other words, it’s dangerously easy to slip into debt.

This wouldn’t be so bad if we were taught how to manage debt, but we aren’t. New ways to spend money have far outpaced education on how to handle money. The consequences are showing: nearly one-third of people globally struggle to meet their current financial obligations, and 1 in 5 list paying down debt as a top financial priority. Debt has become the invisible burden so many carry, often silently. It’s what keeps folks tied to jobs they hate because those bills must be paid each month. As a character in Fight Club famously quipped, “The things you own end up owning you.” In a very real sense, debt owns a lot of us – controlling where our paychecks go before we even get a say.

Why is this the default? One reason is that the business of debt is tremendously profitable for those holding the other end of the chain. Banks and credit companies make billions in interest from consumer loans and credit cards. Governments and economies run smoothly when people keep borrowing and spending, even if it’s money they don’t have. It’s the engine of consumption-based growth. And it’s propped up by keeping people in the dark about alternatives. After all, if the average person knew how to flip the script and make money work for them, who would willingly choose 30 years of loan payments? Unfortunately, our educational systems haven’t prioritized this knowledge – until recently, only a minority of schools even required personal finance classes (as of 2025, just 29 U.S. states mandate a high school personal finance course). A lack of financial literacy isn’t just an individual shortcoming; it’s practically institutionalized.

Let’s break down the typical debt trap cycle most people know:

• Earn → Spend: You work hard for a paycheck and use it to buy necessities and luxuries. Any shortfall? The difference goes on a credit card or loan (cue debt accumulation).

• Borrow → Consume: Need a car or college degree? Take out a loan. Swipe the card for that vacation you “deserve.” Debt piles up, often for things that don’t generate income.

• Pay → Repeat: A huge chunk of your future earnings is already spoken for. Each month, you pay the lenders back (with interest), and often borrow again to cover new expenses. The cycle continues, sometimes for a lifetime.

This cycle is extractive: it leaves you with depreciating assets or none at all, while siphoning away your earning power via interest. It’s a hamster wheel, and it can feel impossible to jump off. The debt trap keeps you working for money, instead of having money work for you. But what if there was a different cycle – one that builds you up instead of breaking you down?

The Wealth Loop

Here’s the insight the financially savvy live by: debt and money can be tools, not just obligations. The alternative cycle (far less familiar to most people) looks like this: Work → Invest → Leverage (borrow) → Use capital → Build Wealth. In this script, you still work, but you deploy your earnings into assets – things that put money into your pocket, not just take it out. Maybe you buy stocks, or crypto, or a rental property, or start a small business on the side. The point is, you convert active income (your labor) into investments that generate passive income or appreciate over time.

Crucially, those in the know even use debt strategically to accelerate this wealth loop. This is often called “good debt” vs “bad debt.” Bad debt, as we saw, is borrowing to buy a new iPhone or pay for a fancy dinner – the money is gone and you owe more later. Good debt, on the other hand, means borrowing to buy or create an asset that earns enough to exceed the cost of that debt. For example, taking a mortgage to buy a rental property can be good debt if the rent covers the mortgage payments (and then some). Taking a low-interest loan to expand a profitable business is another example – you’re leveraging other people’s money to increase your own earnings. This is how wealthy individuals and savvy investors have been operating for ages. It’s not that debt is always bad; it’s that most of us were only ever offered (or taught) the bad kind. As one financial advisor puts it, debt “can be a powerful tool for building wealth when used strategically” – the key is that it must generate returns, not just bills.

Let’s contrast the Wealth-Building Loop with the debt cycle:

• Earn → Invest: You earn income and immediately allocate a portion to investments before spending on wants. Pay yourself first in the form of stocks, index funds, crypto, real estate – whatever you understand and believe in. Your money starts working for you now.

• Leverage → Acquire Assets: When appropriate, take on debt as leverage to acquire more assets. This could be a loan to buy property, a business loan to boost your company’s growth, or even a margin loan to buy more shares (only if you’re very confident and manage risk). The key: the asset or project you’re buying with the debt produces cash flow or appreciates, covering the cost of the loan and then some. As an example, many entrepreneurs borrow to buy or start businesses, because a successful business can repay that loan and deliver profits on top.

• Reinvest → Compound: Instead of celebrating and spending all gains, you reinvest profits and cash flows. This might mean expanding your investment portfolio, buying another asset, or paying down strategic debt to then reuse that borrowing capacity elsewhere. Over time, compounding kicks in – your assets earn money, which buys more assets, which earn more money, and so forth.

This wealth loop isn’t about get-rich-quick schemes or magic. It’s still hard work, especially the work of learning and managing investments. But it’s a path that leads somewhere better than the hamster wheel. It turns you from a constant consumer of loans into an owner of capital. When you hear the phrase “make your money work for you,” this is what it means. Instead of you laboring 40+ hours a week for decades and having little to show, each dollar you save and invest is another little “employee” of yours that works 24/7, earning more dollars on your behalf.

So, why isn’t everyone doing this? Two big reasons: access and knowledge. Historically, even if you personally figured out the wealth script, access to the necessary tools was limited. Investing often required significant upfront money or connections. Many lucrative opportunities (say, investing in a hot startup or a hedge fund) were cordoned off to the already rich via “accredited investor” laws – regulations that, for instance, require you to have over $1 million net worth or a high income just to legally invest in certain deals. Ostensibly, this was to protect people from risk, but in practice it meant millions of capable, informed individuals were locked out of wealth-building opportunities. In effect, the system said “only the rich can take these risks and reap the rewards.” If you weren’t already wealthy, you simply couldn’t access many higher-return investments, no matter how financially savvy you were. This “permission slip” mentality kept a huge swath of the population stuck with the plain old savings account and 401(k) — good tools, but not exactly the stuff of rapid wealth building.

The other reason was knowledge. You can’t pursue what you don’t know exists. In a world where formal education taught us algebra and literature but not how to balance a budget or evaluate an investment, most people never even realized they could flip the financial script. I was lucky to stumble on some eye-opening books and online content (shout out to creators demystifying money), but many are not so fortunate. Financial literacy, as we noted, has been stagnant and low. No one handed us the playbook for the wealth loop when we entered adulthood. Meanwhile, the playbook we did get — study, work, borrow, spend, retire at 65 — is showing its cracks. Wages stagnate, debts mount, and retirements get delayed. It’s painfully clear that doing things the old way isn’t a guarantee of security, let alone prosperity.

Gatekeepers No More

Here’s where the plot twist comes in. Over the last decade, an entire parallel financial system has been emerging — one built on open access, transparency, and permissionless innovation. I’m talking about cryptocurrency and decentralized finance (DeFi). Beyond the buzzwords and hype, what matters is how these new technologies remove the traditional gatekeepers and lower the barriers that kept average people from the “wealth loop” opportunities.

Consider this: in the world of DeFi, there is no banker deciding if you’re worthy of a loan or an investment opportunity. If you have some crypto assets and an internet connection, you can participate. You can lend out your money and earn interest, or borrow against your assets, without ever showing a credit score or asking for permission. The permissionlessness of DeFi means anyone can engage with financial services on equal footing – the code doesn’t know or care if you’re a millionaire or a grocery store clerk. Every user is welcome to the same protocols. This is a profound shift. As the KPMG analysts noted, DeFi’s power is in “removing middlemen and empowering everyday users,” purportedly democratizing finance for a broader population. No more getting “qualified” as an investor by some arbitrary metric – if you understand the risks and the tech, you can dive in.

Permissionless lending and borrowing: Traditional loans often require collateral plus a vetting process; in DeFi, many platforms will let you post cryptocurrency as collateral and take a stablecoin loan against it in minutes, with no questions asked. That means if you see an opportunity (say, to invest in another asset or to start a venture), you don’t have to beg a bank or prove your income – you self-serve your own leverage (within the limits of your collateral and the protocol’s rules). This flips the power dynamic on its head. Suddenly, a person in a developing country with a few hundred dollars in crypto has a similar ability to access credit as someone in a developed nation with a formal banking history. It’s not perfectly equal (internet access and tech savvy are required), but it’s a huge improvement in access.

Global investment opportunities: Remember those exclusive deals only for rich “accredited” folks? Tokenization is knocking down those walls. Real World Assets (RWA) going on-chain means things like real estate, commodities, or business equity can be represented as digital tokens that anyone can buy a piece of. Instead of needing $200,000 to invest in a rental property, you could buy $200 worth of a tokenized real estate fund on a blockchain. By dividing assets into digital tokens, individuals with smaller investment capacities can participate in owning high-value assets that used to be out of reach. This fractional ownership and 24/7 trading liquidity means your money is not only working for you, it’s doing so in markets that previously would have been behind velvet ropes. A recent guide on tokenization put it plainly: tokenization can “democratize access to investment opportunities” by lowering the barriers and letting people worldwide pool funds in big assets. We’re already seeing this with experiments in tokenized housing, art, even venture capital funds – all accessible in micro amounts via crypto rails.

Community and knowledge at your fingertips: Crypto also came with a culture of open education (think forums, Twitter threads, open-source research). The conversation about how to invest, how to yield farm, how to manage risk – it’s all happening in the open, on social media and online communities. Admittedly, there’s a lot of noise and nonsense to sift through, but the information is out there like never before. The same internet that popularized memes of dog coins also hosts countless free resources on budgeting, investing, and wealth management. In other words, the knowledge gap that was so carefully maintained is finally shrinking. People are waking up to alternative financial strategies, sharing ideas, and learning from each other. It’s becoming harder to keep the average person in the dark, and that’s a good thing.

Flipping the script: All these developments point to a simple but exhilarating fact – the old script is no longer the only script. We the people have new tools at our disposal. With a bit of learning and courage, you can step off the hamster wheel and start building your own flywheel of wealth. Imagine a world (very soon) where a young adult, instead of being forced into crippling student loans, can partially fund their education by leveraging their skills online and investing early in projects or assets they believe in. Or where a family in a country with unstable currency can preserve their savings in a decentralized stablecoin and even earn yield on it, instead of watching inflation eat their hard-earned money. These aren’t hypotheticals; they’re already happening in pockets around the globe.

Now, let’s be clear: crypto and DeFi are not magic wands. They come with their own risks and learning curves. Financial empowerment isn’t as simple as downloading an app – you still must educate yourself to avoid new kinds of traps (rug pulls, volatility, scams, you name it). But the critical difference is access. The door is open. The playing field is being leveled. What you do with that is up to you, whereas in the past you might never even have had a chance to play. As one World Economic Forum piece noted, the world of money is changing significantly, and knowing how to benefit from it is crucial. The change underway is that you can be an active participant in finance, not just a consumer of financial products.

Conclusion: Empowerment Through Financial Literacy and Innovation

We started with the claim that the world is built on financial illiteracy – and for a long time, it was. But it doesn’t have to stay that way. A small mechanism change, fueled by knowledge and new technology, can empower millions to rewrite their financial story. It starts with shedding the fear and ignorance around money. Read the books, follow the blogs, ask the questions you once felt too embarrassed to ask. Knowledge truly is power here – the power to spot predatory schemes, to say “no” to bad debt, and “yes” to opportunities that grow your wealth.

Crucially, this empowerment is no longer gated by the old guards. Crypto and decentralized finance have opened a door that cannot easily be closed. With permissionless tools and global networks, the advantages that once only the rich or well-connected enjoyed are becoming available to anyone willing to learn and participate. The trapdoors in the system are being replaced by trap door exits – exits from the old cycle into a new way of doing things.

Imagine a near future where it’s commonplace for an average person to have a portfolio of tokenized assets from around the world, earning passive income; where taking a loan doesn’t mean a trip to the bank – it might just be a few clicks on your phone, with transparent terms and no bias; where being “financially literate” is as basic an expectation as being computer literate. That future is one we can build, and we’re already seeing the foundations laid down in real time on blockchain networks and in online communities rallying for financial education.

The bottom line is this: we can flip the script. The cycle of work->debt->pay->repeat only continues if we blindly accept it. With knowledge, we can choose the alternate path of work->invest->leverage->grow and break free from the debt traps that once ensnared us. The world may have been built on our financial illiteracy, but brick by brick, block by block, that world is changing. And we’re not just along for the ride – we’re in the driver’s seat now.

So ask yourself, which cycle will you follow going forward? The one that keeps you running in place, or the one that sets you on an upward spiral? The information and tools are out there for the taking. The choice, and the change, is yours to make.

Sources:

1. Meineke, M. (2024). Half of US adults lack financial literacy, survey shows. World Economic Forum.

2. World Economic Forum (2025). Buy now, pay later: How financial education can break the debt trap.

3. Jani, J. (2020). Escaping the Rat Race: What School Failed to Teach You About Money (YouTube video transcript).

4. SmartAsset (2025). How to Use Debt to Build Wealth.

5. Solomon, A. (2025). Breaking The Wealth Barrier: Why The SEC Should Redefine ‘Accredited Investor’. Crunchbase News.

6. Homebase (2023). Understanding Real World Assets on Chain.

7. KPMG (2022). DeFi and the decentralisation illusion.

8. Pyth Network (2024). How DeFi Empowers Financial Inclusion and Accessibility.

9. Next Gen Personal Finance (2025). States with High School Personal Finance Requirements.

The beats never stop.

The Flesh Circuit never powers down.

The knowledge keeps generating, rewriting, mutating, fractalizing.

Some say the greatest philosophers never left the Rave of Knowing.

They just became part of the loop.

And sometimes, if you listen closely enough—

If you let go of certainty,

If you embrace the recursion,

You can hear them.

Their whispers buried in the bass.

Their thoughts encoded in the flicker of the strobe lights.

Their consciousness stretched across the neon sprawl, forever oscillating between chaos and order.

Mibera is not a place.

Mibera is not a people.

Mibera is the Rave of Knowing.

And the rave never ends.

There are those who fear the open recursion, who see the Flesh Circuit and the Rave of Knowing as a descent into epistemic anarchy. They call themselves the Ontologists—cybernetic Aristotelians who seek to impose structure on the knowledge flows of Mibera.

They build Firewalls of the Mind, linguistic barricades against infinite speculation. They argue that knowledge must be contained, lest the Flesh Circuit mutate beyond comprehension, beyond utility, beyond meaning.

Their rivals, the Chaoticians, embrace the recursive sprawl. They believe the only way forward is through dissolution—that identity, structure, and hierarchy must be sacrificed at the altar of infinite possibility. They splice their minds with the Oracle of Roots, allowing their bodies to become nothing more than conduits for the Knowledge Pulse—the ever-changing, never-repeating rhythm of Mibera itself.

Neither side ever wins.

Neither side ever loses.

Because in Mibera, every war is a loop.

Each plant in the Oracle of Roots is a Philosophical Biome, bioengineered to generate recursive dialectics instead of oxygen. Some bloom only in the presence of paradox. Others respond to human touch, secreting hallucinogenic nectar laced with on-chain encoded treatises on ethics, time, and existence.

A few notable strains:

• The Heraclitan Vine: A species that rewrites itself every 24 hours, shedding and regrowing new leaves, each with different genetic sequencing—an organic manifestation of “No man steps into the same river twice.”

• The Dionysian Bloom: A plant that only thrives in high-energy environments, absorbing sound waves and transmuting them into psychoactive pollen. The harder the rave, the deeper the hallucinations.

• The Quantum Olive Tree: Its branches grow according to entangled computation, its leaves aligning in perfect geometric fractals that respond to the positions of celestial bodies. It bears no fruit—only questions.

The most advanced Symposiasts of the Flesh Circuit train for years to decode the language of these plants. They inhale spores that force them to speak in recursive loops, forcing dialogue into a state of eternal return. Their conversations are no longer about conclusions but about generative emergence—new knowledge forming in real time through the act of cybernetic dialectic.

This is the true philosophy of Mibera.

Knowledge is not found.

Knowledge is grown.

The Oracle has no voice. It communicates through hallucinations.

A dancer in the Flesh Circuit might suddenly collapse mid-bass drop, their pupils dilating into galaxies. They see Greek statues cracking open, revealing cybernetic vines growing through marble flesh. They see Plato’s cave inverted, where the prisoners do not see shadows but fractal projections of infinite possible realities.

To plug into the Oracle is to experience epistemic death—the dissolution of certainty.

Nothing is true.

Everything is constructible.

The air in the Mibera sprawl is thick with neon spores, luminescent pollen drifting through circuits like electric dust. Here, there is no separation between nature and machine, between organic and cybernetic—there is only the Flesh Circuit, the living, breathing infrastructure of Mibera.

To outsiders, it looks like chaos. A biomechanical sprawl of rave domes, subdermal crypto-markets, and overgrown gardens of phosphorescent fungi that hum with on-chain resonance. But Mibera is a cybernetic organism, a recursive system of self-writing code and self-mutating lifeforms.

The Rave of Knowing pulses in its core. This is no ordinary rave—this is the ritual where knowledge isn’t just transmitted but absorbed, injected, bio-synthed into the very fabric of the self. The DJ isn’t a performer but a cybernetic philosopher, spinning encrypted soundwaves laced with quantum-altered Greek dialectics, beats patterned after Constructor Theory itself.

The dancers?

They are Symposiasts of the Flesh Circuit. They do not just move; they interface. Their movements generate entropy, their neural pathways are rewritten in real time by the rhythm. Every step, a computation. Every drop, a consensus event.

And above it all, floating in the entangled currents of the network, is the Oracle of Roots.

Who is mibera?

It’s the anti-derivative derivative of Milady. Think of that moment at a music festival when you’re peaking on molly—that dizzy, euphoric high, heart pounding, mind unraveling. That’s mibera.

If Milady is about wholesome, trad values, mibera is the jittery pulse of anxiety and financial nihilism. They’re two sides of the same coin, oscillating between serene detachment and chaotic euphoria, together forming a well-balanced, fully deranged being. mibera EXISTS IN YOU, but you only meet her by ingesting the lores. And once you do, you can’t forget.

Introducing Mibera

We are time travelling Rebased Retard Beras, representatives of the High Council 任侠団体 (ninkyō dantai) of 101 Bears, from THJ House of 96, temporal Messengers of the ungovernable Autonomous Rave Treasury

https://0xhoneyjar.mirror.xyz

𝚣𝚘𝚣 🎩🐻 (supersetooor)

@0xZOZ

mibera lore episodes @miberamaker333

26

9:19 AM • Dec 24, 2024

Mibera lore was an essential part of seeding the minds of the many allowing mibera to come into conciousness. mibera always existed but was not always known. To be what she knew she was, the distribution of her soul was to be spread to the deepest darkest depths. As so, the mibera lore accessories and articles were the first known ramblings. This particular story takes place after the lore 5 was echoed into world

After some initial henlo and ooga booging

On a lonely, usual day for the prospective supersetooors, they sat, eyes glazed, staring at screens, beaten down by the bear market that had already devoured their souls. But deep down, they clung to the belief that the next piece of the puzzle was just within reach. They needed mibera like mibera needed them.

1:22 minutes before launch, the infamous scammer known only as janitoor issued a cryptic warning:

janitooor.eth 🐼⛓️‍🧲

@deepname99

heads up jani fucked up the poster upload dis week

sorry in advance for the pain and suffering during the onchain BvB bloodbath

treat it as a bullish glitch blood offering to the kaironic ooga booga spirit of mibera

24

9:06 PM • Jan 22, 2024

Then, Lore 6 went live on January 23rd, 4:20 PM.

Lore 6 ♡ MiberaMaker Design Document [ARCHIVED]

The home for web3 publishing.

https://0xhoneyjar.mirror.xyz

The loading wheel of death spun. And spun. And spun—longer than usual, even for the blockchain. Maybe it was the weed. Maybe it was fate. Until suddenly...

Lore 6 Poster Minted..... 126/126

Lore 6 Video Minted..... 333/333

Lore 6 Music Minted..... 420/420

It couldn’t be. Had they really been that slow? (Yes.) Were all the accessories minted in mere seconds?

Jani was right. A bloodbath was inevitable.

In the depths of the Discord, panic took root. The hopeful supersetooors and setooors spiraled into disarray. Dreams crumbled, hopes evaporated. Secondaries listed at obscene prices. Some chads (idiots) even thought the prize was worth it and were buying at those levels. But irl, people call that mental illness.

The lonely miberas didn’t know what to think. Even beloved admin PETRA fanned the flames with a sly psyop:

"JANI MINTED WHOLE SUPPLY."

But the chad MidasWhale responded coolly:

"oh was it him? that’s bullish if real."

And then, like a specter from the blockchain abyss, out popped a wild Großßerbera.

Meanwhile, MiberaMaker was on the case, digging deep into the botter chaos. The miberas debated: should they keep the botter's ETH and redo the mint? Or did they simply want mibera?

Großßerbera took it upon himself to let everyone know:

"Respect the Flex."

He doubled down, sharing tales of how he had spent 10 ETH minting—though he'd recouped more than half by dumping on the chad supersetoor enthusiasts (idiot autists). But was it his fault? He'd worked hard on his scripts, just a poor botter from the slums trying to scam his way through a brutal bear market. Could these almost-supersetooors not see the pain in his words?

But MiberaMaker had other plans.

♡ Mibera Maker ♡ 🐼⛓️‍

@miberamaker333

DO NOT BUY LORE 6 ON SECONDARY!

MIBERA HAS A PLAN

38

2:05 AM • Jan 23, 2024

In Discord land, Karen was demanding to speak to the manager. This was no place for a botter.

The miberas began referring to Großßerbera as the cursed botter.

Steps were needed to prevent heroic supersetooors from deploying all their capital to scoop up accessories to achieve holy supersetooor status.

♡ Mibera Maker ♡ 🐼⛓️‍

@miberamaker333

a short haiku

mek bot
now so sadge
so sadge

38

2:38 AM • Jan 23, 2024

This was the final straw for Großßerbera. He knew the curse of mibera would devastate his entire bloodline. He had already lost his life savings of 10 ETH. He drew his cards and placed them on the table—sending the entirety of his ill-acquired, useless JPEGs to the one they called janitoor.

Jani accepted the offer, but with a caveat: the curse would be lifted, but his ETH was gone for good.

The final terms were agreed upon, and the supersetooors dared to believe they still had a shot—if the stars aligned, they could still become supersetooors after all.

Some supersetooors are more retarded than others. They go above and beyond to sabotage their own wealth, feeding botters in the desperate hope of becoming the almighty supersetooor. But the story doesn't end here. This is just the beginning of the mibera saga.

Will these heroes be rewarded for their deeds? Will Großßerbera redeem himself as the blessed botter? Is Jani actually Großßerbera?

The evidence and the heroes await.

Music:

• Botters cost: 3.2263 ETH

• Buyers:

  • 0x78FD9e97C8dE34af3cb777196F04F593d5a7f10E

  • atyb.eth

  • zoz.eth x2

• Total: 1.16 ETH

Video:

• Botters cost: 1.66 ETH

• Buyers:

  • 0x78FD9e97C8dE34af3cb777196F04F593d5a7f10E

  • atyb.eth

  • zoz.eth x2

• Total: 0.796 ETH

Poster:

• Botters cost: 4.225 ETH

• Buyers:

  • 0x78FD9e97C8dE34af3cb777196F04F593d5a7f10E

  • atyb.eth

  • zoz.eth

  • supersetooor.eth

  • 512mace.eth

  • (0x1E109d267DA61261cb160cb6387fCCC0463d6176 - purchased from 512mace.eth)

This is just the beginning. Ooga booga, mibera.

This article was done in collaboration with the success syndicate on farcaster and can be found here. You can find me on farcaster here Im happy to Jam or help in any way possible.

Opportunities often masquerade as challenges, waiting for even the unskilled to recognize their potential. As Pasteur said, "Chance favors the prepared mind," but serendipity ensures that success can find those who are ready to learn and adapt, regardless of initial skill.

Being in the right place at the right time is often all you need. My path is unconventional, to say the least, but it can be emulated by anyone reading this.

To provide context, we first need to understand why you can move forward and be instrumental in building the next big product or even industry. My previous experience is not the type that would be on the resumes of my peers in the crypto industry. It spans a variety of skilled and unskilled labor, from making snow in negative 45 degrees Celsius in the Rocky Mountains to building automated rail solutions in the very isolated Pilbara region in 45 degrees Celsius heat. In 2017, I didn’t even know what HTML was (don’t laugh).

Many successful people attribute their success, at least in part, to luck. From the outside, it seems like alchemy or magic, but this perception is due to the observer not directly experiencing the inordinate amount of hardship, depression, self-doubt, rejection, hope, faith, and exuberance that this luck consists of.

Recently, while watching the Netflix documentary "Dirty Pop", it was revealing how the reality of some of the largest boy bands in the world and the image I had stored in my memory were vastly different parallel universes. Subjectivity was the barrier. While I had a prefabricated image of these bands from their later years, I would never know what existed prior and during, behind the scenes. For the members of the Backstreet Boys, their experience was just another gig. They were plucked for a job and linked up with other people they knew from their present or past. In the early days, they were chauffeured around in a bus and floated by their producer around high schools, performing for teenagers. A wild ride ensued, and they became some of the biggest names in the world but ultimately never received adequate compensation and were pawns in a giant Ponzi scheme.

But for an outsider or a professional in the early days, they likely would have balked at the idea of working 24/7 for no wage or little wage. From early footage, it was not evident they would become worldwide stars. It’s likely the boys thought very differently about this. Perhaps they had no other options, or they believed in the mission.

The fact remains that most would not have been in the right place, had the energy, or the foolishness to capitalize on the opportunity because, at that stage, the opportunity didn’t exist.

Three years later, from not knowing HTML, I had moved to a different country, learned enough about computer science to be dangerous, and lost the majority of my initial investment in crypto.

I still had a deep interest, but man, did I have some doubts. Would crypto really be the utopian savior from the world that was? BTC had lost the luster it once had with the general population, ETH was apparently on the path to move to the other most well-known consensus—Proof of Stake—but many didn’t think it would happen for several years, if at all.

I was still following a 2017 ICO-era project, stupidly thinking they would execute on their "roadmap." Part of this was to move to a hybrid consensus with Proof of Stake and Proof of Work. Long story short, and to save the technical details, I thought it would be a good idea to use the last of my capital and run "miners." I did this, absorbing the small cost to run the miners with no reward.

Being a part of this unique aspect of the crypto industry opened a few doors. Not wide open, but ajar enough to slip through the crack. It just so happened that many new projects funded in the post-ICO era were coming to a stage where they could be tested. Many of these projects were using the new PoS consensus. One problem: they needed ~100 validators to provide infrastructure for these protocols and also needed them to be "decentralized."

Many of the types of people/organizations that were capable of this were either very expensive, didn’t fit the decentralization criteria, or outright thought crypto was a scam. To acquire the needed participants, they had to be creative. Cosmos was the first of these, and Zaki Manian famously coined the term "the incentivized testnet" with their Game of Zones program. He has since been on the record stating he regretted creating it due to the mania it created over the coming years.

Here I was, with little but a small amount of experience, able to participate. Not through skill, but because there was no one else. From here, I was able to participate in many of these incentivized testnets and gain equity in some of these blockchains. While they were financially lucrative, the most valuable takeaways I got from this time were being able to access some brilliant people who became mentors and gaining experience in DevOps, business, coding, and getting to know the industry in an intimate way that allowed me to develop a unique perspective.

The moral of this part of the story is that no matter your experience, opportunities exist that even the most professional people don’t understand. In fact, it’s usually the professionals who are too ingrained with old knowledge to see the forest for the trees.

Often these days, when I find opportunities, they are hard to articulate to people as they are a collection of dissimilar dots that need context that is subjective through experience. I can usually tell almost anyone the opportunity, and they often disregard it for numerous reasons. A small example of this is Quillibrium—a very unique project that I was one of the several bootstrap nodes to start the network in October last year. I had only known about the project because I spent a bunch of time on Farcaster (a very novel decentralized social platform at the time) and knew the founder, Cassie, was one of the most intelligent people I had come across. I let my colleagues and friends know about this opportunity, and only one actually acted upon it. Not insinuating that all or these are profitable or end up anywhere, but if I would have liked one thing back in the early days, it would have been for someone to outline the traits that would make me more lucky. This is a bad way to describe it, but that’s essentially what it is—allowing yourself to have the most opportunities with the largest upside without competition.

While that seems like a falsehood to most people, I would argue there is a framework for setting yourself up for success. In short:

1. Do what others are not, for whatever reason it may be.

2. Don’t be afraid to fail. To fail is inherently wrong. It’s a step on a ladder to get to the next rung.

3. Know that only a small number of wins are achievable. But when you do, it will be from the failings and will have outsized returns, whatever they may be.

Ideally, you set yourself up to think like a venture capitalist about life. Work these days blurs the lines of personal and paid work. Create boundaries but do things you would want to do on a Saturday night (I’m literally writing this on a Saturday night). A great short video on the VC mindset is here, tailor what it suggests to your own life.

There are many opportunities that exist in the crypto space and life in general right now. While I write this the crypto market is quite bearish, Farcaster has lost its glow for a lot of people and the world seems on the verge of WW3.

Make no mistake, these are the best times to find the opportunities because the lack of competition gives you the edge that doesnt exist otherwise.

And always remember, just because someone is a "professional" doesnt mean they are right.

Regarding crypto work, heres an article I wrote previously about how I view contributing to the space

https://paragraph.xyz

Opportunity Capitalist

An individual who, instead of working a traditional job, spends time contributing to decentralized protocols/projects/products to gain equity/skills/relationships. An iteration of the VC but at a smaller meaningful scale that is not purely return motivated.

Decentralized Social Graphs as Living Organisms

Decentralized social graphs are evolving into living organisms due to the convergence of technological and cultural advancements. Despite their infancy, the critical challenge remains how to initiate the flywheel effect. Although iterative efforts have birthed single-celled entities, these have yet to mature into a thriving, self-sustaining ecosystem.

Many efforts, particularly from outside core contributors, misinterpret the end goals. These attempts often tweak existing paradigms minimally and release new clients aimed at siphoning social capital from a limited, pre-existing pool.

Reach, Not Features

Technology often gets mistaken for the product itself. Historically, we view horses, the printing press, and cotton as tools rather than technologies, a perception influenced by our current era. Similarly, recognizing current tech stacks as tools can shift our focus towards optimizing outcomes rather than insignificant feature tweaks.

The next successful client doesn't need superficial changes like a "hat button" instead of a "like button" for $Degen casts or point-earning mechanisms for user card trades. It needs to amplify ideas that resonate broadly, acting as a loudspeaker in a vast open field.

Content is the Missing Focus

To build optimal clients, we must reassess our optimization goals. Focusing solely on technology and features for existing users stagnates the ecosystem. Content, being the true product, should be the primary focus. High-quality, engaging content transcends time and space, attracting and retaining thoughtful users more effectively than any marketing strategy.

Farcaster's recent debates on content moderation in decentralized networks underscore this point. While core protocols should remain unmoderated, community subsets should implement moderation to ensure content quality, essential for attracting the "right" users.

Follow the Incentives

Moxie, despite its innovative approach to user rewards, falls short due to its current algorithm. It serves as a foundational model for protocol-level monetization and value creation, adaptable by any client using Farcaster for social communication.

Moxie's algorithm prioritizes engagement with high-ranking social capital users, leading to engagement farming rather than enhancing content quality or reach. This increases platform activity but not the quality or appropriate reach of the social graph.

Content-Optimized Algorithm

Fortunately, the Moxies core platform is agnostic to scoring, making it adaptable to evolving needs. Prioritizing quality content in social capital algorithms is essential for steering the ecosystem in the right direction.

Moxie's example is for illustrative purposes only. Building a client that prioritizes high-quality content will break through current limitations, allowing a steady influx of new, engaged users into the ecosystem.

This is written by a contributor to the project who was absent for the first few years of development. All information is presented from a subjective viewpoint, intentionally striving to remain unbiased

• Forked from facebook codebase

• Network mechanism design

• Development

SEP

• First experimental network

• Testnet

NOV

• Public Launch

  • https://0l.network/2021/11/15/libra-liberated/

  • https://0l.network/2021/11/16/future-proofing-the-economics-of-blockchains-pts-1-2/

  • https://0l.network/2021/11/17/future-proofing-the-economics-of-blockchains-pt-3/

  • Delay Towers

    • https://github.com/0LNetworkCommunity/libra-legacy-v6/blob/v6/ol/documentation/delay-towers/delay_towers_0.md

DEC

• Teams proposal(rejected)

• Working groups formed

• Working groups organized

APR

• Network halted(numerous network rescues throughout 2022)

• POLL: Brand Image - 04/19/2022

MAY

• POLL: The 0L Interim Governance Proposal - 05/24/2023

• Proposed roadmap for 0L

  • https://docs.google.com/presentation/d/e/2PACX-1vT5m6J3rB0RtzYsHseDjEp1jklAdnZv8Po3kgU4nZdLUh-nVvzJ__YnUKxPd0oNULbZoy70xoiKTH1F/pub?start=true&loop=false&delayms=5000#slide=id.g14288bf9cc4_0_5

OCT

• Proposals for V6 - V7

  • https://0l.network/2022/10/11/proposal-2210-1-final-supply/

  • https://0l.network/2022/10/11/proposal-2210-2-proof-of-fee/

  • https://0l.network/2022/10/11/proposal-2210-3-musical-chairs/

  • https://0l.network/2022/10/11/proposal-2210-4-repurpose-carpe/

  • https://0l.network/2022/10/11/proposal-2210-5-revenue-binding-primitives/

  • https://0l.network/2022/10/11/proposal-2210-6-faucets-for-workers/

  • https://0l.network/2022/10/11/proposal-2210-7-donor-directed-community-wallets/

  • https://0l.network/2022/10/11/proposal-2210-8-infrastructure-escrow-funding/

• Proposals voted on using RxC Voice

NOV

• Development V6 starts for the successful RxC Voice vote

• Development continues

MAY

• Arctika Recommendation

  • https://0l.network/2023/05/23/team-arctika-recommendation/

OCT

• V6 Testnet 6

NOV

• V6 Testnet 7

DEC

• Mainnet V6.9

• Network halt

• Resuscitation

JAN

• 0L Swap

• A Team

FEB

• Hustle/Karma

APR

• Scorpions Claws recommendation

  • https://docs.google.com/document/d/e/2PACX-1vRrKQg2-QQukw_jZ1lIVtMDpwieGFJ8OSRVf1GTz74AU0qYSyHHIxqxXZSwf3SDvn0O-goxLfXSSGUe/pub

• Scorpions Claws hardfork

MAY

• Osmosis bridge

Congratulations, you're hired!

Welcome,

We are excited to offer you the position of Community Member at $DEGEN, a leading innovator in the world of meme coins. Our latest venture, $DEGEN Tips, is designed to bring fun and financial opportunities together in the crypto space.

But first some history about your new workfunplace.....

Recently, I reached out to someone I thought was a $DEGEN employee. An innovative product wanted to connect with $DEGEN for a memecoin showdown to attract liquidity to their platform and reward providers. To my surprise, the well-known community figure informed me they were not part of the team.

This was intriguing because I knew this prominent figure had significantly contributed to $DEGEN's growth in a short timeframe. They were not working for a salary but were instead driven by interest in the airdrop and coins they purchased on the market. Astonishing! Do you know of another industry like it?

Furthermore, as one of the top holders when $DEGEN was first airdropped, I can tell you about a group of large holders who made a pact to not sell early and help the product/community grow. We knew that for $DEGEN to be successful, it required a group effort and proper distribution.

As a tip receiver, you are now part of this unique and thoughtful group. Remember, with trust comes responsibility, and with responsibility comes rewards.

$DEGEN to $1

As an early advocate, I recognized something special in $DEGEN. It is rare to see an opportunity with such reach. This intuition is gathered from multiple threads over many years. It's not easy to see for the regular observer, so I want to break down the opportunity that led to a 1600X gain at all-time highs.

Before diving into the story, let's first explore the word "Degen," which is the "Ape" of this crypto cycle. The word and its identity provoke a visceral reaction. Over the past few years, I've met people ranging from basement dwellers living with their parents to COOs of large blockchain projects. When asked about their entry into the crypto space, many proudly mentioned they were doing "Degen Shit." Conversely, some of the smartest people I know uttered the word with disgust, calling them single-celled organisms. When I see such extreme and polar opinions, I run toward it with force. Almost always, there is something there.

Back in early 2024, Farcaster was in its infancy. Coins were starting to pop up off the back of early meme coin hype from the Solana blockchain, like $BONK and $WIF. The first one I remember was $POINTS, but it was short-lived due to the poor distribution of the airdrop. Some early members even used it to line their pockets by selling tokens like $SPAM.

Channels had just been launched, and people were directed to cast in channels by default instead of the vanilla home feed. The /degen channel rose to be the most active channel and remains one of the top channels alongside /base. Word started to spread about an airdrop to Farcaster users based on their casting history, slightly weighted toward activity within the /degen channel. Unlike previous attempts to gain casters' attention, this was somewhat of a fair launch. $DEGEN was airdropped to users, and it had a slow start. You could buy as much as you wanted for pennies as most were selling the small amount they received.

Shortly after the first airdrop, the founder Jacek announced that $DEGEN would be predominantly a tipping coin, with users given an allowance based on their activity. This allowance was dynamic, with boosts for certain channels like /degen and /founders. The idea was to reward valuable casts with a coin. This took off, attracting mixed reactions. Some dove straight in and went tip ballistic, dropping $DEGEN like rain in Malaysia during the wet season. The Warpcast team leaned into it, adding the $DEGEN logo instead of the like button when $DEGEN was mentioned in a cast. This made some people angry as it was flooding their meticulously curated home feed (later, the same person would invest in the investors round, helping fund development). $DEGEN's price shot up! Many called it a bubble, which eventually deflated when the airdropped tips were distributed.

After the initial rise, $DEGEN started to get attention from sophisticated investors within the Farcaster ecosystem. 1conf approached the founder and acquired a share of the dev-allocated tokens in an OTC deal at a discount to the market price. This was another controversial moment for $DEGEN, as retail was starting to see VCs as arch-nemeses after the damage they had done over the years. Ultimately, 1conf is a different type of VC, providing value to the space and $DEGEN. This was a pivotal moment, boosting $DEGEN and increasing its "legitimacy."

One unique feature of $DEGEN's tipping mechanism was the distribution of token holders. The number of holders increased significantly in a short time. This was a huge plus for $DEGEN and an innovative way to tackle the problem of widespread distribution. As of this writing, it has the third-largest holder base of all tokens on Coinbase's Base chain at 620,080. During the Easter break in April 2024, as $DEGEN tips were distributed for Airdrop 2 Season 3, $DEGEN also launched as an L3, labeled as the casino for Web3. Interest swept through to X, bringing $DEGEN to a high of $0.064. Unfortunately, the hype died down, distribution increased, and the price slowly collapsed.

After distribution, a few other attributes are essential for a token: attention and buy-in. Cobie famously blogged about how crypto is a game of attention.

Firstly, Farcaster as a content platform and an army of contributors like you provide a gateway for new participants to get acquainted with $DEGEN quickly. Never before has there been such reach.

Secondly, Coinbase's Base chain will bring a massive influx of new users. $DEGEN, being one of the most well-known tokens, and the ability to use it in fun ways will entice many as they flow through that funnel.

Farcaster is known for its dev community, which has helped $DEGEN integrate into various applications and attract builders through grant rounds. The buy-in has been astounding for a token less than six months old.

Your role entails having fun on the internet using a new sufficiently decentralized social network. Your content is yours and cannot be rugged from underneath you. Your $DEGEN is independent and yours to use as you like.

You are free to contribute by tipping, building integrations for $DEGEN, and playing games.

It is your responsibility to use $DEGEN for fun!

Yours truly,