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RWA, Meet RWL, Part 5: When the Asset Is Already Digital

July 24, 2026 By Scott

Part 5 of 6 in “Real World Assets (RWA), Meet Real World Lies (RWL): Knowing What You Own in the Tokenized World,” a series about what tokenized assets represent, how information about them reaches the blockchain, and why none of this eliminates the need for trust.

Parts 2–4 examined the data, the people, and the failure modes surrounding tokenized assets. We now return to the taxonomy itself. The original split between intangible legal claims and tangible physical assets works for most conventional RWAs. It does not cover the growing set of things that are digital from the start; data, compute, software, network rights, game objects, and credentials. Some of these are assets, some are services or licenses; some may not be assets at all. We need a third branch.

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Filed Under: Crypto

RWA, Meet RWL, Part 4: No Blockchain Solution Fixes Real-World Lies

July 24, 2026 By Scott

Part 4 of 6 in “Real World Assets (RWA), Meet Real World Lies (RWL): Knowing What You Own in the Tokenized World,” a series about what tokenized assets represent, how information about them reaches the blockchain, and why none of this eliminates the need for trust.

The first three parts looked at the assets, the data, and the people involved.

Part 1 asked what kind of thing was being tokenized and what claim the token actually gave its holder. Part 2 looked at the information required to describe and evaluate that thing. Part 3 examined the people and institutions responsible for reporting, checking, and acting on the information.

This part looks at what happens when those trust structures fail.

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Filed Under: Crypto

RWA, Meet RWL, Part 3: Permissionless Does Not Mean Trust-Free

July 24, 2026 By Scott

Part 3 of 6 in “Real World Assets (RWA), Meet Real World Lies (RWL): Knowing What You Own in the Tokenized World,” a series about what tokenized assets represent, how information about them reaches the blockchain, and why none of this eliminates the need for trust.

Parts 1 and 2 examined the underlying assets and the data used to describe them. Now we turn to the people and institutions responsible for that information.

Before getting into that, it’s worth clearing up two terms that tend to get used somewhat loosely.

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Filed Under: Crypto

RWA, Meet RWL, Part 2: The Oracle Problem

July 24, 2026 By Scott

Part 2 of 6 in “Real World Assets (RWA), Meet Real World Lies (RWL): Knowing What You Own in the Tokenized World,” a series about what tokenized assets represent, how information about them reaches the blockchain, and why none of this eliminates the need for trust.

Part 1 asked what kind of thing is being tokenized. Once we know that, we still need information about it: how much gold is in the vault, who owns the building, whether the aircraft was inspected, whether the borrower is paying. That is where the oracle problem begins.

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Filed Under: Crypto

RWA, Meet RWL, Part 1: “Real-World Asset” Is Not an Asset Class

July 24, 2026 By Scott

Part 1 of 6 in “Real World Assets (RWA), Meet Real World Lies (RWL): Knowing What You Own in the Tokenized World,” a series about what tokenized assets represent, how information about them reaches the blockchain, and why none of this eliminates the need for trust.

RWL is what I’m calling “Real-World Lies.” That might seem a little strong. Maybe it is. But I don’t necessarily mean “lies” only in the malicious sense. There are also mistakes, omissions, outdated records, stretched definitions, poorly understood rights, and information distorted by incentives. Right now, there seem to be plenty of ways for something that isn’t quite true to enter a system, become tacitly accepted, and continue moving through it as though it were fact. This series looks at how that happens, at least somewhat. But more importantly, how to classify and evaluate the many parts that finally seem to be coming together to bring more tokenized assets to market.

Much of the following is not new. If you’re already deeply familiar with tokenized assets, you may want to skip this entire series. However, if you’re still new to Real World Asset and trying to get a sense of the whole big picture, perhaps this series will help. It was created as part of a project to help some new to the area to build a wide scope taxonomy of the space and the issues across types of things involved. This isn’t about mechanisms of tokenization. We’ll be more focused on compare and contrast with real world assets and building an evaluation framework that helps us navigate these spaces.

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Filed Under: Crypto

AI Build Copilot, Part 6: MORE Bonus Bits & Pieces

July 22, 2026 By Scott

It doesn’t matter what you’re doing. Product Management work, Creative work. Requirements. Code. We’re all over these tools now and across disciplines, we face some of the same challenges. Here’s yet another set of practical snippets for dealing with AI-assisted coding and building. The tools can move fast and sometimes faster than we can track. This installment collects a few more practical habits for staying in control. These are the small, deliberate practices that keep “trust but verify” from turning into “hope it worked.”

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Filed Under: Product Management, Tech / Business / General, UI / UX

AI Build Copilot, Part 5: Bonus Bits & Pieces

July 21, 2026 By Scott

Intro

I thought Part 4: Preparing for Production was going to wrap up the series. But wait, there’s more! Not every useful lesson about building with AI tools fits neatly into the larger themes of the past articles in this series. So this final section is a collection of extra bits and pieces that don’t quite belong anywhere else, but may still save you a lot of trouble. Yes, I could have just called it “Misc.” I suppose. But in any case, there’s just some random bits that didn’t fit cleanly into earlier docs and fell out of the edits.

Here they are in one random walk place.

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Filed Under: Product Management, Tech / Business / General, UI / UX

AI Build Copilot, Part 4: Preparing for Production

July 14, 2026 By Scott

Part 3 was about building durably, so what happens after that? A successful prototype creates a new problem if you actually put it into some form of production. Not too long ago, if we built what we called prototypes, maybe they went to user testing, or we ‘sort of’ put them out there. But now? A lot of so-called tests really aren’t. That is, they’re really more actual rollouts. Just maybe not necessarily with the same rigor we used to give product launches. The line between that which was intended as prototype testing and so-called Minimum Viable Product (MVP) seems like it might get blurry.

The next challenge with launching a kind of prototype, but that which is maybe real product is someone may decide to use it. So… Is it really a prototype? Meaning… is this just some brochureware for testing or is there actual sign-up functionality, any kind of real billing, private information collection, and so on.

This is when the low-stakes experiment begins accumulating customers, private information, billing relationships, operational dependencies, and consequences. You have to make your own call on this kind of thing. I have to tell you, personally I’ve never loved the “false front door” thing for testing. I get that it can be useful, but sometimes it’s on the edge of unethical in how it seems to trick people. If your prototype is really a test and that’s understood, great. If the truth is it’s not much of a test, but a rollout? Then there’s probably some more risk elements and due diligence that should be covered.

Prototypes have a way of becoming products. Temporary credentials become permanent. Test data becomes customer data. A one-time API connection becomes a critical service. The final part of an AI-assisted build is not clicking Deploy.

It’s deciding whether the thing can be operated safely. One thing that I’ve done along the way when building is created something I’ve called “Launch Blockers.” This is different than a UI backlog or minor changes you find along the way. It’s things like purging schemas, or making sure live production feature flags are set properly. It means a real security review. Things like that.

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Filed Under: Product Management, Tech / Business / General, UI / UX

AI Build Copilot, Part 3: Building a Durable Workflow

July 14, 2026 By Scott

In Part 2, we went over Staying in Control of your build session. Now it’s time to start looking at consistency and reliability issues. AI conversations are useful working spaces. However, they’re not reliable long-term project memory. Sessions end. Context fills up and you have to switch chats. Maybe you switch models from Fable 5 to GreekTragedy-7.6 or ElonsNextWife-10.4. Files are edited elsewhere, etc.

An agent’s understanding of the project can become stale while sounding completely confident. That’s why the project must survive the chat and there’s ways to do that.

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Filed Under: Product Management

AI Build Copilot, Part 2: Staying in Control

July 14, 2026 By Scott

We started in Part 1 with what the bright shiny What AI (Site builder) Demonstrations Leave out… and it was fairly high level. Now we’re going to dive a bit deeper and yes, somewhat into some technical things. My goal remains: to help level up those in generally more non-technical roles, especially product management. (Though of course, what that technical continuum looks like varies widely.) Let’s start with some basics, and drill down from there…

The dangerous thing about AI coding assistants is that they are often very good.

Huh? How could that be dangerous? It’s dangerous because if they were obviously terrible in spots, we would remain more alert as we used them. Instead, they’re helpful enough to earn trust and inconsistent enough for us to experience times where we clearly shouldn’t have trusted them.

The interaction feels conversational. The agent explains what it is doing, uses reassuring language, and often anticipates the next task. Over time, it becomes easy to stop evaluating each recommendation independently.

That’s the lull.

The assistant is called a copilot for a reason. Do not become its passive passenger.

Disclosure: I did use AI in this series for spelling, grammar and to fill out a few bullet lists with minor items I missed. Generally, I draft, do outlines, and write everything; then have AI check spelling, grammar, and completeness; maybe clean up a few items. Here I did miss some bullets though and those are filled in. Where that was true, I checked sources on ‘truthiness’ for everything. The point is, every high-level thing I go into here are things I’ve personally run into – recently – while working on real projects. So it may seem like a lot, but none of it’s fluff. And you ignore them at some degree of risk.

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Filed Under: Product Management, UI / UX

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