TetraMesa

Of Oracles & RWA Headwinds

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Tokenization of Real World Assets (RWA) is on a tear, but will some aspects be held back from mass market adoption for lack of trusted information about certain types of assets? Today’s Oracles, (that supply external, off-chain information to a blockchain or smart contracts), don’t seem ready for richer types of information that we’ll need. Today’s oracle infrastructure is better suited to selected structured data points than to richer, messy reporting packages such as engineering reports, appraisals, legal exceptions, maintenance issues, lease details, or materiality judgments.

Mckinsey estimates tokenization markets worth somewhere from $2T – $4T by 2030. They aptly point out, “Tokenization’s rate and timing of adoption will vary across asset classes” and “Given their characteristics, certain asset classes will likely be faster to reach meaningful adoption.” In other words, easier things will happen faster. Obvious enough. Others assessing future tokenization markets show more of the usual charts with curves bending quickly upwards. The more challenging areas though, will be where they’ve always been challenging in terms of regulatory issues, information flows and so on. When we get into “REAL” real world assets is where things are harder. That is, things like gold, mutual funds, or others that are already virtualized really, should translate more easily to representative onchain tokens than messier deals such as a local shopping center development, a piece of art, or similar. Let’s say a token says you own part of a building. But what if that asset has a problem? Who reports it? Where does the report live? Who’s liable if nobody updates investors?

Traditional finance has longstanding reporting structures. And they still get things wrong sometimes or suffer from fraudulent claims. When we build an abstraction layer like a blockchain on top, we need ways to bridge a reporting gap. Successful adoption here isn’t going to be about just splitting things into smaller pieces with tokens. It’s time to look at why and suggest some solutions.

The idea for this post came out of a LinkedIn thread where Igor Samotesov talked about why trillions aren’t flowing quickly into onchain RWA instruments. And I just happen to be re-reading a book on taxonomies. So this is the result. Here are some more potential reasons for what’s going on and possible solutions.

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Using Skills for AI Builds: Product Safety

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Note: This isn’t about general skill in building things with AI… it’s specifically about things called Skill files or their similar counterparts.

Are you a product person at any level who is either building yourself or managing others that are increasingly doing some direct building?

Like a lot of us, I’ve been making some of my own stuff with AI tools. Or in some cases experimenting to understand their use cases better. Among the hype cycle things of this year are Skill files. (Or more generally skill type instructions for AI tools.) There’s whole marketplaces for them. This post is just a warning I’m throwing out there as a caution along with some ideas for mitigating risk. It’s not meant to be overly alarmist, but the “You can use skills to do anything!” hype is so overwhelmingly thick sometimes, it just needs some balance. And actually, I think it’s not just clickbait, it sometimes feels irresponsible and dangerous.

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Web3 Consumer Protection: Progress, Gaps, and What You Can Do

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Consumer protection in Web3 has come a long way, but it’s still nowhere near what you’d get from a traditional bank or broker. Billions in losses from hacks, rug pulls, and failed platforms tell a story that headlines alone can’t fully capture.

Some frameworks are in place now, and more are taking shape. But the gaps are significant, and they affect you directly.

This post breaks down what protections actually exist in Web3 today, where they fall short, and what you can do to protect yourself while the rules keep catching up.

This article is mostly for ordinary users and investors trying to understand what protections they actually have before using exchanges, wallets, tokens, or DeFi protocols. However, it’s also useful for those building in Web3, because these unresolved protection gaps are the kinds of issues that shape user trust, investor confidence, and adoption decisions.

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Why the Best Web3 Products Feel Less Like Crypto

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For too long, the crypto space has been obsessed with selling the “engine” of the blockchain. Whitepapers of new projects focused on technical features and used a special jargon (e.g. words like “gwei” “slippage” and “hash”). It was like showing users the gears, the grease, and the technical specifications of a new car model, and then wondering why the general public wasn’t buying it.

It finally seems that in 2026, maybe this attitude is giving way to better product sense. Successful Web3 products are starting to make the technology invisible. Or at least trying. They have finally realized that users don’t care about features for their own sake, but instead about the benefit they get.

The era of “crypto-first” because it’s some self-sovereign anti-establishement thing that’s good for you is dead. The era of “stealth Web3” has begun. Here are some guiding principles for this new era.

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What “Decentralized” Really Means in Web3 Products

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The early days of the internet were characterized by a dream that was like a philosophical stance; a “World Wide Web” with no owners that could be used by anyone. (Though infrastructure and control points still existed.) Over time, that dream turned to become a much more commercialized Web, with major portions consolidated in the hands of a few tech giants like Amazon, Google, and Facebook. Web3 was in some ways meant as a “reboot” of that original dream. A reboot powered by a single word, loaded with diverse meanings: “Decentralization”.

However, as the term has moved from whitepapers to marketing billboards, its meaning has become a bit blurred. Much of the time, decentralization is used to refer to technical aspects of elements referred to as crypto or Web3. On the general user side, many may believe that if a product involves a “token” or a “wallet” it is decentralized. This is a potentially inaccurate perception. In reality, decentralization is more like a spectrum than a binary setting. The question isn’t if an app is decentralized, but how much. And the core of such things may be technology based, but there’s other aspects that should be considered.

This post sketches a four-aspect map of decentralization that you can use to look past the branding into the actual way a product functions.

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Security Tokenization Oddities: Unresolved Issues Behind the Hype

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There’s a lot of tokenization discussion in 2026 that seems flooded with hype from issuers, banks, and crypto natives. I’d like to try for some balanced realism vs. breathless promotion or vague warnings.

Ripped from the hands of early pure Crypto exuberance, tokenization is clearly the big innovation in finance right now. Early crypto may have been as much ideological as it was a maybe useful new form of finance. But at this point, the benefits and use cases for more mainstream tokenization are becoming clearer. Securities that move on digital rails could settle faster, reduce reconciliation, support fractional ownership, improve transparency, automate parts of the asset lifecycle, and create new ways to use assets as collateral, and more. What could be better?

Some industry estimates, including Fireblocks’ 2026 tokenization guide, project or report post-trade processing cost reductions of 35–65% depending on asset class, issuance cost savings of 40–50% for certain corporate bonds, faster settlement, and improved capital efficiency through programmable collateral and liquidity management. These efficiencies compound across issuance, reconciliation, and treasury operations. (Check out Fireblocks Executive’s Guide to Tokenization 2026.) As much as I’m a fan of what’s going on, among my favorite things to do is look behind the curtains, around the corners and so on. To understand and help others see what’s not always clear through the hype. And there’s a lot of hype. Though my personal favorite is collapse of settlement times. I’ve always found it interesting that something that started due to the need to reconcile physical paper strips moving around Wall St. persists even with today’s systems.

The benefits show the attractive version. And it’s all generally true enough. Still, even with recent advances, we’re early in these efforts. The deeper version is more complicated. There’s still holes in this area. The more serious tokenization becomes, the less it looks like simply “putting stocks on-chain.” (Or whatever asset we’d be talking about.) It starts looking like a full-stack rebuild of financial market infrastructure, including issuance, investor onboarding, KYC (Know Your Customer), custody, smart contracts (which are of course a new element), transfer agents, settlement assets, corporate actions, reporting, legal records, and dispute handling. And more. Those are just the big pieces.

This is progress. But it also proves the central point. The token is only one layer. And not everything is better. This is an opportunity to make things better. And yet there’s some new issues that get created here that we still have to collectively sort out. A recurring theme in recent tokenization discussions is that institutional tokenization is no longer being framed as crypto experimentation. It’s being framed as regulated, programmable financial infrastructure. This may be obvious. And it’s a good thing, (my opinion anyway), yet we need to pay closer attention than ever. While we create more efficiency, we’re going so fast we may also be creating some possibly dangerous dependencies as we string more of these tools together. (Consider my October, 2025 article on Will RWA Tokenization Growth Increase Systemic Risk? It was focused more on Real World Assets, but a lot of the ecosystem risks are similar.)

Since tokenization is becoming real financial infrastructure, this means the unresolved details matter more.

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Understanding Payments and x402

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Quick History and Value

The web was built to move information quickly, but not value. A page can load in milliseconds, yet payment usually happens somewhere else: through a checkout page, account or subscription. This separation still shapes how digital access is sold. Even when the product is fully online, the payment flow often feels added on top of the web rather than built into it. That separation is finally ending. x402 turns the long-dormant HTTP 402 ‘Payment Required’ status into a native, instant payment layer, letting AI agents and apps pay per request in stablecoins without accounts, logins, or manual steps.”

x402 tries to close that gap by making payment part of the HTTP request itself. When access requires payment, a server can return payment details instead of sending the user or application into a separate billing flow. The client pays, repeats the request and receives the resource. In simple terms, x402 turns payment into part of the same request-response pattern that already powers the web.

Now for the deeper story…

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Can Prediction Markets Help Product Managers Make Better Bets?

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For all the product tools we have, from customer profiles to journey maps, there’s been little use of prediction markets to inform decisions. Unlike AI-driven predictive analytics, prediction markets remain rare in product work. With generative AI pushing teams toward faster experimentation and shipping, they may be worth another look. I became interested in this while playing with a few markets myself and wondering how they might apply to product work.

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Part 2: Practical Tactical AI Tool Challenges: Workflow Reality, Context, and Prompting

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In Part 1, I focused on why AI product work gets messy so quickly. Strategy theater, bad data, dependency problems, and workflow brittleness. This next section moves closer to the machinery itself: workflow tools, notebook sandboxes, exception handling, observability, skill files, context, and prompt engineering.

Workflow Operations – Langchain Type Tools

I use n8n.io for several tasks; a couple professional and several personal. If I was a real developer, maybe I’d be using something closer to pure code. But I’m not. Still, I think tools like these are more than just a crutch. And they’re certainly a fast way to wire things up for some basic testing. (Though some argue newer generative code tools are better and you can skip such things. I’ve been using both though, and find – as usual – what’s best depends on the use case.)

Regardless of platforms, the thing you really need to do with these tools is have an MLOps view from a Product perspective; either something you built yourself or in partnership with your AI/ML/Dev team members. Ideally you have a skilled architect on staff, however you should be collaborating on these things or at least in the loop. In a startup, there might be some more heavy lifting on the product side.

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