As a product person and retail investor, I’m sensing what I would have thought is an obvious risk with Real World Assets (RWA) tokenization, but don’t see much discussion beyond esoteric finance venues. I’m a believer in blockchain and crypto opportunities. However, I prefer a more thoughtful approach than the breathless crypto maxi hype spew. With that perspective, I’d like to offer a primer for product managers and investors interested in this area. I’m trying for a deeper sense of what’s going on than, “The RWA Tokens Are Coming, Invest Before You Miss It!” Or as Darth Crypto would say, “The FOMO is Strong with This One.” Disclaimer: I’m not a finance person… these are my explorations into this world as a retail investor and digital product builder. I’d just like to help my friends and colleagues with informed choices.

Introduction

Short Answer? Maybe not so much. (At least when talking about the core tech vs. specialized solutions.) This won’t be as much about AI as about category and startup creation in general. AI just happens to provide us with a great developing case to review. (At least in terms of Large Language Models (LLMs) and Generative Pre-trained Transformer (GPT) products.)

Product people need to zoom in and out. There’s so much noise around new AI features, we may forget to take a wider view. While we’re often responsible for detailed product roadmaps, some of us also do strategy and business planning. In that role, we need to think about offerings in terms of long term strategy. Rapid growth in AI LLMs / GPTs provides a case worth study in terms of new category creation. We should keep in mind that AI / ML has been around for decades. What’s interesting now is the LLM / GPT space is a great example of a Blue Ocean strategy that is perhaps only fulfilling its promise in a general way. That is, the rising tide is lifting many boats, but early pioneers might not be able to take margin advantaged positions, which should be a benefit in a new category. (If you buy the book, be sure to get the latest edition.) Even though OpenAI bust this category open, they didn’t get to a winner-take-all or even winner-take-most position.

Why?

Companies are missing the mark with AI projects. The headlines are not great.

• Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025
• Research by the RAND Corporation estimates more than 80% of AI projects fail, which is twice the rate of failure of non-AI projects.
• Deloitte shows some benefits accruing such as uncovering ideas and insights with ROI generally positive, and yet, barriers due to mistakes with real-world consequences.
• BCG’s 2025 survey indicates only “25% of executives are seeing significant value from AI”, with projects vulnerable due to unclear use cases and poor data readiness.

What’s going on? And more to the point; can product people help improve these outcomes?

Perhaps there are efforts to solve tech problems, but not always customer problems. From a Product person’s perspective, it’s time to re-visit basics and use cases. In the add AI scramble, there’s confusion about our shiny new toys. You see it in the drive to do anything with them. You see ridiculous job requirements posted from tech to product asking 5 – 10 years experience deploying AI. (For traditional ML, that may be fair. But they usually mean GPTs/LLMs, which have only been around a handful of years.)

Let’s reconsider our new capabilities from customer use case perspectives with focus on LLMs/GPTs. While also growing, we’ll skip traditional ML as it’s a separate and better understood category. (Things like regression models, decision trees, or clustering algorithms used in fields like finance, logistics, or medical diagnostics, which have established frameworks and decades of practical application.)

Why should we try a look from use case perspectives? Because for those of us that work in Product, instead of panicking figuring out how to check off the “We’re doing AI” into a checkbox in an Annual Report or a marketing piece, it might be useful to have clarity of purpose. We’ll focus on Generative Pre-trained Transformers (GPTs) and the Large Language Models (LLMs) over traditional ML or multi-modal GPTs.

I’ve known about the real power of social media for quite some time. (As have we all.) But recently I was deeply reminded of it after struggling with a personal issue. I’m old enough to remember dial-up. And I’ve personally designed, built or managed some of the earliest digital communications platforms. On occasion, those included support groups/forums for people with all manner of issues. The power in these areas isn’t something that AI is likely going to surpass any time soon. And only a few truly authentic marketers can participate. There are just some things that aren’t likely to be handed off to AI agents.

As much as I enjoy some aspects of social media, sometimes over time we tend to forget things. It’s somewhat how we’re designed as people. Our neural pathways are just set up to decay pathways we don’t exercise a lot. And I think I’d forgotten some of the core value of special enthusiast and focused groups. Which is ironic, since I’ve worked on so many of these products across multiple companies.

I’m not sure if now is a great time to be writing this. But I’ve been trying to write more as it both helps me learn better and faster and I’ve always enjoyed teaching and sharing. The hazard right now is I’m a little loopy on pain meds, post ACL surgery after a little ice hockey incident. (The good news was after the fall there was a nice solid wall to stop me sliding.) While I’ve been injured before, plenty of broken bones, etc., this one is a bit heavier. And while I’m always appreciative of the gift of modern medicine, living in a modern economy and the privilege of decent insurance, there’s another benefit. And that’s the information and support available via Digital/social. This is what has me re-thinking about the power of social.

Without the research, the discussion threads, and more I’ve been able to use across Facebook, Reddit and others, there’s no way I’d have done as well as I did my first painful week post-surgery without the help. And I mean help across the board. Information. Intellectual help. Emotional help. Etc. (For the physical help, that goes mostly to my wife who’s putting up with yet another sports injury.)

Anyway, here’s what I’m re-learning as I type this, and since typing is just about the only thing I’ll be able to do for the next week or so, here’s all I’ve come up with this week…

As a Digital Product person, do you think about intrinsic and extrinsic values of your product or service? Traditional marketing has generally considered these issues from a high-level marketing and messaging perspective. But how often do you see them considered deep within the features and benefits of digital products? Not much, with the sometimes exception of gaming products and perhaps some health apps. And yet, the full User Experience (UX) that goes beyond the interface imbues our products with features that touch on one or the other or both. Game designers may deeply consider game mechanics, (rules and interactions), and dynamics, (emergent behaviors, experiences), but what about the rest of us?

The question is, when crafting products, are you considering intrinsic vs. extrinsic dimensions? At the highest level of Vision and Mission through Strategy right down to Story Creation and execution, we’re all imbuing our brands with… with… with what? The answer is likely with both intrinsic and extrinsic values; both in fact and perception. The question is: Are we doing so with intentionality.

Sometimes we can get stuck in feature factory mode and this presents a variety of risks. One is the time drain that prevents us from considering bigger picture items. Such as making sure features are truly aligned with our intended intrinsic and extrinsic values. Some of these product value dynamics might be an exercise for marketing more so than product. This can depend on company size, spans of responsibility, and the nature of cross-functional partnerships and team structures. Whomever is responsible for considering such things, it pays to execute such things properly.

In past articles, I’ve tried to offer some of the basics of crypto as well as simplified understanding of the blockchains themselves. You can see these here, Demystifying Crypto Basics through Metaphor, and here, Demystifying Crypto – Blockchain Layers.

There are a couple of other fundamental topics worth discussing.

Tokenomics

Let’s talk Tokenomics. Tokenomics is a portmanteau word, derived from ‘token’ and ‘economics’.

Tokenomics is the economic design and structure of a cryptocurrency token, encompassing how it is created, distributed, used, and managed within its ecosystem. It includes factors like supply (fixed or inflationary), distribution and allocation, utility (how the token functions within a platform), governance (voting rights or protocol decisions), and incentives (rewards for participation, such as staking or mining). Well-designed tokenomics align incentives for users, developers, and investors, ensuring the token’s sustainability and growth within its network.

If you’re thinking of building a product or service that’s going to find using its own native tokens or coins useful, you’ll need to go a lot deeper than what’s going to be described here. Consider this as just a primer.