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.