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MVPs + Vibe + Moral Hazard = VibeWreck?

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“There’s two classes of failure: those who thought and never did, and those who did and never thought.” – Laurence J. Peter

As product people, we may have some dev experience. Or not. Either way, we should know about risk. We know when deploying products the bottom line is what value we’re putting out there and is it worth more than the costs. The risk comes into play depending on how far out on the edge we’re going. Upon failures, regardless of cause, who takes the first hit and is invited to seek out other career options? Typically product. Perhaps a whole division. And yes, perhaps a whole company. I’ve seen this up close and been a part of it a couple of times. Still, when building the new, at some point we have to do as Richard Branson says, “Screw it, let’s do it.”

I understand I might be hopping on this train bit late. While I thought my rant was semi-on-time, while typically sitting on my draft over weeks, others have piped up and said things well or better. Such as this thread from Simon Wardley about Vibe Coding in general.

Fine. OK. So I’m not first. That’s ok. Here’s my perspective from the product side anyway…

Here’s the Too Long, Didn’t Read (tl;dr): I believe the whole Vibe Programming thing is going to result in some tragically bad outcomes. Yes, perhaps also some exceedingly rare big wins. But mostly not. And yes, many of us now have spectacular tools for faster prototyping and testing. That’s great. Will this collective benefit be worth the costs of what I think might be some stunning failures coming soon to a web thing near you? Maybe. Tough call. Stay tuned. Now I have to mention my favorite quote from that Wardley comment thread I came across: “Just make sure you hire lots of really good lawyers and fire extinguishers then. Or, hire some software engineers … you’ll need them.”

Here’s my bottom line and if you buy off on it, you can just skip the whole rest of the article:

The latest Gen AI tools for coding and product design and production are amazing. I’ve quickly adopted them myself and find them super useful. And yet, they’re still super scary. Anyone actually delivering production product with these things is sending a lot of risk and waste out into the world. And I think we’re going to sense the fallout from that soon enough.

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LLM / Text Vectors for Product Managers

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Intro

Understanding how these things work matters.

Not because you’re going to build the next GPT yourself, but because understanding just enough of how LLMs and vector math work can change how you think about products, teams, and strategy. It can help inspire better solutions, make smarter tradeoffs when AI promises start sounding magical, and maybe even help you call BS when needed. Whatever strategic product decisions you may be making, your implementation team could be internal or perhaps a contract shop. In either case, there’s operations impact and costs that will likely impact your roadmap. If you also have P&L responsibility, you’re going to need to look at the costs here with regard to your business case. And if you don’t, chances are you may be the one who still has to justify the spend to others. As usual in product manager land, even if you’re not the one executing the actual work, you likely need to understand enough about the pieces to know what they can do and what this might cost.

tl;dr

  • LLMs turn text into numbers using math called vector embeddings. We’re going to look at this below.
  • These vectors live in a high-dimensional space, where “distance” equals “semantic similarity.” Again, we’ll look at an example below.
  • Transformers (not the ones from the comics/movies) are the model architectures that makes GPT-style LLMs so powerful.
  • All this lets us build apps that “understand” language enough to generate answers, categorize, summarize, translate, and more.
  • But it’s still math, not magic. And it’s expensive from a lot of perspectives. The question is where do we want to take the expense hit(s) and for what level of benefit.
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De-Dollarization Risks from Crypto and AI

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Edit: Here’s a July, 2025 update. Just a few months after this post, it’s becoming clearer that Stablecoins are on an amazing tear. And most are pegged to the dollar. The U.S. dollar. If this continues to spread worldwide, then that trend is potentially directly in conflict with the thoughts I’d had about the potentially of dollarization via crypto. This may be especially true if official U.S. policy explicitly promotes Cryptomercantilism. Though there will also then be explicit attempts by others to regulate the risk that comes with this. (See the Study: Cryptomercantilism vs. Monetary Sovereignty-Dealing with the Challenge of US Stablecoins for the EU.)

Anyway, to the original article…

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Upgrading an AI with a RAG Vector DB

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In a previous post about Building a PM Helper with AI, I showed how a fun personal AI project I’d built to be my personal Product Management tool searched across multiple sources before synthesizing answers. Unfortunately, I made both a strategic and a tactical error in that 1.0 version. The solution? Using Retrieval Augmented Generation with a Vector Database. What I’m going to do here is offer some super fast high level definitions as I go through the problem space, and maybe in future posts, go more deeply into RAG and Vector databases in terms of value.

tl;dr:

  • If your a product manager working with AI at any level, you will likely need to understand Retrieval Augmented Generation (RAG) to some degree. The following is a small, practical use case to help see the value in action.
  • For the most part, when using LLMs for your own custom work, you’re stuck with the foundation model.
  • Fine tuning can change the weights of the model to various levels, depending on how deep you want to go. These weights basically control how models transform input and can be in the billions. The deeper you want to have impact, the higher the cost. (You’re not likely fine-tuning for personal projects though. And if you are, it will all but certainly be with open source foundational models.)
  • Retrieval Augmented Generation (RAG) doesn’t change weights at all. RAG just passes more information into a Prompt, (which is a fancy name for an information query, unless you really add fuller instructions ), but is limited to something called a context window. Basically, how much info you can pass in. It’s like saying, “Here, read this before answering.” So theoretically RAG reduces the chances of hallucinations and offers more “truthy” answers. (Assuming good data in what you feed it.)
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Building a PM Helper with AI

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What are We Doing?

We’re going to look at a plaything I built in just a handful of hours while digging into agentic AI a bit just for fun. (Well, for career-related things as well, but mostly for fun.) The toy is an AI enabled Digital Product Manager assistant app where you can ask questions about product management. Which, as it turns out, could actually be a real product. I built it as a toy project for fun, but might actually soft launch this thing. Because, why not? Can you just do this with any GPT? Sure. But this one is tuned specifically to product management in general and digital product management in particular. (There are others like it though, so maybe just leave it as a personal tool. We’ll see.)

Why do this? Why bother. And why do you care? Since the vibe coding thing is so very in right now, (maybe for another few months), I figured it was time to jump in a bit. While I’ve built some agent workflows in the past and built a variety of apps, it’s usually been team based. This is one of those, “Hey, if I can do this… anyone can” type posts. The question, of course, is do you have a reason / use case? But the whole argument that used to exist about some things being “too hard” or “too technical” or “too much time” are fading away with some of these tools. Not completely. And some things absolutely – my opinion – require “real” developers. But others? Lower risk things? Personal productivity things? Not as much anymore. So I’m going to go through my process just at a super high level. My goal is to convince other product manager types to dive into this area more deeply than just watching a webinar and learning some of the lingo. Even for senior roles and beyond individual contributor roles, I personally think it’s useful to get a visceral feel for how things work. Doing so helps offer better context for what teams might be going through as well as understanding in what might be possible. And also, getting a sense of what budget implications might be if you’ve got P&L responsibility for a product.

Next we’ll get into the details, but if you want to see the end result, it’s here: (But note, the functionality won’t work as I’ve got the public webhooks turned off so as not to burn up my paid quotas on the services in use. To see it working, check the Loom Demo.)

Direct Link to the App Test Website

App Demo / Loom Video

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AI Vibe Coding – The Good, The Bad, The Tragically Ugly

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There’s good reasons why vibe coding is on a tear right now. In some ways, there’s potentially legitimate value that will come out of this. At the same time, I think it’s likely we’ll see some seriously tragic outcomes. Just in case you’re not up on the meme, vibe coding is simply a term for using AI to write computer code. (See the link for the origin of the meme.) Maybe you could argue that if we’re going to label things, you could call it vibe composing or vibe whatever-ing, given that there’s generative tools for a variety of object types from art to music and so on. But let’s focus on computer code for now.

So. Besides just having programmers possibly be more efficient, what are the benefits? Perhaps one of the loudest benefits right now is being shouted by a cohort of entrepreneurs and product managers. They’re thrilled! “I can just get software now!”

“I am free!”

What they mean is free from the stranglehold technology costs have had on the ability to get digital product done. The problem with the Temple of Tech has always been time, money, and perhaps worst, resource contention. And those pesky coders had something else the Kingpins of history have always had, “They who control the means of production control the world.” But now? Now it’s democratized! No more desperate postings by non-technical co-founders seeking technical help for zero dollars, but a little bit of stock. There’s perhaps some irony here if you look at historical Bourgeoisie Control the Means of Production. But we can skip the philosophy for now. If you’re old enough, you can remember that if you had a development request, you’d maybe literally head to some special room behind secure doors in a basement where florescent lights battled with the glow of the green or orange text emanating from terminals. There’d be a faint smell of ozone as even the loud ventilation system couldn’t quite keep up with all the buzzing electrons. Here, you would make your tech request. Ironically, it’s not that different now in many places. At least, from a relationship perspective, even if the scenery has changed.

So can entrepreneurs and product managers just ditch their tech colleagues now? Are the headlines true? Coding skills are no longer a hot ticket? Let’s take yet another deep breath. Come on with me now… breeethe… in through the nose, out through the mouth… one… two… three… Good. Yes. Things are changing again. And fast. But maybe don’t just lay off everyone just yet.

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Intrinsic / Extrinsic Product Values Framework 

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In a prior post about Intrinsic / Extrinsic Product Value Dynamics, we looked at the basic differences between intrinsic and extrinsic values and consumer perceptions. Here, we’re going over some frameworks you can consider using to define and position your products along these dimensions. The idea is to make sure that everything from features to messaging align with intended values.

Please note that this framework is a suggested proposal; not something I’ve seen used or tested. It focuses on creating and expressing value deeper in product features and structures rather than surface level messaging. (Which is of course still critical.) There is no experimental data of which I’m aware that tests on varying such dimensions against each other. (Though in the Value Dynamics article, we did go over examples of successful and challenged products.) My goal is to offer a structure to explicitly consider these dimensions and use them as input to a larger scope product positioning strategy that would include the other usual elements of actual features, pricing, competition and so on.

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Intrinsic / Extrinsic Product Value Dynamics

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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.

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Demystifying Crypto – Tokenomics & Stablecoins

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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.

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Demystifying Crypto – Blockchain Layers

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In Demystifying Crypto Basics through Metaphor, we started with some of the basic ideas about money and crypto tokens. But we didn’t talk about where these things live.

Blockchains

We kind of skipped this part. But I thought it was more important to go over some of the pieces first, then go big picture. So what’s a blockchain?

Here’s my simple definition of blockchains: It’s a place where you keep track of some stuff and let tons of people keep copies in a really secure way. That is really it.

Here’s the more typical matter-of-fact technical definition: A blockchain is a decentralized, cryptographically secured ledger that records transactions in a tamper-resistant chain of blocks across a distributed and decentralized network. There’s other details like how blocks are formed and linked, timestamps, and more. We’re not going to focus on those. Blockchains are mostly associated with crypto in popular perception, but guess what? They can be used for all kinds of other things; identity management, supply chain management, healthcare records, real estate and land titles, and a lot more. This is why this technology is simmering beneath the surface. It does have several core values and can be made actually useful. It’s not just about the so-called meme coins.

In the hype cycle of crypto, we’ve seen the whole market space rocket up and come back down, then slowly rise again. At least in terms of money and attention, it’s now just kind of there. After all, the Metaverse was popular for a couple of days, but then we got AI to gush about. Meanwhile, we still have the get rich YouTube crypto maxis, but the crypto industry itself has started – more quietly – finding some real world valuable use cases. And the underlying blockchain tech? Players in this area have been just trying to build more things of actual value. So it’s not going away. And if you do product management, it’s quite possible you’ll be looking at where or if some of these technical bits make sense in your offerings.

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