TetraMesa

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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GenAI UX Issues for Product Managers

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In GenAI and Search: Differences from a Product & UX Perspective, we started looking at differences between search and GenAI from a user needs perspective. We looked at the tools in terms of use cases.

Now we’re going to turn more towards how we present things. This skips over everything in the Machine Learning Ops (MLOps) flow. And that’s okay. Because maybe we should be starting with the goal. After all, before you spend what could be millions, whether it’s for a consumer facing startup or an internal Enterprise tool, it’s probably wise to do some prototyping and testing anyway. (For MLOps, See: What is MLOps? (Amazon), What is MLOps? (Google), Why You Need MLOps.) By the way, I’m focusing here on user facing products, whether consumer or business. (As opposed to internal tools for analysis or production, marketing tools, etc. Though these certainly can have UX concerns as well.)

Figuring out how to design products to better serve users is of course not entirely your job. Whether you’re an entry level product manager or senior leadership, you’re ideally living more in the customer problem space. You’re looking across all things. Yes, you’re looking at features, functions, benefits. But also the dozens of other things to do. Which is why you work with your talented Design Leads. Whether direct reports or as a shared service, your design partners need to be getting up to speed on GenAI if they haven’t already. So your job is likely more along the lines of figuring out where or if AI is useful for your business; either for internal production or your actual products. And if it is, you might be the one – or at least be among the several – advocating for the resources to build out capabilities.

The classic question for Product Managers remains… How much do you really have to know in a specific domain; code, design, whatever? If you go too deep in any one area, chances are you won’t be very effective at your cross-functional tasks. This topic treatment is intended for skimming and basic understanding so you can work well with your talented design and tech colleagues. Our goal here isn’t to get anyone in Product to a practitioner level. It’s more to give you the tools to contribute effectively and have customer focused conversations with your specialist colleagues and drive requirements of value. Depth here once again depends on the type of product person you are. If you’re solidly on the business side, your whole product might be a P&L exercise for you and most functions and other team members are ‘just’ a line item on a spreadsheet as far as you’re concerned. A Technical PM? OK, you’re in the weeds with everything from APIs to whatever. Most of us are somewhere in between. One thing generally seems true though… if something comes up in a new domain where no one has clear responsibility yet, chances are you’re going to own it while an organization gets up to speed with where to put the new thing.

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Intro to AI Rubrics for Product Managers

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What is a Rubric for AI Products?

A rubric is about evaluation and quality control, but also standardization, consistency and more. The origin of rubrics is from education and assessment so the term may be new to a digital product person. The general idea is to have a highly structured way to evaluate qualitative judgments. This seemed to be somewhat parallel to what was needed to evaluate AI output, so the model was adapted for that purpose. Rubrics for AI evaluation are used in academia, by tech companies, and regulatory and standards bodies. For traditional development, we have a variety of QA standards. A lot of them involve unit and integration testing and in modern workflows is often part of a continuous development and deployment plan. Rubrics can also be used along a development path, during early evaluation and fine tuning, pre-deployment, and for ongoing testing. However, at least a rough model must be fully available.

In the case of AI model quality assessment, a rubric is a structured framework for evaluation.

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GenAI and Search: Differences from a Product & UX Perspective

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Note: This article is from a Product Manager and Information Architecture perspective. It’s not a consumer guide on how to search better using GenAI.

As Product people, the things we care about most deeply are in the problem spaces. What challenges are we trying to solve? In the fast-changing world of information retrieval, it’s useful to have an understanding of underlying motivations for customer behaviors.

Before we start scrambling to slap an AI prompt input field on top of whatever we’re already selling, we’re going to look at some of the “Why.” Why do people use some of these tools. What is it they really seek? As product managers, we come from diverse backgrounds. Not all have depth in basic information retrieval backgrounds. It’s going to be important to understand some of these concepts as you and your teams will likely be working on projects that will need them. And you may need to consider P&L or similar concerns in these areas.

We’re going to explore use case differences in search vs. some of the newer Large Language Model (LLMs) and Generative AI (GenAI) tools with a longer term goal of how we can do a solid job crafting product that makes use of GenAI experiences. (Including those that go beyond the search use cases.) To do this will take a few steps. The first is making sure we’re thinking about the problem spaces of users and the use cases of traditional search and now generative AI from customer use case perspective. There are many use cases beyond this. Various AI tools can be used for IoT needs, Agent inputs/triggers, Oracle data for blockchain Smart Contracts (arguably these are just agentic triggers as well), and more. (Not to mention multi-modal object types.) These situations offer good cause to evaluate architectures at a deep information architecture level. But for now, we’re going to focus on the day-to-day human interface and we’ll start with basic search. In upcoming articles, we’ll look at design patterns and additional resources for those who want to go deeper.

It’s useful to start with traditional search. Partly as a kind of warm up to get us thinking about how to solve new kinds of problems. Also because there’s overlap with GenAI and we can build on search towards better understanding of how to deploy GenAI.

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MVPs – Refresh, Reimagine or Retire?

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TL;DR:

  • If there’s no market, then your methodology doesn’t matter, MVP or otherwise.
  • This article is going to be a bit about MVP history and where it’s gotten a bit tired. (Or really more about where the idea is misused.) If you’re just interested in some recent thinking on alternatives and extensions, skip this article entirely and go to the Part 2: MVPs – Something.Next.

How is MVP Feeling These Days?

You’ve seen the clickbait headlines… “MVP is Dead. Long Live MVP” Etc. I saw one recently and since I work with several startups that’s what motivated me to create these two posts.

The Minimum Viable Product idea was the new bright shiny object once upon a time, spawning all manner of adherents. It got going around 2001 with Frank Robinson and Steve Blank, but didn’t get popular until 2011 with Eric Ries’ book “The Lean Startup.” Like many management innovations, everyone likes to pile on. There are just two, maybe three issues…

  • It’s rare that one tool can solve every problem.
  • It’s hard to measure. (We see the winners, but does anyone really know how many fail?)
  • Time. We learn things with time, but don’t necessarily adapt well.

It’s time to look into this. Maybe make a few tweaks. For the record, I’m an Eric Ries, Marty Cagan, Dan Olsen fan. So this isn’t about MVP bashing. If you haven’t, read their books and blogs to realize the way some talk and go about MVP is not what they espoused. Ries’ ideas are about rapid experimentation and validated learning, Cagan about product leadership, empowered teams with discovery and understanding user needs. Olsen expands this with ideas of problem-solution fit and more. (If getting Cagan’s “Inspired” make sure to get the latest edition.)

None of them say just fire up your task management software and have at it.

Over time we’ve found holes in the framework, pushed against some edges, and maybe run into a wall here or there. Often perceived problems aren’t the model, but poor interpretation and use. Or perhaps it’s the wrong tool for a challenge. MVP should help validate ideas in the marketplace as early as practicable. What could be wrong with that? Well, if it’s just used as a shortcut to produce something minimal, but not at all viable, maybe you’ve wasted more time and development cycle costs than you would have with better research. Even if your building and iterating early tests super fast with AI prototyping tools, you might just be headed into a wall faster.

It’s time to take another look. First, we’ll go over some basics and where some aging, but still valid, ideas fall down somewhat. Then we’ll look at enhancements to fill in these gaps.

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