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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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Content Moderation: Management Checklist

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Content Moderation for New Products

This is a follow up to “Content Moderation: Where We Are Now.“

If your product has user generated content (UGC), moderation is an essential burden. This is likely the last thing on your mind. You and your team have a vision of some great new thing. Dealing with the dark side here is a time suck distraction and cost. But deal with it you must. Products small to large haven’t fully solved this. The large scale social networks are the lightning rods, but all services contend with this. And you take pain from at least two sides; managing moderation itself, and the criticism from those who think you’re doing it wrong. Maybe too little. Maybe too much. Most likely just not “their” way. You need to have communications management in place to deal with this as well.

Spoiler Alert: At the end of this writeup is a link to a sample of how code classification for moderation can work.

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Content Moderation: Where We Are Now

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This pair of articles is for product managers who might deal with content moderation issues. We’re not going too deeply into the hot debate about moderation at the major platforms. There’s no shortage of folks throwing in on that. Though we’ll dive in somewhat to take away lessons. We’re going to look more at what’s going on for those who are practitioners as managers and builders, not observers.

Rising Volume of Posts and Debate

Besides the top blogworthy targets of debate, there’s tens of millions of sites with some form of user generated content. (Estimate based on Netcraft’s 2024 Web Server survey. Over a billion websites across 270 million domains. If we look at categories of sites that likely have UGC, it’s maybe 40% Even just half of that is still 50 million.)

This means many of us who don’t happen to be Facebook, Twitter, YouTube, Reddit, etc. have the benefit of not being hauled before Congress to testify on our content moderation policies. However we still must deal with the issues. Especially since manipulation isn’t just about politics, it’s serious dollars. Among the statistics CrowdRiff reports, they include the GrandView Research estimate of 2023 UGC market at $5.36 billion, growing at a 29%+ CAGR through 2030.

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Content Moderation: Product History

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This is mostly for product managers who might be involved with products that have content moderation involved. Given the increased pitch around this topic right now, having built or managed several products with these features, I thought I’d do a series on some of the issues involved. This first part will be historical. If you’re not into that, you can skip to an upcoming article, (once I write it), to go right to some suggestions.

Our collective societal debate over content moderation moving from a simmering debate to a boiling over mess was inevitable. As one of the first product people to be faced with managing consumer masses as they gained access to the open internet, I thought it might be interesting to take a look at how we got here and where we might go next. Especially because so many of us working in product management have some degree of this issue to deal with. You don’t have to be a Facebook or Twitter or Reddit to face issues in these areas.

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Strategic Responses to GenAI from Search

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I initially planned to write about how Generative AI (GenAI) might impact traditional search in a classic startup vs. incumbent scenario. Over time, as the landscape rapidly evolved, first I shared thoughts on “Search Tools in a GPT World” and then “Traditional Search vs. GPT Business Models.” This all leads to the obvious question… what are traditional search engines doing and what else might they do to respond?

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