TetraMesa

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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DeepSeek and AI – Take a Breath

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As a user of several GPTs and subscriber to several, of course I had to check out the new bright shiny object. There seems to have been a lot of very fast reaction to this thing. Sometimes it’s useful to just take a moment and gain some perspective.

Last week, when DeepSeek released its open source AI model, it seemed like all hell broke loose in AI world. The quality of this Chinese-led effort seemed to be a surprise, especially given the apparent cost differences and supposedly use of much lesser compute infrastructure to create it. Various stock prices took hits, concerns about information sharing based on use of the DeepSeek product, similar to TikTok issues, were surfaced, and so on.

Let’s survey the issues and put some of this in perspective.

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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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Traditional Search vs. GPT Business Models

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What About Business Models?

In the first part of this article series, “Search Tools in a GPT World,” we looked at Search Tools in a product environment where AI GPTs are clearly on a tear. Now we’ll look at how technology and consumer sentiment shifts are impacting economics and business models.

The rise of GPTs introduces significant shifts in business models underpinning search and information retrieval. Search engines operate primarily on ad-driven models, based on traffic, clicks, and ranking. This impacts income to both search engines and the publishers to whom they drive traffic. We’re going to focus on the search engines themselves. In contrast to traditional search, GPTs seem mostly out of the starting gate with pay-per-use, subscription, or freemium models. This may be a reflection of the resource-intensive nature of generating real-time responses. It’s a simpler business model than search, which depends on a complex ad services ecosystem. As well, ads alone might not be sensible from a P&L perspective. Let’s review some of the business models.

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Search Tools in a GPT World

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I’ve always enjoyed search, both as user and builder. So from a product perspective, I’ve been fascinated by its evolution and the recent fires lit under the traditional tools thanks to the ascendence of AIs. This will be a three part series. First, Search Tools in a GPT world, then business models, and lastly, how traditional search might respond.

So… How might the “traditional” Search industry evolve in the face of AI GPTs? Let’s take a historical tour to consider some customer pain points and values that various tools deal with and how these are morphing. It’s not as simple as GPTs are better search and it might be useful to consider other technology shifts. Did Video Kill the Radio Star? Maybe. But video didn’t kill radio. At least, not completely. Yet. OK, yes, perhaps the shift decimated revenues, but niche use cases survived through both television and even through more recent digital streaming. Even satellite radio was also able to find a place. Will the information retrieval industry experience something similar with what’s been billed as an even more disruptive technology? Or is this truly something radically different if we consider this shift on the level of industrial revolution?

Will the future of Search follow a similar path? Perhaps somewhat, but maybe not quite the death blow some have suggested given there seem to be a lot of niche values for Search. AI driven GPTs, (Generative Pre-trained Transformers), are already changing the search landscape. But their evolution is not as simply obvious as “this is a better search” for at least two related, but separate reasons. First, GPTs can likely excel past traditional search for a wide variety of use cases. But perhaps not all. And second, GPTs can and are used for significantly different use cases than search.

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Ironies of Safety vs. Risk for Personal Self-Sovereign Assets

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Crypto is on a new tear again. With 40% of U.S. adults supposedly now owning some crypto, are we moving from early adoption of a couple of years ago into early majority in terms of the technology adoption curve?

Wherever we are in the larger scale hype cycle, I’ve had too many conversations recently indicating poor info out there. A lot comes from the influencer class on YouTube who often breathlessly extol the virtues of how if you don’t do or don’t do this or that your financial future will lay in tatters. If you were to somehow measure the emotional tenor of some of these folks, it’s similar to if you watch the manically effusive presentations of the gamers playing everything from Minecraft to other kids’ games. Most of them probably mean well and many provide great value, but there’s things they’re leaving out. And one major issue is the irony that having more control of your assets could incur more risk than one otherwise might.

The last thing the world needs is another “Intro to Crypto.” And this isn’t that. But I do want to go over some issues considering use of self-sovereign wallets vs. custodial type accounts. For those just starting to get exposure to crypto as an asset class, with Custodial wallets/accounts, a third party holds your crypto, manages any account keys and executes transactions on your behalf. (Such as Coinbase, Kraken, etc.) They’re like your traditional bank or brokerage. Alternatively, (or additionally), with Self-sovereign wallets you fully control your assets using private keys in your wallet and what it holds with no intermediaries involved. Whether you’re transferring funds, trading on decentralized exchanges, purchasing things, connecting and transacting with other applications, or whatever, you have to do everything in terms of managing transactions and you own all associated risks; of which there are several. My goal isn’t to dissuade you from this category. It’s just to shine a brighter light on some of the downside risk and offer some risk mitigation strategies.

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Customer Quests Part 2: Solutions for the Journey

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The Search Problem

In Part 1, we explored the idea of reframing customer journeys as a quests; purposeful pursuits with challenges, obstacles, and transformative moments. Now we’ll look at some ideas to possibly solve challenges along the way and empower customers navigating decision-making complexities. Let’s focus on brands as guides, clearing obstacles and providing tools for success.

The Search Problem Revisited

Every quest starts with a need and evolves into a search. Consumers gather information from diverse sources to evaluate their options and make decisions. This search, however, is often full of friction points that can delay or derail the process. In crafting better experiences, we should understand not just what customers seek, but how they seek it and barriers they encounter along the way. The sources consumers rely on during the search stage can vary depending on the type of purchase, the consumer, and the circumstances.

I’d like to introduce the idea of some general quest archetypes. This first framing of this idea is probably not exhaustive. Individual customer personas are useful, but I’d like to try to focus more on general types as that can also help us figure out what we need to provide. I expect to expand and refine this over time.

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