TetraMesa

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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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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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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Customer Quests Part 1: Decision Making as a Search Problem

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The ideas for the following thoughts came out of a search project, not a customer journey mapping effort. But the ‘filter bubble‘ idea came up in conversation and it struck me how we often only find that which we’re capable of seeing based on our perspective. And even a subtle shift can make a difference in helping us reach our goals. So here you go…

In Marketing and Product Management we often refer to the Customer Journey. Maybe we should be thinking about a Customer Quest instead. And treat the process as a search problem.

This may seem to be a subtle difference; somewhat more about attitude than a significant departure of definition. But consider the value in just a small shift. Our outlook can impact how we think about and handle customer touch points. A journey is a trip or process of traveling from one place to another, whereas a quest is a purposeful pursuit. For our purposes, the quest is more about the motivation and goal, whereas the journey is just the path we’re following trying to solve a search problem. But we’re often not just following a path. We might be blazing this new trail. Yes, some enjoy the journey. Shopping is fun for some people, maybe a learning or social activity. More so if the category is fun; new skis, golf clubs, etc. For commercial goods and services, we might enjoy the journey as we learn useful things on our quest. Nevertheless, we didn’t choose a meandering cruise with the all-you-can-eat buffet. We’ve got a goal. The cliche for a variety of things in life may be “it’s not about the destination, it’s the journey.” That’s a nice thought. But not always true. Sometimes we just want to get things done. Just mapping a consumer path and trying to inject a marketing message here or there might not be enough.

If you’re still not sure about buying off on this concept, think about your own behavior over the past year or so. Have you started trying out Generative AI tools like ChatGPT, Claude, Gemini, etc? Have you used them to solve any challenges you’ve had as a consumer of goods or services? If so, why did you try this new tool? What was traditional search or the company’s customer service not doing for you? If you’ve done this, then you’ve experienced exactly the kind of challenge we’re talking about here. There was a search/discovery need we all had that was not being met. (Or was being poorly handled.) And no one was really helping us adequately. We got lucky and the world gave us yet another tool. The lesson though, is that we had existing unrecognized needs. What else are we missing as product creators and marketers in terms of customer information seeking needs? Maybe… maybe just this subtle shift in thinking about this aspect of customer experience can help find out.

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How Badly Will the GPTs Kick Google’s Teeth In

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Notice I didn’t ask “if” newer Artificial Intelligence tools like Generative Pre-trained Transformers (GPTs) will impact Google. I asked “How badly.” While Google isn’t going anywhere, these new tools will chip away at its search market share and perhaps overall value. Google and parent company Alphabet develop properties from mobile operating systems to devices like Nest and Fitbit, Waymo autonomous driving, cloud services, and more. Still, advertising from search, YouTube and their network was almost 80% of their revenues in 2023. Google’s dominance remains and its advertising revenue is still its golden goose. However, GenAI tools introduce a serious competitive threat to the core search business. How badly will Google feel the impact?

Here’s a high level summary of the main points, after which I’ll try to defend each.

  1. Basic Search Needs: AI GPTs are increasingly satisfying basic search needs. While not perfect, they meet many use cases where users are seeking answers; not links to maybe answers.
  2. AI Stickiness: GPTs offer capabilities beyond search, encouraging users to stay in that space. Even with some of the challenges with AI accuracy, people may stick with a “good enough” solution.
  3. GPTs are Improving: These tools are rapidly evolving, fueled by intense investment and innovation.
  4. Google Is Somewhat Stuck: Google’s brand is so tied to traditional search that pivoting may alienate users or undermine its core business.
  5. Everyone is Attacking from Multiple Vectors: Yes. OpenAI’s ChatGPT may have been the alarm bell, but there’s a whole lot more coming; both consumer and business.
  6. Business Use Cases beyond advertising: People are paying premium prices.

That’s the tl;dr. Stop here. Or if you want the backup rationale behind these points, continue…

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Content Publishers Must Navigate the Rise of GenAI

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In a prior post, Will Generative AI Hurt Search & Publishers, I discussed how Generative AI could impact content publishers. Here I’ll suggest some strategic options for publishers, limited as they may be. It’s been a while since I’ve personally built large scale content products, but the category is in my blood from a deep history in digital publishing. As I’ve studied and worked with AI, I’ve felt compelled to share some ideas.

Generative AI and other machine learning tools are not just the latest tech flavor of the month. Their value is powerful and accelerating, especially for information like text, imagery, audio, and video. Numeric data as well of course, though finance and science have their own tools. We’re not focusing on coding, life sciences, atmospheric science, killer robots, or other technical fields here; rather on general content publishing.

Things aren’t all bad for publishers. GenAI brings both opportunities and challenges, offering efficiencies and creative possibilities, but also introducing complexities in quality and originality, with business model and revenue impacts.

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Product Discovery – Part 2 – Moving from Concept to Execution

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Introduction

This is a follow up to Product Discovery – Part 1 – Beyond the Checklist where we looked at a story as a lesson before considering the more tactical get-it-done checklists.

Here is product discovery at the highest level, summed up in two bullet points.

  • Living in the Problem Space: Asking good questions.
  • Living in the Solution Space: Coming up with answers that are: 1) viable; with market demand via value propositions(s), and 2) feasible; technically and operationally producible within some defined financial criteria, as well as compliant with any regulatory issues.

That’s it! Everything that follows are the details.

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Product Discovery – Part 1 – Beyond the Checklist

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Some of the best products happened through one of two means; the first is the traditional market research, deep study and so on. The other is practically by accident; often the flash of inspiration entrepreneurial route. Everything else is somewhere in the middle. This series is about how we can maybe do better in either case.

I’m going to handle this topic in two parts. This first is just a story illustrating the value we can learn by going deep into the weeds and emphasizes the value of deep discovery; in this case for a B2B product. Why bother? Because I think otherwise we may be missing things. After that, in a separate article, we’ll run through the more basic discovery checklists.

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