TetraMesa

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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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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The Real Core Value of Blockchain Tech

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Note: This post is going to be a bit more philosophical than suggestions for applied solutions, which is where I usually focus. If you’re not interested in such things, by all means, skip this one.

Feel like diving in with me to some random thoughts I’ve had recently? Great! Welcome. Let’s go…

What’s the real core value of blockchain technology? At this point, we have some idea about the many things that run on top of it. Or rather, there’s lots of things people are trying to run on top of this foundational layer and build value. Let’s see what we have here so far… Crypto, Smart Contracts, Decentralized Applications (dApps), Decentralized Autonomous Organizations (DAOs), Supply Chain Logistics with solid provenance tracking, Identity Management, Tokenization Platforms, Decentralized Storage, IoT Networks, Governance and Voting, and more coming to a hype cycle near you soon! What have I missed? Probably a lot. Each of these is at varying places on the marketplace adoption curve. As usual with our bright new shiny technologies, all of this is unevenly deployed with various niche solutions gaining a little traction here or there, core infrastructure still nascent with LayerThisOrThat still sorting themselves out, and so on. Perhaps most ironically, (I personally love this one), there’s a Root of Trust problem with the identity layer for all of this; from initial verification to who verifies the verifiers. And more. (That’s probably an article / rant all on its own.)

So what? What’s the point? What are we all really trying to do here and why?

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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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Will Generative AI Hurt Search & Publishers?

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The short answer seems to be “Yes.”

This isn’t really a new idea at this point. But I wanted to offer my take on it as I don’t think I’ve seen a good overall summary and I wanted to offer one.

The deeper questions seem to be: How will it hurt? How much? And what will Search companies and Publishers do in response? While Generative AI is different from Traditional Search and has different use cases, some aspects are taking market share from traditional search. According to Similarweb back in 2023 “ChatGPT Grew Another 55.8% in March, Overtaking Bing.” And there’s the early 2024 prediction by Gartner of Search volume dropping 25% by 2026. Then AdWeek said that Google’s use of AI alone could hit publishers with a $2B annual ad revenue loss.

This will likely continue and is somewhat ironic. Search will take market share hits because someone else is using content that isn’t theirs to create value. Sound familiar? Seems like the foot is on the other shoe. And yet, while Search might be facing some disruption, their core value propositions remain just fine. Also, they typically are integrated with several products; e.g., Google across web, video, maps, and more. The real question is how they’ll deploy billions to respond, not that anything’s replacing them soon. Still, with hundreds of billions at stake, even a small percentage hit is non-trivial. (Even if Google is growing ad revenue faster than others lately.) We should be more concerned with the smaller search engines and the publishers themselves.

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Prompt Iteration for Consumers – Getting the Best from Generative AI

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There’s a somewhat well-known joke about a photographer that went to a fancy dinner party. The host who was also the chef said to the photographer-guest, “I love your wonderful pictures; you must have a fantastic camera.” After a great dinner, the photographer said to the host, “Dinner was great. You must have a fantastic stove!”

The moral is clear enough. Though our tools can make a difference, going from good to great isn’t usually about the tool, it’s the craftsperson. The question is which kind of user do you want to be? This article isn’t about AI builders using what’s referred to as “Prompt Engineering.” The focus here is for generative AI users who want to improve results without deep technical exploration. Let’s do a little background first to set some context, then bullet point some techniques to try out. (Skip down to the bullet point list if you don’t care for the background.)

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Using AI for Mediocrity at Speed

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I’m a huge fan of LLMs… but this will be some of the subtle dark side. This isn’t an attempt to show AI doing horrid things. No. My concern is more subtle. It’s about insidious degradation of thought; in spite of the awesome power of AI to also help us learn and produce new things. My concern is that AI, eventually feeding upon itself, will regress us to average faster than ever.

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