TetraMesa

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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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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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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Prompt Engineering: What It Is and Isn’t

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Is Prompt Engineering just an overly fancy way of saying, “Here’s a better search query?” Maybe it should just be called Search 2.0? True enough, the output of an AI large language model is more than just a bunch of results, but the query itself is still ‘just’ an instruction of sorts, right?

In some cases, yes, it’s essentially the same as a fancy query. But mostly not. There are obvious differences in the use cases for prompts using AI Large Language Models (LLMs) vs how keywords get used in traditional search engines. And for all their potential faults and risks, LLMs can provide stunning new capabilities across a variety of use cases. At the same time, there seems to be some overblown expectations as to what prompts can do. For example, at least in some places, a misunderstanding that prompt engineering can make models better. While it may be true that prompts and responses can be iteratively honed and fed back into the fine-tuning of models to actually make models better, for the most part, they’re not used this way. I’d like to try to clear this up because I think it’s important we understand how we can use our tools and where they’re limited. Just to be clear, I’m not talking about the handful of folks who really are evaluating prompt output to adjust models. (If you’re one of those folks, you’re ideally operating more at the data science kind of level of prompt engineering.) For our purposes here, I’m talking about the typical consumer or business use that seems to have some people believing prompt input alone changes how the models themselves work.

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Will AI Fraud Finally Push Blockchain Products To Solve a Real Problem?

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We may be reaching the point where fraud and fakes are becoming so prevalent that the costs and pain are overwhelming enough that we’re ready to take the effort to try new solutions. Blockchain technology might finally have a mass market use here. But we’re not quite there. Except for crypto most of the work has been in infrastructure. Consumer use cases are few. This might be a natural evolution. The progression from infrastructure to platforms to applications is a movie we’ve seen before. And consumers likely don’t see safety and security issues as the most fun to deal with. At a certain point though, the answer to “where do you want to take the pain” turns into “ok, we have to deal with this now.”

The challenge remains getting adoption when there’s serious effort. After all, isn’t it annoying enough to have to identify how many traffic lights are in a picture just to get to your own account somewhere? Well, dealing with today’s crop of blockchain identity solutions is a bit more of a hurdle. At least for now. You may be thinking, “But don’t we already have all manner of fraud solutions, from credit checks to heuristic rules and now even AI trust scores?” Yes we do. And how is all that working for us?

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Foundations for Effective Remote Workforce Management

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So why not force return to office? There’s clearly benefits to the traditional model, right? Well, it should be obvious at this point there’s a great many benefits for remote work and hybrid options – at least for roles where it’s possible – for both employees and employers. (See Remote Work Statistics & Trends.) Potentially reduced costs on both sides, better work/life balance for employees, wider range of talent selection, etc. (However, note that cost reduction might be less than originally thought; stipends for remote gear, travel costs for team meetings that might not otherwise have been necessary, etc. So cost issues will as always be highly situational.)

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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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