TetraMesa

Archives for July 2025

GPTs and LLMs from a User Use Case Perspective

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Companies are missing the mark with AI projects. The headlines are not great.

  • Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025
  • Research by the RAND Corporation estimates more than 80% of AI projects fail, which is twice the rate of failure of non-AI projects.
  • Deloitte shows some benefits accruing such as uncovering ideas and insights with ROI generally positive, and yet, barriers due to mistakes with real-world consequences.
  • BCG’s 2025 survey indicates only “25% of executives are seeing significant value from AI”, with projects vulnerable due to unclear use cases and poor data readiness.

What’s going on? And more to the point; can product people help improve these outcomes?

Perhaps there are efforts to solve tech problems, but not always customer problems. From a Product person’s perspective, it’s time to re-visit basics and use cases. In the add AI scramble, there’s confusion about our shiny new toys. You see it in the drive to do anything with them. You see ridiculous job requirements posted from tech to product asking 5 – 10 years experience deploying AI. (For traditional ML, that may be fair. But they usually mean GPTs/LLMs, which have only been around a handful of years.)

Let’s reconsider our new capabilities from customer use case perspectives with focus on LLMs/GPTs. While also growing, we’ll skip traditional ML as it’s a separate and better understood category. (Things like regression models, decision trees, or clustering algorithms used in fields like finance, logistics, or medical diagnostics, which have established frameworks and decades of practical application.)

Why should we try a look from use case perspectives? Because for those of us that work in Product, instead of panicking figuring out how to check off the “We’re doing AI” into a checkbox in an Annual Report or a marketing piece, it might be useful to have clarity of purpose. We’ll focus on Generative Pre-trained Transformers (GPTs) and the Large Language Models (LLMs) over traditional ML or multi-modal GPTs.

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The Continuing Sandwich Attack Irony in Crypto

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In DeFi, you’re not always eating the sandwich, sometimes you are the sandwich.

Crypto trade front running is hardly news. So why bring it up again? There have been rises in this practice in 2025 so it’s worth taking another look. More importantly, there’s somewhat recent changes giving us options to cut down the risk. Part of the latest rise might be due to the latest crypto bull cycle, or new blockchain ecosystems. It’s also plausible that some are using newer AI tools to level up their bot game. In any case, I’ll cover the core issue for those not already familiar, then lay out some of the relatively new tools we can use. Some have been around awhile, but only recently started showing up in user-friendly wallets and services.

This problem of front running trades shouldn’t exist at all in a supposedly permissionless and trustless environment. Let’s remember part of why crypto was invented. There’s a lot about traditional finance (TradFi) that’s opaque to retail traders. No one seems to pay much attention until something breaks. We can infer the regulators don’t, can’t, or won’t do much to stop this. At least they haven’t so far, beyond a handful of show trials and fines that are small enough compared to gains that they’re just a cost of doing business. Some might argue front running in crypto is not even a flaw in the “trustless” design (which relies on code and consensus) but is just a tradeoff of openness, which happens to enable attacks without breaking rules. That may be true from a purist standpoint. But I’d argue it’s not a viable position for the long-term health of the ecosystem.

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Agents for Content Discovery and Ideation

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TL;DR

If you’re a product manager crafting content production workflows for yourself, a client, or your team, start by explicitly defining your values and strategy. (After all, values like authenticity can be core to your strategic edge.) What follows are practical ideas and a walkthrough of how this plays out in a real-world example: a niche medical site powered by AI agents for discovery and ideation.

AI tools don’t need to be garbage generators. They can be useful partners when used thoughtfully with humans in the loop. Here’s just a couple of ways AIs can help in market discovery and content creation, backed by real-world examples from my niche medical community site, ACLSupport.com. Key takeaways: Leverage AI to uncover customer insights quickly and streamline production, while maintaining your unique voice. In a market where AI content tools are projected to reach $2.38 billion by 2032, and the Generative AI In Content Creation Market $134.23 billion, businesses that integrate these tools ethically will gain a competitive edge.

The AI Content Debate: Addressing Key Concerns

As AI reshapes content marketing, backlash centers on two main issues: quality and authenticity.

Quality: The “AI Generates Garbage” Debate

Critics argue AI output often feels generic, optimized for clicks rather than value. This may be true for low-effort prompts churning out clickbait, but it’s not inherent to the technology. Content creators, writers, marketers, understandably worry about disintermediation. Yet, as with any tool, results depend on the user. Poor input yields poor output, much like spellcheckers can’t fix flawed ideas.

Authenticity: The Authenticity Dilemma

The deeper unease stems from the “uncanny valley.” If you haven’t heard of this, it’s kind of a creepy feeling when something mimics humanity imperfectly. We dislike being deceived, especially where source transparency matters. Blockchain might have a partial solution by verifying origins as I’ve written about, but for now, we don’t have this industry infrastructure. According to the Content Marketing Institute’s 2025 B2B Content Marketing report, while 81% of B2B marketers use generative AI, 45% lack usage guidelines, underscoring gaps in prioritizing authenticity. As is often the case, our practices lag the cutting edge tech.

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