TetraMesa

Archives for February 2026

Hidden Stablecoin Trade-Offs

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Note: the primary discussion here is about basic, issued fiat reserve backed stablecoins, not algorithmic or any other types of stablecoins.

Stablecoins may be the first truly mainstream useful thing to come out of crypto. Here’s a deep dive into some things to be aware of though…

First, why should you care about anything written here? Stablecoins matter because they may be crypto’s first broadly useful product at scale. But they are often misunderstood. Many people think they are simply “digital cash on-chain.” In practice, they are usually privately issued, reserve-backed, compliance-constrained financial instruments with token interfaces. That makes them powerful and also means they inherit legal, banking, liquidity, and control-point realities that many users and even some builders don’t fully account for.

Many consumers, and even some crypto product builders, understand stablecoins at the price peg layer, but not at the control points, redemption mechanics, and compliance constraints layer.

We should always know what we’re buying and what assets we’re holding. Stablecoins are useful and increasingly foundational to crypto payments and settlement. Crypto traders may hold a fair amount of stablecoins as they use them as a basis currency for trading. They’re a way to park value without going back to fiat, and manage collateral / margin.

Stablecoin. The name itself inspires confidence. Something to be aware of, though, is that stablecoins are typically far more centralized than most might expect. Issuer and administrator controls can freeze, pause, and sometimes effectively claw back funds, even when holders think they are holding something “cash-like.” This is not necessarily a bad thing. Though those who see crypto’s dream of self-sovereign finance think so. Typical consumers and product people in finance businesses likely see the value in the controls. I promise I am not here to trash stablecoins! They are one of the best uses for crypto to come along yet. I just believe consumers should understand what they own and how their tools work. And product / businesspeople building workflows around them as well. Looking at how this asset is often described, it at least appears a lot of folks don’t know a lot of the following issues about stablecoins. These are the kinds of things I like to explore.

Consumer and business crypto users should understand there is a quiet structural reality that’s under-discussed outside of more technical or compliance-aware circles. The general crypto meme that often gets sold is that crypto is “self-custody so no one can touch it” and it may feel that way if looking at a balance in your personal wallet. Assuming you use a self-custody wallet and keep your keys safe, this is true for bitcoin, but not necessarily true for issuer-administered ERC-20s tokens. The phrase “custody” can get over-applied. For personal wallets, self-custody really means: you control the private key that can sign transactions. It does not automatically mean: the asset can’t be frozen, transfers can’t be blocked, redemption can’t be denied, or that the rules can’t change via upgrades and admin roles. With bitcoin, controlling the key is basically the whole story. With many stablecoins, controlling the key is only one layer.

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Are Boomer & GenX Workers About to be More Valuable?

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This isn’t about a contest about what cohort is more or less valuable. It’s an exploration into different types of skillsets and some of what’s been going on lately with AI.

Let’s run a thought experiment about workers in general and ageism in particular. With all the talk of AI displacement, I keep wondering if there’s a less dystopian view. A lot of roles may change or vanish, but we could also see growth in niche areas. And maybe the loud claim that “we won’t need so many people” turns out to be overstated. If so, do deep skills and hard-earned judgment become more valuable, not less?

All of a sudden, some who shed too much staff, (and as is often the case, the wrong people), need to hire at least some back. Meanwhile, the nature of expertise changes such that “older” workers, wherever you want to draw that line, turn out to have a lot more value because a) the smarter machines are amazing, but turn out to still have limits, and b) AI may hollow out some early-career task bundles, and that can raise the relative value of people who can frame problems, validate outputs, and take responsibility for outcomes. I’m a huge fan of the latest AI tools and a frequent user of multiple models, several bots and agentic workflows. I’m fully buzzword compliant! And yet, in spite of the dire warnings of the viral Shumer post “Something Big Is Happening“, there may be a still be a place for talented and experienced humans. See Joe Procopio’s “It Turns Out, AI Agents Suck At Replacing White-Collar Workers” for one of many examples.

Some companies who claim they’ve cut staff thanks to AI may discover they cut too deep, losing exactly the people they really need. Meanwhile, expertise may be repriced. AI is impressive, but has limits, and it can hollow out early-career task bundles. That raises the value of people who can frame problems, validate outputs, and own outcomes. I’m a heavy user of modern AI tools and workflows, yet even with the “Something Big Is Happening” hype, there still seems to be plenty of room for talented, experienced humans See Joe Procopio’s “It Turns Out, AI Agents Suck At Replacing White-Collar Workers” for one of many examples.

Maybe this sounds naïve, but perhaps multiple cohorts will remain valuable, just in different ways. Through it’s looking to be a rocky transition. Yes, Skynet could wake up next week, but there’s also a world where things mostly work out fine. I know I’m supposed to say “if you’re not using AI in the shower, you’re doing it wrong.” I’ll work on the clickbait. For now, let’s talk about what’s actually changing.

Kids These Days

They’re often far more fluent with modern tools than we were, and they’ve grown up swimming in information; more volume, more variety, better teaching methods. But by definition, most early-career workers have limited lived experience. They may have had a few jobs in high school and college, maybe even a small side hustle, but the rest has been school, hobbies, and a first job or two.

They may also have less intuition than older cohorts did at the same age, not because they’re incapable, but because so much is abstracted away. More services handle more of life, reducing cognitive load in ways that can erode “practice-based” skills (navigation via GPS is the cliché example). Sol Rashidi calls this “Intellectual Atrophy™.” Even if that term is AI-focused, the broader pattern predates LLMs. And yet, younger workers can be astonishingly capable especially in tech while still missing some “common sense” that usually comes from scar tissue plus environment.

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Maintaining Healthy Cognition Living With AI

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We all have a choice about how we use these new tools. And if you are a parent, how you teach your kids to use them. If you lead a team, the same questions apply. How should your business use them, and where do they add value for your people and customers?

This is an exploration of the “why” behind a lot of what is going on. I reference behavioral research along the way.

This will not be “Here’s how you build a chatbot to take over the world tomorrow.” Or “this will replace your workforce tomorrow.” It is also not a “Here’s what you should do checklist,” though there are practical ideas near the end. Think of it as a tour of how AI can shape how we live, work, and think, with background on how it works and perspectives you may want to consider for yourself, your teams, and family. The topics are not new, and not all original. The goal is to revisit common themes and add depth by getting closer to their primary drivers. Not just what to think, but why the assertions may be true.

Note: this is a long form article, not the usual LinkedIn bullet points. Some articles get built in bits and pieces over several years as I learn about a topic They’re really my research notes. I usually include a quick summary up top. Not this time. This one is for deep background and context. I think these issues matter for the next set of our collective societal decisions. If you want a “what can I do right now” checklist, this isn’t it.

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Re-Thinking Build vs. Buy: AI’s Hidden Costs in Product P&L

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Time to step away from the AI hype and look at real costs for everyday AI use cases. This article focuses on internal tooling and operational spend, not so much more exciting user facing products; though the same cost dynamics apply.

My motivation here is I keep hearing AI will let teams “vibe code” their way out of SaaS, but AI can also turn the resulting products’ predictable Operating Expenses (OpEx) into volatile, usage-based costs. So I want to think through ongoing variable costs. Not ROI or quality risk, which matter too. Or the reality that AI talent may still be hard to find; just really more ongoing variables costs, which are different than our more predictable past tools.

Spoiler Alert: I’ll run through the thought process and offer up a spreadsheet. But the bottom line is this choice can be very business specific. Building your own with AI can easily spin up costs faster than you’d expect, as can burning tokens with SaaS solutions that add AI. But even speedier app development with AI assisted coding might cost you more than SaaS. Scroll down and just grab the spreadsheet or read on for all the details. It’s about more than just tokens vs. seats.

Quick Summary: SaaS often seems pricey per year, while “vibe coding” your own replacement can look cheaper early but gets expensive once you count labor, operations, and risk. The biggest drivers are seats, interaction complexity, and hidden costs like evals, testing, and ongoing auditability. DIY can win for small, short efforts, but at enterprise scale SaaS can still be cheaper on TCO even if the subscription line stings. In either case, for AI enabled products, there’s inference/token and other costs. Here’s the sheet if you’re skipping the rest of this article. (Note that older non-AI enabled build vs. buy examples are there just for historical reference and comparison.)

Also note that the cost assumptions I’ve put in here to start are radically higher than what simple token pricing might be compared to what you’ll find on a vendor’s chart. I’ve tried to add blended costs for things that reflect real production costs, like API costs, vector database queries for RAG, caching and so on. The point is for you to plug in your own numbers. If you want you can split out more granular costs to their own lines.

And, oh yes… don’t forget token costs are not the whole story. This piece is focused on costs, but when you shift to pricing and ROI, it’s worth reading John Rowell’s Context Is the Next Frontier in AI Economics.

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Bot Convergence for a 24/7 Economy

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There’s a lot going on right now. But I’m sensing there’s a unifying theme. I think it’s something to do with driving towards a fully always on 24/7 economy. As crypto truly merges with traditional finance (TradFi), and AI continues in its overall capabilities plus agentic and bot autonomy, what do we get? Or rather, what are we driving towards; good, bad or otherwise?

I like to try to write about things related to digital product management or at least somewhat practical things. This isn’t that. This is more digital culture and culture in general. These are just some thought explorations I’ve had while playing across multiple technologies. It’s an attempt to look around a few corners based on an admittedly vague sense of where some of these things could be converging. And it’s going to feel like a somewhat random walk to try to get all the puzzle pieces in place. And there are several pieces. I promise I’ll eventually get to a point though.

If you have other things to do, now’s the time to bail out! Otherwise…

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