TetraMesa

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 in general. Everyone is of course different. It’s an exploration into different types of skillsets, some of what’s been going on lately with AI and challenging some assumptions.

What I want to try is to 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?

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, even if 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 to automate everything, you’re doing it wrong.” I’ll work on the clickbait. For now, let’s talk about what’s actually changing. By the way, that’s quickly become the clickbait headline style I find most ridiculous. Maybe it’s just my feed, (or maybe I am doing things wrong), but have you noticed how many use that framing, “If you’re still doing X – or not doing Y – you’re doing it wrong?” Since just about everything is about context, these seem especially foolish attempts at FOMO.

Kids These Days

Younger generations, (however you want to personally define that), are often far more fluent with modern tools than those of us in the GenX plus cohort were at their age. (Obviously in some cases, some of the tech wasn’t even there.) 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. Their tech, however, is moving incredibly fast. One interaction I found highly amusing was when my 20-something nephew commented on something my elementary school age daughter was doing and said, “yeah, we didn’t have anything like that when I was growing up.”

Through all of this, or perhaps due to all of this, some 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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Why Crypto Cards Will Finally Disrupt Credit Cards (Soon)

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Maybe I’m just being targeted with more card offers, but between my experience and research for a small payments project, I’m seeing more crypto companies roll out traditional-looking cards. It’s a smart strategy: crypto is still cryptic, so familiar packaging becomes a Trojan Horse into the market. People have been talking crypto-card growth spurts for years (see i2c in 2022), and some still call it a slow roll, especially after multiple “crypto winters.” However, it feels the pace is picking up, so I dug in and wrote up why.

What’s changed? This article is my take, part as crypto enthusiast, but mostly through a strategic product lens. The bigger picture is an industry that’s enjoyed near-unchallenged dominance for decades, and suddenly the landscape is getting complicated. Card networks remain entrenched, but the last few years added simultaneous pressure from regulation, real-time bank rails, and shifting consumer payment preferences, making the ecosystem materially more complex. It may look like the upstarts cooled off as the hype faded. I think that’s a dangerous assumption and a setup for surprise. Let’s look at what’s been happening and why I believe we’re closer to new inflection points than ever.

By the way, this isn’t a prediction that revolving credit disappears. Many find value in it regardless of how destructive it can be to personal wealth. This is more a prediction that the card bundle (payments + rewards + account relationship) shifts to wallets, leaving traditional issuers with less pricing power.

TL;DR Spoiler: You don’t have to do anything just yet. Though you may want to explore as a consumer to see if you can get better rewards. As a merchant, just keep an eye out for evolution here. Customers may abandon shopping carts or avoid you if you don’t offer their payment method.

That’s it. Stop right here!

However, I believe in deeper holistic and strategic marketplaces views. So if you want the long form in-depth reasoning, here you go…

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Web3 / DeFi Trust Deep Dive

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We should understand the nature of trust in some of our newer evolving financial systems, especially as they merge or outright collide with traditional systems. Part of the whole sell for DeFi is about being able to securely transact in so-called – and unfortunately labeled – trustless environments. What “trustless” really means and enables here is permissionless execution without dependence on discretionary gatekeepers. Or rather, trust comes from sources other than the default historical authoritative nature of traditional centralized institutions within traditional finance, as enabled and regulated by government. Regardless of whatever more self-sovereign dreams of DeFi may have been or remain, this realm does not eliminate the influence of law, regulation, or government, especially once assets touch the real world. Still, DeFi reduces reliance on operational discretion of institutions, and provides a variety of new values from Global, always-on settlement to programmable money, interoperability, inclusion, and so on.

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Tech Driven Financial Systemic Risks to Watch

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Note… Nothing here are predictions. Only what I believe are plausible issues worth attention. Nothing is intended as doom and gloom. The point of identifying risks is to consider ways to mitigate or eliminate them. With that in mind, there’s some things I’ve been getting concerned with lately.

Crypto, DeFi, and AI, seem to have things in common besides being disruptive and interesting, and that’s risk beyond their spheres of influence. I’m optimistic about long-term outcomes, at the same time success usually isn’t a straight line. Risks include how individual sectors can impact wider markets. Some technologies don’t just succeed or fail internally, but reshape industry plumbing, incentives, and reflexes in the larger scale marketplaces, often faster than institutions can adapt.

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Identity Phonership – You, Yourcellf

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First off, apologies for the bad puns in the headline. I am a dad though. So bad dad puns just come with the territory. Here, I saw a chance for a double, so had to take it.

Today it’s time to discuss some pros, cons, risks, and mitigations for the reality that our cellphones have accidentally become our gatekeepers to all manner of things digital. And often physical as well. You likely already know how integrated, (and dependent), a lot of digital activities have become on our mobile devices. But how? And what might this mean? Smartphones as identity gatekeepers has been discussed before. However, what we’re experiencing now in the mid 2020s is arguably a new level.

How did our cell phone companies become the gatekeepers of our identities?

How many things now push for multi-factor authentication via our smartphones? There’s products where it seems if you don’t have a smartphone, you’re simply not going to be able to participate. How might this play out? Do phone companies know about this? Of course. Will they try to exploit this role to just extract more fees given they’re arguably in strategically poor commodity businesses with competitive margin pressure?

Phones are no longer just credentials; they’re becoming identity custodians. Security is often thought of as three things: What you have, (such as debit card), what you know, (PIN code), and what you are, (biometrics.) With our phones we seem to have shifted from just something you have to the thing that vouches for everything else.

Recovery, coercion, or loss were not first-class design considerations.

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Child Digital Threat Surface Area Roundup 2025

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Not long ago I wrote about GenAI and kids and what we do in my own family to try to make them useful and avoid the pitfalls. It feels weak now, even just a short time later. As I’ve looked into this more, the numbers and trends are disturbing. More kids are dying or having other bad things happen. It’s not just the usual “if it bleeds it leads” news cycle impinging on my personal awareness. More incidents are happening. And what we’re facing as parents is getting worse fast across multiple venues.

Any of us building digital products, (parents or not), should have some awareness of what’s going on regarding the widening and deepening digital threats faced by kids and do what we can do to deal with it, though this writeup is targeted more at parents. Yes, it might be true that we all have digital and online risks, but of course, kids are all the more vulnerable. I’m writing this because I’ve been having more conversations about this lately with other parents, especially some that don’t work in digital.

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Product Managers as AI Ethics Officers

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You’ve probably read about AI ethics. I want to go beyond the basics and sell you on the ROI of AI Ethics. We’re talking basic consumer and B2B issues here and more accuracy, not wider scale issues like battlefield autonomy or similar use case issues. I’ve worked on a few AI projects over the past year: one more retrieval-based (prompt/response) and another more generative (content creation with human oversight), plus some testing work. With today’s tools and vendors, you can often kick something out that seems solid pretty quickly, whether it’s a generative product or something using more traditional tools like predictive analytics. Doing it truly well, though, is sometimes orders of magnitude harder. It can be tough to justify the resources to do it right.

Here’s a cynical thought about AI ethics. Often, companies don’t really care. Actually, that’s too cynical. People may care, however, if you look at where pressure and resourcing usually go, it’s often more towards speed to market and growth, with maybe a thin layer of regulatory compliance. A few companies differentiate on quality. But most seem to be more in the feature race. “Non-functional” requirements? We’ll get to them. At some point.

This doesn’t mean everything needs to be perfect. If we waited for 100% certainty, nothing would launch. We make tradeoffs: a light/dark mode toggle is not an emergency cardiac-alert system. People get that. Especially AI, which is harder to understand and harder to control. How can we deal with these realities?

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