TetraMesa

Digital Dollars, Real Debt: Hidden Connections Between Stablecoins, Treasuries, and Banks

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As a non-finance person who’s done work in crypto, I’ve come across something I think worth sharing from a product perspective. Part of what we do as product managers is look around corners. We can’t help it. We try to understand what happens when systems interact, dependencies change, and something in one place creates unintended consequences elsewhere. One thing we learn is that optimizing one part of a system doesn’t necessarily optimize the whole thing. In fact, sometimes making one aspect tremendously successful can create unintended consequences elsewhere. That’s especially interesting when the products themselves start becoming part of critical infrastructure. And that’s what’s increasingly happening with stablecoins.

What interests me here is there are three worlds that don’t seem to get discussed together in general business or product management circles. These are: government fiscal policy, traditional banking, and digital assets. There’s increasing attention in specialized finance research. But I suspect most general businesspeople and even product folks working in the space don’t necessarily encounter this info. And there’s dependencies emerging that I don’t think get enough attention.

Most people still think of stablecoins as “crypto stuff” and U.S. debt as “government economics or politics.” But what happens when growing worldwide demand for digital dollars also creates demand for U.S. government debt? And what happens when that same process shifts funding away from traditional banks or creates new channels through which financial stress can spread? That’s what I want to explore. Not as a prediction of an impending crisis, but as a series of connections that I think deserve more attention, especially among those who create products or services in this area. So I’m going to try to untangle this.

What prompted me to write this was this post from famed investor Ray Dalio called The Numbers Behind the Bond Bear Market. Some aspects may be disturbing. This article might be as well. Personally, I’m an optimist. However, some of what I’m going to present suggests some potential storm clouds. Maybe not. Either way, it’s worth understanding the connections to have a sense of which way things can lean as we see trends in one place put pressure in other areas. In any case, here we go.

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Human-in-the-Loop, But Not in Control

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What Digital Product Managers Need to Know About Meaningful AI Oversight

TL;DR

Human-in-the-Loop (HITL) is frequently presented as a safeguard for AI and other automated systems. But simply inserting a person into an approval process doesn’t necessarily make the system safer, more accurate, or more accountable. Sometimes it’s little more than a checkbox.

Effective HITL requires more than a qualified person with the authority to intervene. That person must also have sufficient information, situational awareness, time, and practical ability to exercise meaningful judgment. Here’s the paradox: the more successfully we automate a task, the less capable its human supervisor may become of intervening when something goes wrong. This isn’t a new problem. Human-factors researchers were warning about it decades before modern AI. What’s changing is the speed, scale, complexity, and increasing autonomy of the systems we’re deploying.

If we’re going to insist on keeping humans in the loop, we need to design systems that keep them capable of being there. We’ve already seen real-world automation failures in systems that were supposed to have human oversight. In some cases, that oversight proved ineffective when it was needed most.

For digital product managers, this isn’t just an AI governance question. It’s a product design question. Where should human intervention happen? What should the person see? What authority should they have? And how do we know whether that oversight actually works? These choices belong in product requirements, workflow design, and acceptance testing, not just a compliance document.

That’s the premise. If you want the deeper version, read on…

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Pricing and Why “Unlimited” Digital Offers Are Generally Bad

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tl;dr

Saying “Unlimited” might be a great marketing pitch and plenty of products try it. Personally, I’m a big fan of this at the buffet. And maybe it works in some contexts where there’s clearly some kind of consumption limit such that even the outliers don’t utterly destroy your margin. (For example, try as I might, there’s only so much chocolate pudding I can eat.)

However… even though we’ve seen this offer for digital products it’s probably a bad idea most of the time, and I’m going to tell you why.

Here’s the short answer and you can skip the rest if you buy it. Across years and products we’ve seen outlier abuse of unlimited plans can cause havoc or painful exception processes. Even honest usage can become a negative if costs go up in unexpected ways and you’re stuck with a brand and contractual promise you can’t keep.

Do you agree? Don’t do unlimited unless you have clear rationale and systems to deal with issues. That’s it. If you want more justifications for the assertion, read on. If you read further, as you do, ask yourself or your team members this one question: “Is the benefit of this tired claim worth all the trouble it could cause?”

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Product Leader Workflow API Literacy: n8n

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Postman is useful when you want to look closely at one API conversation: send a request, inspect the response, change a parameter, see an error.

A workflow platform such as n8n.io becomes useful when you want to watch several of those conversations turn into a workflow. That makes it a useful product-literacy tool even if you are not trying to become an automation engineer. A requirement such as “get a location, retrieve the forecast, decide whether rain is likely, then return a useful result” can stay abstract on a whiteboard. In n8n, we can wire the real calls together and watch data move from one step to another.

So we’re going to build a single companion workflow that goes with the article. It supports a manual learning path, useful failure handling and an externally callable webhook path, all using the same underlying geocoding, forecast and product-decision logic.

The completed workflow, its README and the downloadable n8n JSON are available in the public GitHub repository PM API Literacy Lab — Umbrella Weather Check. You can inspect the workflow structure there before importing it, or download the JSON and import it directly into your own n8n instance. (Note that there’s no guarantee that this will be continually updated, but it’s all been checked and working as of this writing.)

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The API Bill Is a Product & Finance Decision

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Bonus Hands On article for the product manager’s guide to digital connectivity series.

tl;dr

Unit Economics, ROI and P&L for Product Leaders

“API-first” is becoming another default for a lot of product teams, perhaps especially with AIs and agents potentially consuming data delivered through such channels. While APIs may have started more as internal plumbing, they’re more product-like than ever.

This has serious impact on product work. Product managers spend a lot of time connecting behavior to product metrics. Conversion. Engagement. Retention. Task completion. Adoption. The next level is connecting these pieces to financial outcomes, both cost and revenue. So we’re going explore that.

This is the idea behind Productify’s P&L guide for product leads. Growth metrics and financial impact should be understood together. A feature that increases engagement but damages margin may not be working economically unless that tradeoff is deliberate and justified elsewhere. A feature that costs more to serve but creates more revenue, retention or risk reduction may be a terrific investment.

APIs can sometimes make this relationship more visible.

The business questions are what it costs to create a successful customer outcome, what that outcome is worth, and whether the economics get better or worse when the product wins.

Stop here if you like. You can skip the rest of the article as this was as the main point. Everything that follows is details. If you want a sample spreadsheet to start building your own model, see this link to a GitHub Repository for a copy: API Product Unit Economics Model

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Product Leader API Literacy: Learn by Doing With Postman

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Bonus Hands On article for the product manager’s guide to digital connectivity series.

There’s a point where reading about APIs stops being very useful. At least this was true for me. The theory is fine. You can learn what an endpoint is, what JSON looks like, what a GET request does and why a 404 is different from a 500. But sometimes the fastest way to make those ideas stick is to actually send a request and look at what comes back. As I’ve pointed out elsewhere, our goal is not necessarily to become experts here. Though that’s ok. It’s to have a reasonable depth of understanding as to how the plumbing works so we can participate meaningfully where such tools impact product craft or P&L if we have financial responsibilities as well. This will be mostly about hands-on learning for some basic API usage; nothing to do with actually designing them or considerations for AI-first or revenue. Those are larger scope topics for another time.

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Product Leader’s API Interface Literacy Field Guide (5 of 5)

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In this part 5 of 5 for the series, we’re going to tie everything we’ve gone over about digital product connectivity together with a In this part 5 of 5 for the series, we’re going to tie everything we’ve gone over about digital product connectivity together with a field guide with learning references.

The first four parts are intended to offer a survey of product technical communications interfaces from APIs to agents and so on. This page is a See Also reference for more detailed information. A lot of it was used to craft the article series. Practically by definition, as a curated list of web things, it is likely to be outdated quickly. So consider this as just a snapshot for what the landscape of this space looks like in 2026.

Start with the landscape

Core interface references

Enterprise messaging and batch integration

Not every important integration is an always-on API or event stream. Many industries still rely heavily on structured business-document exchange and scheduled or batch-oriented flows.

Device and industrial connectivity

IoT is not one separate interface category. Connected devices may use request/response, publish/subscribe messaging, streams, command/control or other patterns depending on constraints such as bandwidth, power, connectivity and scale. These are useful starting points:

Agent interfaces

Product-manager-oriented explainers

How to use this guide

A PM does not need to read every specification front to back. Start with the taxonomy from Part 2, identify the interaction you are trying to understand, read one PM-oriented explanation, then go to the primary specification when a decision depends on precise behavior.

The companion code from Part 3 is the hands-on layer. The goal is not certification in protocols. It is enough fluency to recognize the decision, ask better questions, and know where to go deeper.

Choosing How Your Product Talks (4 of 5)

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In this part 4 of 5 for the series, we’re going to look at the “So What?” of digital connectivity among products.

The product goal and product manager’s role with regard to things like products’ technical communication structures is to make sure the team has correctly defined the interaction the product needs. This includes customer and business consequences that will help engineering choose implementation options. This way, engineers can better determine what interface patterns and technologies are worth consideration.

There’s also a second-order goal. Product needs to determine if something should become a long-term supported product surface at all. Whether it’s an external API, webhook, event stream, device interface or something else, the moment there are external dependencies on these things, they become long-term promises involving versioning, reliability, support and so on. There are clearly costs to this, and Product, likely along with Finance and Engineering, needs to have those costs and benefits modeled.

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One Product, Many Interfaces (3 of 5)

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In this part 3 of 5 for the series, we’re going to look at the “How”of digital connectivity among products.

This will be a deeper dive, and links to some interactive tools and exercises will be included below. A taxonomy of anything is useful until it becomes a giant set of overwhelming boxes. So instead, one way to illustrate the differences for this set of things to use examples and show where the parts can be applied in useful ways. This will ideally be more effective than just a basic chart alone. Several practical test examples will be built and linked into this article so. you can manipulate some of these things yourself if you like.

Let’s pretend we have a logistics service. We’ll call it ScottsHouseOfLogistics. Because no one is likely going to bother getting that URL and this way I have a place to put all the stuff I want to sell. Ideally we’ve got customers. We’ll also have packages, drivers, delivery stops, tracking events and support cases. Our overall realm will be the same. What changes are the interaction scenarios.

We won’t just pick a solution and use that function repeatedly. These tools aren’t necessarily competitors. Several may coexist because they’re solving different interaction problems. Any PM that’s been around awhile has seen choices made based on what “we have on hand and know right now.” That might be a bad idea, but often can be the best course of action. You use the tools you have and know. But is that always the best practice? Unlikely. This isn’t about product managers learning to call every one of these shots. Still, if it’s your product and you’re on the hook for success / failure, maybe P&L, then you should at least be asking the right questions or understand what your colleagues are telling you about choices.

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The Interface Taxonomy Beyond APIs (2 of 5)

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In this part 2 of 5 for the series, we’re going to look at the “What & When”of digital connectivity among products.

The fastest way to make this whole topic area a confusing mess is to just list out technology definitions. This might not be the best way to learn how things work. Though we do need to quickly visit buzzwords like REST, GraphQL, gRPC, webhooks, MCP, and some others. They don’t all sit at the same conceptual level, and they’re not interchangeable answers to one problem. What we’re going to do in this article series is look at some use cases and a handful of popular options. And in the very next installment, some live examples and work products of things you can try yourself if you want to dive in a bit. Again, we’re not trying to be coders. We won’t be experts. But sometimes just seeing something actually work can bring those Aha moments we can keep with us over time vs. just spewing buzzwords. It’s the difference between sleepily scanning another bullet point list vs. “oh, yeah… I see how that works now.” (We are going to ask some questions and run some lists first before we get to the fun stuff though.)

A useful direction in this space can perhaps start by asking ”What interaction are you trying to create?” Then look at technologies that can do it. Or you can always ask your dev, architect or AI team members. Yeah, I said it. “AI team members.” While I believe in the “mostly sort of trust, but verify” framework here, at this point, I’ve found our still somewhat new AI friends rise to that level. Maybe they still have that fresh new AI smell, and we still need to sort out best ways to use them, but there you go.

Diving deeper now, we can see things like OpenAPI and AsyncAPI can describe interfaces. OAuth 2.0 handles delegated authorization. JSON and Protocol Buffers represent data. SDKs package interfaces for developers. HTTP, WebSockets and MQTT move information. All these things matter, but they are not all separate product interaction categories.

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