Letter from the Editor: Finance is becoming ambient infrastructure underneath everything, but what are we giving up - Tearsheet
Introducing our new ‘Letter from the Editor’ series featuring exclusive insight and opinion-driven analysis from Tearsheet editor Sara Khairi. The focus is to link ideas, question assumptions, and track shifts across both mature and emerging trends in financial services.
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Issue # 3
For years, we’ve talked about digitization as if it were ‘the’ destination. We built apps, dashboards, APIs, embedded widgets, AI copilots. We optimized access, sped up onboarding, and compressed decision times.
Doing this made finance feel easier but not necessarily more present. It still shows up in bursts when you open an app, check a balance, apply for credit, or reconcile at the end of the month. The system is faster, but it remains episodic. You still go to it rather than it staying with you.
That model is starting to give way, and that is what I want to talk about today. Financial services are starting to move beyond the old request-response model. In its place is an incoming, always-present layer that interprets context in the background, responds dynamically, and participates alongside the user instead of merely waiting for input.
We’re already seeing the early contours of this across different parts of the stack. The recent Plaid-OpenAI integration around ChatGPT is one of them. On the face of it, it resembles another AI-powered personal finance assistant: users connect accounts through Plaid, and ChatGPT responds with contextual insights drawn from live financial data like budgeting support, spending analysis, debt management, savings recommendations.
Useful, sure. But also slightly too small as a way of describing what’s actually changing.
Historically, financial experiences lived inside financial products. What OpenAI is effectively testing is finance embedded inside a conversational intelligence layer people already inhabit constantly throughout their day.
That changes the center of gravity. The banking app is no longer the primary interface; conversation increasingly is. And conversation doesn’t behave like traditional software. It doesn’t reset every time you open it. It carries context, stretches across workflows, and stays present while decisions are forming.
This is why I think the industry narrative around “AI in finance” only captures part of what is happening and understates the shift underway; what is actually emerging is more like always-on financial interpretation.
And this evolution didn’t start with ChatGPT.
Embedded finance already moved things in this direction by pulling financial functionality closer to behavior. Shopify embedded capital and payments directly into commerce. Klarna and Affirm brought credit into discovery and intent, not just checkout. Banking capabilities stopped behaving like standalone destinations and started merging into workflows.
Emerging AI systems are what push that logic further.
Agentic AI in wealth and banking, payments and commerce
What’s taking shape now is embedded interpretation. Systems are increasingly expected not just to process transactions, but to understand patterns, maintain continuity across fragmented financial activity, surface relevance proactively, and eventually participate in decisions.
That is a much bigger transition than another chatbot layer. Previous fintech cycles optimized transactions; this one is beginning to optimize financial cognition itself. That changes the competitive landscape in ways I don’t think incumbents are fully prepared for yet.
Historically:
- Banks owned accounts
- Fintechs owned experiences
- Now AI systems are positioning themselves to own interpretation
That third layer may become a highly valuable layer in financial services going forward. Because once a system becomes the place where users continuously interpret financial reality, every action – spending, saving, borrowing, investing, planning – flows through that layer.
This is why the Plaid-OpenAI partnership is gaining eyeballs, even if the product itself evolves, never fully scales as imagined, or struggles commercially. Some skepticism around the launch is warranted, though. Transaction data is incomplete, advice without execution still leaves friction, and consumer demand for AI-powered financial guidance does not necessarily mean they will pay for it at scale. Additionally, behavioral finance has historically been much harder than fintech companies assume or product demos suggest.
But those critiques mostly speak to product viability. The deeper shift is interface migration.
Finance is moving out of banking environments and into persistent intelligence systems that people already use to organize information, interpret decisions, and navigate daily life.
We can also see this in the way AI is being introduced into core banking and wealth workflows. Take the idea behind capabilities like Citi Sky.
Across these examples, AI isn’t acting as just an assistant added on top of finance but as a bridge or layer between raw activity and meaning. This is what distinguishes the current AI wave from everything that came before.
We’ve had digitization. Then automation. Then embedded finance. Each wave made finance more efficient, more distributed, and in some cases less visible. But this is about continuity. Continuity is not just availability, so to speak. It is context preserved over time, understanding what changed, what matters now, and what is likely to matter next, without requiring the user to rebuild the frame each time they interact with the system.
That’s a very different expectation to place on financial infrastructure. And it also reorders what ‘good’ actually looks like.
For years, the goal was to make finance invisible. API-first banking accelerated that by modularizing financial capabilities so they could appear anywhere. Embedded finance distributed those capabilities across commerce, payroll, and software ecosystems. Now AI introduces systems that continuously interpret financial context without being asked.
More intelligence does not automatically mean more clarity
A system can be highly responsive and still create noise. It can surface constant insights while still leaving users responsible for stitching meaning together. And in finance, that stitching has always been the user’s burden.
The ‘always-on’ narrative is often labeled as progress, but its real impact turns finance into an ambient layer.
That shows up in concrete ways in how systems begin to behave. A portfolio that doesn’t just report performance but contextualizes movement in relation to goals and macro conditions. A banking interface that doesn’t wait for queries but flags emerging patterns in cash flow or risk. A wealth tool that doesn’t just answer questions, but anticipates the framing of the question itself.
At this point, the line between ‘user action’ and ‘system interpretation’ starts to blur. And that is where incumbents face a harder challenge.
Financial institutions have always been strong at producing answers. What they are now being asked to build is continuity of understanding. Not correctness in moments, but relevance over time. That is a different operating model. And it is not yet clear that the industry is structurally set up for it.
There’s also a deeper question underneath all of this. If finance becomes continuously present – interpreting, explaining, and responding in real time – what happens to the moments where users used to pause, think, and decide?
Historically, friction was not always a flaw. Sometimes it was the point where attention was forced. A moment to pause, compare, reconsider. Remove too much of that, and you don’t just reduce friction; you potentially reduce visibility into the decision itself.
This is where the industry’s obsession with ‘seamlessness’ starts to feel questionable. Seamlessness feels effortless, but it is not neutral in effect.
This is not an argument against AI in financial services. It’s more of a reminder that presence changes behavior. Systems that are always available tend to become systems that are always shaping.
And that is the real design problem ahead: how much intelligence should stay in the foreground, and how much should disappear into the background until it is needed.
Because the endgame, at least as I see it, is not a constant stream of financial outputs, nor simply better UX or faster payments. It is about moving away from fragmented financial management and toward a system that understands a person’s financial life as it unfolds without overwhelming them.
– Sara
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