Rethinking the financial interface in the age of data and intelligence abundance

The interface products from data monopolies were never just software; they were monopolies sustained by control. Control over data, pricing, and how professionals accessed information. That control wasn't arbitrary - it was the logical outcome of incentives built around data margins rather than user outcomes.

But that control is eroding. The explosion of alternative data, the rise of AI agents, and the stubborn focus on selling data over serving users have created a fracture the old model can't contain.

A new model is emerging: interface-first, data-neutral, and AI-native. A model where the software layer serves the user, not the vendor's data sales team. Where the interface becomes a true operating system for finance: open, modular, and intelligent.

The shift is structural. And it's being driven by the same forces that once built the terminal, an explosion of data, new forms of intelligence, and an industry that can no longer afford misaligned incentives.

The incentive flip

For decades, financial software bundled everything: the data, the tools, and the user experience. You paid one price for all of it - a package so tightly coupled that you could never separate what you were really paying for.

That bundle created an empire. But it also created a distortion. Vendors optimized for selling data, not for improving the user experience. Every design choice, from the limited export features to the lack of integration with external datasets, served the protection of data margins. (I wrote extensively about this in Part I.)

A decoupled interface breaks that dynamic.

Once the UI stands on its own, its incentive flips. A standalone interface doesn't need to protect data entitlements. It only needs to serve the user. Its success depends on usability, speed, and how well it integrates with whatever the analyst already uses - not on how many internal datasets it can upsell.

The interface becomes an impartial workbench. Analysts can plug in their preferred data sources - proprietary, public, or alternative - and get to work without vendor interference. They stop paying for access to data they don't use and start paying for software that helps them move faster.

This separation forces competition where it matters most - speed, interoperability, and design - rather than who controls the data contracts. In a world where most professionals already subscribe to multiple feeds, the real value isn't in who sells the data. It's in who helps you make sense of it.

Why this moment

Finance has always been data-driven. But today, it's data-saturated.

Satellite imagery, credit card transactions, app usage, ESG disclosures, web traffic, social sentiment... New datasets are arriving faster than any incumbent can ingest them. Most of them don't fit neatly into a pre-built terminal. They're messy, dynamic, and often proprietary.

No closed platform can keep up with this level of diversity. The data landscape has simply outgrown the idea of a single vendor controlling the flow.

Closed systems once thrived on scarcity; open ones now thrive on abundance.

What's needed is an open interface, one built like a network. It should treat data as modular and composable, like APIs that can be plugged in and swapped out. Analysts should be able to connect to a new data source, build a model, or run an AI workflow without waiting for a vendor's permission.

Modern platforms embrace this modularity. They're designed as pipelines rather than products. They see finance as a system of moving parts - some internal, some external, all interoperable. The contrast with legacy systems is stark. A monolithic terminal moves slowly because it's built around the assumption of completeness: that everything you could ever need lives inside its walls. That assumption no longer holds.

Neutrality becomes a survival strategy, not a philosophy.

An interface built for neutrality doesn't push one feed, model, or dataset over another; it simply exposes the best tools and lets the user decide. This is the opposite of the incentive structure that has dominated finance for decades. In the old world, every UI was also a distribution channel. Every search result, chart, and default setting subtly guided users toward in-house content.

A neutral interface ends that. It competes not on exclusivity but on openness. Its job is to make the user's workflow faster, cleaner, and more powerful - not to shape what data they consume. When users can bring any data or tool they want, innovation moves faster. Vendors compete on quality rather than access, and analysts can finally mix the best of everything in one coherent environment. Their coherent environment, shaped to their workflow.

The architecture of openness

If you follow this logic to its conclusion, an open, extensible, AI-native interface becomes something larger than an app. It becomes the operating system of finance.

Like a computer OS, it provides a unified environment for every tool, every dataset, every agent. In this world, an analyst doesn't open five different applications. They open the same workspace every morning, and it is their environment. It holds their context, connects to their data, and runs their automations & workflows.

Building this requires solving three hard problems:

The data problem: Ingesting structured and unstructured data at enterprise scale while maintaining governance and latency.

The interface problem: Designing a UX flexible enough for research, analytics, and reporting without having the user leaving the platform.

The ecosystem problem: Convincing vendors, users, and developers to build on top of it rather than around it.

That's why few have tried. And why those who do will define the next generation of financial software.

Extensibility: the expression of freedom

Modern analysts don't just consume data; they create it. Every model, notebook, and internal database is a new form of proprietary signal. A next-generation interface needs to treat that as first-class input.

Bring your own data. Analysts can connect internal systems - their Snowflake warehouse, their portfolio data, or even unstructured files like research memos - and blend them seamlessly with vendor data. No conversions, no export limits, no awkward detours through Excel.

Bring your own agent. The analyst can integrate their own intelligence layer. One day it might be a general-purpose LLM; the next, a fine-tuned model that excels at analyzing FOMC minutes. The interface shouldn't care. It should provide an open protocol for intelligence - where agents and tools can be orchestrated together without friction.

Bring your own workflow. This is where data, models, and agents converge to automate complex, multi-step tasks - like drafting an investment memo, building a risk dashboard, or monitoring credit exposures. A workflow is no longer a hard-coded process inside a terminal; it's something the analyst defines. You might start with your firm's internal data, enrich it with a vendor's ESG feed, and pass both through an AI agent that writes the first draft of your memo - all within the same interface. The workflow can then route the output to a colleague for review, trigger an update in a dashboard, or feed the result into another model.

Extensibility is no longer a technical feature, it's the expression of freedom inside the software.

Intelligence: context as currency

An AI-native interface isn't a chat window. It's an environment that understands context, remembers state, and reacts to changes. It's aware of your portfolio, your preferences, your recent queries, and even your compliance boundaries. It can reason across multiple data sources and agents - not by pretending to know everything, but by knowing how to connect to everything.

In this world, context becomes currency: the better your interface understands it, the smarter every workflow becomes.

This demands event-driven architecture. Legacy systems were designed around menus and files; AI-native systems are built around events and context. Every user interaction carries metadata: who asked, about what, under what constraints. Every answer links back to its provenance. The system becomes self-documenting - every step traceable, every output auditable.

And because it's event-driven, it's always on. The interface doesn't wait for you to ask "what changed overnight?" - it tells you. It doesn't wait for you to open a chart, it updates it as new data arrives. The interface becomes a living environment. It evolves with you, learning from every query, refining context, and surfacing insights before you ask.

The shift to this financial software OS will move even faster with AI, not because AI is flashy, but because it turns the entire system into an alpha-generating machine. Every integrated data source becomes a signal. Every workflow becomes repeatable edge. The interface doesn't just help you find alpha; it compounds it.

Beyond hedge funds

This transformation isn't limited to the quant elite. It's spreading across the entire industry.

Every firm, from asset managers to corporate treasuries, now operates in a multi-data world. Analysts are technically literate, often fluent in Python, and increasingly empowered by AI. Non-developers can build small automations or workflows that once required entire IT teams. The power dynamic is shifting from vendor-driven tools to user-driven environments.

Meanwhile, the open-source ecosystem has exploded. Powerful financial libraries, AI agents, and visualization frameworks are freely available. The infrastructure that once cost millions can now be assembled by a small team.

The monopoly of access has collapsed. Firms no longer want to rent the same interface as their competitors; they want to own their workflows. The transition won't be smooth. But the pattern is clear: openness outcompetes control. Modular systems outpace monoliths. And the users, now more empowered than ever, are driving the change themselves.

The path forward

The story of financial software has always been a story about control. Who controls the data, who controls the interface, who controls the user experience.

That control is now up for grabs.

The winners of the next era won't be those who hoard data, but those who make it usable. They'll build interfaces that adapt, not entrap. They'll align incentives with users, not against them.

And they'll understand that a true financial OS isn't built in isolation, it's built in community.

Open ecosystems matter. A modern financial interface needs an open developer base to grow new connectors, agents, and applications organically. It needs users contributing back to the system, expanding its capabilities faster than any single company could.

This is exactly what we're building at OpenBB. An open, AI-native workspace that's data-neutral by design. A platform that integrates seamlessly with any dataset, any model, any agent. A financial operating system that's not owned by a data vendor, but powered by its users.

Soon, we'll announce the Open Data Platform which is the largest open source project in the finance category, proof that the appetite for neutrality, openness, and innovation is real. And as the ecosystem grows, every new connector, every new AI agent, every new app built by the community compounds the network effect.

The era of walled-garden financial software is ending.

The next generation of financial software won't sell you data, it will give you power.

The interface is striking back.

And this time, it's on the user's side.