Everyone Has the Same Data. Now What?

A quiet shift has been reshaping public equity research over the past few years. Data that once required expensive subscriptions, specialized infrastructure, or exclusive vendor relationships is becoming widely accessible. AI hasn't just made analysis cheaper; it's made access cheaper. The result: many of the inputs that used to create an advantage are now available to anyone willing to point an LLM at the right sources.

Ten years ago, simply having structured transcript data or clean KPI feeds created a noticeable gap between firms. Today, the same information can be collected, cleaned, and synthesized in minutes. Anyone can pull filings, summarize 10-Ks, run cross-year comparisons, or generate factor breakdowns without a quant team or a premium terminal. The moat around access is shrinking fast.

The Collapse of the Old Data Moat

AI has broken apart workflows that relied on human labor, manual processes, and expensive tooling. What once required analysts and costly infrastructure now takes a single prompt.

A few examples illustrate the shift:

EDGAR filings can be ingested, parsed, and structured instantly. Earnings transcripts, once tightly controlled by premium vendors, are now everywhere and easily summarized or scored. Entry-level alternative data (hiring trends, product reviews, web pricing) can be scraped and normalized with AI-powered pipelines that cost almost nothing.

The entire data ecosystem is moving from pay-for-access to pay-for-interpretation, speed, and integration. If everyone has the same raw material, the raw material is no longer an advantage.

A More Level Field, With New Pressure

When the data everyone uses converges to the same sources, differentiation has to come from something else. Alpha becomes less about who has the dataset and more about how fast and how intelligently a team can synthesize what the market is already seeing.

This is why more firms are increasing expert calls to gather context they can't scrape, exploring niche datasets not yet widely distributed, building proprietary frameworks to score signals in unique ways, and mixing public information with internal insights, opinions, and real-world conversations.

Speed matters. Context matters. Interpretation matters. Creativity matters more than ever.

The pressure rises because the baseline is higher. If every firm can run LLM sentiment on transcripts, then sentiment alone is no longer an edge. If everyone can summarize filings, then summaries aren't an edge either. The advantage shifts to how well you combine standard inputs with differentiated ones.

A Quick Example of Creative Edge

I read Dave Wang's newsletter this week and thought he highlighted a great example of how to think creatively about AI and data. (Link: https://www.davewang.ai)

Instead of waiting for official reports or full model cards on Google's Gemini 3 preview, he used Grok to pull early reactions, leaked benchmarks, and developer sentiment directly from X in real time. Because Grok has native access to that data, it gave him a faster read on how the model might be received commercially.

This is the kind of thinking that earns an edge in a world where most datasets are flattening. The advantage didn't come from having a private dataset. It came from using a tool differently and interpreting the noise before everyone else caught up.

The Next Frontier Is Inside the Firm

As external data becomes more uniform, the most differentiated dataset left is the one no vendor can sell: the data already inside your firm.

Every research team has a massive pool of untapped intelligence spread across analyst notes, email threads, Teams chats, spreadsheets and models, long-form research, draft theses, and updates that never get captured in a system.

This internal information reflects your team's judgment, worldview, and process. It's proprietary by definition. Yet most firms treat it as disposable because it's spread across tools, personal drives, and unstructured conversations.

We believe this internal dataset is where the next meaningful edge will come from. It's not from collecting more public data, but from unlocking what your team already knows and giving it structure and visibility. A firmwide intelligence layer that listens across the organization, organizes what it hears, and surfaces what matters in a single place. Not a dashboard. Not a search box. A living internal brain that reflects how your people think.

Closing Thought

Public data is flattening. Tools will continue to democratize access, and information will continue to flow faster. In this environment, the advantage will shift toward firms that can interpret more creatively and connect more internally. The firms that treat internal intelligence as a strategic asset, not an afterthought, will build the strongest research cultures and the most resilient edges over the next decade.

The Basis Engine

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About Shift
Investment research shouldn't be this hard. Shift turns your firm's scattered knowledge into powerful insights with AI built for how you actually work. We're a team of builders and finance experts based in Charlottesville, VA.