The Future is a Machine Customer: Why Expert Networks Are Becoming Data Refineries

INFLXD.

Previously
Inflexion Transcribe

The Future is a Machine Customer: Why Expert Networks Are Becoming Data Refineries

James Nguyen, Founder & CEO

Aug 06, 2025

The Future is a Machine Customer: Why Expert Networks Are Becoming Data Refineries

Expert networks began in the business of connecting people, but that may not be true for long. Their true value lies not in the connections they make, but in the data they produce—data that machines will soon consume. As AI advances, the transcripts from expert calls will feed directly into algorithms, not human readers. This shift is quietly turning expert networks from service providers into data refineries, where the end user isn't an investor scanning a document but an AI agent synthesizing insights at scale.

Asymmetry

Expert networks emerged to solve for one thing: information asymmetry. They give buyers access to expertise they can’t easily find elsewhere. Think of financial due diligence: an investor needs to understand a niche industry, like semiconductor supply chains, but lacks direct access to insiders. Traditionally, networks like GLG or AlphaSights recruit former executives for hour-long calls, charging fees that reflect the scarcity of that expertise.

These calls can cost anywhere from a few hundred to several thousand dollars per hour, a premium paid for a temporary edge.¹

This model has been incredibly successful. The core product has always been insights that aren't available on the open web or in public reports.

But the value isn't just in the call itself; it's in the captured knowledge. Calls are recorded, transcribed, and stored, creating proprietary datasets that clients pay to access.

Yet, humans have limits. An analyst might review a handful of transcripts in a day, manually piecing together patterns. Machines, on the other hand, don't tire. They can ingest thousands of conversations simultaneously, cross-referencing details across industries and time periods.² This isn't speculation. We are already observing the roots of this shift in how investment firms use AI to accelerate due diligence.³ Where a process once took two weeks, AI can reduce it to just a day or two,⁴ all while analyzing a greater number of opportunities. The asymmetry persists, but the consumer is changing.

Aggregation

Transcript libraries were the first major evolution beyond one-off calls. Instead of scheduling a new conversation for every query, networks began building repositories of past interviews. Companies like Tegus and others with similar offerings have validated the demand for this model, emphasizing high-quality, searchable content that serves as a powerful research tool.

These libraries effectively productize expertise. A single call, once ephemeral, becomes a reusable asset. The business model shifts: after the initial interview, the marginal cost of replicating that insight drops to near zero, enabling immense scalability.

This was the second act for the industry, moving from personalized insights (Act I) to productized insights (Act II). Clients trade the personalization of a direct call for the breadth of aggregated knowledge, increasing their odds of finding relevant information without redundant effort.

But aggregation exposes a flaw in human-centric thinking. Reading through dozens of transcripts is time-intensive, and critical nuances get lost in the process of skimming. AI agents excel here. They don't just search for keywords; they synthesize.

For example, an agent could aggregate views on electric vehicle battery trends from hundreds of transcripts, identifying points of consensus and contrarian opinions in seconds—a task that would take a human analyst days. The misconception is that these libraries are for human consumption alone. In reality, they're becoming raw material for machines.

Synthesis

Synthesis is where machines truly outpace humans. To an AI, an expert transcript isn't just static text; it's structured data ripe for processing. An agent can parse a conversation, extract key themes, and correlate them with external sources like market reports or financial filings. This isn't mere summarization—it's the creation of entirely new insights through combination.

We can see the beginnings of this in adjacent fields. Asset managers are already deploying AI agents that automate complex research workflows, pulling from proprietary data to inform investment decisions.⁵ The efficiency gain is immense, as these agents can run concurrently, synthesizing information at a scale impossible for even large teams of human analysts.

This leads to the next logical step: "synthetic expertise."

This is the productization of personalized insights (Act III). AI-generated personas, trained on vast libraries of expert conversations, can simulate expert responses based on aggregated and continuously updated data.

Because these systems learn with every new transcript added, they evolve, mimicking how a real-world expert's knowledge grows over time.

Critics might dismiss this as overhyped, pointing to current limitations like hallucinations in large language models. But this is a bet on direction, not position. The models we use today are the worst we will ever use again.

If we accept that agents will inevitably become faster and more comprehensive at synthesis, it follows that they will become the primary consumers of this data.

Datasets

At the heart of this shift are the datasets. Expert networks are collectors of vast troves of transcripts that are proprietary, nuanced, and compliant with regulations. These aren't generic web scrapes; they're vetted conversations with real-world experts, covering hyper-specific sectors from biotechnology to financial technology.

In the AI era, data is the most durable moat. While synthetic data has gained traction for training models, expert transcripts offer something far better: authentic, domain-specific, high-fidelity input.

This is why firms that specialize in annotating and cleaning data for AI models have become so valuable; they prepare the raw material that powers intelligent systems.⁶

For expert networks, building these datasets now creates compounding value. Each call adds to a corpus that can be used to train and refine AI, enabling the creation of "synthetic experts" that evolve and improve with each new input.

The defensibility of this future business model will be conditional on the decisions made today. The nuance lies in quality. Incomplete or "dirty" data leads to poor synthesis, which is why the focus must be on coverage, completeness, and credibility. Networks that invest in the hygiene and annotation of their data now are positioning themselves for long-term defensibility.