Rethinking AI Value Chains:
Who Really Benefits from Your Data?
We’re often told that data is the new oil. But in today’s AI economy, most data owners don’t feel like oil barons.
Whether you’re a small business, a cultural organisation, or a public body, chances are you’ve contributed, knowingly or not, to training the models that power today’s AI platforms. But as those models become billion-pound assets, it’s worth asking: who’s really benefiting from your data?
To answer that, we need to unpack the modern AI value chain, because the real value doesn’t always sit with the people who generate, curate, or even own the data.
The AI Value Chain, Simplified
Let’s break down the commercial AI pipeline into four simplified stages:
Data Generators
Individuals, businesses, and organisations producing everything from text to photos to 3D scans.Model Developers
Firms building foundational models using aggregated data, often scraped or licensed from public and private sources.Platform Providers
Cloud providers and API platforms who deliver the models at scale.End-User Applications
Businesses or consumers using AI tools via interfaces like chatbots, image editors, or analytics dashboards.
Each stage adds value, but not equally. Most revenue and control is concentrated in stages 2 and 3: model development and platform delivery.
Where the Value Accumulates
It’s easy to assume that if your organisation is using AI, you’re benefiting from the value chain. But in many cases:
You’re paying for access to models trained (in part) on your own content
You lack transparency into how your data was used
You have no share in the model’s commercial upside
This is especially troubling for sectors like heritage, education, journalism, and the arts, where outputs are often rich, structured, and highly valuable as training material.
Meanwhile, foundational model providers are building defensible IP and market power on top of a largely invisible labour force of data contributors with no contractual stake.
The Risks of Centralised Value
This imbalance raises concerns beyond fairness. It also affects:
Data sovereignty: Public institutions may lose control over how their datasets are used and monetised.
Innovation ecosystems: SMEs are often priced out of using large models they helped train.
Sustainability: Relying on opaque platforms discourages in-house innovation and long-term skills development.
When value accumulates upstream, those working at the edge, where real-world problems are being solved, have fewer resources and less influence.
What Organisations Can Do Differently
You don’t need to stop using cloud AI tools. But you should be more intentional about where your data goes, and how it could serve your interests.
Here are some practical steps:
Track what you’re contributing: Understand whether your datasets are being shared, scraped, or reused via third-party platforms.
Push for explainability and auditability: Work with vendors who are transparent about data usage, not just model performance.
Consider self-hosted or open models: These offer more control and flexibility, especially for sensitive or high value use cases.
Explore collective bargaining: Cultural institutions, researchers, and public bodies can collaborate to set clearer terms for data licensing.
Final Thoughts:
Don’t Just Feed the Machine
The current AI economy thrives on data, but not always in ways that reward its original creators. If we want a more equitable, transparent, and sustainable AI landscape, we need to rethink how value is created and shared.
At Aralia, we believe in tools that empower users without extracting unchecked value. That’s why we build platforms like Elata with transparency and sovereignty in mind, from training to deployment.
If your data is powering the AI economy, you deserve a seat at the table.
