Data Scarcity and Trust

The Exhausted Wells

Ran through it all, what's left?

For years, the scaling paradigm was simple: scrape for more than a decade, feed the models a mixture of noise and signal, and hope for a breakthrough every few model generations.

That paradigm is hitting the law of diminishing returns. Scaling the volume of web text yields marginal gains when the marginal token is increasingly repetitive, low-value, or redundant.

Synthesis

Overcoming the data obstacle requires a shift from passive extraction to active engineering.

The most promising version is verified synthetic generation: models produce training data inside environments that check it.

That means code executed in sandboxes, proofs checked by formal verifiers, and simulation loops with measurable outcomes.

The point is to move away from unregulated self-consumption, where models learn from their own unchecked output, toward generation with a ground-truth filter.

The Private Wells

The Artisanal Mines

Plenty of valuable data sits outside the public web, especially where digitization has been slow: clinical records, factory sensor logs, freight manifests, agricultural supply chains.

Take a regional tea cooperative: harvest yields, weather, grading, pricing, and shipping data that could teach a model how a real supply chain behaves, and that no crawler will ever touch.

This data is useful, but it’s untapped, unprocessed, and, most importantly, illiquid: hard to price, hard to verify, hard to transfer.

That will change, as it does with everything we keep optimizing.

Selling Trust

When we run out of things to sell, we sell values

Providing the service first builds something that paying up front never would. Trust works like that: it’s earned before the transaction, and it’s expensive when it’s missing.

Unlike gold, trust isn’t fungible, but it does behave like a priced asset: it takes effort to build, it can be spent, and buyers pay for it. In data, the price of skipping it shows up later. You can bypass certification and train on the dataset anyway, but you’ll pay in lawsuits, in lost partnerships, and in looking like a parasite sucking society’s wells dry. There’s no free lunch there either.

The Data Passport

One solution is to give every dataset a publicly verifiable Data Passport:

• Where it came from: chain of custody, with timestamps and hashes
• Who may sell it: the contract clause that allows resale and permitted uses
• What was removed: a redaction report and re-identification test results
• What was left: quality statistics, schema, and usage terms

A sample entry might look like this:

Field	Example
Source	[Cooperative name], 2019–2025, via signed data-sharing agreement
Integrity	SHA-256 hash of each release, timestamped
Resale rights	Permitted for model training; no resale of raw records
Redaction	Names and GPS coordinates removed; re-identification test passed at [threshold]
Quality	[X] records, [Y]% completeness, schema attached

Two honest caveats. Hashes and timestamps prove a dataset hasn’t been altered, not that it was sourced with consent, so the contract and audit layers matter as much as the cryptography.

And a passport is only as trustworthy as whoever issues it, which is why the track record of the certifier is part of the product, and why the process should be open to independent audit.

What we're building

We propose a compliance certification with a track record (the Data Passport), and handling the sale of the resulting datasets.

That’s the purpose of Voidback: to find alpha where others aren’t looking. Antonlytics is the first of many alpha theses that will live on Voidback.

This is a clear gap, and this is a path to filling it. Visit Antonlytics for more information.