Scaling Garbage or Sealing Truth? Why AI Survival Depends on Data Provenance at the Edge

My friend Ali Razeghi of Immutable AI Labs makes a sharp point in his recent posts which echoes my views and the work we do at Open Code Mission: AI biggest vulnerability is not compute limits, regulation, or even model collapse. It is bad data.

This is the crux of where AI stands in 2025:

• Models are hungry, ingesting at an unprecedented scale
• Pipelines are moving closer to the edge: IoT sensors, routers, phones, decentralized devices
• The closer we get to the edge, the weaker the control, the wider the attack surface, and the deeper the risks of poisoning

The Poisoning Problem

Poisoning here is not metaphorical. If a compromised edge device feeds tampered data into your model, there is no undo button. Once baked in, you either roll back and retrain (losing months and millions) or accept the corruption until it manifests in billion-dollar disasters.

Zillow collapse was just the opening act.

The DePIN Dilemma

Now layer DePIN on top of this. Decentralized Physical Infrastructure Networks promise scale and democratization, but they are built on other people devices.

When Helium has already paid out $40M to faulty sensors, that just the visible cockroaches. The real infestation is unseen: hidden tampering, silent contamination, cascading into the models we are supposed to trust.

This is where Ali call for cryptographic verification at origination intersects with my own work. Decentralization without verifiability does not democratize AI. It accelerates collapse.

Why Current Solutions Fall Short

The solution is not more synthetic filler. Synthetic data is already reinforcing its own statistical noise rather than expanding the truth.

Nor is it "better Web2 exhaust." Random web scrapings are riddled with bias, misinformation, and adversarial contamination.

The Only Viable Solution: Data Provenance

The only viable solution is data provenance, cryptographically enforced at creation.

In the Verum Sphere architecture we have been building, I call these Lumens: data objects that carry a cryptographic lineage from the moment they are created or ingested.

A Lumen is not just data. It truth with a seal. Its provenance is embedded, not bolted on later.

How Lumens Change Everything

With this approach, the model ingests not raw exhaust but verifiable atoms of trust. Attack vectors like:

• Adversarial poisoning
• Shadow AI contamination
• Latent drift

...are radically constrained because the data itself carries integrity from the outset.

The CrowdStrike Moment for AI

We should think of this as the CrowdStrike moment for AI. Just as cybersecurity shifted from patching compromised systems to active defense and threat intelligence, AI must shift from retroactive cleaning of corrupted data to preventing corruption at the point of origin.

The Choice Before Us

If we do not make this shift, we are simply scaling garbage. Faster, bigger, and more catastrophically.

But if we do—if provenance, verification, and transparency are built into the data itself—then we can scale truth.

Truth, not compute, is the real fuel of the next generation of AI.