Beyond Memory: Why AI Trust Will Be the Hardest Lesson Yet

I’ve spent decades watching new technologies hit the same roadblocks—big ideas that sound great in theory but break down when they hit the real world.

The latest example? Agentic AI.

Everyone is racing to build AI systems that learn, adapt, and make autonomous decisions. The industry is moving fast—too fast in some ways.

The last big challenge? Memory. AI without memory is stateless, repetitive, and frustrating. That’s why vCons (Virtualized Conversations) have emerged as the essential memory layer for AI.

But there’s a bigger problem that many aren’t thinking about yet: Trust.

AI that remembers is one thing. AI that remembers accurately—and can prove it—will be the defining challenge of the next phase of AI adoption.

The Next Problem: AI Trust Isn’t Just About Compliance—It’s About Reality

We’ve spent years worrying about bias in AI models. But what happens when AI memory itself is manipulated?

I don’t believe people are talking about this enough.

• What happens when an AI financial advisor learns from altered transaction data?
• What if an AI doctor makes patient recommendations based on manipulated medical records?
• What if an AI-powered legal system “remembers” past rulings incorrectly?

This isn’t science fiction. It’s a problem that’s already emerging.

Memory alone doesn’t solve these issues. Trust does.

AI Trust is Not One-Size-Fits-All

This is where companies are making a mistake. Many assume that every AI system needs the same level of trust enforcement. But that’s not the case.

SCITT (Supply Chain Integrity, Transparency, and Trust) isn’t a universal requirement—it’s a strategic one.

• vCons provide memory. Every AI system benefits from structured memory.
• SCITT ensures that memory can be trusted. But not every AI system needs cryptographic verification.

Some industries won’t have a choice.

Which Industries Will Be Forced to Adopt AI Trust First?

Regulators are already circling. The first industries that won’t be able to move forward without AI trust frameworks include:

• Finance – AI-driven trading, lending, and fraud detection require tamper-proof audit trails.
• Healthcare – AI-assisted diagnoses and patient history demand verifiable medical records.
• Legal – AI-generated case research and contract creation must be immune to data corruption.
• Government & Policy – AI-driven decision-making in public sectors will face the highest scrutiny.

For these industries, vCons provide the memory. SCITT ensures it can be trusted.

The Shift from AI Memory to AI Accountability

I’ve seen this before.

There was a time when people I know thought VoIP wasn’t viable. Some skeptics couldn’t see beyond the limitations of early deployments—or were deflecting because they saw disruption coming. The companies that figured out how to build trust in the technology changed everything.

This is no different.

We’ve reached a pivotal moment. The AI conversation is shifting from “Does it remember?” to “Can we trust what it remembers?”

The companies that solve this first won’t just lead the AI revolution—they’ll define its foundation.

Where does your AI strategy stand? Are you relying on memory alone, or are you preparing for trust and accountability? Let’s talk about how companies can get this right.

Let’s discuss.

📍 Upcoming Events Where This Conversation Continues:

Spring ’25 VON Evolution – April 9, 2025 | Bentonville, AR A deep dive into the future of AI-driven communications, trust, and compliance.

Spring ’25 vCon – April 22-24, 2025 | Hyannis, MA Exploring the next generation of AI, vCons, and SCITT—where trust meets technology.

📢 Learn More About vCons and SCITT:

Subscribe to The Pulver vCon Report: HERE

You can learn more about the on-going work of vCon at the IETF: HERE

You can learn more about the ongoing work of SCITT at the IETF: HERE