The Human Side

A NEARCON 2026 Recap

by Sam Rogers
6 min read
video
governance
trust
collaboration
conference
announcement

I just returned from two days at NEARCON in San Francisco with 300-400 founders, investors, researchers, developers, and leaders from companies you've heard of. All of them converging toward the same core problem: How do you make something trustworthy?

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The Room That Knew

Whether the conversation was about artificial intelligence, blockchain, security, or web3 more broadly, the same pattern emerged in panel after panel. AI product managers from Google, Oracle, and Robinhood. CEOs from Kraken, Brave, and Anchorage Digital. Senior architects from Intel, eBay, and Circle. Researchers from OpenAI, Perplexity, and Snowflake.

They were all measuring the same things: adoption rates, efficiency gains, output quality. Important metrics, certainly. But whenever the discussion turned to trust, the room would stall.

Not because they didn't care about it. Because they were looking for trust inside the technology and measuring the AI and tooling behavior when the actual risk lives on the human side of that system.

Three Conversations That Crystallized the Problem

The Existential Trajectory Conversation

Over lunch, I sat with an early NEAR investor, someone who's been in this space for decades and (like me) remembers life before the internet. Also at the table was an AI researcher, formerly at Google and NVIDIA, now bootstrapping his own startup building agentic infrastructure. The three of us talked passionately about the trajectory of intelligence itself: where it's headed, how fast, and what we as a society and as individuals are doing to navigate it.

We're working on similar problems from different angles. That convergence felt significant.

The Brave Browser Conversation

The Brave team has shipped TEE-protected inference (Trusted Execution Environments) that make it verifiably certain that nobody sees your data during AI inference. It's built into their product and being openly tested via their nightly builds right now.

I had built a similar integration for the PAICE behavioral assessment and entered it in NEAR's Innovation Sandbox hackathon with a working demo, then rolled the production code out within a week. It was validating to see we're solving similar problems with the same architecture. The technical capability is new, but it exists. The question is what you do with it once trust in the infrastructure is established.

The Students at Lunch

On the second day, I had lunch with three students. Ages twenty to twenty-two, different genders, backgrounds, and universities. Smart, passionate about data analytics, AI, and blockchain, and completely unsure about how to navigate their next steps.

Not because they lack technical skill. Because nobody's connected that skill to a path forward. They're waiting for a framework that doesn't exist yet in their world.

The Generational Split Nobody Acknowledged

Here's what I noticed across the whole event: there's a generational divide that went unspoken.

The younger participants are brilliant and eager and drawn to the energy of AI but don't see any means of engaging with it responsibly. The technology is exciting; the pathways are unclear.

The more experienced participants are accomplished and risk-aware, but overwhelmed by the speed and genuinely afraid of liability exposure. "No agentic actions without humans at the kill switch" even though everyone knows that certain actions (like trading) need to move faster than human speeds.

I heard people talk about accomplishing a quarter's worth of goals in a two-week sprint. And yet the consensus was that enterprises still take eight to ten months just to approve a new AI model.

Both of those things are true at the same time. And nobody had a way to bridge the gap.

The Missing Wall

Every talk I attended, every panel discussion, everyone was essentially building the same house. But from my perspective, there was a load-bearing wall missing. And nobody could see it, because they were standing on the technical side.

What's missing is the human side.

Organizations are investing heavily in infrastructure, models, and tooling. They're building technical trust through cryptographic proofs and trusted execution environments. But they're not measuring how their people actually collaborate with AI:

  • What happens when AI gives a confidently wrong answer?
  • Do they verify or defer?
  • Do they catch the error or pass it on until it compounds?

What PAICE Measures

PAICE measures how people actually collaborate with AI. Not what they know about it, not whether they've completed a training module, but what they do when the stakes matter.

At NEARCON, I released a new whitepaper on verifiable People+AI collaboration architecture that proves (not promises, proves) that nobody including employers can see your conversational data, and that assessment scores can't be tampered with after the fact.

But the architecture is only half the story. The other half is that your people need measurement and development—not on their AI knowledge, but on their AI behavior.

Seeking Pilot Partnerships

We're seeking pilot partnerships now. Here's what you get:

  • A behavioral capability baseline showing what your people actually do with AI
  • Visibility into where the gaps are and where risks are hiding
  • Governance-grade results you can defend

What you don't need:

  • No accounts required
  • No personal data collected
  • No integrations required
  • Twenty-five minutes per person

I had over a dozen meaningful conversations at this one event—with leaders, researchers, investors, and students—all trying to figure out their future. Every conversation came back to the same place: We've built the technical layer. Now what?

That "now what?" is your people. Measuring their work respectfully, developing their skills honestly, and literally proving what they're ready for—and what they're not. That's what I built: the human side.

The best way to find out if it fits your organization is the same way everything good happens at a conference: a straightforward conversation between people who care about getting this right.

I'm here. Let's talk.

Get Involved:

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