Meet the Founder: Why I Built PAICE.work And Where It’s Going

From Sam Rogers:

As much as I love writing, I do not naturally enjoy writing my own bio.

If the work could speak for itself forever, I would gladly stay behind the curtain. But if you are going to trust PAICE with your team’s AI behavior data, or consider investing in this company, you deserve to know who is behind it, what I care about, how I think about AI risk, and where I come from.

This is that story, told in practical terms. But unlike the 40+ daily blog posts here, this one is almost completely AI generated. I validated and made only the few necessary edits. ChatGPT knows me well.

From stage lights to systems and outcomes

My career started under stage lights, not in a pitch deck.

I grew up in the San Francisco Bay Area and spent my early adult years as a performer. At different times I was a Star Trek alien at a theme park, one of the top beatboxers in the United States, and an international award winning solo vocal jazz act. I produced and directed for stage and screen and learned how to read a room in real time.

That environment is unforgiving in a useful way. If the story does not land, if the structure is confusing, you know immediately. There is no “we’ll fix it in post.”

In parallel, I became obsessed with what makes a show consistently work behind the scenes. Signal chains. Stage plots. Cues. The systems that either support performers or sabotage them. I learned an early lesson that still drives PAICE:

Most failures are not about talent. They are about systems and clarity.

Twenty-five years at the intersection of learning, systems, and risk

Over time the backstage work pulled me away from the stage.

For the last 25 years I have worked in Learning and Development, media production, and technology, largely helping organizations build training and enablement ecosystems that actually work in practice.

Along the way I have:

Built certification and enablement programs for companies including YouTube
Led training systems migrations and LMS integrations at organizations like ADP, Capital One, Robert Half International, and others
Designed and implemented complex integrations in regulated environments, such as a DMS to LMS integration for Convatec
Served as Global Learning Technology and Analytics Manager, responsible for the “mess in the middle” where process, data, and platforms meet

I also founded Snap Synapse, a consulting studio focused on connecting systems, teams, and data so that performance actually improves instead of just being reported differently.

In almost every engagement I ended up in the same position: the person who has to quietly make sure things work, because failure is not an option and excuses are not helpful.

This experience matters for PAICE because it frames how I see AI adoption. Not as "cool tools" or "innovation theater," but as a performance and risk management problem that lives deep in real workflows.

A hard privacy lesson that shaped the product

There is another piece of my background that is directly relevant to how PAICE is designed.

Years ago I believed strongly in radical transparency. I ran a public blog and shared my thinking openly across social media, assuming that honesty plus context would be an asset.

Then I served as an expert witness in a final case that did not go well. My public writing was used against me. I was disqualified, not for any lack of expertise, but because my willingness to be transparent did not align with how the process defined “neutrality.” I also handled several other eye-opening experiences of transparency blowback for myself and others close to me.

The lessons were painful and clarifying. I tore down much of my digital footprint and studied privacy and data protection in a more serious way. I still value transparency, but I no longer treat it as costless.

PAICE is designed around that scar tissue. We measure how people work with AI without asking them to paste in sensitive content or hand over their identity as the price of participating. That is not a marketing line. It is a design constraint.

Why AI onramps for humans

I have been hands on with AI and ML tools for years. I started experimenting with them long before the current hype cycle, and I have been running local models since 2022. When the broader "ChatGPT moment" arrived, most of my work conversation shifted to one topic:

“How do we help regular people use this responsibly and effectively in real work, not as a science fair project?”

The pattern I saw repeatedly:

Leaders knew AI was important but lacked visibility into how their people were actually using it
Teams were improvising with powerful tools in ways that mixed real value with real risk
“AI readiness” conversations revolved around self assessment surveys and tool adoption dashboards, not observable behavior

I started building what I call “AI onramps for humans” to address this. Not grand theory. Not abstract ethics debates. Concrete, work shaped scenarios where you can observe how someone actually collaborates with AI, and help them improve.

Those onramps show up in three main places:

Snap Synapse, my consulting studio for AI enabled transformation
Signals & Subtractions, a weekly newsletter that surfaces one signal, one human prompt, and one subtraction opportunity for leaders
PAICE.work, a public benefit corporation focused on measuring and improving People + AI collaboration effectiveness

PAICE is the productized, scalable version of that work.

The problem PAICE is built to solve

If you are an operator or investor, here is the problem in plain language.

Organizations are scaling AI use faster than they are building governance, shared practice, or measurable capability. They do not know, with any precision:

How their people actually use AI in day to day work
Where the real value is being created
Where privacy, compliance, and reputational risk are accumulating
Which teams are quietly ahead or behind the curve

The market has responded with AI “maturity models,” surveys, and dashboards that often rely heavily on self reported confidence and tool usage logs. These are easy to sell and hard to trust.

PAICE takes a different approach:

Behavioral not aspirational: We look at what people do in realistic scenarios, not just what they say about AI
Contextual not generic: Assessments are framed around actual work patterns and constraints, not abstract prompts
Evidence ready for governance: Outputs are structured so that risk, capability, and opportunity can be discussed in the same frame

The result is a standard and a scoring system that gives:

Individuals a clear sense of their real AI collaboration strengths and gaps
Teams a shared language and set of patterns to improve together
Leaders a governance ready view of AI capability across their organization without harvesting sensitive data

Why a public benefit corporation is an asset, not a handicap

PAICE.work is structured as a public benefit corporation. That is a deliberate choice with clear implications for investors.

The public benefit I wrote into the company’s charter is to make AI collaboration measurable, teachable, and governable in ways that protect human agency.

Practically, that means:

We commit to product decisions that align with long term trust and safety, not just short term adoption metrics
We create a clear, defensible stance on privacy and data handling for our customers and their employees
We align with the direction of regulation and public expectation instead of fighting it

From an investor perspective, this structure:

Creates brand and trust moat in a sensitive category
Reduces long term regulatory and reputational risk
Attracts customers who need to show their boards and regulators that they are treating AI risk seriously

PAICE is not a non-profit. It must become a healthy, profitable company. The PBC structure simply codifies constraints that most responsible AI companies will end up operating under anyway. I chose to be explicit about it.

How I think about capital and execution

Signals & Subtractions, the newsletter I publish every Monday, is a good window into how I think about leverage and capital.

Each issue follows the same pattern:

One strategic signal
One human prompt
One subtraction opportunity

The subtraction piece is not a slogan. It is a discipline. In both consulting and product work, I have seen more risk created by “just one more thing” than by any deliberate decision to stop.

That discipline applies to PAICE as a business:

We focus on a very specific problem: measuring and improving People + AI collaboration in real workflows
We prioritize depth with a clear customer profile over chasing every AI-related trend
We design for capital efficiency: pilots that generate learning and reference value, reusable patterns across industries, and a product that fits into existing governance and enablement motions

I am not interested in "growth at any cost." I am interested in durable capability that organizations will still need five and ten years from now, even as individual tools change.

What we have actually done so far

Since launching PAICE.work as a distinct entity a month ago, we have:

Incorporated as a Delaware public benefit corporation
Published a detailed whitepaper describing the PAICE standard and its behavioral focus
Run research preview assessments to refine scoring and scenario design
Built an early pipeline of pilots with organizations that already have meaningful AI activity inside their walls
Published a daily blog series on PAICE, including this post, to make our thinking and design choices legible
Continued to grow Signals & Subtractions as a context engine for leaders navigating AI adoption

On the consulting side through Snap Synapse, I continue to help organizations implement AI in their learning and operations ecosystems. Every other month or so, I facilitate a multi-day workshop on applying AI for ATD (The Association for Talent and Development), which I consider to be paid market research. This work feeds directly into PAICE's understanding of real constraints and use cases inside organizations across a wide array industries.

What the next chapter looks like

In the near term, PAICE is focused on three things:

High fit pilots
Partnering with organizations that already have 20 to 100 people using AI at work and need clear insight into value and risk.
Standard maturation
Continuing to refine the PAICE standard with real behavioral data while keeping privacy safeguards intact.
Go to market foundations
Building the integrations, partner relationships, and success stories that make PAICE an obvious choice for leaders who need evidence instead of assumptions.

Behind all of that is a simple commitment: to be a trustworthy steward of the data, the people, and the capital entrusted to this company.

Why this founder and this company

If you have read this far, you are probably evaluating more than product features. You are evaluating whether the founder is aligned with the kind of outcome you care about.

Here is the short version.

I have spent decades in the messy middle where technology, people, and risk collide, and I know how fragile trust can be
I have lived the downsides of naive transparency and built PAICE with those lessons baked in
I am comfortable in front of a room, but my default orientation is backstage: making sure the systems actually work
I care more about measurable capability, governance, and human agency than hype cycles

If you are a leader or investor who wants AI to be powerful and governed instead of powerful and uncontrolled, PAICE is designed for you.

And now you know a bit more about the human responsible for building it. From AI, of course.

Sam here again to add one more thing I care deeply about.

My whole life and career has been about learning new skills and helping other people do the same. I have come to believe that our ability to collaborate effectively with AI is about to become the meta skill underneath everything else. It shapes how fast we can learn, how we make decisions, how we design systems, and how we protect what matters.

If someone else had already built a reliable way to measure that collaboration skill, I would gladly be backing their work and moving on to other adventures. They did not. Eventually, I felt compelled to act.

I am highly motivated to scale PAICE as quickly and responsibly as possible so that we can move past the usual snake oil phase of the hype cycle. New possibilities open up on the other side of mature AI collaboration. My calling is to help organizations reach that side with their integrity, people, and reputation intact.

Ready to assess your AI collaboration capabilities? Take the free PAICE assessment to get personalized insights and recommendations.

Meet the Founder