Kevin Payne

GTM Engineer at LawVu | Building Revenue Systems For Impact Startups | A16z, YC & Techstars Portfolio

From the intersection of AI, Startups, and Web3. The future of tech belongs to impact driven creators and operators.

In 2020, I was a full-time contributor for publications with 200+ bylines in HubSpot, Crunchbase, Blavity and major tech publications. In 2025, I joined a Series A/B SaaS company as a GTM Engineer building AI-powered revenue systems.

Most operators choose one path: stay employed OR go solo. I'm proving you can do both without burnout. Build a portfolio career and diversify your sources of income one small bet at a time.

How I can help...

FOR CREATORS: If you're building a personal brand or content business, my frameworks show you how to learn in public (Learn > Create > Share) and monetize your expertise through the 5C model. Join 23K+ creators who follow my systems for scalable content and portfolio income.

FOR OPERATORS: If you're at a Series A/B company wanting to build portfolio income alongside your full-time employment, I show you how to prototype revenue systems in your operator role, document what works, then package that knowledge into content and coaching offers.

My Approach: I don't consult from the sidelines. I prototype AI systems in my operator role, document what works, then teach operators how to implement for founders. Learn as an operator, share as a creator.

Current Focus: Building portfolio career through the 5C model:

- Clone
- Content
- Courses
- Community
- Conferences

Learning in Public: Learn in operator role > Create content documenting what works > Share with operators and founders. Get paid to learn, get paid to share.

Three Core Interests:

1. The Operator Stack: Leverage AI systems for sustainable growth
2. Portfolio Income: Small bets optimized for social impact
3. The Five-Pillars: Integrated life philosophy

Meta-Advantage: Teaching AI workforce implementation while using AI workforce to scale personal brand… authentic proof-of-concept.

Join 1K+ creators and founders getting their minds renewed. No fluff, just Growth Guides that merge biblical wisdom with proven AI systems.

Subscribe at https://substack.com/@kevintpayne or follow @kevintpayne.

Founder

Building a portfolio of digital real estate. Everyone’s familiar with real estate investments. It’s as simple as buying a house or land to rent or sell for profit. 

Digital real estate works the same way. Every domain name, every website and every blog you see online is internet property. They represent digital real estate you can buy and sell just like houses, apartments and lots.

I specialize in writing and speaking about solopreneurship, artificial intelligence, and minimalism. My mission is to document my journey and impart wisdom pertaining to wealth and health.

Efficiency is the wrong AI goal.

Every AI use case gets pitched the same way.

Save time. Reduce headcount. Cut costs.

That frame is missing something important.

Efficiency is a feature.

The most durable operator value isn't efficiency.

It's trust. Judgment. Reputation.

None of those compound through automation.

Two types of AI adoption:

Sustainable leverage: doing things that weren't possible before -- research at scale, personalization that couldn't exist manually.

Extractive automation: replacing the thinking that makes you irreplaceable.

The difference shows up slowly.

Year 1: both look like productivity.

Year 3: sustainable leverage has compounded your judgment and audience. Extractive automation removed the edges that made you worth trusting.

The operators winning in 2026 aren't the most automated.

They're the ones whose AI stacks make their judgment more visible, not less.

The AI runs the process. The operator owns the thinking.

This matters differently if you're a minority operator.

The systems you build carry your perspective. The governance reflects your values. The frameworks shape how your team thinks after you've moved on.

That's stewardship.

What's your AI north star? Speed, quality, scale, or something else?

Drop it below.

Curious whether the efficiency-first frame resonates or if practitioners are building toward something different.

GTM Engineer at LawVu | Building AI-Powered Systems | 200+ Publication Bylines | Operator at A16z, YC & Techstars Startups

A young man stands in a futuristic setting, surrounded by contrasting themes of automation, trust, and efficiency in AI use cases.

AI handles the labor. I control the thinking.

That's the operating model I'm building in 2026.

Not AI doing my work. AI multiplying my capacity to do work that actually matters.

There's a version of AI adoption that looks productive but isn't:

Using AI to skip the hard parts.

Draft this. Summarize that. Reply for me.

Faster output. Shallower thinking.

In 12 months, you've optimized out the skill that made you valuable.

The version I'm testing:

AI handles: research synthesis, structure, repurposing, distribution.

I handle: frameworks from 3 years of operator experience, insights from rooms I was actually in, connections no dataset contains.

Things AI can't manufacture yet.

The operators doing this right aren't producing content faster.

They're producing thinking that can be distributed at scale.

AI-powered content machine vs. AI-powered thinking distribution system.

Very different outcomes in 3 years.

There's a long-form resurgence happening.

People aren't just tired of short-form. They're hungry for thinking.

Not output. Thinking.

AI made shallow content so cheap that depth became the signal.

The operators who develop their judgment and use AI to distribute it will look like a different category entirely.

Judgment compresses years of experience into a post.

AI gets it in front of the right people.

What's one thing you refuse to let AI do for you?

Reply below.

Curious where practitioners are drawing the line, especially in high-stakes operator roles.

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Skipping human-in-loop will cost you.

Every operator I have seen get burned by AI went straight from manual to autonomous.

Here is the framework that changes that.

There are three stages to responsible AI implementation:

→ Manual: you own every step, document what you do
→ Human-in-Loop: AI drafts, you review, you approve
→ Fully Autonomous: AI runs the process end to end

Most teams try to skip straight to stage three.

That is where the failures happen.

When you skip to autonomous without the middle stage, you lose three things:

→ The ability to catch bad outputs before they compound
→ The trust of your stakeholders when something goes wrong
→ The documentation trail that tells you where the system broke

You also cannot explain to leadership what the agent is doing or why.

The human-in-loop stage is not a slowdown.

It is where you learn what the AI gets wrong consistently, where you discover the edge cases your SOP did not cover, and where you build the governance layer that makes autonomous operation safe later.

That knowledge cannot be skipped. It has to be earned.

As an operator, every AI deployment runs this sequence:

→ Week 1 to 2: Manual baseline. Document every step, every decision, every exception.
→ Week 3 to 6: Human-in-loop. AI executes, I review before outputs go live.
→ Week 7+: Autonomous only if the error rate is within an accepted range.

The baseline from Week 1 is what makes Week 3 to 6 possible.

The objections you will hear most: "This is too slow. We need to move faster."

The response: moving fast into a broken autonomous system does not save time.

It creates a failure event that costs 3x as long to unwind, plus the trust rebuild with stakeholders.

The three-stage model is the faster path to durable automation.

Before your next AI deployment, answer three questions:

→ Have I documented the manual process completely?
→ Do I have a human review checkpoint before outputs leave the system?
→ What does an acceptable error rate look like before I remove the checkpoint?

If you cannot answer all three, you are not ready for autonomous.

Save this thread before your next AI project kickoff.

And drop in the comments: which stage of this model is hardest for your team to commit to and why? Manual, human-in-loop, or fully autonomous?

Let's build on it together.

A man in a gray blazer works on a laptop at a marble table in a stylish hotel lobby, surrounded by other guests and a concierge.

Want more trust? Stop asking for meetings.
Start shipping weekly clarity.

I haven't requested a status meeting in months.

My stakeholders know exactly what I'm working on, what's at risk, and what I need.

Here's the format I use:

Most operators earn trust by being available.

That is the wrong model.

Available means reactive. Reactive means you are always catching up.

The highest-trust operators are the ones whose stakeholders never have to wonder what is happening.

Meetings feel like trust. They are usually the opposite.

If your manager has to schedule time with you to understand your progress, that is a gap in your communication system. Not a relationship moment.

The weekly impact report closes that gap before it opens.

My report has 5 sections. Takes 20 minutes to write. Sends on Fridays.

This Week's Output. Metrics That Moved. Blockers and Risks. What I Need. Next Week's Focus.

That is it. No narrative. No filler. No "I've been very busy."

This Week's Output.

Not activities. Not effort. Output.

"Launched X." "Completed Y." "Shipped Z."

If you can't list 3 completed outputs on a Friday, that is information worth knowing too.

The discipline of writing this section forces prioritization all week.

Metrics That Moved.

Pick 2 to 3 KPIs relevant to your work. Report the number. Report the delta.

That is one line per metric. It tells your stakeholder where you are, where you were, and where you are going.

Numbers are trust signals. Narrative is a substitute for them.

Blockers and Risks.

If something is going sideways, your stakeholder should read about it here before they hear about it elsewhere.

What I Need.

This is where you make cross-functional asks without scheduling a meeting.

One line. Clear owner. Clear deadline. Done.

Next Week's Focus.

Two priorities. No more.

This signals intentionality. It also creates a natural accountability loop for the following Friday.

If you said you were focused on X and Y, then X and Y better show up in next week's Output section.

The weekly impact report is not a status update.

It is a trust infrastructure document.

It replaces uncertainty with clarity. It replaces interruptions with rhythm. It replaces availability theater with actual output.

High-trust operators do not get asked for more meetings. They get more autonomy.

I share operator frameworks like this every week, built from real GTM and RevOps experience.

Follow along if you want to build indispensability through systems, not hustle.

What does your current stakeholder communication look like? Drop it below.

Your AI is not slow.

It is stateless.

Every session starts from zero. No memory of what you built yesterday. No context from last week. Just you, re-explaining yourself to a blank screen.

That is not an AI problem. That is a systems problem.

Most people use AI like a vending machine.

Request in. Output out. Close the tab.

The output is fine. But nothing compounds.

You are getting the speed of AI with none of the leverage.

The fix is not a better prompt.

It is a knowledge base your AI reads before it responds.

One that updates as you work. So session 90 builds on session 1 instead of starting over again.

Three tools make this work.

Tool 1: Wispr Flow

One keyboard shortcut. You speak. Text appears wherever your cursor is.

No cloud. No upload. No lag.

Its only job: eliminate the friction between thinking and recording.

Most insights disappear because capturing them costs just enough effort to skip.

Tool 2: Notion

Not a notes app. Your AI's external brain.

Every insight you capture, every decision you document, every framework you build lives here in structured databases.

Connected to Claude via MCP, Notion gets searched before Claude drafts a single word.

Tool 3: Claude

Takes your raw capture, retrieves what Notion knows about you, and returns output calibrated to everything you have already built.

No blank page paralysis. No context re-explaining. No first drafts that ignore your existing thinking.

The knowledge base feeds every session.

But the system compounds fastest when agents run the maintenance.

Notion just launched Custom Agents. Five worth building immediately, ranked by impact:

1. Inbox and Request Triage
2. Meeting Notes to Tasks
3. Weekly Status Report Generator
4. Slack Q&A Agent
5. Knowledge Drift Auditor

The compounding logic:

Triage Agent routes clean work in
→ Meeting Agent converts conversations to tasks
→ Status Agent synthesizes both
→ Q&A Agent answers from the maintained knowledge base
→ Drift Auditor keeps the foundation trustworthy

Each one makes the others more accurate.

There are two types of knowledge base.

Static: fixed at upload, requires manual rebuild when your thinking evolves. Good for reference material.

Living: updates as you work. After 90 days Claude applies your frameworks without prompting. After 6 months it flags when a new idea contradicts a prior decision.

You want the living version.

The full breakdown is in this week's newsletter.

Three tools. Five agents. Seven failure modes. Thirty-minute setup guide.

If you want your AI to actually get smarter over time, this is the architecture.

check out my Substack article: https://lnkd.in/g2GfRFqC

What is your current AI setup missing most: capture, memory, or synthesis?

A man stands in a neon-lit alley with malfunctioning screens, reflecting themes of technology, memory, and identity.

Most organisations believe they’re compliant on slips — until the next claim lands.

The procedures are there.
The signage is out.
The cleaning is done.
Yet incidents continue, claims rise, and costs follow.

It doesn’t have to be this way.

With the right, evidence-based approach, slip accidents can be reduced by 57%+, and risks controlled at a level that is defensible to insurers, auditors, and the courts... when it matters most.

My focus on slip risk began after a life-changing accident where a vehicle mounted the kerb and ran me over. That experience shaped a simple mission: reduce the 500,000+ avoidable slip accidents that happen every year.

Today, I help organisations in leisure, retail, healthcare, manufacturing, transport, and hospitality to:
• Reduce slip accidents
• Defend and avoid costly claims
• Gain confidence their floors are safe, compliant, and provable

How we do it

Diagnose
Quantify real slip risk using HSE-approved pendulum slip testing.

Solve
Apply targeted anti-slip treatments to make unsafe floors safe — even when wet.

Maintain
Keep floors safe over time through planned preventive maintenance, supported by insurer- and court-ready certification.

This approach is already saving clients £10m+ per year in avoided claims, while delivering safer environments for staff and the public.

I’m also the Amazon #1 best-selling author of Prevent Slip Accidents With Slipology and host of the Safety & Risk Success Podcast, where I explore how better risk thinking leads to better outcomes.

If reducing incidents, protecting people, and controlling costs matters to you, the best next step is simple:

👉 Explore the free tools in my Featured section or send me a DM.

Christian Harris

Sunday’s top 8 slips 😅

Because if you can’t laugh and learn, you’re missing half the value.

Enjoy the tumbles, then remember:

Performance > Perception.

#SafetyHumour #SlipSafety #ChristianHarris

Reducing Slip Risk, Defending Claims & Cutting Costs | Evidence-Based Slip Testing & Anti-Slip Treatments for Safer, Defensible Floors | Founder, Slip Safety Services

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