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 and 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.

Most Series A/B startups obsess over GTM systems but ignore the operational bottleneck quietly killing velocity: legal workflows.

When you're scaling from 20 to 200 people, this is what breaks:

→ Contract reviews pile up (sales waiting days for redlines)
→ NDAs get lost in email threads (prospects go cold)
→ Compliance questions slow product launches
→ No one knows where the signed MSA actually lives

Your revenue team is ready to scale. Your legal ops? Still running on email and shared drives.

The companies that win apply the same systems thinking to legal operations that they apply to RevOps:

• Identify inefficiencies (where's time bleeding?)
• Score impact vs effort (what moves the needle?)
• Align stakeholders (legal AND business priorities)
• Build the business case (ROI, not feature lists)

That's what this webinar covers.

Legal Tech Readiness Scorecard Workshop

Hosted by LawVu.

Wednesday, Jan 28 | 9am PT / 12pm ET / 5pm GMT

Neil Smith walks through a practical framework for:

✓ Mapping your biggest legal time traps
✓ Scoring workflow inefficiencies
✓ Aligning legal priorities with business needs
✓ Turning readiness scores into actionable next steps

No vendor pitch. Just a repeatable system you can use immediately.

If you're building systems that scale revenue, you need legal ops that don't slow you down.

Register: https://lnkd.in/gkWi6S9J

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Newsletter

Free Press

Every week I write a newsletter covering a SaaS bootstrapped founder's journey to acquire their first 1,000 customers. 

You're eligible for free press if:

1. You're a bootstrapped founder
2. You have achieved a $10K+ MRR
3. You've acquired 1K or more customers
4. You have time for a 60-minute coffee chat

Simply respond by saying "eligible and interested."


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

Building AI agents backward? 

Starting with tools, copying prompts, and then wondering why it all sounds like garbage?

The real issue isn't downstream... it's upstream.

Discover the game-changing framework here:

Your AI agents are mirrors.

They reflect your clarity of thought, your decision-making frameworks, and your values... whether you've articulated them or not.

Feed them confusion, and you get confident noise at scale.

I call this the Confident Idiot Problem.

AI tools are incredible at sounding certain. They deliver mediocre ideas with Fortune 500 conviction.

If you don't have your own convictions locked in, you mistake that confidence for quality.

The fix isn't better prompts.
The fix is clearer thinking.

Before I write a single prompt, I articulate:
→ What I've learned through direct experience
→ What I believe that peers would disagree with
→ What I refuse to do, even for money

Opinions vs. Convictions:

- **Opinion:** "AI will transform business."
- **Conviction:** "I only build AI that augments human judgment. Autonomous AI without oversight creates fragile systems."

One is a position you hold.
The other is a position that holds you.

The Conviction Stack has three layers:

1. **Identity** - Who am I?
2. **Framework** - How do I think?
3. **Output** - What does quality look like?

Most operators only provide AI with Layer 3, similar to asking someone to paint your house without specifying the color.

Here's the Conviction Audit:

- What have you learned the hard way? (Earned beliefs)
- What do you believe that peers would disagree with?
- What would you refuse to do, even for money?
- What frameworks do you use repeatedly?

Document everything.

Compile this into an Operating Philosophy.

~2,000 words that capture:

Who you are
How you think
What quality looks like
What you won't do

This becomes the master prompt for every AI system you build.


**Old approach:**
- Copy templates → Generic results

**New approach:**
- Load Operating Philosophy → AI uses your mindset for solutions

Same tools, but conviction transforms outcomes.

AI is your ultimate leverage tool, magnifying whatever you pour into it.

Fuel it with clarity → Watch your returns skyrocket.
Feed it confusion → Scale up a symphony of confident noise.

Dive into the full Conviction Audit framework here:

https://lnkd.in/g2GfRFqC

Urban night market with neon signs; a man stares at a holographic, digitized reflection displaying neural network visuals and code.

Justin Welsh wrote something that stopped me mid-scroll:

"Nobody is coming to save you."

My first reaction? Honestly, it stung. But sitting with it changed how I think about building.

Here's what I've come to understand (and why it's actually hopeful):

I get why this hits hard. We grow up believing someone will notice our work, that the right opportunity will find us, and that if we just keep showing up, we'll be rewarded.

And sometimes that happens.

But betting your future on "sometimes" is a fragile strategy.

Here's the thing nobody tells you:

Waiting to be discovered is exhausting.

The anxiety of hoping someone notices.
The disappointment when they don't.
The feeling that your worth depends on external validation.

That's not security. That's dependency.

What shifted for me:

"Nobody is coming to save you" isn't a threat. It's permission.

Permission to stop waiting.
Permission to build something yours.
Permission to bet on yourself instead of hoping others will.

That's actually freedom.

The real safety net isn't a job title or a platform's algorithm. It's:

- Skills that travel with you
- An audience that knows YOUR name
- Income streams you control
- Systems that work while you rest

These don't require anyone's permission to build.

I know what you might be thinking:

"But I have a job. I have responsibilities. I can't just leap."

You don't have to leap. You can build alongside. One hour a day. One post at a time. One small asset that compounds.

The goal isn't to escape. It's to have options.

What I'm building (slowly, alongside everything else):

- A newsletter that grows whether I'm "on" or not
- Content that compounds over months and years
- Skills that help people regardless of my employer

Not an escape plan. An expansion plan.

The mindset that helps me:

From: "How do I make sure I'm never vulnerable?"
To: "How do I build so that vulnerability doesn't break me?"

You can't prevent hard seasons, but you can build resilience that carries you through them. That's what this work is really about.

"Nobody is coming to save you" sounds lonely at first.
But flip it: nobody can stop you either.

No gatekeeper to convince.
No permission to request.
No ceiling someone else installed.

The path is open.
And you're more capable of walking it than you think.

Most creators separate learning from earning. I don't.

Here's the framework that lets me get paid to learn AND get paid to teach what I learned:

SODOTO:

- Solve your own problem
- Document the solution
- Teach others
- Monetize
- Repeat

Each step funds the next.

How it works in practice:

I'm a GTM Engineer at a B2B SaaS company. Every week, I solve real problems: AI automation, RevOps workflows, outbound systems.

These aren't theoretical... they're live projects with real stakes.

Step 1: SOLVE

I don't manufacture problems to write about.
I document what I'm already doing.

Real projects. Real deadlines. Real learning curve.

Step 2: DOCUMENT

While solving, I capture everything:

• Personal notes
• Decision points
• What failed
• What worked

Takes 10 extra minutes. Creates week of content.

My notes inspire newsletter drafts.

Step 3: TEACH

Every newsletter starts with a problem I actually solved.

Then I break it down so someone else can replicate it.

One newsletter = one solved problem = one teaching moment.

Step 4: MONETIZE

The newsletter builds an audience.

Audience attracts sponsors.

Content sponsorships turn your expertise into income without selling your time.

Same knowledge. Leveraged distribution.

Step 5: REPEAT

Each project creates new problems.

New problems create new solutions.

New solutions create new content.

The flywheel never stops because I'm always working on something real.

The repurposing stack:

Newsletter (deep dive)
  ↓
LinkedIn post (key insight)
  ↓
X thread (step-by-step)
  ↓
Threads (punchy version)

One solved problem = 4+ pieces of content.

The secret nobody talks about:

I'm not an expert waiting to teach.

I'm a student documenting the journey.

Learning in public. Applying in real projects. Sharing what works.

You don't need credentials. You need curiosity.


Transform your job into a content powerhouse and your content into growth guides with SODOTO.

You're a problem-solving pro. Now, start capturing those moments.

Which solution did you find this week that others would pay to master?

Ditch the cookie-cutter GTM playbook.

After 2 years of crafting a universal signal infrastructure, I've cracked the code.

No matter your vendor, this tool-agnostic framework will give you the edge. Ready to level up?

The problem: Most GTM teams are signal-poor.

They invest in intent tools, get "in-market" lists, and blast generic outreach. Reply rates stay flat.

The issue isn't the data; it's the depth of the data.

Typical intent tools track:
→ Website visits
→ Keyword research
→ Buying stage scores

What they miss:

→ M&A announcements
→ Champion job changes
→ New leadership hires
→ Funding rounds

That gap is your edge.

The Signal Hierarchy:

Tier 1: First-party (you own it)
Tier 2: Second-party (you have access)
Tier 3: Third-party (you rent it)

First-party tells you WHO is looking.
Third-party tells you WHY now.

Layer all three. That's the magic.

Playbook That Works: M&A Signal

Trigger: Company completes an acquisition
Timing: 30-60 days post-announcement
Angle: "Post-merger integration support"

While competitors send out generic emails, you're addressing the specific business event that created the buying opportunity.

Champion tracking is underrated.

Your customer's employee moves to a new company. They already know your product. They already trust you.

Problem: LinkedIn updates lag 3 months.
Solution: Real-time job change APIs.

Warm intros convert 5-10x better than cold outreach.

My controversial take:

Stop evaluating tools by feature lists.

Start evaluating by integration criteria:

→ API (composable workflows)
→ MCP (AI agent access)
→ SOC2 (enterprise ready)

If a tool fails any of these, think twice.

The automation progression:

- Stage 1: Human-in-the-loop (learn edge cases)
- Stage 2: Agentic workflows (AI drafts, humans approve)
- Stage 3: Fully autonomous (after extensive testing)

Shortcuts create chaos. Patience creates systems.

The architecture that scales:

- Signal Layer
- Enrichment Layer
- Personalization Layer
- Orchestration Layer
- CRM

The CRM is the only permanent fixture. Everything else is interchangeable. That's the point.

Your intent data provider isn't your competitive advantage; your signal infrastructure is.

Strategy first. Tools second.

This week, audit your stack. How many of the 20+ signal types are you actually capturing?

Dive into the entire growth guide here: 

https://lnkd.in/g-whPnTv

Few truly grasp Elon Musk's mindset. While many get lost in the chaos and headlines, they overlook the five core principles that empower him to juggle rockets, cars, AI labs, and brain tech all at once.

Discover his secrets and learn to apply them without derailing your own life.

Mechanism 1: Maniacal Sense of Urgency

Urgency = compressing the feedback loop between problem and solution.

2018 Tesla production hell: Most CEOs email managers. Elon slept under his desk on the factory floor until the bottleneck was fixed.

Traditional: Problem → Meeting → Test (3 weeks)

Elon: Problem → Test (3 hours)

100x compression.

Your move: Compress one project timeline by 80%.

Make decisions with 80% of data. Most are two-way doors.

Where competitors make 1 decision in 2 weeks, you make 100.

Decision velocity is your advantage.

Mechanism 2: The Algorithm

Elon's 5-step algorithm (applied to everything):

• Question every requirement
• Delete parts of the process
• Simplify what remains
• Accelerate cycle time
• Automate last

Most companies automate too fast and drown in inherited complexity.

"That's how we've always done it" is a disease.

Mechanism 3: Build Unreasonable Teams

Elon doesn't hire for culture fit.

He hires aligned maniacs obsessed with the mission.

November 2022: "Fork in the Road" email to Twitter staff.

Hardcore mode or severance.

Great companies are built by unreasonable people doing unreasonable things.

Reasonable = consensus. Unreasonable = breakthroughs.

Mechanism 4: Iterate at Speed of Thought

SpaceX treats every launch like a data pipeline.

April 2023: Starship explodes 3 minutes after launch.

Media: "Disaster!"

Elon: "Congrats team. We launch again in 3 months."

NASA: 5 years, $20B, 10 cycles

SpaceX: 2 years, $3B, same result

Optimize for iteration, not perfection.

Every failure = data point.

Every data point = intelligence.

Every piece of intelligence = faster iteration.

When you optimize for perfection, you optimize for comfort.

When you optimize for iteration, you optimize for truth.

Ship messy. Learn fast. Iterate.

Mechanism 5: Scale Beyond Yourself

Elon doesn't run 6 companies. He designs operating systems with lieutenants.

Gwen Shotwell at SpaceX: 23 years of history. Started as engineer, now COO.

Great founders don't centralize power, they distribute effectively.

5-minute time blocking:

Mon/Tue: SpaceX
Wed/Thu: Tesla
Fri: Everything else

You don't need 5-minute blocks.

But you need to know:

• What only YOU can do
• What to delegate
• What to delete

If your business needs you to operate, you don't have a business.

You have a job.

The 5 mechanisms:

• Maniacal urgency (compress feedback loops)
• The algorithm (delete → simplify → automate)
• Unreasonable teams (mission-obsessed)
• Iterate at speed of thought (optimize for learning)
• Scale beyond yourself (distribute effectively)

Which one will you implement this week?

90% of AI implementations fail in the first 90 days.

But the 10% that succeed?

They all do one thing differently.

Here's how to be the 10%...

Every week I see companies announce "AI transformation":

"We're implementing AI agents!"
"We're automating outbound!"
"We're rebuilding with AI at the core!"

Six months later:

• AI produces garbage outputs
• Automation breaks constantly
• Team lost trust in "AI"

What went wrong?

They optimized for tools instead of systems.

Here's the uncomfortable truth:

If your CRM is a mess, your intent data is stale, and your outbound has 2% reply rates...

Adding AI won't fix it.

You'll just amplify the dysfunction faster.

The principle that saves everything:

"Optimize before you automate."

Broken process + AI = dysfunction at scale.
Optimized process + AI = compounding returns.

The 10% who succeed?

They fix the foundation before adding the intelligence layer.

Before adding AI, fix these:

CRM:
→ Lead sources tracked accurately?
→ Pipeline stages reflect reality?
→ Forecast you can trust?

Intent Data:
→ Scoring aligned with sales?
→ Signals trigger actions?

Sales Engagement:
→ Reply rates above 10%?

Be honest.

The sequence that works:

1. Optimize current stack (3-12 months)
→ Clean data, adoption, documented processes

2. Test agentic workflows (2-6 months)
→ Internal tasks, edge case discovery

3. Automate what's proven (ongoing)
→ Connect systems that work

Technology is 20% of AI adoption.
Behavior change is 80%.

The 10% who succeed build trust:

• Small pilots → prove value
• Document wins → show ROI
• Slow rollout → earn adoption
• Human oversight → maintain control

They don't mandate AI.

They earn it.

Enterprise reality vs startup advice:
SMB: "Just try the tool and see"

Enterprise: "Every tool needs 90-day security review"
SMB: "If it fails, pivot fast"

Enterprise: "Failed experiments cost 12 months of political capital"

Know your context.

You're ready for AI when:

✅ 90%+ team adoption of core tools
✅ 85%+ data quality score
✅ Each tool has documented ROI
✅ Processes are written SOPs
✅ Stakeholders agree stack is optimized

If you're not there yet?

That's not "falling behind."

That's building right.

Be the 10%:

Don't chase tools. Build systems.
Don't skip stages. Earn progression.
Don't optimize for speed. Optimize for compounding.

The question isn't "how fast can we deploy AI?"

It's "what foundation are we building on?"

Foundation first. AI second.

AI operators think in workflows, not words.

Stop tweaking prompts. Start designing systems.

Months of optimization led me to the breakthrough strategy of focusing on workflow design.

Here's how it reshaped the game:

Marginal gains. Infinite loops. Shaky outcomes.

Then it hit me: the flaw wasn’t in my prompts... it was in the architecture.

The Prompt Engineering Dilemma:

Solitary prompts are delicate. They shine in trials but crumble quietly in real-world use.

Context changes. Edge cases appear. The model confidently produces garbage.

You can't solve architectural problems through prompt engineering.

The Prompt Architecture Shift

Stop asking: "How do I improve this prompt?"

Start by asking: "How do I design a system for graceful prompt failures?"

This involves:

Decomposition

Break a complex task into simpler ones. Each prompt handles one function, and together they accomplish what a single prompt can't.

Validation Layers

The output of every prompt gets checked before the next step:

- Does this look right?
- Does it match the expected format?
- Does it contain the required elements?

Failures get caught. Reruns happen automatically. Bad outputs never propagate downstream.

Context Management

What information does this prompt actually need?

- Not everything.
- Not the whole document.
- Just the relevant context for this specific task.

Smaller context windows. More focused instructions. Better outputs.

Fallback Paths:

What's the plan for model errors?

Human help. New prompts. Quick recovery.

Our design handles setbacks. Failures are expected.

Prompt engineering refines; prompt architecture shapes the vision.

The other makes entire workflows reliably excellent.

The operators who win at AI aren't better at writing prompts; they're better at designing systems where prompts are just one component.

What's one workflow you've built that chains multiple AI calls together?

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This is what a Series A/B AI SDR engine looks like in 2026.

The teams winning aren't sending more emails. They're reaching prospects 2-3 days before competitors even know they're in-market.

Real-time intent is the new moat.

Here's the 6-layer architecture: 

The old playbook:

→ Buy static lead lists
→ Blast cold emails
→ Hope you catch them at the right time

The 2026 playbook:

→ Capture real-time intent signals
→ Reach them while they're actively researching
→ Beat competitors to the conversation

Speed to signal is everything now.

Layer 1: Real-Time Intent Signals

This is the competitive moat. When a prospect researches your category, you need to know within hours... not weeks.

Tools I've seen work:

→ 6sense (real-time intent + account identification)
→ Qualified (instant website visitor alerts)

First to the conversation wins.

Layer 2: AI Enrichment + Scoring

Raw signals need context. Fast tools I've seen work:

→ Clay (firmographics + technographics + news)
→ Apollo (contact data + org mapping)
→ ZoomInfo (enterprise-grade enrichment)

Key requirement: robust API for real-time data flow. No batch uploads.

Layer 3: AI-Assisted Sequencing

Speed matters here too. Signal → sequence in minutes, not days.

Tools I've seen work:

→ Outreach (real-time triggers + AI suggestions)
→ API-first architecture (connects to your signal layer)

If your sequencer can't trigger from live intent, you're already late.

Layer 4: Multi-Channel Orchestration

The channels need to talk in real-time. Architecture I've seen work:

→ Qualified triggers Slack alerts when target accounts hit the pricing page
→ Outreach fires sequence within the hour
→ LinkedIn touchpoints coordinated (not random)

Real-time signals demand a real-time response.

Layer 5: Enterprise-Grade Infrastructure

Series A/B can't ignore this anymore:

→ SOC2 compliance (non-negotiable for enterprise deals)
→ API-first architecture (tools must talk to each other)
→ MCP capabilities (AI agents need direct tool access)

Security + flexibility = scale.

→ Salesforce (multi-threading depth tracking)

The pattern: real-time data beats batch data at every layer of the funnel.

Layer 6: Deal Intelligence

Real-time signals don't stop at prospecting. Tools I've seen work:

→ Gong (call analysis + real-time coaching)
→ Salesforce (multi-threading depth tracking)

The pattern: real-time data beats batch data at every layer of the funnel.

Why real-time intent is the moat:

→ Prospects research for 2-3 weeks before talking to sales
→ First vendor in the conversation wins 60%+ of deals
→ Static lists mean you're 2 weeks late

The teams capturing signals in real-time are eating everyone else's lunch.

This is the stack I've seen Series A/B teams use to beat larger competitors.

Not more headcount. Not more emails. Just faster signal capture and real-time response.

Next step! building your own proprietary intent signals from scratch...

Follow for more GTM strategy posts .

Last week I talked about AI Ops, RevOps, and Growth Ops.

I showed you how to build systems that 10x revenue for your company.

But here's what I didn't tell you:

I was unemployed for 18 months while mastering these skills.

I could architect revenue systems that generated millions.

But nobody knew who I was.

That's when I realized something that changed everything.

Elena Verna talks about growth loops. Brian Balfour teaches GTM frameworks. 

But neither of them taught me this:

Operational excellence without public documentation is invisible leverage.

You're a genius at building systems for others.

You optimize pipelines.
You automate workflows.
You scale revenue.

But at the end of the day, that expertise lives inside company walls.

Nobody outside your org knows what you can do.

This week, I'm expanding beyond Ops.

Building a growth loop that turns your operational work into career leverage.

I call it the Creator-Operator Loop:

Learn (at your W2) - Solve real problems at scale
Build (systems that work) - Create frameworks from experience
Share (publicly) - Document what you're learning
Grow (authority + opportunities) - Attract relationships, speaking, etc.

Most operators only do the first two.
The ones building portfolio careers leverage do all four.

This is how you go from invisible operator to recognized authority.

From builder in the shadows to builder with an audience.

From single income stream to diversified career.

While keeping your day job.

What's your biggest challenge turning operational skills into public authority?

I just spent 3 hours testing Claude's new Co-Work feature.

This isn't AI that answers questions.

This is AI that controls your computer and executes tasks.

Here's what changed for GTM operators today:

Every AI tool you've used works like this:

You ask → AI answers → You copy → You paste → Repeat 50 times

Co-Work works like this:

You give task → AI makes plan → AI implements it → You review

This is the shift from assistant to operator.

This unlocks AI Ops (Automate) from my Four Impacts framework.

Before: AI suggested what to do
Now: AI does it for you

Organize 40 desktop files in 10 seconds.
Create reports from scratch in 2 minutes.

Deploy websites without code.

Test 1: Desktop Cleanup

I had 40 screenshots cluttering my desktop.

Told Co-Work: "Organize by date folders"

It analyzed, created folders, moved files automatically.

30-minute task → 30 seconds. Zero errors.

Test 2: Research Reports

Command: "Create a report on Claude Co-Work for an copywriting framework"

Co-Work researched, wrote, formatted, saved a 6-page document locally.

Took 3 minutes. Better quality than I expected.

This replaces hours of VA work.

Test 3: Website Prototyping

Built a landing page for my online portfolio.

Co-Work designed, coded HTML/CSS, deployed live.

8 minutes total. No coding knowledge required.

Just clear instructions and iteration.

Test 4: Social Media Posting

Told Co-Work to post an announcement to X.

It navigated browser, wrote post, and scheduled.

Ran in background while I kept working.

This is what virtual assistants used to do.

3 hours of testing Co-Work:

→ Organized 40 files (10 seconds
→ Created research report (3 minutes)
→ Built and deployed website (8 minutes)
→ Scheduled for social media (automated)

Time saved vs manual: 4+ hours

And this is day one with zero optimization.

Here's what matters for GTM operators:

Co-Work removes the technical barrier to AI automation.

No coding. No complex setup. Just clear instructions.

Series A/B teams can now compete with enterprise resources.

The gap just closed.

Question for GTM operators:

What's the one manual task eating up 5+ hours of your week right now?

Reply below and I'll show you how Co-Work could automate it.

Retweet if this changes how you think about AI automation.

I've watched $50K+ implementations crash and burn because teams skipped the basics.

Here's what nobody talks about:

Automation amplifies what's broken.

Let me show you the 9 patterns:

Pattern 1: Dirty CRM Data

AI enriches garbage records → More garbage, faster.
Fix: 85%+ data quality before automation.

Pattern 2: Undefined Lead Routing

AI creates leads with no destination.
Fix: Document routing rules + territory logic first.

Pattern 3: No Process Documentation

AI can't automate what isn't written down.
Fix: SOPs before sequences.

Pattern 4: Low Tool Adoption

AI pushes to tools nobody uses.
Fix: 90%+ team adoption of core stack first.

Tribal knowledge doesn't scale.

Pattern 5: Misaligned Scoring Models

AI prioritizes wrong leads → Reps lose trust.
Fix: Validate scoring with sales feedback first.

Pattern 6: Broken Integrations

AI triggers cascade of sync errors.
Fix: Two-way data sync working before automation.

Pattern 7: No Stakeholder Alignment

AI deployed → 50 people resist → Project dies.
Fix: Sales, Marketing, RevOps, IT buy-in at each stage.

Pattern 8: Missing Edge Case Library

AI breaks on exceptions nobody documented.
Fix: Pilot first, document edge cases.

Pattern 9: No Human-in-the-Loop Design

AI makes decisions that require human judgment.

Never automate:

→ Discovery calls
→ Crisis management
→ Strategic pivots
→ Deals over $100K (without review)

Technology amplifies human judgment.

It doesn't replace it.

The fix is simple (not easy):

Stage 1: Optimize current stack
→ Clean data, documented processes, tool adoption

Stage 2: Test agentic workflows
→ Internal tasks, edge case discovery, behavior change

Stage 3: Automate what's proven
→ Connect optimized systems

Enterprise reality vs startup advice:

❌ "Add AI first, optimize later"
✅ "Optimize first, AI accelerates what works"

❌ "Move fast, break things"
✅ "Breaking things = losing stakeholder trust for 12 months"

Different context. Different playbook.

James Clear said it best: "You do not rise to the level of your goals. You fall to the level of your systems."

Your goal: AI-powered GTM motion.

Your system: Enterprise tools configured correctly, adopted consistently, producing clean data.

The system compounds.

The bottom line:

Before you buy another AI tool, ask:

→ Is our data clean?
→ Are processes documented?
→ Do people use what we have?
→ Can we measure ROI?

If any answer is "no"...

Fix that first.

Automation amplifies. Choose what you amplify wisely.

The Momentum.io GTM AI Awards just exposed the biggest GTM mistake.

Companies aren't failing at AI adoption. They're failing at execution design.

What separated winners from the rest? One thing.

Hundreds of companies submitted. Only a few actually redesigned how work gets done.

Momentum's CEO: "Most say they're using AI, but very few have redesigned how work actually gets done."

That gap? It costs you everything.

The winners weren't using better AI tools.

They built GTM infrastructure where AI has a defined job and clear integration points.

AI as a tool ≠ AI as an operating layer.

Most teams are still doing the first. Winners moved to the second.

Winners had 4 things in common:

✓ AI-specific role definitions (who does what)
✓ Feedback loops (AI learns from outcomes)
✓ Measurable handoffs (human ↔ AI clarity)
✓ Regular audits (is it still working?)

Your stack probably has zero of these.

The companies that won: Uber, Deloitte, Databricks, Pinterest.

Not because they had bigger budgets.

Because they designed AI into their GTM architecture like they'd design a new sales process.

It's systems thinking, not tool thinking.

Here's the real question:

Your team is probably using AI somewhere. Email automation. Lead scoring. Forecasting.

But are you orchestrating it? Is there a system that knows which AI does what and why?

Or is it scattered chaos with automation sprinkled on top?

Most GTM leaders think the shift is AI adoption.

Wrong.

It's moving from point automation → integrated intelligence.
From "we use Salesforce AI" → "AI is woven into how we sell."

That's the real 2026 advantage.

Quick audit:

If your AI tools disappeared tomorrow, could a human replicate your process?

If yes: AI is still a layer on top. You're not resilient yet.
If no: You've designed something real.

Which are you?

If you enjoyed this content, please follow me for more.

Silence is my secret weapon for productivity as I navigate the chaos of a portfolio career.

No calls, no content... just stillness.

Discover why this daily practice sets the stage for success.

I used to wake up and immediately check Slack.
Then email.
Then LinkedIn notifications.

By 7 am, my brain was already reactive... responding to everyone else's priorities.

None of my own.

The challenge of crafting a varied career: there's an endless list to tackle.

- Employment commitments
- Newsletter outlines
- Research projects
- Creative concepts

Without clear priorities, you chase after the loudest demand. 

The urgent takes precedence, while crucial tasks fade into the background.

So I flipped the order.

Now, the first 30 minutes of my day belong to silence.

Phone stays face down.
Do Not Disturb stays on.

The chaos can wait.
It always can.

What happens in stillness:

- You remember why you're building this.
- You reconnect with purpose before productivity.
- You create space between stimulus and response.

The notifications don't disappear; they just lose their power over you.

Here's the tactical version:

- Phone charges outside the bedroom
- First 30 min: prayer, reading, journaling
- No screens until stillness is complete
- Only then does the world get access

Simple. Not easy. Worth it.

The irony of building with AI and automation:

The more systems you create, the more noise you generate.

More notifications. More dashboards. More metrics.

Stillness is the counterbalance. It keeps the machines from running you.

I've noticed a pattern with burned-out operators:

- They're never alone with their thoughts.
- Every gap is filled with podcasts, scrolling, content consumption.

The mind never rests. The soul never recalibrates.

Breakdown becomes inevitable.

Portfolio careers demand more from you. More output. More context switching. More plates spinning.

But they also demand more stillness. More clarity on what matters. More space to hear what the noise drowns out.

If your mornings start with other people's emergencies, you've already lost the day.

Take back the first 30 minutes. Let the chaos orbit, but protect your center.

That's where purpose lives.

Calm man meditating cross-legged on floating platform, glowing "PURPOSE" orb at chest, muted apps and notifications hovering around him.

"Move fast and break things."

This advice works for startups.

It destroys enterprise GTM teams.

Don't learn this the hard way deploying AI with 50+ stakeholders.

Here's what actually works:

Startup reality:
→ Founder can mandate adoption
→ 5 stakeholders to align
→ Pivot takes a meeting
→ Failed experiment = learning

Enterprise reality:
→ 50+ stakeholders across departments
→ 90-day security review before touching data
→ Failed experiment = 12 months distrust

In startups, breaking things costs a weekend.

In enterprise, breaking things costs:

→ Trust with sales leadership
→ Budget for future initiatives
→ Political capital with executives
→ 6-12 months rebuilding credibility

The stakes are different.

The playbook must be too.

Enterprise Constraint #1: Security Review

Startup: "Let's try this tool today!"

Enterprise: "Every tool needs 90-day security review before touching production data."

Solution: Start with cloud-based tools that have existing enterprise compliance (SOC2, GDPR).

Enterprise Constraint #2: Stakeholder Alignment

Startup: CEO says go, team goes.

Enterprise: Sales, Marketing, RevOps, IT, Legal, Finance must align.

Solution: Build coalition before deployment.

Sell different value to each stakeholder.
Adoption follows buy-in.

Enterprise Constraint #3: Behavior Change

Startup: Small team, fast adoption.

Enterprise: 50+ people with established workflows.

Solution: Pilot with 5-10 users first.

Prove value → Document wins → Expand slowly.

Don't mandate AI. Earn it through results.

The Enterprise AI Playbook:

Week 1-4: Stakeholder mapping + buy-in
Month 2-3: Security review + pilot design
Month 4-6: Small pilot + edge case discovery
Month 7-9: Documented wins + expanded rollout
Month 10-12: Full deployment + optimization
Slow is smooth. Smooth is fast.

What actually works in enterprise:

Stage gates, not timelines
Stakeholder buy-in, not mandates
Documented processes, not tribal knowledge
Human-in-the-loop, not full automation
Proven pilots, not big bang launches

Boring? Yes.
Effective? Absolutely.

Reframe the goal:

❌ "Deploy AI as fast as possible"
✅ "Build organizational capability that compounds"

The company that deploys AI in 18 months with full adoption beats the one that deploys in 3 months with 20% usage.

Speed of deployment ≠ speed of value.

The enterprise truth:

Technology is 20% of AI adoption.
Behavior change is 80%.

You're not racing competitors.

You're building systems that compound for the next decade.

Move deliberately. Build coalitions. Earn trust.

That's how enterprise AI actually works.

I ask 5 questions before adding any tool to our GTM stack.

These questions have saved me from 12 bad decisions (and probably $100K+ in wasted budget).

If you're evaluating tools right now, save this thread:

Question 1: Does it have API capabilities?

→ RESTful APIs for custom integrations?
→ Webhook support for real-time triggers?
→ Comprehensive documentation?

No API = vendor lock-in.

You can't integrate what can't connect.

This is non-negotiable in 2026.

Question 2: Does it support MCP (Model Context Protocol)?

→ Native AI agent connections?
→ Secure data exchange?
→ Standardized protocols?

MCP is how AI agents will connect to your tools.

If your tool doesn't support it, you'll be ripping it out in 2 years.

Question 3: Does it have two-way data sync?

→ Changes in Tool A update Tool B automatically?
→ No manual exports/imports?
→ Conflict resolution built-in?
One-way sync = data silos.

Two-way sync = single source of truth.

This is how enterprise stacks actually work.

Question 4: Is there strong R&D investment?

→ Regular product updates (monthly/quarterly)?
→ Clear product roadmap published?
→ Investment in emerging tech?

Tools that stop innovating become legacy debt.

Choose platforms that evolve with you, not against you.

Question 5: Does it integrate with SSO/security requirements?

→ SAML/OIDC support?
→ SOC2 compliance?
→ GDPR ready?

Enterprise security review can take 90 days.

Tools without proper compliance = 90-day delay before you can even test.

Check this first.

The 5-Question Framework:

1. API capabilities → Can it connect?
2. MCP support → Is it AI-ready?
3. Two-way sync → Is data unified?
4. R&D investment → Will it evolve?
5. Security/SSO → Can it pass review?

Score each 1-5.
Below 20? Don't buy.

Tools I've passed on (and why):

• Popular AI tool → No MCP support
• Trendy automation platform → One-way sync only
• Cheap alternative → No SSO, failed security review
• Incumbent vendor → 6-month update cycles

Shiny features don't matter if the foundation is broken.

The principle:

Tools you choose today are your foundation for 5+ years.
Choose platforms that:

→ Connect to everything
→ Prepare for AI integration
→ Keep data unified
→ Evolve with the market
→ Pass enterprise security

Short-term convenience = long-term debt.

Before your next tool purchase:

1. API capabilities?
2. MCP support?
3. Two-way sync?
4. R&D investment?
5. Security compliance?

Screenshot this. Share with your team.

Save yourself from expensive mistakes.

Follow for more GTM engineering insights.

Everyone's asking: "Will AI influencers replace content creators?"

After diving deep into this topic, I think most people are missing the nuance entirely.

Here's the real answer (and it's not what Gary Vee says):

First, some context:

AI influencers aren't new. Lil Miquela was "born" in 2016... that's almost ten years ago.

She now has millions of followers, collaborates with Prada, and has released actual music.

The concept of non-human influencers has been around longer than most realize.

The market is exploding:

→ Thousands of AI influencers already exist on TikTok and Instagram
→ The AI influencer market is worth billions
→ Projected to hit $45B by 2030

But here's where most predictions go wrong...

Gary Vee's take: AI influencers will replace human creators because they're cheaper.

"I'd rather pay an influencer that looks just like you for $5 a post."

Respectfully, I disagree.

Here's the nuance everyone's missing:

There's a critical difference between:

• Influencers: people with communities who have actual influence over purchasing decisions.

• UGC creators: people who create content for brands without a personal following.

AI will absolutely replace the second category, not the first.

The insight that changes everything:

Influence is expensive. Content is not.

Brands don't pay IShowSpeed millions because of what his content looks like.

They pay for his influence... built through sharing his journey through life.

That trust is irreplaceable.

What AI WILL replace:

→ Product photography
→ Billboard ads
→ Generic UGC content
→ Stock-style marketing assets

Basically anything that doesn't require human connection or authentic influence.

What AI WON'T replace:

→ Real stories from real people
→ Authentic community building
→ Trust built over years of vulnerability
→ The "I feel like I know you" factor

Being human is about to become your greatest competitive advantage.

The future isn't replacement. It's coexistence.

Remember when celebrities joined TikTok and everyone freaked out?

Same energy.

We'll adapt. New lanes will open. The game will change—but real creators aren't going anywhere.

The AI influencers that will actually WIN?

The ones where we *know* they're AI and love them for it.

Think: brand mascots, faceless content vessels, entertaining characters.

Not AI pretending to be human. AI that's transparent about being AI.

The bottom line:

If your business is creating generic content without building real connection → start pivoting.

If you're building genuine influence through authenticity → you're more valuable than ever.

Being a real person is about to be your superpower.

What's your take? Are you worried about AI influencers?

The hardest season of my career became the foundation for everything after.

Here's what I learned building toward a portfolio career:

A few years ago, everything shifted.

The traditional path felt uncertain.

Instead of panic, I chose differently:

→ Rebuild intentionally
→ Stack skills strategically
→ Document everything

Here's what worked:

Lesson 1: Time is your most valuable asset.

Amanda Goetz talks about ruthless prioritization.
I mapped my week in 30-minute blocks.

The result:

60% of my time → 10% of outcomes.
Clarity changed everything.

Lesson 2: Your skills compound.

Myron Golden teaches that wealth follows value creation.

Instead of chasing any opportunity, I doubled down:

→ GTM systems
→ AI automation
→ Content creation

Three skills. Top 25% in each.
That combination is rare.

Lesson 3: Build in public.

Every problem I solved → documented.
Every framework I developed → shared.

This did two things:

Built credibility
Attracted opportunities I couldn't manufacture

SODOTO: Solve Own problems, Document, share. Others pay you to solve Theirs.

Lesson 4: Portfolio beats all-in.

One income stream = single point of failure.
I stopped chasing one big break.

Started building multiple smaller ones:

→ W2 stability
→ Content leverage

Different risks. Compounding together.

Lesson 5: Faith over fear.

The hardest part wasn't tactics.

It was mindset.

Trusting that disciplined action over time produces results.
Even when you can't see them yet.

"The plans of the diligent lead surely to abundance." — Proverbs 21:5

The key word: diligent.

Not frantic.
Not desperate.
Not scattered.

Diligent.

Consistent effort. Clear direction. Patient execution.
That's what builds foundations that last.

If you're in a season of transition:

The foundation you build during difficulty becomes the platform you launch from later.

Don't waste the struggle.

What season are you in right now?

I built a Second Brain in Notion that powers all 5 pillars of my life from one system.

Most knowledge systems fail because they're built for storage, not action.

Here's how I built mine differently:

Why most Second Brains fail:

→ Too complex to maintain
→ Capture everything, use nothing
→ No connection to output
→ System becomes a burden, not leverage

Information isn't valuable.
Information connected to action is valuable.

I rebuilt my system around this.

The SODOTO Framework:

Solve Own Problems
Document the solution
Teach Others

Every note I capture must pass the SODOTO test:

"Can I use this to solve a problem, document it, and teach it?"

If no → don't capture.
If yes → capture + connect.

My Notion Structure:

1. Inbox → Everything starts here
2. Projects → Active work 
3. Areas → Ongoing responsibilities (5 Pillars)
4. Resources → Reference material
5. Archive → Completed/inactive

Based on PARA, adapted for portfolio career.

I organize around 5 Life Pillars:

→ Faith (spiritual growth, social impact)
→ Fitness (health, longevity, energy)
→ Family (marriage, kids, homeschooling)
→ Finance (operator + creator)
→ Freedom (location independence, systems)

Every note connects to a pillar.

My Capture Workflow:

• Podcast insight → SuperWhisper → Notion inbox
• Article highlight → Readwise → Notion database
• Meeting note → Quick capture → Tag project
• Content idea → Voice memo → Inbox for processing

Capture is fast. Processing happens weekly.

Weekly Processing (30 min):

1. Empty inbox completely
2. Connect notes to projects/areas
3. Identify SODOTO opportunities
4. Create content ideas from learning
5. Archive what's not actionable

No note lives in inbox longer than 7 days.

Backlog = system failure.

The key: Connect to Output

Every learning connects to:

→ Blog post idea
→ Newsletter topic
→ Tweet thread
→ Consulting framework
→ Work project

I don't just capture.

I capture → process → connect → create.

The Second Brain feeds the Content Engine.

The Stack:

• Notion → Second Brain home
• Readwise Reader → Article/book highlights
• SuperWhisper → Voice to text
• Claude → Processing + connections

Total cost: ~$30/month

ROI: Entire portfolio career runs on this system.

The principle:

Don't build a Second Brain for storage.

Build it for action.

Every capture → connects to a pillar
Every note → passes SODOTO test
Every week → processing clears inbox
Every learning → becomes content

Information + action = leverage.

Start there.

Most AI transformations fail in the first 90 days.

Not because of bad tools.
Not because of bad timing.

Because teams skip Stage 1.

I built a 3-stage framework that changes everything:

The popular narrative:

"We need AI agents!"
"We need automation platforms!"
"We need the latest tech!"

The reality I keep seeing:

Teams are layering new tools onto broken systems.

You can't AI your way out of bad processes.
You can only accelerate them.

The GTM Stack Maturity Framework:

→ Stage 1: Optimize current stack (Foundation)
→ Stage 2: Layer agentic workflows (Intelligence)
→ Stage 3: Integrate automated workflows (Scale)

Each stage unlocked by completion + stakeholder buy-in.

Not calendar.

Stage 1: Optimize Your Current Stack

Before you add AI, ask:

• Is CRM adoption at 90%+?
• Is data quality above 85%?
• Are processes documented?
• Does each tool have measurable ROI?

If you can't answer "yes" to 80%+ of these...

You're not ready for AI.

Stage 2: Agentic Workflows

Start with internal, non-customer-facing tasks:

• Lead Research Agent (enrich leads with intel)
• Meeting Prep Agent (brief reps in 2 min vs 20)
• Competitive Intel Agent (monitor landscape)

Cloud-based = faster security approval.

The hidden value of Stage 2:

You're not just testing AI.

You're building a living wiki of edge cases:

"Research agent struggles with subsidiaries"
"Reps want briefs in Slack, not dashboards"
"Deals over $100K need human review"

This is your change management gold.

Stage 3: Automated Workflows

Now you connect everything:

Trigger: High-intent visitor identified

→ Enrich with data tools
→ Call Lead Research Agent
→ Create CRM lead
→ Route to correct rep
→ Notify via Slack
→ If >$100K, trigger human review

Zero manual work.

The principle that changes everything:

"Automation amplifies what's broken."

Broken process + automation = dysfunction at scale.

Optimized process + automation = compounding returns.

Automation is an amplifier, not a fixer.

Fix first. Automate second.

Technology is 20% of AI adoption.

Behavior change is 80%.

Don't sell "AI transformation."

Sell:

→ Sales: "Clean CRM = accurate forecasting"
→ RevOps: "Documented processes = less firefighting"
→ Executives: "System compounds over time"

Adoption follows trust.

Don't chase tools. Build systems.
Don't skip stages. Earn progression.
Don't optimize for speed. Optimize for compounding.

You're not racing to deploy AI first.

You're building capability that compounds for the next decade.

Follow for more.

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