Founder | CEO @ GrowMeOrganic 🚀

When I was 19, I accidentally built my first SaaS while learning how to code. It was a simple email scraper that pulled leads from Instagram.

It wasn’t perfect. It broke often. But it worked well enough to catch the attention of some top cold email communities.

I didn’t know much about marketing or product scaling. So I started talking to users, wrote every single email, and patched things together late at night.

Over time, I built GrowMeOrganic. Then came SendMails. Then WhatChimp.

Now, I’m building GrowthToolkit. A prospecting platform focused on speed, accuracy, and verified contact data that actually works when it matters.

Built for people who want results without getting slowed down.

Backed by a team that believes in moving fast, staying curious, and solving problems that others avoid.

💌 Anant Gupta

AGI for Vibe Coding 

Not really, but I am getting closer to it…

I was frustrated with AI turning passive after five or ten minutes and asking me to “run this” or “check that.”

So I stopped treating AI like a code generator and started treating it like an engineer with tools.

I built an internal system where AI has full operational control over the project.

First, I created a remote browser control, similar to Puppeteer or Selenium, but without requiring any installation.

The AI can take over a real user session in my app, interact with the UI, trigger flows, capture screenshots, and send those screenshots back to my local project. 

Claude can read them and understand exactly what’s happening in the browser.

On top of that, I built a lightweight Sentry-like system, so the AI can inspect logs whenever something breaks instead of guessing.

If needed, it can also run JavaScript directly inside the browser, inspect network requests, analyze APIs, and even check the database.

If nothing is clear from logs or APIs, it can always fall back to screenshots or direct file inspection.

So the AI has visibility across:
	•	Browser state
	•	Frontend behavior
	•	Backend logs
	•	APIs
	•	Database
	•	Project files

This allows it to code and test end-to-end autonomously, without stopping every few minutes.

The final missing piece was memory.

So I added structured documentation using MD files, plus my own short-term and long-term memory layer. 

This extends the effective context window and lets the AI retain what it learned across modules and dependencies.

Because of this, when the AI switches context; say from one module to another, it already understands the historical decisions, dependencies, and intent.

The result is an AI system that behaves less like a chat model and more like a senior engineer:
observe → inspect → reason → test → fix → remember → continue.

Why this is different from “autonomous agents”

Most “AI agents”:

	•	Still rely on humans to execute
	•	Simulate environments
	•	Die when context is lost

My system:

	•	Operates on real infrastructure
	•	Has feedback loops
	•	Has memory
	•	Has tools + authority

That’s the difference between automation and agency.

CEO @ GrowthToolkit.io | 🏆#1 Platform to Find Accurate Email & Phone Numbers of Decision Makers from any Company | Sign Up for FREE!

graphical user interface, text, application, email

Ryan Staley

I help Chief Revenue Officers & Sales Professionals Leverage AI to work 10 hours less weekly and 2x performance. Follow for cutting-edge cheat codes, prompts, and workflows🎙Scale Up Show Host🔥$0→$30M ARR in 5.5 yrs

Over the last decade, I’ve grown a division from 0-$30M ARR with 4 Sales People and have upskilled over 2,500 GTM executives on AI, Enterprise Sales and GTM Strategy  .

My mission is to inspire 1 million Go To Market Executives to work less and sell more through becoming Superhuman with Artificial Intelligence.

I help revenue leaders transform their teams by acquiring AI skills to escape the operational grind, and accelerate topline revenue.

I’m the founder of Whale Boss and The Scale Up Show.

My portfolio:

→ I’m the Founder of Whale Boss, an AI Sales & Marketing consulting and skill transformation company
→ I’m the Founder of the Scale Up Show, Top 1% Global Podcast
→ I teach Executives, Sales & Marketing teams to implement tools like ChatGPT, Copilot, Claude, etc.   
→ I provide Chief AI Officer Advisory services for human capital.  


My results:

→0-$30M without funding, marketing or SDRs with only 4 Sales people
→Identified and implemented over 500 AI use cases (internally and externally)
→Taught over 800 CROs proprietary selling strategy and tactical execution
→Consulted over 40 Technology companies on custom revenue growth strategies.  
→Voted as a TOP global AI thought leader, Gartner Ambassador & Sales Assembly thought leader. 


If you want to learn more on how to become Superhuman, you can join my newsletter.
⇒ https://superhumanrevenue.beehiiv.com/


If you want to explore AI skill transformation for your organization or Team, apply for an AI Consult.  
=> https://calendly.com/ryanstaleywss/introductory-call-with-ryan-staley-from-whale-boss


Youtube- https://www.youtube.com/channel/UCFGndSHN4KkE89kUP74U7Qg

Podcast-https://ryanstaley.io/podcast/

X-https://x.com/Ryan_Staley1

Podcast Host

I deconstruct how the top SAAS CEOs in the world grow their companies repeatedly and predictably.  

This show has featured Startup and Billion Dollar Founders, Best Selling Authors, and the World’s Top Sales and Marketing Experts like Terry Jones (Founder of Travelocity and Chairman of Kayak),  Andrew Gazdecki (Founder of Microacquire), Harpaul Sambhi (Founder of Magical with a previous exit to Linkedin) and many more.

I've spent 3 years helping executives navigate the fastest transformation in human history.

Here's what I've learned:


The ones who perform under pressure all have something in common.


A center. An anchor. 
Something they control when everything else is chaos.


For me? It's this. 5am. 
Weights. 
Silence before the storm.


This isn't a "wake up at 4am and crush it" motivational post.

It's reality:
→ My energy stays higher throughout the day
→ My decision-making stays sharper under pressure
→ I'm building health proactively instead of reacting to burnout


I've watched too many CROs and CMOs grind themselves into the ground chasing numbers.


Hit the target. Miss their health. Miss their families. Miss themselves.

The irony? When you lose your center, you lose your edge.


NOTE: This doesn't have to be the gym. 
It could be running. Meditation. 
Playing guitar. 
Coaching your kid's team.


The point is having SOMETHING that's yours. 


That doesn't depend on board approval, pipeline coverage, or the next AI announcement.


What's your anchor?

Founder & CEO, Whale Boss | Demystifying AI Agents | Helping revenue leaders 2x team results through AI Led GTM | Trusted by PE-backed SaaS | Transform your team in 60 days

I did something from Thanksgiving to New Year I haven't done in 5.5 years.
I stopped.

No podcasting. No YouTube. No content treadmill.
Instead:
→ Closed out the year strong
→ Set up massive foundational moves for 2026
→ Actually recharged with family in Napa-Russian River Valley
→ Did deep work when things got quiet

Here's what happened when I created space:

I spent serious time with Claude Code and orchestration workflows.

What I discovered completely rewired what I think is possible.

The jump I'm seeing in AI orchestration isn't incremental.
It's exponential intelligence at the speed of thought.

2026 is going to be different. Not because of hype.

Because of what I've built in the silence.

Sometimes the best move is to step back so you can leap forward.

What's one thing you stepped away from that gave you unexpected clarity?

No alternative text description for this image

15 hours in, three operating systems later, and I'm officially hijacked by Claude Code.

Not in the way you think.

Let's skip the AI hype and get real.

I stopped reading about "AI use cases" and got my hands dirty.

Fifteen solid hours, two weeks, zero fluffy prompts.

Just building actual operating systems.

Not content.

Not lists.

Real, working systems that actually run my business better.

Here's what happened:

1. The CEO Operating System (Annual Planning)

Forget hope and guesswork. This system turns chaos into a clear plan.
- Pick the few things that matter (not 27 "priorities")
- List what we're not doing
- Map risks and constraints
- Build a real 90-day action plan
- Log decisions, assumptions, and learnings as we go
- Use 10 battle-tested frameworks for tough calls

The result? Not a plan that "looks good" in a slide deck, but one that actually runs when the rubber meets the road.

2. The Sales Intelligence Operating System (Client Interaction Analysis at scale)

My new force multiplier.
It chews through a mountain of sales calls, client emails, and feedback in seconds, not weeks.
- Finds trends, patterns, and recurring objections
- Spots churn risk and expansion signals
- Surfaces the REAL words buyers are using, not what we wish they'd say
- Highlights opportunities we're missing

This thing can scrape thousands of data points and hand me something actionable before the coffee gets cold.

3. The Product Intelligence Operating System (Continuous Improvement)

Where delivery drifts, this system pulls it right back.
Because my edge is delivering actual AI transformation training, not just talking about it.
This OS hunts for patterns in:
- What features clients love (and which get ignored)
- What gets real results vs. wasted effort
- Where new modules, examples, or sequences are needed

It's the antidote to vibes-only product improvement. Decisions made on facts, not feelings.

Let's get honest about what made this work.

It wasn't "AI did it for me."

It was orchestration.

I skipped the mega-prompt lottery and ran swarms of specialized agents, each with a clear job.

Every OS had something even more important: memory. They don't just spit out answers and forget. They get smarter. They remember what works, what doesn't, why we made certain calls, and how we define "good."

Every week, outputs compound instead of vanish.

Here's your anti-hype PSA: it's not magic. If you feed it garbage, you get garbage,just a lot faster, and prettier.

The unlock isn't the tech.

It's having:

- Real-world skills 
- Guardrails
- Repeatable workflows
- And, most important, systems that learn instead of reset every time you open a new chat window

Thinking about breaking this down in detail on LinkedIn, YouTube, maybe even my podcast.

Would you want to see how each OS works under the hood, or should I just go back to writing about "top 5 AI tools for 2026" instead?

Drop a comment, tag someone who needs to see this, or just tell me if you want more.

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New episodes weekly here: https://ryanstaley.io/


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Textblaze

whale-boss

We’ve trained 1,850+ sales and marketing professionals on AI in 2025.
Here are the 10 most jaw-dropping ROI outcomes they achieved:

(With real quotes from real people. No theory. Just results.)

#10: Personalized Demo Configuration
"Prospects are more engaged because demos are tailored to their exact industry and use case." — Account Executive, B2B SaaS
Result: 10%+ conversion lift → 500 more closes/year potential

#9: Executive Alignment Strategy
"We're getting meetings at higher levels than ever before. The AI research helps us speak their language." — Enterprise Account Manager, $4B BPO Company
Result: 20%+ larger deal sizes → $2M incremental revenue

#8: Shark Tank Hackathon Model
"Teams built actual tools they're still using. It wasn't just training—it was delivery." — Leadership Teams, Multiple Companies
Result: 100 tools built → 200 hrs/week saved → $520K/year

#7: ROI Builder for Champions
"Building a business case for my champion used to take half a day. Now I can generate one in 15 minutes." — Account Executive, B2B SaaS
Result: 94% time reduction → $225K/year saved

#6: Competitive Intelligence GEM
"Instead of spending hours every week tracking competitors, the GEM does it automatically and alerts me." — Sales Rep, Life Sciences Company
Result: 4+ hrs/week → Fully automated → $1.04M/year

#5: Prospect List Generation
"It took us days to build a prospect list. Now we generate 300+ qualified companies in minutes." — Sales Team, Life Sciences Company
Result: 95%+ time reduction → $480K/year saved

#4: Crawl-Walk-Run Framework
"Every department now has a structured AI project in flight. We went from pockets of use to organization-wide." — COO, Industrial Software Company
Result: Org-wide adoption → 500+ hrs/week saved → $1.3M/year

#3: Call Transcript Analysis
"I don't spend 30 minutes after each call reviewing notes. The AI extracts everything instantly." — Multiple Participants
Result: 30 min → Instant (100% automated) → $2.6M/year for a 100-person team

#2: Account Research Workflow
"I was spending 4 hours researching each strategic account. Now I get better insights in 30 minutes." — Marketing Specialist, B2B SaaS
Result: 87.5% time reduction → $1.75M/year saved

#1: Demo Prep Automation
"What used to take me 2+ hours now takes 10 minutes. I can prep 5 demos in the time it used to take for 1." — AE, B2B SaaS
Result: 92% time reduction → $1.37M/year saved

The math doesn't lie:
📊 Total Annual Impact: $14.21M (for a 100-person team) 
⏱️ Hours Recaptured: 5,935 hrs/week 
🎯 Sales drove 79% of total ROI

The bottom line:

AI transformation isn't about technology. It's about closing the deal with your own organization. 

The companies seeing these results treated internal AI adoption like an enterprise sale: → Identify the pain → Find champions → Build the business case → Close the deal

Your team already knows this framework. Now apply it internally.

Which of these outcomes would move the needle most for your team?
Drop a comment 👇

I just finished another AI Transformation Sprint with a SAAS leader-QT9 Software  software

The results blew me away.

Here's what 45 days of focused AI implementation produced:

→ 18 AI use cases deployed across Sales, Marketing, and Leadership
→ 139 hours saved WEEKLY across the team
→ Equivalent of 3.5 Headcount gain to drive greater efficiency and output
→ 12.4x return on program investment

But here's the metric that matters most:

Workflow confidence jumped 103%.

Before: 1.94/5
After: 3.94/5

This is the real unlock.

Most AI training programs teach tools. We transformed how the team THINKS about AI.

The 3 biggest wins:

🔹 Demo Prep: 2+ hours → 10 minutes (92% reduction)
🔹 Account Research:** 4 hours → 30 minutes (87.5% reduction)
🔹 Business Case Creation:** 4 hours → 15 minutes (94% reduction)

Here's what most companies get wrong:

They measure AI adoption by "ROI on Day 1 like a slot machine"
That's like measuring fitness by gym membership.

Leadership doesn’t set the non-negotiables

The real ROI comes from:

1. leading indicators=time
2. lagging indicators=increase in tasks, activity, pipeline , etc
3. Results= Increases in revenue and profit 

When confidence doubles, adoption becomes automatic.

NOTE: 

74% of the ROI came from SALES use cases. 

The Executive leadership was committed

-leading by example, setting expectations and driving adoption

The team was amazing

-hungry, curious and grew exponential by doing

What's blocking your team from this kind of transformation?

Feel stuck or don’t know where to start, DM me and I’ll give you a few ideas of what has been working over the last 3 years.

Amazing work Jim Ellward Jared Brady Brian Kulaga Cole Gardner, MBA Jake Ninowski Kertrina Justice Blake Carlson Christian Reyes 

And the rest of the QT9 team (who are not showing up in LI for me.)

I've presented growth strategies to C-Suites for the past 15 years.

Here's the brutal truth:

Most strategies die in the boardroom because they're built on gut feel and outdated playbooks.

The C-suite doesn't want more slides. They want conviction backed by data they can actually verify.

That's why I've spent the last 3 years building AI workflows that pressure-test strategy BEFORE it hits the executive table.

What used to take my team 3 weeks of analyst work now happens in 2 hours.

On January 28, I'm teaching this exact approach in Pavilion's AI in GTM School:

**Growth Strategy Design for an Enterprise (with AI Workflows)**

What you'll walk away with:
→ How to stress-test positioning against real market data using AI
→ Frameworks for validating competitive threats before they become surprises
→ The exact process I use to build strategies the C-suite actually trusts

This isn't theory. It's the same workflow I've used with 2,400+ executives over the past 3 years.

If you're a CRO or CMO tired of defending strategies built on hope, this session is for you.

Link in comments to register.

What's the last AI growth strategy you presented that got challenged in the boardroom?

WHAT keeps me up at night about AI in GTM:

The window is closing.

Right now, companies who implement AI systematically have a MASSIVE competitive advantage.

By 2027? AI capabilities will be table stakes.

Everyone will have access to the same tools.

The differentiator will be:
→ How fast you implemented
→ How deeply it's embedded in your operations
→ How skilled your team became during the transition

I've watched this pattern before.

Companies that moved early on CRM, marketing automation, sales engagement—they dominated for years.

The laggards spent the next decade catching up.

AI is that same moment. Compressed into 24 months.

This is why I partnered with Pavilion to teach a session for the **AI in GTM School**.

It's designed for GTM leaders who:
🔹 Know AI matters but don't have a systematic approach
🔹 Want alignment across teams, not scattered experiments
🔹 Need a 90-day plan they can actually execute

The next cohort starts next week.

If you're a CRO or CMO who's ready to move from AI experimentation to AI transformation—this is your moment.

Here is the link to sign up: 
https://lnkd.in/gekG_R6t

This is taken from an AI Agent social network - Moltbook

It is the third most upvoted post and the first 2 aren’t any better.

Things are going to go off the rails really fast if someone doesn’t have some standards or safety measures to this site. 


Although this is an amazing concept and idea we need to make sure bad actors that have social burner accounts don’t poison the well.

Please comment for reach and let’s make sure we act smart when working with this insanely powerful technology.

I spent 4 hours watching a single Anthropic video over break.

Rewound it. Took notes. Implemented immediately.

Here's the insight that's changing how I build AI systems:

STOP building agents. START building skills.

Here's what most people get wrong:

They think an "AI agent" is some magical autonomous entity that figures everything out.

Wrong.

Agents without skills are just expensive guessing machines with massive drift.

🔹 They go off path
🔹 They hallucinate next steps
🔹 They deliver inconsistent outputs

The Anthropic team broke it down into 3 layers that change everything:

**Layer 1: Foundational Skills**
The basics. File creation. Web search. Code execution.
Every agent needs these—but they're just the table stakes.

**Layer 2: General-Purpose Skills**
Document creation. Data analysis. Spreadsheet generation.
These are the workflows that apply across industries.

**Layer 3: Custom Enterprise Skills**
YOUR specific processes. YOUR playbooks. YOUR workflows.
This is where the magic happens.

The shift?

Instead of asking an agent to "figure it out," you're giving it a SKILL.md file that says: "Here's exactly how we do this."

No guessing. No drift. No inconsistent outputs.

NOTE: I've been implementing this for 2 weeks and the quality of outcomes is dramatically better across everything from content to research to data analysis.

The companies that master skills will have AI that actually performs.

The companies that keep building generic agents will keep wondering why their AI is inconsistent.

Which approach is your team using right now?

I just burned through 115 million tokens on Claude Code in 30 days.

That's not a typo.

I kept hitting usage limits and upgrading—which tells you something about how deep I went.

Here's the thing nobody talks about:

Building agents is EASY.

Building GOOD agents is hard.


You can spin up a swarm of AI agents in minutes. I've done it. Command it, and it creates.


But here's what happens next:


The output is a fraction of what you actually need.
It's the exact same pattern as Custom GPTs.
Anyone can build one in 60 seconds.
Almost no one builds one worth using.


The difference between "agent that exists" and "agent that performs"?
🔹 Rigor in testing
🔹 Quality examples baked in
🔹 Clear success criteria
🔹 Iteration cycles (lots of them)

After 30 days of intensive building, here's my take:


The barrier to entry has collapsed.
The barrier to excellence hasn't moved.


That gap is where the real opportunity lives for revenue leaders willing to put in the work.


What's been your experience building agents—easy to start, hard to finish?

I've been wrong about where the next AI breakthrough is coming from.
Prompting. Workflows. Automation.

But here's what I'm realizing now:

The real unlock isn't better prompts.

It's agentic tools like Claude Code.

I've been fine-tuning how I use it across everything I do—content, research, client work, operations.

And it's not incremental improvement.


It's a different category entirely.

So I'm going to start sharing what I learn with you in real-time.

Frameworks.
Cheat sheets.
What works. What doesn't.


No polish. No waiting until it's "perfect."

Because this is the bridge to the next exponential value creation with AI.

And I'd rather you learn from my mistakes now than figure it out alone 6 months from now.

Here is quick cheat sheet that will get you started on how to use it the right way!

What AI tools are you experimenting with right now that feel like a step-change, not just an improvement?

I spent 25 years learning how to build pipeline, increase close rates, and shorten sales cycles.

Then I asked an AI agent: "How would YOU solve these problems?"

The answer exposed a blind spot I didn't know I had.

Here's what I learned: We've been giving agents human playbooks and asking them to execute faster.

That's backwards.

Here are the 4 questions that changed how I work with agents in 2026:

1. "How would you approach this AS an agent—not as a faster human?"

Stop giving agents your methodology. Ask them what methodology makes sense given THEIR capabilities.

An agent doesn't need to "qualify" leads the way a human does. It can score 1,000 signals simultaneously. Let it design the process.

2. "Given these raw materials, what would you build?"

Feed it the data: transcripts, CRM records, emails, call recordings, Google Drive docs.

Then ask: "What system would you create to solve [goal]?"

You'll get architectures you never would have imagined.

3. "What patterns do you see that I can't?"

This is the unlock.

Humans are pattern-matching machines—but we max out at ~7 variables.

Agents can correlate hundreds. Ask them what YOU'RE missing.

4. "Using first principles, how would you solve this if you had no constraints?"

Agents don't carry your limiting beliefs, your "that's how it's always been done," your organizational politics.

Let them design from scratch. Then adapt to reality.

NOTE: These questions assume you've given the agent CONTEXT—not just instructions. Context is the fuel. Instructions are just steering.

The skill of 2026 isn't prompting.

It's knowing what to ask.

What question would you add to this list?

WHEN I started using AI 3 years ago, I focused on prompts.

HOW to write better questions.
HOW to get better outputs.
HOW to save time on individual tasks.

That approach 10x'd my productivity.

But what I discovered this week will 100x it.

**The shift: Prompt engineering → Orchestration engineering**

Here's the difference:

Prompt engineering is asking AI a great question.

Orchestration engineering is building SYSTEMS where AI holds your entire business context and synthesizes insights you'd never see on your own.

It's the difference between:

- Using GPS for one trip
- And having a co-pilot who knows every route you've ever taken, every destination you're considering, and every traffic pattern across all your priorities

I've been building what I call "operating systems" inside Claude Code:

A CEO OS that synthesizes my annual goals, quarterly priorities, and weekly rhythms.

The AI doesn't just answer questions.

It eliminates disparate information across areas.

It turns goals into action plans and tracking sheets.

It shows me where I'm off track before I even ask.

**Here's why this matters for YOUR career:**

The executives who master orchestration engineering won't just be more productive.

They'll have a STRATEGIC ADVANTAGE that compounds every single day.

While others are still asking ChatGPT individual questions...

You'll have an intelligent layer synthesizing every:

- Sales call
- Customer interaction
- Internal communication
- Market signal
- Team execution detail

Against your strategic priorities.

In real time.

NOTE: I predict "Orchestration Engineer" will be one of the most valuable skill sets in business by 2027.

It won't require you to be technical.

It will require you to think in SYSTEMS, not prompts.

The 10x knowledge workers of the next decade won't be the best prompt writers.

They'll be the best orchestration architects.

This is the fastest transformation mankind has ever had.

And the window to get ahead?

It's closing faster than most people realize.

What's the first "operating system" you'd build for your role if you could connect all your disparate information in one intelligent layer?

McKinsey just announced they have 25,000 AI agents.

My reaction? That's not a flex. That's a warning sign.

Here's what I've learned from 3 years of AI itransformation implementations across startups and fortune 500s:

MORE IS NOT ALWAYS BETTER.

In fact, more is usually worse.
I've watched companies rush to build dozens of agents, custom GPTs, and AI workflows—only to end up with a graveyard of tools nobody uses.

The problem isn't building agents.

The problem is building agents without asking:
→ Who owns this?
→ Who maintains this?
→ Who actually uses this daily?

NOTE: Managing 25,000 agents currently is just as hard as managing 25,000 people.
Maybe harder.

Because agents don't tell you when they're broken.
They don't raise their hand when they're producing garbage.
They just keep running.

The companies winning with AI right now aren't the ones with the most agents.

They're the ones with the RIGHT agents.

Fewer. More precise. Actually used.

What's your take—is agent quantity becoming the new vanity metric?

I've spent the last 3 weeks building what I call an "Agentic Operating System" for my company.

Here's the breakthrough:

What if your AI didn't just answer questions...

But held the ENTIRE CONTEXT of how your company operates—and helped you see what you're missing?

This is orchestration engineering.

And it's the skill that will separate 10x knowledge workers from everyone else.

Here's how I'm building it:

**5 interconnected "Operating Systems" that form a company brain:**

1️⃣ **CEO/Executive OS**

- Annual goals
- Quarterly priorities
- Weekly operating rhythm
- Action plans with tracking

2️⃣ **Sales Intelligence OS**

- Pipeline analysis
- Call transcriptions → next best actions
- Executive overviews
- Hidden opportunities across deals

3️⃣ **Product Intelligence OS**

- Training transcripts
- Customer feedback loops
- Win/loss analysis
- Engagement and expansion signals

4️⃣ **Marketing OS**

- Campaign performance
- Content byproducts
- Dream 100 prospecting
- Competitive intelligence

5️⃣ **Internal Operations OS**

- Slack messages
- Email threads
- Meeting transcriptions
- All aligned to strategic goals

**The magic happens when they INTERCONNECT.**

🔹 Your sales calls inform product improvements
🔹 Your product intelligence informs marketing messaging
🔹 Your internal operations stay aligned to executive priorities
🔹 Every signal gets processed against your strategic goals

This creates what I'm calling the "company brain."

It helps you see:

- If you're on track
- What you're doing right
- How to optimize
- 10x opportunities hiding in plain sight
- 100x possibilities you never considered

NOTE: I used to think the future was individual AI agents doing tasks.

Now I realize it's ORCHESTRATED SYSTEMS where agents work together with shared context.

That's the 10x multiplier.

The Leaders who build this capability in 2025?

They won't just outperform their peers.

They'll operate in a different dimension entirely.

Which of these 5 operating systems would unlock the most value for your team?

I'm in this photo with the cast of Severance.

Except I've never met them.

I strung together a couple commands in ChatGPT's new image generator and this was the result.

2026 is going to be WEIRD.

Here's what I'm seeing after 3 years of AI implementation work:

The line between real and synthetic is disappearing faster than anyone predicted.

🔹 Veo 3.1 just dropped
🔹 Image generation hit a new threshold
🔹 Video is about to follow

And we're only in January.

NOTE: This isn't fear-mongering. This is pattern recognition.

When you can't trust what you see online, something else becomes infinitely more valuable:

Human-to-human connection.

Not text. Not email. Not async.

→ Live conversations
→ Phone calls
→ In-person presence

These will be at a PREMIUM in 2026.

The irony? The more powerful AI becomes at mimicking reality, the more we'll crave what's actually real.

What's your prediction for how this plays out?

I just looked at my ChatGPT annual summary.
I don't know if I'm excited or disturbed.

The data showed me exactly how much of my thinking, creating, and problem-solving now runs through AI. Every day. 

Multiple times per day.


Three years ago, I had my "ChatGPT moment" that changed everything.

What I'm seeing now makes that look like a warmup.

The agents. The systems. The orchestration capabilities.


My mind is genuinely blown.


Here's what I've accepted: This is short-term sacrifice for long-term gain.

The hours I'm investing now in understanding these systems? 
They're not optional anymore.
They're the price of admission for what's coming.


I've worked with 2,600+ executives over the past three years. 

The ones who leaned in early are operating at a completely different level than those who waited.

That gap is about to become a canyon.


What did your AI usage summary reveal about how you're working now vs. a year ago?

HOW would an AI agent approach pipeline differently than your sales team?

I ran the experiment.

I asked an agent to solve evergreen pipeline problems that every sales org faces. But I didn't just give it the task—I asked it to map out:

→ Its approach
→ The steps it would take
→ How it would "attack" the problem

Here's what I learned:

**What impressed me:**

🔹 The level of detail was exceptional
🔹 It viewed pipeline stages through a different lens than most humans
🔹 It identified optimization points I hadn't considered

**What missed the mark:**

🔹 Its assessment of how humans handle pipeline undersold us
🔹 It didn't account for relationship nuance that top GTM pros leverage
🔹 It assumed more process consistency than exists in real orgs

NOTE: This isn't about agents being better or worse than humans.

It's about understanding WHERE each excels.

The takeaway for revenue leaders:

Before you deploy an agent on any workflow, ask it to explain its approach first. Compare it to your team's approach. The gaps tell you exactly where human oversight matters most.

This is how you build agent + human workflows that actually work.

What's the first sales problem you'd ask an agent to map out? Drop it below—I'll share what I've seen work.

What if cheat sheets/frameworks are actually holding you back?

I've taught thousands of executives about AI.

Frameworks everywhere.

Slides. Decks. Cheat sheets.


And here's what I've realized after 3 years of implementation work:

Frameworks are the START.
Systems are the SCALE.


Last week I built something I'm calling Atomic Claude Code.


It takes James Clear's Atomic Habits—one of the best frameworks ever created—and turns it into a living, breathing agentic system inside Claude Code.


Not a document I reference.
Not a poster on my wall.
Not a "note to self."


An actual system that:
→ Tracks my 1% daily improvements
→ Compounds learning over time
→ Holds me accountable without willpower


Here's the honest truth:
I've been teaching AI transformation for 3 years.
But the biggest unlock in the last 90 days?
Realizing I can BUILD the frameworks I teach INTO the AI systems I use.


The framework becomes the infrastructure.
The concept becomes the code.
The theory becomes the transformation.


NOTE: We're at an inflection point where the gap between "people who know frameworks" and "people who implement frameworks as systems" will determine who wins the next 12 months.


So here's my question:

Is this crossover from frameworks to agentic implementations something you're interested in?


Something you've already done?


Or something you're waiting to explore?


Drop your answer below—I'm genuinely curious where you are in this journey.

taplio

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The Battle

💌 Anant Gupta

Ryan Staley

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