Hi, I’m Ryan. I help high performers become founders by using LinkedIn to build real, profitable businesses.

My career didn’t fall apart. I didn’t burn out. I finished every role cleanly - Director, VP - leading hundreds of engineers across the world and helping build multiple billion‑dollar companies.

And still, I knew I wanted to build on my own.

I’ve founded 5 companies and earned multiple exits while scaling teams behind $1B+ in outcomes. I loved the work, but I wanted a life where I could build big things and be present for my family. I wanted ownership. I wanted flexibility. I wanted proof I could do it on my terms.

LinkedIn became the path. I grew from 1,200 to 100,000+ followers in nine months, built a brand that opened doors I didn’t know existed, and stepped fully into my own business.

Since then, I’ve coached 50+ founders to design careers, income streams, and opportunities that match the life they actually want - not the life their company dictates.

Here’s how we work:

- Identify the opportunities you can’t see in your past experience.
- Build a clear, practical brand that attracts the right people.
- Turn your skills into simple offers, real customers, and predictable income.

Today, I own my time. I build, coach, and spend my days with my family - and help others do the same.

If you’re a high performer who wants a business that fits your life, DM me.
Partnerships, speaking, press: ryan@ryanyockey.com

Professional highlights:
• 5× Founder | Multiple exits
• Scaled teams responsible for $1B+ outcomes
• Grew to 100,000+ followers in 9 months
• Coached 50+ founders
• Trusted by leaders at Google, Adobe, Meta

Ryan Yockey

I’ve been watching people for years. The patterns are wild.

The loudest person in the room? Usually compensating.

The flashiest? Probably broke or close to it.

The one who won’t shut up about how smart they are? Not that smart.

Here’s what I’ve noticed about people who actually have it:

The strongest are gentle.
They don’t need to intimidate. Their presence does the work.

The smartest are quiet.
They listen more than they talk. They know they don’t know everything.

The wealthiest are simple.
No logos. No flex. Just compound interest and time freedom.

The happiest are private.
They’re not performing for an audience. They’re living for themselves.

Real power doesn’t need to prove itself.

It just exists.

The person who’s actually built something doesn’t need to tell you about it every five minutes.
Their results speak.

The person who’s actually confident doesn’t need external validation.
They’ve done the internal work.

If you find yourself constantly trying to prove your worth, your intelligence, your success—pause.

Ask yourself who you’re trying to convince.

Because the people who’ve made it?
They stopped performing a long time ago.

They just started building in silence.

That’s what I write about every Saturday. One short note to 135,000+ entrepreneurs who care more about real progress than public applause.

It takes 4 minutes.

And its free: https://lnkd.in/gfyweeTM

No noise.
Just the work.

Actionable strategies to grow your audience, monetize your skills, and build leverage online. | Trusted by leaders at Google, Adobe, Meta

The real luxury isn’t making millions.

And it’s definitely not your internet dream.

It’s easy to confuse money with freedom.

Fast cars.
Big houses.
Screenshots and timelines.

But watch closely.

The people who actually "made it"
optimize for something much quieter.

I control my time.
I can choose when to workout.
I dont have nearly the level of stress I once did.

Slow mornings without guilt.
Rested afternoons whenever I want.
Sleep that isn’t borrowed from tomorrow.

And most importantly:

People you love.
Who love you back.

Money can certainly help create these things and more, theres no denying that.

But chasing money at the expense of them usually destroys the very life you’re trying to build.

I learned this the hard way.

The goal isn’t more.
It’s enough — designed intentionally.

That’s what I write about every Saturday.

One short email to 135,000+ entrepreneurs on building work and lives that don’t collapse under success.

It takes 4 minutes to read.

It’s free. Join here: https://lnkd.in/gfyweeTM

Slow down.
You’re closer than you think.

I waited longer than I should have.

Not because I didn’t have ideas.
Not because I wasn’t capable.

Because I kept telling myself I needed:
More clarity.
More confidence.
More proof it would work.

I thought I needed a better plan.

I didn’t.

I thought I needed permission to try new things.

I didn't

I thought I needed someone to tell me it was okay to start.

I didn’t.

What I actually needed was to stop questioning myself.

Every meaningful thing I’ve built started the same way:
Messy.
Incomplete.
Uncomfortable.


The people I know who’ve built real businesses didn’t wait for the right time.
They started before it made sense.
They figured it out while delivering.
They gave themselves permission to start.

Most people do the opposite.

They plan.
They ask.
They wait.

Your competition isn’t other people.

It’s your own hesitation.

Stop asking if you’re ready.
Start doing the thing you keep thinking about.

That’s what I write about every Saturday.

One short note to 135,000+ entrepreneurs who stopped waiting and started building.

It takes 4 minutes.

It’s free: https://lnkd.in/gfyweeTM

Start sooner than you feel comfortable.

The most valuable skill isn’t productivity.

It’s knowing what matters.

Anyone can work harder.
Anyone can move faster.
Anyone can optimize their calendar.

That’s not the edge.

The edge is knowing what not to do.

Because efficiency on the wrong things is still wasted energy.

I see this constantly.

People obsess over:
• Morning routines
• Task managers
• AI workflows
• Productivity hacks

But they’re applying them to work that doesn’t matter.

So they feel busy.
They feel exhausted.
And they wonder why nothing compounds.

Real leverage comes from clarity.

Knowing:
• Which decisions actually move the needle
• Which problems are worth solving
• Which work creates momentum instead of maintenance

Most professionals don’t need better systems.

They need better filters.

Once you know what matters, productivity becomes obvious.
Until then, it’s just motion.

That’s what I write about every Saturday.

One short email to 135,000+ entrepreneurs on building conviction, making clearer decisions, and designing work you’re proud to stand behind.

It takes 4 minutes to read.

It’s free. Join here: https://lnkd.in/gfyweeTM

Keep building.
The right people notice.

Manage AI risks with interconnected tips | Lawyer | AI GRC | DPO | Ambassador for Kapfou | Global AI Delegate | Lead Auditor ISO 27001 | Founder: RTivara Advisory|

Most organisations want to use AI with confidence. What slows them down is not ambition, but uncertainty. Who is responsible when AI influences decisions? What happens when something goes wrong? What will regulators, insurers, or courts expect to see?

That is where my work sits.

I help insurers and regulated organisations prepare for AI use in a way that can be explained, defended, and carried responsibly. My focus is AI assurance and liability readiness, before incidents occur, not after damage is done.

I work at the intersection of law, governance, and operational reality. The questions I help answer are practical ones:

Where does accountability sit?
What evidence supports your decisions?
What risks are being accepted quietly, without oversight?

I have spent over a decade working across law, data protection, cybersecurity, international legal work, and AI governance.

This background allows me to support organisations operating under EU and cross-border regulatory expectations, where mistakes carry financial, legal, and reputational consequences.

I also advise organisations expanding into or operating across African markets, where clear due diligence and legal structure often determine whether partnerships hold or fail.

What clients value:
Clear understanding of AI-related liability and responsibility
Reduced exposure through defensible governance and assurance structures
Stronger confidence from insurers, regulators, and senior leadership

Background: Lawyer, Certified Chief AI Officer, Data Protection Officer, and Lead Auditor for ISO 27001 and ISO 42001. I bring legal depth and careful judgement to decisions that carry long-term consequences.

How I support organisations:

AI assurance and liability readiness
AI risk and governance design
Data protection and regulatory compliance
Cross-border legal and contractual advisory

I have worked with organisations across Belgium, Cameroon, India, and the EU.

If your organisation is introducing AI into regulated or high-impact environments and needs clarity on responsibility and exposure, you are welcome to message me.

Remy Takang (CAPA, LLM, MSc, CAIO).

Stress isn’t the problem. Holding onto it is.


I came across a short video that left a lasting impression.

𝐈𝐭 𝐬𝐚𝐲𝐬:
→ Think about something stressful for a few minutes, no big deal.

→ Hold it in your mind for an hour, it starts to weigh on you.

→ Carry it all day and it paralyses you.

That’s exactly how it feels.

Stress is like holding a glass of water. The glass doesn’t change. But the longer you hold it, the heavier it feels.

And yet, so many professionals do this daily:

💼 Carrying pressure from meeting to meeting

💬 Replaying conversations in their heads

🕒 Holding problems overnight, and into the next day

Sometimes the most productive thing you can do is put the glass down.

Take a break. Breathe. Step away.
Then return with strength, not strain.

What is that thing you have been holding on to for far too long? 

Put it down!

-----------------------
👉For actionable insights on meaningful AI implementation for business leaders and individuals who want value beyond the hype, 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/eRe8VFtz

The Liability Challenges of Automated Systems


This week felt like a natural progression.

We talked about AI assurance and AI insurance,

Then we attempted an answer to the question, “Is AI insurance a risk allocation mechanism”, or is it quietly becoming a signal of governance maturity?

That question led directly to this week’s issue.

In Issue #31 of Meaningful AI, I focus on the liability challenges of automated systems.

Not from a technical angle, but from a responsibility one. When systems act, learn, and change after deployment, liability no longer sits neatly in one place. It moves across designers, deployers, operators, and decision-makers.

This issue looks at how responsibility travels through automated systems, why traditional liability frameworks strain under that movement, and what this means for boards, insurers, and regulators.

Automation does not remove responsibility. It tests whether we are willing to stay present when decisions scale.

That question feels harder to ignore now.

I invite you all to explore this issue and share your thoughts. Is there something I missed? Let's connect the dots together.

Introduction There is a quiet moment after something goes wrong. A machine makes a decision. A plane reroutes. A vehicle brakes too late. A credit app

Open article: Issue #31: The Liability Challenges of Automated Systems by Remy Takang (CAPA, LLM, MSc, CAIO). • 7 min read

Issue #31: The Liability Challenges of Automated Systems

Is AI insurance a risk allocation mechanism?


We started yesterday with AI assurance and AI insurance. Today, let's answer the question of ‘if AI insurance is a risk allocation  mechanism.’

AI insurance does not remove risk.

It forces a decision about where that risk sits.

Every automated system already distributes responsibility across many hands. Designers shape behaviour. Deployers decide context. Operators choose when to trust outputs. Leaders decide how much oversight is “enough.”
𝐈𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞 𝐬𝐢𝐦𝐩𝐥𝐲 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐚𝐭 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐞𝐱𝐩𝐥𝐢𝐜𝐢𝐭.

When insurers ask questions, they are not only pricing harm. They are testing:
 
》Whether responsibility has been consciously assigned or quietly assumed. 

》Whether someone is accountable for monitoring drift. 

》Whether escalation paths exist beyond theory. 

》Whether failure has been rehearsed.

Where answers are vague, risk pools awkwardly. It lingers between teams, vendors, and contracts, waiting for an incident to expose it.

Seen this way, insurance becomes less about compensation and more about structure. It reveals how organisations think about consequences before they arrive.

𝐈𝐟 𝐫𝐢𝐬𝐤 𝐜𝐚𝐧𝐧𝐨𝐭 𝐛𝐞 𝐜𝐥𝐞𝐚𝐫𝐥𝐲 𝐚𝐥𝐥𝐨𝐜𝐚𝐭𝐞𝐝, 𝐢𝐭 𝐢𝐬 𝐮𝐬𝐮𝐚𝐥𝐥𝐲 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐰𝐚𝐬 𝐧𝐞𝐯𝐞𝐫 𝐩𝐫𝐨𝐩𝐞𝐫𝐥𝐲 𝐬𝐞𝐭𝐭𝐥𝐞𝐝.

And automated systems are unforgiving of that kind of ambiguity.

If this resonates, then drop your thoughts in the comments.

List three to five accomplishments that reflect your best work.


That is the question Amazon’s leadership is now asking employees to answer. 

On the surface, it appears to be a routine performance exercise. In context, it says something deeper about how relevance is being judged as AI reshapes work.

Senior executives are no longer impressed by activity, hours logged, or proximity to tools. They are asking people to point to outcomes. 

What changed because you were there?

What moved forward because of your judgment, not just your access to systems?

In an AI-shaped workplace, many tasks can be assisted, sped up, or drafted by machines. What cannot be replaced so easily is responsibility—choosing what matters. Seeing second-order effects. Improving a process that others depend on. Holding a problem when the answer is unclear.

This kind of question forces a quiet reckoning. It asks employees to articulate value in human terms, not technical ones. It also asks leaders to listen carefully, because not all meaningful work is loud or easily counted.

Relevance today seems less about how advanced your tools are, and more about whether you can clearly explain why your work mattered at all.

Most AI failures are advisory failures.


Not because advisers lacked knowledge.

But because they did not ask the right questions early enough.

In AI assurance work, the first risk is not the model.

It is a misunderstanding of what the client thinks assurance actually means.

Many organisations believe assurance will tell them their AI is “safe”.

What they really need is clarity about where responsibilities reside, who is responsible for them, and what happens when things go wrong.

That is why serious AI assurance starts before any system review.

It starts with questions like:

>Who will rely on this advice to make decisions?

>What harm is foreseeable if this system fails quietly rather than dramatically?

>Where does accountability blur once the system is live?

>What confidence level is being implied, even if not stated?

If these questions are skipped, everything that follows becomes fragile.

The report may look polished, but it will not protect the organisation, or the adviser.

Good AI assurance does not remove risk.

It makes risk visible enough for leaders to take responsibility for it.

If you are deploying AI, procuring it, or being asked to sign off on it, this distinction matters.

If you want help asking the right questions before assurance becomes a liability, DM me.

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Ryan Yockey

Remy Takang (CAPA, LLM, MSc, CAIO).

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The Battle

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Remy Takang (CAPA, LLM, MSc, CAIO).

Followers

Posts

Engagements

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