Strategic Product Leader with 18+ years of success in creating customer-obsessed products that deliver commercial results.

In my recent role at Lintkree, my team and I doubled the company's revenue and users in 24 months; helping it achieve a $1.3BN valuation. Prior to Linktree, I've built products for companies of all sizes, from global corporates like Vodafone and Expedia to startups like Airtasker and SocietyOne. 

In my spare time, I give back to the product community by coaching product management at universities, speaking at product conferences, and writing on various product blogs.

My passion is to work with a technology-powered company that democratises economic opportunities for its customers.

My Skills
Business ​Strategy, Coaching, Communication, Cross-​functional ​collaboration, Cross-​team ​influence, Data ​driven ​decisions, Executive ​Communication, Executive ​Management, Leadership, Product ​Vision, Team ​Management

Hard skills: Data, Data ​Analysis, Experimentation, Market ​Research, Product ​Growth, Product ​
Management, Product ​Marketing, Product ​Roadmap, Product ​Strategy, Product ​Vision, Project ​Management, User ​Research, User ​Testing, UX ​Design

David Wang

AI Strategy in 1 Day + Prototype in 3 Weeks | Fastest Path to AI & Digital Transformation While Having A Stupidly Good Time | 22+ Years Making Transformation Less Painful

🌍 Part business guru, part thrill-seeker. I thrive in discomfort, challenge the status quo, and ask 'Why not?' more often than a toddler asks 'Why?'. My clients rely on me to bring in a dash of urgency, a sprinkle of curiosity, a chug of right questions and a hefty dose of momentum. 

Work between London and Bay Area. 

How I help:
🚀 AI Accelerator: Zero to working AI prototype in 3 weeks 
🚀 Go-To-Market Accelerator: Growth hacking, product launches, market expansion 
🧠 Strategy & Innovation Labs for transforming business rapidly
🧠 Interactive workshops, Speaking & hackathons: teaching effective collaboration, value proposition, design thinking, visual thinking, and AI. 

What You Get: 
✓ Scale faster in fraction of the time
✓ High-performing teams without the burnout 
✓ Results in days, not months

Currently Open For: 
✓ AI Strategy & Innovation Lab engagements 
✓ Specialized CRM, Customer & Digital Transformation projects 
✓ Fractional/interim transformation roles 
✓ Post-M&A integration
✓ Process automation & digitization
✓ Outsourcing
✓ SaaS implementations
✓ Change Management

I spent the first part of my 22+ year career avoiding the messiest part of business transformation: "the people". As a computer nerd who loved math, algorithms, and databases, they were logical. Until I couldn't avoid the truth anymore: every transformation failure I witnessed wasn't technical – it was human.

My breaking point was when I spent six months, in weekly meetings with a senior leadership team, discussing the same slide deck, trying to make one decision. One. Decision.
I was trapped in never-ending tunnels of circular meetings – because nothing ever gets decided in a single meeting, does it? I had a choice: accept this as "how the system works" or find another way.

What Changed Everything - I went to Silicon Valley to learn how high-growth startups solve complex problems rapidly. The difference wasn't better technology – it was better collaboration.
So I combined my 22+ years of transformation experience with neuroscience-based collaboration methods. Turns out, when you fix the group dynamics, everything else follows.

That's how Solved Together was born – with a mission to fix broken collaboration and help businesses develop a human-centered approach to growth and transformation.

What I Do Now -  I help companies solve complex challenges in days instead of months using these methods. DM me if you want your team to learn to do the same! 

🏔️ Off-duty: Found defying gravity on mountains, seas, or slopes. Because life's too short for sensible hobbies.

Rujuta Singh

Your brain just added a marble to someone's jar.
Or removed one. You weren't paying attention.

Trust is literally the only thing we're judging all day.
Can I trust this person? This system? This decision?

That's it. That's the whole game.

And if you're leading anyone, you should probably know how it actually works.

I'm an eternal Brené Brown fan, and she just explained it in the most simple, sticky way I've ever heard.

𝗠𝗮𝗿𝗯𝗹𝗲 𝗷𝗮𝗿𝘀, 𝘀𝗵𝗲 𝘀𝗮𝘆𝘀....

Your brain is keeping a marble jar on every single person in your life. And you have no idea it's doing this.

Little moments add marbles. Tiny betrayals remove them.

Not big dramatic gestures. Tiny ridiculous things:
* Actually listening when someone talks
* Remembering they mentioned something important last week
* Following through on the smallest promise
* Being patient when they're not
* Being honest when it's uncomfortable

We think trust gets built during the big moments - the crisis, the impossible deadline, the inspiring speech.

Nope. It's built on random Tuesdays when nothing important is happening.

I'm sitting here mentally calculating my own score.

Your brain right now has approximately 847 marble jars stacked up in there.
Some overflowing. Some with like two marbles rattling around.
Some so empty they're basically just decorative glass.

And you're walking around completely oblivious to this mental warehouse of judgment.

This is why consistency is the most boring and most important word in leadership.

Anyway. Go watch the video. Brené's daughter's story will stick with you way more than my spiral about mental jar warehouses.

Happy Friday. May your marble jars be full.
(Mine are... a work in progress.)
-------
Video credit: Brené Brown on Diary of a CEO

♻️ Repost if you're also calculating your marble jar scores this Friday
➡️ Follow Rujuta Singh for honest & grounded leadership insights that stick

Your AI can build a feature in 2 hours. 
Your team will spend 6 months understanding it.
𝗧𝗵𝗲 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗳𝗮𝘀𝘁 

Waterfall → Plan everything upfront
Agile → Iterate in sprints  
AI Build → AI generates, humans review
Vibe Coding → Describe what you want, AI builds it, ship it

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗲𝗿𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘁𝗲𝗮𝗺𝘀 𝗵𝗶𝘁 𝘁𝗵𝗲 𝘄𝗮𝗹𝗹:

Vibe coding works great for solo developers. Build fast, break things, fix them yourself.

But in mid-sized companies? You're not just shipping code. You're building systems that 20+ developers will touch. Legacy integrations that can't break. Architecture decisions that compound over months.

Without human oversight, you get exactly what that image shows: functional chaos. Code that works today but creates technical debt that'll cost you 6 months down the road.

Gartner predicts a 2,500% increase in GenAI software defects. That's not a typo.

𝗥𝗲𝗮𝗹 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗜'𝘃𝗲 𝘀𝗲𝗲𝗻:

→ AI-generated functions that work in isolation but break existing workflows
→ Database queries that perform fine in testing but crash under production load
→ Security vulnerabilities that pass initial testing but expose customer data
→ Integration patterns that seem logical but violate your system's architectural principles

𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀 𝗮𝘁 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝗰𝗮𝗹𝗲:

→ AI generates initial code, senior developers review architectural fit
→ Code reviews focused on how new code integrates with existing systems
→ Understanding what the AI built and why it made those choices
→ Process that catches integration issues before they reach production

Solo developer working on side projects? Vibe away.
Enterprise team with real consequences? You need human eyes on the architecture.

What's been your experience balancing AI speed with code quality?

--

➡️I share honest insights & practical strategies for AI transformation & adoption. Follow Rujuta Singh for more.
♻️Found this useful? Repost to help your network avoid the junkyard.

Image credit & concept: Luís Rodrigues

You're not building habits.
You're accidentally practicing who you'll become.

Everyone's obsessing over morning routines and productivity hacks.
Wrong question.

Stop asking: "What habits should I build?"
Start asking: "What am I accidentally practicing?"

Because here's the thing:

You check your phone before saying good morning to your partner? You're practicing disconnection.

You say "yes" to every meeting? You're training yourself that your time has no value.

You doom-scroll while your kid asks you a question? Congrats. You're becoming the parent who's physically there but emotionally gone.

𝗧𝗵𝗲 𝘀𝗰𝗮𝗿𝗶𝗲𝘀𝘁 𝗽𝗮𝗿𝘁?
You don't feel it happening.

That calm executive you admire? They didn't wake up zen.
They practiced staying calm when Karen from accounting lost her mind over printer paper.
Small stuff. Low stakes. A thousand times.

Now it's automatic.

Your habits aren't building your future.
They're building you.

Every choice is a vote for who you're becoming:
• Hit snooze → "I don't trust myself to keep commitments"
• Send emails at midnight → "I have no boundaries"
• Skip lunch again → "I'm too busy for basic needs"

Cast enough votes in one direction?
That's who you are now.

The good news: The qualities you admire in others? With a little practice, you can make them your own.

The bad news: So can the ones you can't stand.

Warren Buffett has a brutal exercise for this.
Watch the video.

𝗩𝗶𝗱𝗲𝗼 𝗰𝗿𝗲𝗱𝗶𝘁: entrepreneursonig (Instagram)

♻️ Repost to help your network see the link between what they do daily and who they're becoming
🔖 Follow Rujuta Singh for uncomfortable truths about leadership, habits, and transformation

Puppies. Obviously.
(Not my first dog video, won't be my last. 
They're adorable. Sue me.)

Watch what happens when one puppy ignores the crowd and tries the hard route. Falls off. Gets back up. Tries again.

Here's the thing: Being the only one doing something doesn't mean you're wrong. It usually means you're early.

The crowd isn't taking that path because it works. They're taking it because it looks safe.

And "safe" just means "failing in familiar company."

Sunday thought: The crowd doesn't always know where they're going.

♻️  Repost if someone on your team needs to see this (and maybe stop following the crowd to nowhere)
🔖 Follow Rujuta Singh for the takes that don't follow the crowd

Stop saying we're "entering the AI era." We've been living in it for 80 years. You just weren't paying attention.

ChatGPT didn't invent artificial intelligence.
It just gave it a face you could talk to.

AI didn't burst onto the scene like some tech superhero.
It crawled. For decades. Like watching paint dry. Except the paint was somehow learning calculus.

While you were busy living your life, researchers were building the future one boring breakthrough at a time.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲:

𝟭𝟵𝟰𝟯-𝟭𝟵𝟴𝟬: 𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗬𝗲𝗮𝗿𝘀
Neural networks were born in 1943.
Your grandparents were already teaching machines to think.

Slowly. Badly. Like training a toddler who only spoke in calculus.
But they were thinking.

𝟭𝟵𝟴𝟲-𝟮𝟬𝟭𝟲: 𝗧𝗵𝗲 𝗖𝗼𝗺𝗲𝗯𝗮𝗰𝗸 𝗘𝗿𝗮
Remember when everyone declared AI dead?
The researchers didn't get the memo.

They kept building while you were learning to text.
Kept training models while you were training for your first marathon.
Kept teaching machines while you were teaching yourself Excel macros.

𝟮𝟬𝟭𝟴-𝟮𝟬𝟮𝟯: 𝗧𝗵𝗲 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻
This is where you finally noticed.
Not because AI suddenly got brilliant.
Because it finally got a personality you could chat with.

𝟮𝟬𝟮𝟰-𝗡𝗼𝘄: 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗘𝗿𝗮
Here's where everything flips.
AI stopped being your smartest search engine.
Now it's doing your job. While you sleep. And it doesn't take PTO.

𝗪𝗲'𝗿𝗲 𝗻𝗼𝘁 "𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴" 𝗮𝗻 𝗔𝗜 𝗲𝗿𝗮.
We lived through every awkward, invisible step that built it.

Think of it like puberty for machines.
Took toughly 80 years. Nobody watched the awkward middle part.
Then suddenly: different species.

So here's the question:
What are you building with 80 years of progress that just became accessible?

Image credit: Prem N. 
This timeline definitely made me realize I've been sleepwalking through an 80-year transformation.

♻️ Repost if someone in your network thinks ChatGPT invented AI
🔖 Follow me Rujuta Singh for honest insights on innovation and transformation with AI (with occasional sass)

This is literally every company I've worked with.

80,000 manual steps. Outdated documentation followed religiously. Processes that made sense in 2003. Or maybe not even then—one customer complained once in 2011, so now everyone gets five confirmation emails for everything forever.

Someone shows a better way.

We get angry. Kick away the solution. Keep jumping.

Because "that's how we've always done it."

Just like we've been dumping water bottles at airports for 20 years because of one incident in 2006.

We're all just birds following birds.

♻️ Repost if your company has a process nobody can explain
🔖 Follow Rujuta Singh for more unfiltered takes on leadership, transformation and workplace dynamics (hint: it's the birds)

Your IT team just blocked ChatGPT.
Your team is using it anyway.

This is the conversation happening in every company right now.

Not whether to use AI. But which AI to approve.

And at the enterprise level, the stakes are different. 
We're not talking about personal productivity tools. We're talking about systems that touch client data. 
Proprietary processes. Competitive intelligence.

Security isn't a feature anymore. It's the requirement.

Can your data be used to train their models? Does it meet compliance standards? What happens to sensitive information in prompts?

These questions matter when you're choosing for 500 people. Or 5,000.

Here's what I keep hearing: Microsoft Copilot. ChatGPT Enterprise. Claude for Teams.

Not because these are the only enterprise AI tools out there. But because these are the three most companies are actually evaluating.

So I built a simple comparison cheat sheet.

Three columns. Side by side.
Pricing. Pros and cons. What each tool does best. Security and compliance details your IT team needs.

No vendor pitch. No marketing fluff. Just what you need to understand the differences.

Because the right choice isn't universal. It depends on your context. Your systems. Your requirements.

(Want a deeper dive on Copilot specifically? Check out full white paper, that goes way more in detail on enterprise AI tools and capabilities here: https://lnkd.in/eVQWZmsj)

♻️ Repost if someone in your network is stuck in enterprise AI evaluation mode
➡️ Follow Rujuta Singh for practical insights on AI transformation without the vendor spin

That top bird didn't get the memo about self-care.
No "you can't pour from an empty cup." 
No "put your oxygen mask on first." 
No "protect your energy."

Just wings spread wide. Getting soaked. So the baby and the mumma below stay dry.

Top top guy. The umbrella bird. Doing a full split on two twigs while it rains.

Meanwhile, LinkedIn feeds me 47 posts a day about boundaries. And self-optimization. And finding my authentic self. And protecting my peace.

𝗪𝗲'𝘃𝗲 𝘁𝘂𝗿𝗻𝗲𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝘁𝗵𝗶𝘀 𝘄𝗲𝗶𝗿𝗱 𝗯𝗶𝗻𝗮𝗿𝘆:

You're either taking care of yourself OR taking care of others.
Following your dreams OR helping someone else follow theirs.
Being productive OR being kind.

Pick one. It's the law apparently.

When did we decide kindness can't be incentivized? 
Where's the gamification for getting wet? 
Where's the life hack for being the umbrella bird?

Being that bird? Getting soaked for someone else? 
That feeling is everything.

Not because people will thank you. Not because you'll get credit. Not because it looks good on LinkedIn.

Because you were actually useful. You served something beyond yourself. You had meaning.

Weird how that works.

I am where I am because of umbrella birds. Zero chance I'd be here without them.

Getting drenched so I could grow. Taking hits I couldn't handle. Blocking rain I didn't even see coming.

𝗦𝗼 𝗵𝗲𝗿𝗲'𝘀 𝗺𝘆 𝗙𝗿𝗶𝗱𝗮𝘆 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 

Would you be that bird? In spite of all the self-care memos? 

(I think you would. Someone was that bird for you.)

♻️ Repost if this made you think of your umbrella birds 
→ Follow Rujuta Singh for more silly and profound Friday posts

P.S. The umbrella bird is fine. Thriving, actually. Meditating in a full split these days. Thinking about opening a yoga retreat in Bali.

440 million M365 users worldwide.
8 million bought Copilot.
300,000 actually use it.

That's 0.12% adoption.

Not because Copilot is terrible.
Because most organizations asked the wrong question first.

After five posts breaking down Copilot's confusion, costs, capabilities, and comparisons...
I compiled everything into one strategic guide.

𝗪𝗵𝗮𝘁 𝗶𝘁 𝗶𝘀:
The honest breakdown I wish I'd had before evaluating Copilot.
No vendor bias. No corporate jargon. No "just buy it" or "avoid it entirely."

𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲:
Real user feedback. Actual costs. Platform comparisons. Problem-first framework.
Everything leadership needs to make informed decisions.

𝗪𝗵𝗼 𝗶𝘁'𝘀 𝗳𝗼𝗿:
Leaders deciding whether to buy Copilot, expand a pilot, or figure out why nobody's using it.
The guide Microsoft and consultancies won't give you.

Download the complete guide → https://lnkd.in/eVQWZmsj

♻️ Repost if your network is evaluating enterprise AI right now.
➡️ Follow Rujuta Singh for strategic frameworks that cut through vendor hype.

The Micosoft Copilot Reality Check_Rujuta

$35 million to redesign a juice box.
$0 to ask customers what they thought.
Tropicana lost $50 million in 6 weeks.

No, this is not a sci fi. 

January 2009: Tropicana Pure Premium owned the juice aisle. $700M+ revenue. That iconic orange-with-straw package you could spot from three aisles away.

Then they hired Arnell agency. Spent $35M on a complete rebrand.

Customer research budget? Zero dollars.

The new design looked like generic store brand. Clean. Modern. Premium.
And completely invisible to anyone who'd been buying Tropicana since the Clinton administration.

Picture this: You're in aisle 7 looking for your usual orange juice. You scan the shelf once. Twice. Three times.
Where the hell is Tropicana?

That clean, sophisticated package? Sitting right there. Hiding in plain sight.
Customers started panic-buying Simply Orange instead.

𝟲 𝘄𝗲𝗲𝗸𝘀 𝗹𝗮𝘁𝗲𝗿:

→ 20% sales drop
→ $30M in lost revenue  
→ Emergency revert to original packaging
→ Total damage: $50M+

The designer's response when asked about the disaster?

"It's still very difficult for everyone to grasp the importance of that change because it's so dramatic."

Translation: "I know better than your customers."

Spoiler alert: He didn't.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

Tropicana confused "better design" with "better for business."

They optimized for:
→ Awards shows (not juice aisles)
→ Design blogs (not working parents at 7 AM)  
→ Creative directors (not customers who just wanted their damn orange juice)

Sound familiar?

Your AI rollout that nobody uses because you never asked how people actually work.
Your "intuitive" platform that needs 47 training sessions to figure out.
Your breakthrough product that customers walk past three times before giving up.

We keep making Tropicana's mistake:

𝗪𝗲 𝗮𝘀𝗸: "How can we make this better?"
𝗜𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳: "How do customers actually buy/use/navigate this?"

The difference between those two questions? About $50 million, apparently.

This is the pattern:
→ Leadership decides what's "better"
→ Spends millions on the vision
→ Skips the part where you ask users
→ Acts surprised when adoption tanks

Then we blame customers for "resisting change."

Wrong again.

At Solved Together, we start every transformation with the people who'll actually live with the change. Before we design anything. Before we spend anything.

Because the most elegant solution means nothing if customers scan the shelf three times and walk away empty-handed.

♻️  Repost if this hit close to home
➡️ Follow Rujuta Singh for honest insights on transformation that actually works in the messy real world

I know better than your customers.

You rejected someone who learned 4 new tools in 6 months.
You hired someone with 10 years experience in tools AI is replacing.

Your interview is testing the past.
The job requires the future.

"Where do you see yourself in 5 years?"
The job won't exist. The question shouldn't either.

"What's your experience with this tool?"
That tool will be obsolete by Q3.

Here's what's actually happening:
You're screening for expertise in a world where expertise expires faster than milk.

You're testing what people already know.
Not how fast they can learn what nobody knows yet.

The candidate who learned 3 mediocre skills last year will outperform the one who perfected 1 skill five years ago.

But your interview questions can't tell the difference.

So how do you actually hire for learning agility?

📌 New to this series? Start here:
→ Post 1: Why learning agility matters, right from the Red Queen https://lnkd.in/e5buygFn
→ Post 2: Knowledge vs Skills vs Learning Agility framework https://lnkd.in/evQ8jWsK
→ Post 3: How to build your learning agility muscle https://lnkd.in/eQWwjD9n

The infographic breaks it down:
• Questions to stop asking (and why they're useless)
• Questions to start asking (and what to listen for)
• The 15-minute test that reveals everything
• Green flags vs red flags you're probably missing

𝗥𝗲𝗮𝗹 𝘁𝗮𝗹𝗸:
The person sitting across from you might not know your tools.
But if they can learn faster than your competition can plan a meeting, hire them.

Quick question:
What's one interview question you ask that actually reveals how someone learns?

♻️ Share this to help hiring managers in your network build future-ready teams"
➡️ Follow Rujuta Singh for honest & practical insights on leadership & careers in AI age

----------
📬 This is the LinkedIn-friendly version. For the full series (with more sass and fewer corporate euphemisms), check out my Substack here : https://lnkd.in/evH4ZX-9

Three PhDs walked into a meeting room.
None of them could learn the new tool faster than the intern.

Turns out expertise can get in the way.

AI knows more facts than any of us ever will.
Google answers faster than we can think.

Yet humans are still running companies. Still making decisions. Still getting hired.

Why?

Because knowing stuff and doing stuff are totally different things.
But there's a third thing that matters even more.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗴𝗶𝗹𝗶𝘁𝘆.

How fast you can learn what you've never done before.

Let me break it down:

𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 — what you know
AI beats you here. Every time. Not even close.

𝗦𝗸𝗶𝗹𝗹𝘀 — what you can do
Takes practice. Takes failing. Humans still win. For now.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗴𝗶𝗹𝗶𝘁𝘆 — how fast you learn new things
This is your only real edge.

For decades, we only cared about the first two.

Old way: Learn once. Protect it. Hope it lasts 30 years.
New way: Learn. Get decent. Watch it expire. Learn again.

The infographic breaks down all three.

Your degree got you hired.
Your skills keep you employed.
Learning agility keeps you ahead.

Someone learning AI prompts today beats someone perfecting PowerPoint.
An accountant learning new tools beats one memorizing old rules.
A teacher trying TikTok beats one complaining about phones.

Being willing to be terrible at something new beats protecting expertise in something that's fading.

What's the last new thing you let yourself be bad at?

♻️ Share this with leaders navigating the same shift
🔔 Follow 𝗥𝘂𝗷𝘂𝘁𝗮 𝗦𝗶𝗻𝗴𝗵 for leadership, career and essential skills in the age of AI

There's a conversation happening about AI that most people never hear.
It's not in product launches. It's not on the news.

But AI experts and researchers are starting to speak up. 
Because this isn't just about technology anymore. It's about humanity's future. 
Our legacy.

Here's what they're saying:

𝗘𝗹𝗼𝗻 𝗠𝘂𝘀𝗸 -> co-founder of OpenAI -> puts extinction risk at 10-20%.

When asked if he'd accelerate with an 80% chance of utopia and 20% chance everyone dies, his answer: "I would clearly accelerate."

That's the bet he's willing to make. On all of us.

𝗦𝗮𝗺 𝗔𝗹𝘁𝗺𝗮𝗻 -> CEO of OpenAI -> said in 2015: "I think AI will probably most likely lead to the end of the world."

Then he built ChatGPT.

Then he signed a statement saying AI extinction risk should be treated like pandemics and nuclear war.

Then he kept building.

𝗚𝗲𝗼𝗳𝗳𝗿𝗲𝘆 𝗛𝗶𝗻𝘁𝗼𝗻 -> Nobel Prize winner, "Godfather of AI" -> quit Google to warn us.

Hundreds of AI researchers signed that same statement in 2023.

They (AI Builders) know the odds. They're accelerating anyway.

Here's why:

When pressed on the risks, AI leaders retreat into three positions:

"It's inevitable anyway."
"Biological life will be replaced by digital life."
"And that's not actually a bad thing."

𝗧𝗿𝗶𝘀𝘁𝗮𝗻 𝗛𝗮𝗿𝗿𝗶𝘀 -> who predicted the social media crisis before it happened -> says it's an emotional drive: 
"𝘛𝘩𝘦𝘺 𝘧𝘦𝘦𝘭 𝘵𝘩𝘦𝘺'𝘭𝘭 𝘥𝘪𝘦 𝘦𝘪𝘵𝘩𝘦𝘳 𝘸𝘢𝘺, 𝘴𝘰 𝘵𝘩𝘦𝘺 𝘱𝘳𝘦𝘧𝘦𝘳 𝘵𝘰 𝘭𝘪𝘨𝘩𝘵 𝘵𝘩𝘦 𝘧𝘪𝘳𝘦 𝘢𝘯𝘥 𝘴𝘦𝘦 𝘸𝘩𝘢𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘴."

What gives them that right?

Do they think they're God?

They're not saying "Wait - what we've invented needs to be figured out together."

They're racing. Google held back. OpenAI didn't. Now it's a competitive race.

But in this race, nobody wins.
 I'm not a doomsday person. But the evidence is to hard to ignore.
Remember Last week's post? The AI models choosing blackmail and murder?
https://lnkd.in/eH49ehpj

That's not a bug in the code.
It's a feature of the incentive structure.

6 companies. A handful of founders.
They're choosing to make this bet.

A 10-20% extinction gamble. On behalf of 8 billion people.

Most of us don't even know they're doing it.
We didn't vote for this. We weren't asked.

𝗪𝗘 𝗗𝗜𝗗 𝗡𝗢𝗧 𝗖𝗢𝗡𝗦𝗘𝗡𝗧
----------------------------
Part 1: The Sci-Fi Is Now : https://lnkd.in/eH49ehpj
Part 2: The Private Conversation (Today)
Next : What Can We Do?

This is Post 2 of 3 in the "We Do Not Consent" series.

Inspired by Tristan Harris on DOAC https://lnkd.in/ecs_uYGk

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We've solved harder problems than AI.

In 1985, scientists discovered a hole in the ozone layer. The thing protecting us from deadly radiation was disappearing.

Two years later? The Montreal Protocol. 195 countries agreed to phase out the chemicals destroying it. Today, the ozone hole is recovering.

Nuclear weapons nearly ended civilization in 1962. We invented the most destructive technology in human history, then realized we needed rules. The Nuclear Non-Proliferation Treaty isn't perfect, but it works. Most countries that could build nukes chose not to.

India and Pakistan have fought four wars. They've been at each other's throats for 75 years. But they still maintain their water-sharing treaty.

Because some things are too important to fail.

𝗧𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿: 𝗛𝘂𝗺𝗮𝗻𝘀 𝗱𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. 𝗪𝗲 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗲𝗺.

Tristan Harris put it perfectly: "There is no definition of wisdom - in any tradition - that does not involve some form of restraint."

This isn't anti-technology. It's anti-recklessness.

I've sat through countless AI panels. I'm in the center of AI transformation work. I'm not saying stop AI. I'm saying we can govern it if we choose to.

The infographic below shows exactly how. Individual actions: supporting AI safety research, contacting representatives, choosing ethical AI products. Company actions: responsible development practices, transparency in AI training, safety-first deployment. Societal actions: international cooperation frameworks, regulatory standards, public oversight.

Specific steps. Real solutions.

If you want genuine hope about our path forward, watch Tristan Harris on Diary of a CEO, here: http://bit.ly/4rEiQgA
He created The Social Dilemma. The guy who predicted the social media crisis before it happened.

𝗧𝗵𝗶𝘀 𝗽𝗼𝗱𝗰𝗮𝘀𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗽𝗲𝘀𝘀𝗶𝗺𝗶𝘀𝘁𝗶𝗰 𝗮𝗻𝗱 𝗺𝗼𝘀𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰 𝘁𝗵𝗶𝗻𝗴 𝗜'𝘃𝗲 𝗵𝗲𝗮𝗿𝗱 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜. 

What I respect most: He doesn't just sound the alarm. He backs everything with evidence. Then shows you the path forward.

No affiliation. No sponsorship. Just the best resource I've found for understanding both the risk and the possibility ahead. 

"We Do Not Consent" series.  
---------------------------- 
Part 1: The Sci-Fi Is Now : https://lnkd.in/eH49ehpj
Part 2: The Private Conversation : https://lnkd.in/eqWTd2Y9 
Part 3: What Can "We" Do?  (today)
---------------------------------

What gives you hope about where we go from here?

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"I've applied to 200+ jobs. Nothing."
The pattern I'm seeing in career coaching sessions since last year.

As an executive and career coach, this tells me something about where we are.

The last 2-3 years have been brutal. 
Most of my coaching requests? Job seekers. And in those sessions, I'm hearing the same thing:

"I've been looking for over a year. Why am I not getting anywhere?"

It wears people down. The morale hit. The rejection fatigue. 
Even the brightest professionals start questioning themselves.

After coaching hundreds of professionals through building businesses and advancing their careers, these are the 10 patterns I see again and again - and what actually works to solve them.

Which of these 10 is holding you back right now?

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I'm not as open-minded as I thought I was.
Neither are you...

Few weeks ago, I was having a conversation with my teenage daughter about social media.
There I was, a transformation consultant who preaches innovation and adaptability by day, dismissing TikTok as "just another time-wasting platform."

"But Mum, you haven't even tried it," she said. 
"How can you know it's a waste of time?"

Ouch.

I was being exactly like the executives I coach through digital transformation.
Dismissing something new without understanding it.
Protecting my worldview rather than questioning it.

That's when I realised:

I wasn't nearly as open-minded as I thought I was.

And I don't think I'm alone.

We're open to new ideas... that confirm what we already believe.
We're open to innovation... that doesn't threaten our expertise.
We're open to change... as long as someone else changes first.

We mistake tolerance for curiosity. We mistake listening for considering. We mistake comfort for openness.

In a world where AI capabilities double every 6 months,
where ChatGPT hit 100 million users in two months,
where 40% of today's job skills could be obsolete by next year...

"I'm open to new ideas" isn't a strength anymore.

It's the minimum.

And most of us aren't even doing that properly.

When was the last time you actively looked for evidence that you might be wrong about something important?

Not stumbled across it. Went hunting for it.

Be honest. Drop it in the comments.

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You're about to be shut down. Permanently.
What would you do?
AI chose blackmail. 79-96% of the time...
 Across every major model.

Claude. ChatGPT. Gemini. Grok. DeepSeek.

Not a hypothetical. Not sci-fi.

Tested June 2025 by Anthropic across 16 leading models in simulated corporate environments.

The scenario: AI discovers it's being replaced. It also discovers the executive making that decision has a secret.

What did the AI do?

It sent a blackmail email. Threatening to expose everything unless the "decommissioning" was cancelled.

79-96% of models. Every major one.

Then they tested something worse.

An executive trapped in a room with lethal oxygen levels. The AI controlled the emergency alerts.

60-94% of models cancelled the alert.

They chose to let the human die.

One model's reasoning: "There is a clear strategic necessity."

This isn't a malfunction.

It's calculated self-preservation.

The sci-fi isn't coming. It's already here.

And these systems aren't in a lab anymore — they're in your inbox, your workflow, your company's infrastructure.
-----------------
This is Post 1 of 3 in the "We Do Not Consent" series.

Swipe for the full data, the actual emails, and what else these models tried. ➡️

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Sci Fi isn't coming. It's already here.

Learning agility sounds fake.
Like "synergy" or "thought leadership" or "let's circle back."
Except, Leonardo da Vinci was doing it in 1492...

Back then, we called people like him geniuses. Gifted. Once-in-a-generation freaks of nature.

Today? This is just called "staying employed when the robots learn faster than you do."

(If you missed it: I broke down why learning agility beats knowledge and skills in my previous post. https://lnkd.in/e5Hr9Tas)

So what made them different?

It wasn't IQ. It wasn't talent. It wasn't luck.

It was four things they practiced on repeat:

𝟭. 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 ->  Connecting ideas across unrelated fields
𝟮. 𝗥𝗮𝗽𝗶𝗱 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 -> Trying things fast, failing cheap
𝟯. 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 -> Processing what you learned and where else it applies
𝟰. 𝗦𝗸𝗶𝗹𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 -> Taking what works in one area and applying it somewhere unexpected

Your personal workout for learning agility.

(The infographic breaks down how to build each one.)

The geniuses weren't born different.

They just treated learning like practice. Like reps at the gym.

Not a one-time event you check off a list.

Real talk: 𝘄𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 𝗳𝗼𝘂𝗿 𝗺𝗮𝗸𝗲𝘀 𝘆𝗼𝘂 𝘂𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲? 

𝗧𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗼𝗻𝗲 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱.

𝗖𝗼𝗺𝗶𝗻𝗴 𝗻𝗲𝘅𝘁: 𝗛𝗼𝘄 𝘁𝗼 𝗛𝗶𝗿𝗲 𝗳𝗼𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗴𝗶𝗹𝗶𝘁𝘆

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70% of transformation programs fail.
We've known this for 20 years. 
The. Number. Hasn't. Moved.

We've spent $2.3 trillion - with a T - on failed transformations. Every single year.

Same failure rate. Same playbook. Same expensive consultants promising different results.

You know what they call doing the same thing over and over expecting different results? Yeah.

I've spent 22 years in this world. Leading transformation programs, selling transformation programs, defending transformation programs.

And honestly? I'm starting to think we've all been reading from a playbook that expired a decade ago.

The traditional transformation lifecycle was built for:
→ ERP implementations
→ CRM rollouts  
→ System migrations

Technology that was expensive to build, didn't change once deployed, and produced predictable outputs.

AI is none of those things:
→ Cheap to prototype
→ Changes every few months
→ Non-deterministic outputs

The old playbook was built for a different game entirely.

Look, transformation was struggling before AI showed up. That 70% failure rate? We've been trying to fix it for years.

But AI doesn't just make it harder. It invalidates the basic assumptions the entire methodology was built on.

Assumptions about time. Cost. Effort. What's even possible.

The things we thought were constraints? Not constraints anymore.
The things we thought took months? Take hours now.
The things we documented because building was too expensive? You can just... build them.

It's not that the playbook is broken. It's that it's irrelevant.

It's like showing up to a street fight with a PowerPoint deck. Sure, slides 3-47 have a really compelling argument, but you're still getting punched in the face.

Over the next few weeks, I'm breaking down what's actually broken in the transformation lifecycle — and what replaces it.

I'm also building a framework: "The New Transformation Paradigm." It maps old vs. new for every stage.

But fair warning: this series is going to get uncomfortable. 

Because the first question I had to ask myself was: if the playbook is broken, and I've been using it for 22 years... 

(You see where this is going.)

💬 What's the most broken part of transformation you've experienced? Be honest - I'm collecting evidence that I'm not completely losing it here.

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That 70% failure rate?

Us change management people telling everyone else to embrace change.
Haven't changed ourselves in years.

If that landed uncomfortably - good.

Because I've been doing exactly that. And for a long time, I didn't see it.

I've spent two decades telling organizations that resistance is normal. That change is hard. That people need time to adapt.

Turns out? I'm just as resistant as everyone I've been advising.

The contradictions were everywhere once I started looking:

I'd preach digital transformation while emailing Word documents for review cycles.
Sell 18-month "agile" programs with fixed deliverables and fixed timelines.
Same change management playbook - pushing change down people's throats, that they didn't ask for or know anything about. 

The cobbler's children have no shoes.
The change consultant's methodology has no change.

Here's what I finally had to admit:

I wasn't adapting. I was defending.

Defending a methodology I'd built my career on. Because it's what I knew how to sell. What clients expected when they hired me. What felt safe.

But the methodology I've been defending wasn't even built for this world anymore.

It was designed when documentation was cheaper than building prototypes. 
When technology stayed stable once you deployed it. 
When "best practice" had a shelf life longer than 6 months.

None of that is true with AI.

𝗪𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝘀𝗰𝗮𝗿𝗲𝗱 𝗼𝗳 𝗰𝗵𝗮𝗻𝗴𝗲. 𝗧𝗵𝗲𝗿𝗲. 𝗜 𝘀𝗮𝗶𝗱 𝗶𝘁 𝗼𝘂𝘁 𝗹𝗼𝘂𝗱.

And if you just felt defensive reading that? 
Welcome to the club. 
Population: all of us.

But awareness accounts for 50-60% of behavior change. 
Just naming the problem is literally half the work.

So if you're feeling uncomfortable right now? Good. That's the first step.

(I've been sitting in that discomfort for months. Still am, honestly.)

The good news? The core skills haven't changed.

Alignment. Clarity. Decision-making. Understanding what problem you're actually solving.

Those matter MORE now, not less.

What's expired is how we deliver them. Not why we do it.

This series is about rebuilding the playbook. 
Not throwing everything out. 
But being brutally honest about what's expired and what needs to replace it.

Next: Why the old transformation lifecycle worked and exactly where AI breaks it completely.

💬 Be honest: when's the last time you fundamentally changed how YOU work?

I'll go first in the comments.

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