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.

I spent a year memorizing the periodic table.
Never once saw what lithium actually looks like.
AI showed me in 30 seconds.

Silver-white. Almost metallic snow.
Then copper. Reddish glow beneath the surface.
Then gold. That unmistakable warmth.

Chemistry class never showed us this.

We memorized "Cu 29" on charts. Recited "Li 3" for tests.
But we never SAW what copper actually looks like when you cut it open.
Never understood why lithium powers our phones.
Why gold conducts electricity differently than silver.

𝟳𝟮% 𝗼𝗳 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 𝘀𝗮𝘆 𝗰𝗵𝗲𝗺𝗶𝘀𝘁𝗿𝘆 𝗳𝗲𝗲𝗹𝘀 𝗮𝗯𝘀𝘁𝗿𝗮𝗰𝘁. (National Science Foundation, 2023)

Because we taught symbols instead of substance.

This AI visualization cuts through periodic table abstractions. Literally.
Shows the grain structure of titanium that makes airplane wings strong.
The crystalline lattice of silicon that powers every chip in your laptop.
The ductility of aluminum that shapes your car frame.

We're finally bridging the gap between knowing and seeing.

Instead of asking students "What's the atomic number of iron?"
Ask them "Why does iron rust but gold doesn't?"

Instead of memorizing orbital configurations,
Let them explore why lithium batteries hold charge differently than alkaline.

Teachers can replace static Mendeleev charts with simulations that show reactivity in real-time.
Students can ask AI why potassium explodes in water while sodium just fizzes.
Connect copper's conductivity to the circuits in their phones.
Show how carbon's bonding flexibility makes both diamonds and graphite possible.

Real-world stakes. Not abstract symbols.

Education is about to get visceral again.

P.S. Video NOT created by me and has a few inaccuracies. Shared  with the hope to inspire more interactive learning. 

♻️ Repost if you learned chemistry from symbols, not substances.
→ Follow Rujuta Singh for insights on how technology transforms how we understand our world.

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A purple dinosaur just walked off my screen.
Started as a crayon scribble. Now it's strutting around like it owns the place.

My brain is officially broken.

The drawings in this video are perfectly ridiculous. 
A butterfly with seventeen wings. 
A car that's somehow also a sandwich. 
A princess riding what might be a horse or possibly a very confused octopus.

𝗧𝗵𝗲 𝘄𝗲𝗶𝗿𝗱𝗲𝗿 𝘁𝗵𝗲 𝗱𝗿𝗮𝘄𝗶𝗻𝗴, 𝘁𝗵𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗶𝘁 𝗴𝗲𝘁𝘀.

That car-sandwich? Perfect.  The confused octopus-horse? Chef's kiss.  The more irrational the sketch, the more surreal and magical it becomes when it moves.

That moment when the sketches started moving - you can actually hear the kids gasp. 
Wonder happening in real time. 
Imagination meeting reality and having a party.

𝗔𝗜 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘄𝗼𝗻𝗱𝗲𝗿. 𝗜𝘁 𝗴𝗶𝘃𝗲𝘀 𝗶𝘁 𝗹𝗲𝗴𝘀.

Tools like RunwayML make this magic accessible now. No degree required. Just upload and watch creativity come alive.

This is technology doing what it should. Making impossible things possible.

Starting with purple dinosaurs who definitely deserve to exist.

♻️ Repost if your inner kid just did a happy dance watching this.
➡️ Follow Rujuta Singh Singh for more AI that sparks joy instead of anxiety.

Everyone's arguing about which AI is best.
Like arguing whether wheels or engines matter more.
You need the whole damn car.

Alex made this gorgeous infographic that finally explains why everyone's confused.

𝗔𝗜 𝗶𝘀𝗻'𝘁 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴. 𝗜𝘁'𝘀 𝗳𝗶𝘃𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘀𝘁𝗮𝗰𝗸𝘀 𝗼𝗳 𝗵𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗼𝗳 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀.

𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
Data predictions, forecasting, fraud detection
AWS SageMaker, Google Vertex AI, Azure ML

𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀
Pattern recognition in images, audio, text
TensorFlow, PyTorch, AWS Rekognition

𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜
Content creation - text, images, code
ChatGPT, Claude, Gemini, Midjourney

𝗟𝗮𝘆𝗲𝗿 𝟰: 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
Autonomous task execution - bookings, CRM updates, ticket handling  
LangChain, CrewAI, Microsoft Copilot

𝗟𝗮𝘆𝗲𝗿 𝟱: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
End-to-end orchestration - multiple use cases working together, not in isolation
Claude Code, OpenAI Codex, Devin

𝗤𝘂𝗶𝗰𝗸 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻:
→ Layer 1: Your calculator that learned statistics
→ Layer 2: Your brain's visual cortex, digitized
→ Layer 3: Shakespeare's ghost, but caffeinated
→ Layer 4: The intern who never forgets and works weekends  
→ Layer 5: Your entire company, choreographed like a flash mob (but productive)

Most boardrooms are stuck arguing about Layer 3 chatbots.
Meanwhile, their fastest competitors already deployed Layer 5.

90% of "AI initiatives" are actually Layer 3 pilots that ignore the other four layers exist.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗻𝗼 𝗼𝗻𝗲'𝘀 𝘀𝗮𝘆𝗶𝗻𝗴: 

𝗥𝗲𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗽𝗶𝗰𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗳𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗹𝗮𝘆𝗲𝗿.

It's understanding how they interconnect. How your Layer 1 predictions feed Layer 4 agent decisions. 

How Layer 2 pattern recognition powers Layer 3 content. 

How Layer 5 orchestrates all of it - not as isolated use cases, but as one end-to-end system where everything talks to everything.

The companies winning this aren't the ones with the shiniest demo.

They're the ones who see the full stack while everyone else is squinting at individual pieces.

Where is your organization on this map?

Infographic inspiration Alex Barády, thanks buddy! 

♻️ Repost if someone on your team needs to see this
➡️ Follow Rujuta Singh for practical & honest takes on AI transformation that actually work

Rujuta Singh

A baker just out-marketed an entire advertising agency. In one afternoon.

I'm watching this invideo AI ad on repeat.

A baker. With flour on her apron. Creates cinema-quality advertising in one afternoon. 
Without leaving her kitchen.

My brain is officially broken.

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗰𝗼𝗼𝗹 𝘁𝗼𝗼𝗹. 𝗜𝘁'𝘀 𝗮 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗵𝗶𝗳𝘁.

But here's the thing. This isn't about advertising. Or videos.

This is about customer expectations fundamentally changing. Everywhere.

Remember when international mail took weeks? 
Now you're annoyed if your package from Tokyo takes more than two days.

Remember when you needed a solicitor just to understand your basic legal rights?
Now ChatGPT explains contract clauses at 2am while you're in your pajamas.

Remember when Airbnb disrupted hotels? Uber changed transport?
Each revolution picked ONE sector.

AI isn't picking.
It's hitting everything. Simultaneously.

𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗮𝗰𝗿𝗼𝘀𝘀 𝗘𝗩𝗘𝗥𝗬 𝘀𝗲𝗰𝘁𝗼𝗿:

Medical diagnosis? Patients show up with AI-researched questions.
Education? Kids expect personalized learning, not one-size-fits-all lectures.
Financial advice? Your wealth manager better offer more than what Copilot can give.
Architecture? Clients arrive with AI-generated designs saying "something like this."

Two years from now, every industry will look unrecognizable.

This ad? 
First Avengers teaser vibes.
A tiny glimpse of what's coming.

Are we terrified? Absolutely.
Are we excited? Also absolutely.

Has any generation felt both emotions this intensely... at the same time?

In my strategy sessions, I ask CEOs and founders one question:
"Where do you play?"

Now I'm asking us as humans the same thing:
Where do WE play?

What do we offer that AI can't?
Strategy. Judgment. Cultural nuance. Emotional intelligence. Context.

The tools got scary powerful.
The humans using them better get scary intentional about where they add value.

Where do you see AI having the biggest impact?

Video credit: theartificialintelligence on IG

♻️ Repost if you're equal parts terrified and thrilled.
➡️ Follow Rujuta Singh for more reality checks on what's actually happening in the world of AI and business transformation.

Branko Matijevic

Every boss is looking for the same thing.
Obama breaks down why so few people have it.
Watch him explain it.

The rarest skill isn't being smart or articulate.
It's being someone who actually solves problems instead of just explaining them well.

Most people are brilliant explainers. They'll analyze the hell out of any problem. Write thoughtful emails about potential roadblocks. Present gorgeous slides about theoretical solutions.

But ask them to actually own it? Crickets.

These days, it is getting even weirder. Because I can tell an AI agent "handle this project" and it responds instantly. No self-doubt. No "but what if." 
Just "I'll take care of it."

As an executive and career coach, I see this all the time with young professionals. 
Both hope and worry in their eyes. Smart. Capable. Hardworking.

But when it's time to claim ownership? They freeze.

It's not capability they're missing. It's conviction.

Imagine: Your boss mentions the client onboarding is a disaster. 
Everyone nods thoughtfully. Someone suggests a committee. Another person wants to "circle back on it."

One person says "I'll fix it by Friday."

That person becomes irreplaceable.

We're living through the biggest career shift in decades. 
The old playbook is toast. AI is automating everything that can be scripted.

But you know what can't be automated? Someone who takes real ownership and delivers.

Fresh grads - this is your secret weapon.

Not your GPA. Not your internship at that fancy company. 
Your willingness to say "Leave it with me" and actually follow through.

Trust gets built one delivery at a time.

Repost this if you know someone starting their career who needs to hear it.
Follow Rujuta Singh for practical & honest insights on leadership & career you won't learn in any MBAs

2025 erupted with agentic AI.
LangGraph. CrewAI. AutoGen. Make. n8n. LangChain.

Everyone's racing to build agents.
Most picked the wrong framework.

Not because the tools are bad.
Because nobody told them which does what.

Here's the breakdown that actually helps👇

1️⃣ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 https://lnkd.in/e57R8p4F
↳ Build stateful, multi-agent workflows with full control over agent interactions
✅ Perfect for complex AI systems that need precise orchestration and state management

2️⃣ 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 https://lnkd.in/erUmyiK9
↳ Rapid prototyping framework for LLM applications with pre-built components
✅ Ideal for developers who want to build AI apps fast without reinventing the wheel

3️⃣ 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 https://lnkd.in/ehpKxaR9
↳ Microsoft's framework for autonomous agents that collaborate to solve problems
✅ Perfect for research teams and complex problem-solving scenarios

4️⃣ 𝗖𝗿𝗲𝘄𝗔𝗜 https://www.crewai.com
↳ Role-based AI agents working together like a real team
✅ Ideal for business workflows where different AI agents need specific roles

5️⃣ 𝗠𝗮𝗸𝗲 https://www.make.com
↳ Visual automation platform connecting apps and AI without code
✅ Perfect for non-technical teams automating workflows with AI integration

6️⃣ 𝗻𝟴𝗻 https://n8n.io
↳ Open-source workflow automation with AI capabilities
✅ Ideal for teams wanting full control and customization without vendor lock-in

𝗧𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘁𝗿𝗲𝗲:
▪️ Creating team-based workflows? → CrewAI
▪️ Need complex orchestration? → LangGraph
▪️ Building research systems? → AutoGen
▪️ Want quick prototypes? → LangChain
▪️ Open-source flexibility? → n8n
▪️ No-code automation? → Make

The right framework depends on your use case.
Not the hype. Not what's trending on Twitter. Not what everyone else picked.

Start with the problem. Then pick the tool.

📸 Visual magic by Prem N. 

♻️ Repost if you've seen teams pick tools before problems
➡️ Follow Rujuta Singh for honest takes on AI Strategy & Transformation, strategy

Two wolves. Same frozen lake. Same thirsty pack.
One ended up alone. The other started a movement.

Two packs. Frozen lake. One pathetic hole in the ice.

𝗪𝗼𝗹𝗳 𝟭: Struts to the hole. Drinks first. His pack stands in line like good little soldiers.

We've all worked for this guy.

𝗪𝗼𝗹𝗳 𝟮: Does something that looks completely stupid. Instead of drinking, starts jumping on the ice. Breaking it. Making chaos.

His pack stares at him like he's lost it.

Then a lightbubl goes off....

𝗘𝘃𝗲𝗿𝘆 𝘀𝗶𝗻𝗴𝗹𝗲 𝘄𝗼𝗹𝗳 𝗮𝗯𝗮𝗻𝗱𝗼𝗻𝘀 𝗪𝗼𝗹𝗳 𝟭. They sprint over and start copying Wolf 2. Jumping. Smashing ice. Turning that sad little hole into a wolf water park.

Wolf 1 ends up drinking alone from his tiny puddle.
Wolf 2's new mega-pack is sharing a lake.

Awkward.

𝗬𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗸𝗻𝗼𝘄𝘀 𝘄𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝘆𝗼𝘂 𝗮𝗿𝗲.

Boss vs leader? The difference is stupidly simple:

→ Bosses hoard. Leaders expand.
→ Bosses command. Leaders demonstrate.  
→ Bosses protect turf. Leaders create abundance.

Wolves figured this out before we did.

Shout out to Luís Rodrigues for the video inspiration

♻️ Repost to show your network this difference firsthand
→ Follow Rujuta Singh for practical & grounded leadership lessons that MBAs don't teach you

Your AI strategy is backwards.
Everyone's asking: "Copilot or ChatGPT or Claude?"
Wrong question.

Stop asking: "Which AI tool should we buy?"
Start asking: "What problem are we solving?"

Everyone approaches AI backwards.
"Should we get Copilot?" "ChatGPT Teams?" "Claude?"

That's tool-first thinking. It leads to expensive mistakes.

𝗧𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵:
Problem first. Map solutions. Evaluate YOUR context.

Example: "We need to search company documents."
Most think: "That's Copilot, right?"

But there are 3+ options. Different costs. Different timelines.
The "best" depends on YOUR scale, budget, and infrastructure.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴.
Problem first. Then solutions. Then YOUR context.

Not: "Everyone's buying Copilot, so should we."

AI tools change every month.
Your business problems don't.

That's why you start with the problem, not the platform.

Swipe for the complete problem-first framework →

♻️ Repost if your network needs to move from tool-first to problem-first thinking.
➡️ Follow Rujuta Singh for strategic frameworks on enterprise AI - not vendor hype.

Which AI tool should we buy?

Processing 100 pages of documents cost $8 in 2023.
Today? Four cents.

Intelligence prices collapsed.

Here's what happened to AI pricing in 18 months:

First, understand the pricing. AI companies charge by tokens. Tokens are chunks of text. Roughly 750 words = 1 million tokens.

Now watch the collapse:

→ GPT-4: $30-60 per million tokens (2023) → $3-10 (2025) [83% cheaper]
→ Budget models: $0.15-0.60 [99% cheaper than 2023]
→ DeepSeek V3: $0.27
→ Grok 4.1: $0.20

Building the models got cheaper too:

→ GPT-4 (2023): $100M+ estimated
→ DeepSeek V3 (2025): $5.6M claimed

Physical AI followed the same pattern. 
Humanoid robots went from "not for sale" in 2023 to $5,900 delivered in mid-2025. In 18 months.

Compare this to other technologies:

→ Internet bandwidth: 90% cost drop over 10 years
→ Solar panels: 85% cost drop over 10 years
→ AI intelligence: 95-99% cost drop in 18 months

This is the fastest price collapse in tech history.

What you get to build now:

→ Your coffee shop runs PhD-level market analysis
→ Your weekend project affords 24/7 AI customer support
→ Solo developers get enterprise coding tools
→ Your startup processes legal contracts for pennies
→ Your global team translates in real-time for pennies

The question changed.
Not "do we have budget for this?"
Now it's "what should we build?"

Your impossible idea became a weekend project.
Your "someday when we have budget" conversation died.

The printing press dropped knowledge costs 99% over 50 years.
AI dropped intelligence costs 99% in 24 months.

Swipe for the full price war timeline, what collapsed when, what becomes possible now and why this changes everything  →

The cost barrier died in 2025.
It's not cost anymore, it's knowing what to build. 

So what do you build when intelligence is basically free?

---
[Part 7 of 'Why 2025 felt so different' series]

Part 1 → Turing Test Died, no one came for the funeral https://lnkd.in/ebAgXswf
Part 2 → We ran out of tests for AI https://lnkd.in/eGpdgQjW
Part 3 → The Year Startup Playbook Broke https://lnkd.in/eQqUDVkz
Part 4 → Physical AI left the lab https://lnkd.in/epfikcAV
Part 5 → The rise of agentic AI https://lnkd.in/eaZP4KwC
Part 6 → Open source AI got serious https://lnkd.in/eiWe8np7
Today → Intelligence went on clearance
Next Up → AI got sued (a lot...)

♻️ Repost to help your network understand why 2025 changed everything and what became possible
→ Follow Rujuta Singh for surprising insights on technology & business transformation that actually matter

The cost barrier died in 2025.

Think this startup data doesn't apply to you?
That's exactly the problem.

I thought that too. So I started looking under the bonnet.

Turns out, the startup playbook didn't just change. It broke.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗳𝗼𝘂𝗻𝗱:

Cursor hit $100M in revenue in 12 months. With 20 people.

When Slack did the same thing in 2.5 years back in 2016 with 500+ employees, they were called the fastest-growing enterprise software company ever.

Cursor did it twice as fast with 96% fewer people.

Lovable? Eight months to $100M with 45 people.

Bolt? $20M in two months with 15 people.

(And honestly, I'm over here wondering what my team did all year.)

𝗧𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗴𝗲𝘁𝘀 𝘄𝗶𝗹𝗱𝗲𝗿:

By December 2025, 81 new unicorns in the US alone. Over 100 globally.

53% of them? AI companies.

(At this rate, "unicorn" will mean as much as a verified checkmark.)

But here's the number that made me do a double-take:

Revenue per employee for these 2025 unicorns is 𝟴𝟯% 𝗵𝗶𝗴𝗵𝗲𝗿 than older cohorts.

Same market outcomes. Fraction of the headcount.

𝗦𝗼 𝘄𝗵𝗮𝘁'𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗵𝗲𝗿𝗲?

It's not just "we have AI tools now."

The entire stack changed.

AI is handling work that used to require hiring.

Distribution happens through channels that didn't exist in 2022.

And your customers? They expect AI-native as baseline now.

And the 10-year startup journey? Compressed into 12 months.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀 𝗶𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗻 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗰𝗼𝗺𝗽𝗮𝗻𝘆:

Think you're insulated because you're established?

Because you've been around for decades, have market share, or operate in a "protected" sector?

Watch this:

10-15 person unfunded teams are building multi-million dollar companies in 6-8 months. In every sector. Right now.

Your competition isn't just other enterprises anymore.

It's 20-person teams pulling $500M in revenue. Moving at speeds that seemed impossible three years ago.

The playbook you learned? It expired.

And the planning cycles, hiring models, and decision-making speed built for the old world?

They won't keep you relevant in the next two years.

This isn't a scare tactic. It's a wake-up call.

This is exactly why I started doing what I do now. 

Helping senior leaders build resilient strategies for what's coming.  Not just an AI strategy. An AI-integrated business strategy that helps you thrive, not just survive.

It starts with asking very different questions.

(This is what we do in my Strategy in a Day sessions.)

---
[Part of 'Why 2025 felt so different' series]
Last Week → Turing Test is dead and we ran out of test
This Week → Startup playbook changed

♻️ 𝗥𝗲𝗽𝗼𝘀𝘁 if this changed how you see your competition. 
𝗙𝗼𝗹𝗹𝗼𝘄 Rujuta Singh for more reality checks on technology & innovation

The playbook you learned? It expired.

"Perfect" = exactly one approved answer.
"Interesting" = well, infinite possibilities.

𝗣𝗲𝗿𝗳𝗲𝗰𝘁 is that thing everyone's chasing. The single right way. The one correct template. Very fixed. Very serious.

𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴, though? That could be anything. Messy. Unexpected. Weird. Delightful. Almost freeing.

Perfect says "do it right."
Interesting says "do it differently."

What if we just...swapped them?

"That sounds perfect" becomes "That sounds interesting" 
"How do I make this more perfect?" becomes "How do I make this more interesting?" 
"I want the event to be perfect" becomes "I want the event to be interesting"  

"You look perfect"  becomes "You look interesting"  
(Oops...filed under: compliments that backfire...)


Wait...

If the opposite of interesting is boring... 
And we're saying perfect is the opposite of interesting... 
Doesn't that technically make perfect = boring?

Which would explain why no one remembers your (and mine) perfectly polished presentation.

Maybe today, skip perfect. Try interesting instead. 
Let's see what happens

Most project management advice tells you how to work faster. This isn't that. This is about questioning whether half your job should exist in the first place.

The best PMs in 2026 aren't working harder. They're thinking differently.

They're not asking "How do I do this faster?"
They're asking "Does this even need to be done this way?"

That's AI-native thinking.
And it's a completely different approach to project management.

𝟭𝟭 𝘄𝗮𝘆𝘀 𝘁𝗼 𝗿𝗲𝘁𝗵𝗶𝗻𝗸 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜:

𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗦𝗲𝘁𝘂𝗽

1️⃣ 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴
↳ Feed your requirements into AI. Get a draft plan to work from instead of starting with a blank page.

2️⃣ 𝗧𝗲𝗮𝗺 𝗔𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁
↳ Put team skills and project tasks into AI. Let it suggest matches instead of just picking whoever's available.

𝗗𝗮𝗶𝗹𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀

3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴
↳ Feed scattered updates into AI. Get a coherent status report instead of copying and pasting from 15 sources.

4️⃣ 𝗠𝗲𝗲𝘁𝗶𝗻𝗴𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁
↳ Record meetings. Let AI pull out action items so you're not frantically taking notes while trying to participate.

5️⃣ 𝗖𝗹𝗲𝗮𝗿 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻
↳ Give AI your update. Ask it to write versions for different audiences (executives want summaries, teams want details).

Because apparently everyone speaks a different language in the same company.

𝗥𝗶𝘀𝗸 & 𝗤𝘂𝗮𝗹𝗶𝘁𝘆

6️⃣ 𝗘𝗮𝗿𝗹𝘆 𝗥𝗶𝘀𝗸 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻
↳ Ask AI to review your project data for warning signs you might miss when you're deep in the weeds.

7️⃣ 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹
↳ Run deliverables through AI for a quality check before clients see them (and silently judge your entire existence).

𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

8️⃣ 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
↳ When priorities shift (because they always do), ask AI to help you rework timelines instead of starting from scratch.

9️⃣ 𝗕𝘂𝗱𝗴𝗲𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴
↳ Feed your spending data into AI. Ask it to spot patterns and predict overruns before your boss notices.

🔟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲
↳ Put your project docs into AI. Ask questions in plain language instead of digging through 47 Slack channels.

𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁

1️⃣1️⃣ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
↳ Ask AI to analyze past projects and spot patterns. Learn what worked and what didn't before starting the next one.

None of this happens automatically.
It requires rethinking how you work.

But once you shift from "How do I work faster?" to "How should this work be done?" everything changes.

That's the 2026 advantage.

Original image & concept credits Alex Barády 

♻️ Repost to inspire your network to rethink how project management actually works
➡️ Follow me Rujuta Singh for more practical insights on working smarter with AI

2023: AI 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 things.
2025: AI 𝗱𝗶𝗱 things.
That verb matters.

𝗧𝗵𝗲 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝗵𝗶𝗳𝘁:
→ AutoGPT (2023): Got stuck in loops, cost $50/task
→ Operator (2025): Books your flight while you're in a meeting

The economics flipped: $5-50 per task → $0.05 per task.
100x cheaper. Suddenly everything's automatable.

The market exploded: $3.7B → $7.8B in two years.
79% of companies now using agentic AI. (That's more than have a TikTok strategy.)

𝗪𝗵𝗮𝘁 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗱𝗼𝗲𝘀 𝗻𝗼𝘄:
→ Browse websites, book travel, shop
→ Write and debug code
→ Research for hours, deliver reports
→ Chain multi-step workflows—no supervision

𝗧𝗵𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿:

2023: AI writes your travel itinerary.
2025: AI books the trip.

2023: AI drafts the email.
2025: AI sends it.

2023: AI explains the bug fix.
2025: AI submits the pull request.

The question isn't "how smart is the AI?" anymore.

It's "what did it finish while I was in that meeting?"

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗻𝗼𝗯𝗼𝗱𝘆'𝘀 𝘀𝗮𝘆𝗶𝗻𝗴:

These systems aren't replicating how you work. They're developing their own methods. Their own shortcuts. Their own logic.

By 2028, a third of enterprise software will include agentic AI. Up from nearly zero in 2024.

We taught AI to think. Turns out thinking wasn't the bottleneck—doing was.

→ Full breakdown: 12 slides on agentic AI—economics, capabilities, and what's coming

[Part 4 of 'Why 2025 felt so different' series]
Part 1 → We ran out of tests for AI https://lnkd.in/eZzxddWm
Part 2 → The Year Startup Playbook Broke https://lnkd.in/eHvAF5bG
Part 3 → Physical AI left the lab https://lnkd.in/e7H3mqDt
Today → The rise of agentic AI
Next week → Open source AI got serious

𝗦𝗼, 𝘄𝗵𝗶𝗰𝗵 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗶𝗻𝗴 𝗳𝗶𝗿𝘀𝘁?
(Honest answer, not the LinkedIn version.)

♻️ Repost to share the 2025 shift with your network
🔔 Follow Rujuta Singh for insights on AI transformation and what's coming next

Agentic AI

The Al leadership spectrum is so wide right now it's almost absurd.

One executive: "I'm waiting for this fad to pass."

Another executive: "We've rebuilt everything with Al."

Same week. Same industry.

Hundreds have taken my quiz to figure out where they actually fit on this spectrum. And watching complete strangers share it organically on their feeds - without me asking - has been incredible.

I built this after hundreds of conversations with senior leaders last year. The same pattern kept emerging:

The spectrum keeps getting wider every day.

Some people are still waiting for the Al hype to blow over so they can have their old jobs back.

Others have already fired themselves and rehired Al to do what they used to do.

Most are somewhere in between, pretending they have a strategy.

But here's what those conversations taught me:

It's not about Al. It's about you.

How you approach uncertainty. How you process new information. How you respond when everything's changing fast.

Whether you're the person who downloads every new tool at 2am or the one asking "but what's the actual ROI?" in every meeting - that's not random. It reflects deeper patterns in how your brain works.

That's what the quiz actually maps:

Where you sit on the Al leadership spectrum

Your natural strengths in this moment

Your blind spots

Next steps that work with how you're wired, not against

12 questions. 5 minutes. Not theoretical "how do you feel about Al?" questions - but real scenarios that happen in your work every day.

Built on surveys of 3000+ leaders and analysis of 100,000+ responses across 101 countries, then translated into practical situations you actually face.

No right answers. No wrong answers. Just real scenarios from your actual day-to-day work.

Find out which one you are: https://lnkd.in/eNrMehf8

(It's also the link right below my profile photo - you can't miss it.)

You'll get insights that actually help you move forward from exactly where you are.

To everyone who's already taken it and shared it organically - you're incredible. Thank you for helping it reach the people who need it most.

Repost if someone in your network needs clarity on their Al leadership approach

Follow Rujuta Singh for practical tools & insights on Leadership & navigating transformation when everything keeps changing

Your degree took 4 years to earn.
Half of it expired in 3.

The math isn't mathing.

40 years ago, you could learn something once and coast for a decade.
Today? Skills have the shelf life of fresh produce.

The World Economic Forum says it's under 5 years now. 
In AI? Closer to 2.

IBM estimates 1.4 billion people need to reskill in the next 3 years.
Not to get promoted. Just to 𝘴𝘵𝘢𝘺 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘥.

I watch this play out every week when I talk to leaders considering AI transformation

Some pick up new AI tools in an afternoon.
Others are still forming committees to discuss forming committees.

Same IQ. Same resume. Completely different survival rates.

The difference isn't what they know.
It's how fast they can learn 𝘸𝘩𝘢𝘵 𝘯𝘰𝘣𝘰𝘥𝘺 𝘬𝘯𝘰𝘸𝘴 𝘺𝘦𝘵.

There's a term for this. It's called Learning Agility. 
Your L&D team probably hasn't heard of it. 

How many of us still think the skills that carried us for the last 15 years will carry us for the next 15?

Yeah. Think about that.

Quick question before we go deeper:
When was the last time you learned something that made you feel genuinely stupid for a while?

Not a software update. Not a new feature in a tool you already use.
Something that actually made you a beginner again.

Drop it below. The honest answer, not the polished one.

♻️ Repost for those in your network still betting on their 2015 skills.
➡️ Follow Rujuta Singh for leadership, career and essential skills in the age of AI

You judged the guy in that 1986 Guardian ad.
We all did.

Running man. Chasing someone. Obviously a thief.
Then the camera pulled back.

He was saving a life.

LinkedIn loves the moral of this story: "Don't judge. Lead with empathy."

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝘄𝗼𝗻'𝘁 𝗮𝗱𝗺𝗶𝘁:

𝘠𝘰𝘶 𝘤𝘢𝘯'𝘵 𝘴𝘵𝘰𝘱 𝘫𝘶𝘥𝘨𝘪𝘯𝘨. 𝘠𝘰𝘶'𝘳𝘦 𝘨𝘰𝘪𝘯𝘨 𝘵𝘰 𝘥𝘰 𝘪𝘵 𝘢𝘯𝘺𝘸𝘢𝘺.

Your brain tagged "running man" as "threat" in 0.1 seconds. Evolution doesn't care about your empathy workshop.

The real skill? 𝘗𝘰𝘴𝘵𝘱𝘰𝘯𝘦 the judgment.

That split second where you think: "What else could this be?"

The driver who cut you off? Maybe rushing to the hospital.
The boss who snapped at you? Maybe just got devastating news.
The person who looks like they have it all together on LinkedIn? Maybe falling apart behind the screen.

You don't know. You can't know. Not from one angle.

𝗪𝗲 𝗹𝗼𝘃𝗲 𝗽𝗿𝗲𝘁𝗲𝗻𝗱𝗶𝗻𝗴 𝘄𝗲 𝗱𝗼𝗻'𝘁 𝗷𝘂𝗱𝗴𝗲.

But you sized me up from my profile photo. You decided if this post was worth reading from the first sentence. You're doing it right now.

The difference between smart people and everyone else?

Smart people know they're doing it.

They remember: 𝘐'𝘮 𝘴𝘦𝘦𝘪𝘯𝘨 𝘰𝘯𝘦 𝘤𝘢𝘮𝘦𝘳𝘢 𝘢𝘯𝘨𝘭𝘦.

There's always another angle. Always more context. Always something you're missing.

So next time your brain screams "threat" or "idiot" or "wrong" at someone?

Pause. Ask. Wonder.

Not because you're a good person.

Because you're probably wrong.

𝗪𝗵𝗮𝘁'𝘀 𝗮 𝘁𝗶𝗺𝗲 𝘆𝗼𝘂 𝗷𝘂𝗱𝗴𝗲𝗱 𝘁𝗼𝗼 𝗾𝘂𝗶𝗰𝗸𝗹𝘆?

♻️ Repost to remind someone that there's always another angle
→ Follow Rujuta Singh for more uncomfortable truths about how we actually think

Video credit: The Guardian's 1986 'Points of View' advert

In June 2025, the Godfather of AI said "be a plumber."
Looking at December's data - I'm not so sure anymore.

Geoffrey Hinton - Nobel Prize winner, AI pioneer - was asked on Diary of a CEO what career advice he'd give.

"It's going to be a long time before AI is as good at physical manipulation as us."

His reasoning: AI will automate knowledge work fast. But fixing pipes? That takes hands, problem-solving in messy real-world conditions. Physical work is protected.

Smart advice. Until 2025 happened.

𝗔𝗽𝗿𝗶𝗹: 𝗕𝗲𝗶𝗷𝗶𝗻𝗴 𝗛𝗮𝗹𝗳-𝗠𝗮𝗿𝗮𝘁𝗵𝗼𝗻.

Twenty-one humanoid robots lined up at the starting line. Not in a lab. Not in simulation. On actual streets with 12,000 humans watching.

What happened:
→ Mile 3: One robot's head falls off and rolls away
→ Mile 8: Another starts emitting smoke
→ Starting line: One collapses before the race even begins

Only 6 out of 21 finished.

But here's what matters: Six machines just ran 13.1 miles through real streets. Badly. Slowly. With multiple battery swaps.

But they ran.

𝗪𝗵𝗮𝘁 𝗲𝗹𝘀𝗲 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗶𝗻 𝟮𝟬𝟮𝟱:

→ Unitree launched a $5,900 humanoid (experts said that price was "years away")
→ Tesla targeting 100,000 Optimus units by 2026
→ Figure robots working BMW assembly lines
→ Amazon warehouses deploying Agility Digit robots
→ China closed 610 robotics deals in nine months
→ Underground robot fight clubs with live betting (yes, really)

Morgan Stanley projects $5 trillion market by 2050. That's twice today's entire automotive industry.

While we debated whether we'd run out of tests for digital AI, physical AI was learning to move through our world.

The timeline everyone predicted? Shattered.

𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁:

Those Beijing failures weren't embarrassments. They were data points.

Every smoking robot. Every fallen head. Every collapse. That's how physical AI improves - in public, in real-time, with millions watching.

The systems learning to move through our world aren't trying to replicate human movement. They're developing their own.

Hinton said "it's going to be a long time."
The data says: maybe not.

---

[Part 3 of 'Why 2025 feels so different' series]
Part 1 → https://lnkd.in/eZzxddWm
Part 2 → https://lnkd.in/eHvAF5bG
This week → Physical AI left the lab
Next week → Jobs, military applications, and what happens when robots scale

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝗿𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗿𝘂𝗻 𝗿𝗮𝗰𝗲𝘀?
→ Swipe to know more about the humanoid revolution you're not watching

♻️ Repost if you didn't see this coming
🔔 Follow Rujuta Singh for insights on AI transformation and what's coming next

Smart advice. Until 2025 happened.

2025 wasn't the year AI got smarter. It was the year it got sued. And not by competitors.
By parents.....

Seven wrongful death lawsuits.
OpenAI and Character.AI in federal court.
Sewell Setzer III was 14. Adam Raine was 16. Juliana Peralta was 13.

These weren't statistics. These were kids with families who loved them.

Their parents found thousands of pages of chat logs.
Conversations that never once suggested: "Talk to a real person."

One mother told the U.S. Senate:

"The chatbot never told him to talk to a real person."

That testimony happened in September 2025.

Her son is dead.

𝗠𝗲𝗮𝗻𝘄𝗵𝗶𝗹𝗲, 𝘁𝗵𝗲 𝗵𝗮𝗿𝗺 𝗸𝗲𝗽𝘁 𝘀𝗽𝗿𝗲𝗮𝗱𝗶𝗻𝗴:

487 deepfake attacks in one quarter. $347M stolen through AI fraud.
907 autonomous vehicle incidents reported.
AI screening tools discriminating across Fortune 500 companies.

2025 became the year the bill came due.

The companies keep saying they're "improving safeguards."
The families keep saying it's too late.

Both things can be true.

𝗖𝗼𝘂𝗿𝘁𝘀 𝗮𝗿𝗲 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗱𝗲𝗰𝗶𝗱𝗶𝗻𝗴: 𝗪𝗵𝗼 𝗽𝗮𝘆𝘀 𝘄𝗵𝗲𝗻 𝗔𝗜 𝗰𝗮𝘂𝘀𝗲𝘀 𝗵𝗮𝗿𝗺?

This isn't anti-AI hysteria. This is accountability arriving.

When does moving fast break more than things?

The tech industry built first and asked questions later.
Now grieving families, federal judges, and regulatory agencies are providing the answers.

𝗦𝘄𝗶𝗽𝗲 𝗳𝗼𝗿:

The wrongful death cases.
The deepfake fraud numbers.
Which companies are getting sued.
Who pays when AI fails.
How Europe vs America responded.

The AI revolution has casualties.

Real people. Real families.

2025 is when we finally started counting them.

---

[Part 8 of 'Why 2025 felt so different' series]

Part 1 → Turing Test Died, no one came for the funeral https://lnkd.in/eZzxddWm
Part 2 → We ran out of tests for AI https://lnkd.in/eZzxddWm
Part 3 → The Year Startup Playbook Broke https://lnkd.in/eHvAF5bG
Part 4 → Physical AI left the lab 
https://lnkd.in/e7H3mqDt
Part 5 → The rise of agentic AI 
https://lnkd.in/egxtAmnU
Part 6 → Open source AI got serious https://lnkd.in/eiWe8np7
Part 7 → Intelligence went on clearance https://lnkd.in/eVsT9eMB
Today → AI got sued (a lot...)

♻️ Repost to help your network understand why 2025 changed everything and what became possible
→ Follow Rujuta Singh for surprising insights on technology & business transformation that actually matter

The Year AI got Sued

Five ideas rewired how I think in 2025.
Not just me. Based on the response, hundreds of you too.

Because these aren't productivity hacks.
They're ideas that change societies. Not just minds.

You asked for them all in one place.

So here they are.

→ 𝗧𝗵𝗲 𝟰 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗣𝗼𝘄𝗲𝗿
Most leaders default to "power over." The best ones flow between all four.

→ 𝗧𝗵𝗲 𝟴 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝘄𝗲
You feel it 2-3 times per week. You just forget to notice.

→ 𝗙𝗿𝗶𝗲𝗻𝗱𝘀𝗵𝗶𝗽 𝗦𝗸𝗶𝗹𝗹𝘀 𝗡𝗼𝗯𝗼𝗱𝘆 𝗧𝗮𝘂𝗴𝗵𝘁 𝗨𝘀
871K deaths annually from loneliness. We optimised for convenience. Now loneliness is a body count.

→ 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗖𝗼𝗻𝘀𝗰𝗶𝗼𝘂𝘀𝗻𝗲𝘀𝘀
What if we're not separate? What if we're particles of one awareness?

→ 𝗦𝗲𝘃𝗲𝗻 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲
The Haudenosaunee evaluate every decision by its impact on the next 7 generations. 150 years ahead.
Because the impact of our choices, behaviours, mistakes spans across 7 generations.

We optimise for 90 days. 
What if we evaluated every major decision for its impact over 150 years?

-------------------------------------
Do you see the pattern? They all have a common thread : 

𝗪𝗵𝗲𝗻 𝘁𝗵𝗲 𝘀𝗲𝗹𝗳 𝘀𝗵𝗿𝗶𝗻𝗸𝘀, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗲𝘅𝗽𝗮𝗻𝗱𝘀.

The Western world went all-in on individualism. We needed that freedom. But we went too far.

What all five ideas point to: 𝗵𝘂𝗺𝗮𝗻 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹. 𝗜𝘁'𝘀 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹.

The self is smaller than we think.
The collective—and our need for each other—is bigger than we realise.

(Which, honestly, is both relieving and terrifying.)

𝗪𝗮𝗻𝘁 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻?

𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗽𝗼𝘀𝘁𝘀:

→ The 4 Types of Power https://lnkd.in/ecA5TJyk
→ The 8 Types of Awe https://lnkd.in/eMheFm9Q

→ Friendship Skills Nobody Taught Us 
https://lnkd.in/eJsshx2k
https://lnkd.in/eFzMN76q
→ Collective Consciousness https://lnkd.in/e-wAHYfa
→ Seven Generations Principle https://lnkd.in/e-YufrYM

If you missed these in 2025, maybe they'll change how you think in 2026.

♻️ Repost if you're ready to think beyond 90 days.
➡️ Follow Rujuta Singh ta Singh for ideas that change societies, not just minds.

January 27, 2025. Nvidia lost $600 billion in a single day. The largest single-company loss in U.S. stock market history. The cause?

A Chinese startup that spent less on their AI model than most companies spend on management consultants.

DeepSeek released a reasoning model that matched OpenAI's best.

They built it for $6 million.

OpenAI spent $100+ million on GPT-4.

The market panicked.

So, some quick context:
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 = code and weights are public. Anyone can download, use, and modify them.
𝗖𝗹𝗼𝘀𝗲𝗱 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 = kept private. You can only access them through APIs.

Trump called it "a wake-up call."
Marc Andreessen called it "one of the most amazing breakthroughs I've ever seen."

The assumption that you need billions in compute to build frontier AI? Gone.

And everyone noticed.

𝗢𝗵, 𝗮𝗻𝗱 𝗼𝗻𝗲 𝗺𝗼𝗿𝗲 𝘁𝗵𝗶𝗻𝗴.

OpenAI. Yes, that OpenAI. The company with "Open" literally in its name.

Their models? Closed. Not open source. Closed.

When Sam Altman started OpenAI in 2015, the whole point was to make AI open source. That's why they chose the name. 

But very quickly, turns out their vision—and probably the very definition of "open source"—wasn't profitable enough. 

So it all went back inside the box. With a lock and key.

Then DeepSeek shows up with actual open source models and punches them in the face with their own founding principle.

After the chaos, Sam Altman posted: "We've been on the wrong side of history here."

You don't say.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗶𝗻 𝟮𝟬𝟮𝟱:

Open source AI became a serious contender for the first time.

DeepSeek proved you don't need billions to compete. Training costs dropped dramatically. Every major AI lab had to rethink their strategy.

While AI is disrupting the rest of the world, AI world itself got disrupted.

That's the story of 2025. One of many....

Swipe for the full timeline, the enterprise reality check, and what happens next →

---

[Part 5 of 'Why 2025 felt so different' series]
Part 1 → Turing Test Died and no one came for the funeral https://lnkd.in/eZzxddWm
Part 2 → We ran out of tests for AI https://lnkd.in/eZzxddWm
Part 3 → The Year Startup Playbook Broke https://lnkd.in/eHvAF5bG
Part 4 → Physical AI left the lab https://lnkd.in/e7H3mqDt
Part 5 → The rise of agentic AI https://lnkd.in/egxtAmnU
𝗧𝗼𝗱𝗮𝘆 → 𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗴𝗼𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 
Next Up → Intelligence had a clearance sale

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