Forward Deployed AI Engineers for teams shipping real products. We build and deploy AI agents + RAG systems that survive production.
Joined June 2015
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Top 50 Blockchain influencer in India by 25, Author by 26, Father by 27, and an Entrepreneur by birth.
Just a middle-class boy from Jalandhar who used to think I can never be successful without:
-> An IIT Tag
-> Family Connections
-> Good backups
From a tier 2 college, got campus-placed into a "safe" sales job in Mumbai. Looked perfect on paper. Felt like the wrong life.
So I did the thing everyone warned me against. I walked away.
Took a ₹5,000/month internship just to learn Blockchain.
From freelance content writing to starting a marketing agency in Web3 and to independent consulting, Life took me through a roller coaster.
Then something shifted. Too many failures but too much ambition.
Every failure taught me one thing nobody told me:
Technology doesn't win. Distribution does.
So I went all in. Strategy Head in consulting. Wrote a book "Why Blockchain". Enterprise world debut.
I just hit reset.
Back to zero. Back to learning.
Chasing one thing that I always believed in,
Mastering distribution for emerging technology innovation.
So let me ask you 👇
What's the one "safe" path you're scared to walk away from?
Harman Puri retweeted
Everyone knows Nvidia.
But one of the biggest winners of the AI revolution has been hiding in plain sight.
It’s SK Hynix.
The South Korean semiconductor company doesn’t build chatbots.
It doesn’t launch flashy AI models.
Most people have probably never even heard of it.
Yet it manufactures the High Bandwidth Memory (HBM) chips that power many of Nvidia’s AI accelerators—the hardware behind today’s most advanced AI systems.
And now it’s preparing for a $28 billion Nasdaq listing.
That’s after its stock has surged 273% this year, fueled almost entirely by demand for AI infrastructure.
This is a reminder of something the market often teaches us.
When a technological revolution begins, the biggest winners aren’t always the companies everyone is talking about.
Sometimes they’re the companies quietly building the picks and shovels.
During the gold rush, fortunes weren’t made only by the miners.
They were made by the businesses selling the tools.
AI is following a similar pattern.
While headlines focus on ChatGPT, Claude, Gemini, and the next breakthrough model, billions of dollars are flowing into the companies that make AI possible chipmakers, memory manufacturers, networking providers, and data center infrastructure.
SK Hynix is also doubling down on the future, announcing plans to invest 100 trillion won (around $64 billion) in new semiconductor manufacturing facilities.
That tells us something important.
The AI race is no longer just a software race.
It’s an infrastructure race.
The companies building the foundations of AI may ultimately create just as much value as the companies building the intelligence itself.
As investors and business leaders, perhaps we should spend less time asking, “Which AI model will win?”
And more time asking,
“Who is building everything those AI models can’t function without?”
OpenAI has raised the stakes in the AI race with its new GPT-5.6 model family: Sol, Terra, and Luna.
According to the company, Sol is 54% more token-efficient for coding and outperforms Anthropic’s Fable 5 while using less than half the tokens, taking less than half the time, and costing around one-third less.
OpenAI also introduced ChatGPT Work, bringing AI-powered documents, spreadsheets, presentations, and enterprise workflows into one platform.
The AI battle is no longer just about intelligence.
It’s becoming a race for speed, efficiency, and lower cost.
Meta just reversed one of its most controversial AI features.
Days after launching Muse Image, the company has removed the option that let users generate AI images of public Instagram accounts simply by tagging them.
The feature sparked backlash because:
• Public accounts were opted in by default
• Users weren’t notified if their likeness was used
• Privacy concerns quickly went viral
Meta admitted it had “missed the mark” and pulled the feature.
The AI race isn’t just about building powerful tools anymore.
It’s about earning user trust before pushing new capabilities.
Anthropic just gave AI a usage report.
Its new Claude Reflect feature lets users track how they use AI across the past 1, 3, 6, or 12 months—from writing and coding to research and brainstorming.
It also adds Quiet Hours, Break Reminders, and asks a simple question:
“What should you still do yourself, even if AI could do it faster?”
The next generation of AI may not just make us more productive.
It may help us use AI more intentionally.
Harman Puri retweeted
Today isn’t just about TCS earnings.
It’s a reality check for India’s entire IT industry.
With TCS kicking off Q1 earnings season, investors will be watching more than revenue and profit.
They’re looking for answers.
Can AI create more business than it disrupts?
Can Indian IT giants adapt as enterprises rethink tech spending?
With TCS shares down 36% YTD, today’s guidance could shape the industry’s AI narrative for the rest of 2026.
Last year, everyone wanted Prompt Engineers.
This year, companies want Forward Deployed Engineers.
Why?
Because building AI is no longer enough.
Businesses need people who can take AI from a demo to a real product inside hospitals, banks, factories, and enterprises.
AI deployment roles have reportedly grown 42x since 2023, with FDE job postings up 700%+ over the past year.
At leading AI companies, senior FDE compensation can approach $1 million.
The next AI boom may not belong to model builders.
It may belong to the people who can make AI actually work.
The AI race is entering a new chapter.
According to Bank of America, the next AI winners may not be just the U.S. and China.
South Korea tops the list thanks to its semiconductor dominance and rapid AI adoption.
UAE leads the world in AI adoption, with 70% of its working-age population already using AI.
India is emerging as a major AI powerhouse, becoming the largest market for ChatGPT, Gemini, and Perplexity users.
The future of AI won’t be defined only by who builds the best models.
It will be shaped by who adopts AI at scale.
Meta has launched Muse Image, a new AI feature that lets users remix public Instagram posts and generate new visuals using AI.
The update also allows users to import public Instagram content into AI creations unless creators choose to opt out.
As AI creativity expands, so does the debate around consent, originality, and how public content should be used.
The next AI battle may not be about capability it may be about trust.
Today isn’t just about TCS earnings.
It’s a reality check for India’s entire IT industry.
With TCS kicking off Q1 earnings season, investors will be watching more than revenue and profit.
They’re looking for answers.
Can AI create more business than it disrupts?
Can Indian IT giants adapt as enterprises rethink tech spending?
With TCS shares down 36% YTD, today’s guidance could shape the industry’s AI narrative for the rest of 2026.
Meta just launched Muse Image, its new AI image generator.
But it’s not the image quality that’s grabbing attention.
The controversy?
Muse can use photos from public Instagram profiles to generate new AI images simply by tagging the account—unless users manually opt out.
Critics are calling it a privacy nightmare, while Meta says users have control through their settings.
The AI debate is shifting.
It’s no longer just about what AI can create.
It’s about who gets to decide how your data is used.
For years, Microsoft spent billions building the AI future.
Then it ran into a problem.
Every AI prompt sent to models like Claude and ChatGPT came with a growing bill.
So Microsoft made a quiet decision.
Instead of paying other AI companies forever, it reportedly started replacing OpenAI and Anthropic models with its own MAI models inside Excel and Outlook.
The AI race is no longer just about building the smartest model.
It’s about building one that’s smart enough and cheap enough to run at global scale.
Here’s a punchy Twitter/X post:
Meta is making AI-generated ads harder to miss.
Facebook & Instagram will now clearly label ads created or edited using AI—whether with Meta AI or third-party tools like Photoshop or DALL·E.
As AI-generated content grows, transparency is becoming a competitive advantage, not just a feature.
#AI #Meta #DigitalMarketing #Advertising #GenerativeAI #SocialMedia
Imagine driving through Chennai during rush hour.
The traffic lights don’t follow a fixed timer anymore.
They watch.
They learn.
They adapt.
That’s the vision behind Chennai’s new AI-powered Intelligent Transport System (CITS).
Using real-time traffic data, the system will optimize signal timings, reduce congestion, detect violations, and help emergency responders react faster.
For years, we’ve talked about AI changing the future.
Now it’s changing something far more ordinary—and far more important.
The daily commute.
Smart cities aren’t coming.
They’re already being built.
“If I were starting today, I’d build an AI business.”
Shark Tank’s Kevin O’Leary says he’d focus on just two opportunities:
• AI agents that automate business workflows
• AI-powered cybersecurity
His message is simple: the biggest AI winners won’t just build models—they’ll solve real business problems.
#AI #Startups #Business
Harman Puri retweeted
78% of organizations now use AI.
But Gartner projects 60% of enterprise AI initiatives could fail due to poor execution, not poor technology.
The next AI advantage won’t come from buying better models.
It will come from leaders who know how to turn AI into business outcomes.
Microsoft is cutting 4,800 jobs while continuing to pour billions into AI.
The company says the layoffs are part of improving efficiency as it ramps up AI investments.
The bigger picture:
• Microsoft shares are down 23% in the first half of 2026.
• Big Tech is expected to spend over $700 billion on AI this year.
• Microsoft has projected $190 billion in spending for 2026.
This isn’t just another round of layoffs.
It’s a sign that the AI race is entering a new phase—where companies are under pressure to prove that massive AI investments can generate real business returns.
The AI era isn’t just about building intelligence anymore.
It’s about making the economics work.
Everyone knows Nvidia.
But one of the biggest winners of the AI revolution has been hiding in plain sight.
It’s SK Hynix.
The South Korean semiconductor company doesn’t build chatbots.
It doesn’t launch flashy AI models.
Most people have probably never even heard of it.
Yet it manufactures the High Bandwidth Memory (HBM) chips that power many of Nvidia’s AI accelerators—the hardware behind today’s most advanced AI systems.
And now it’s preparing for a $28 billion Nasdaq listing.
That’s after its stock has surged 273% this year, fueled almost entirely by demand for AI infrastructure.
This is a reminder of something the market often teaches us.
When a technological revolution begins, the biggest winners aren’t always the companies everyone is talking about.
Sometimes they’re the companies quietly building the picks and shovels.
During the gold rush, fortunes weren’t made only by the miners.
They were made by the businesses selling the tools.
AI is following a similar pattern.
While headlines focus on ChatGPT, Claude, Gemini, and the next breakthrough model, billions of dollars are flowing into the companies that make AI possible chipmakers, memory manufacturers, networking providers, and data center infrastructure.
SK Hynix is also doubling down on the future, announcing plans to invest 100 trillion won (around $64 billion) in new semiconductor manufacturing facilities.
That tells us something important.
The AI race is no longer just a software race.
It’s an infrastructure race.
The companies building the foundations of AI may ultimately create just as much value as the companies building the intelligence itself.
As investors and business leaders, perhaps we should spend less time asking, “Which AI model will win?”
And more time asking,
“Who is building everything those AI models can’t function without?”
The Supreme Court of India just delivered one of the strongest messages on AI yet.
After tribunals relied on AI-generated, non-existent case laws, the Court quashed the orders and directed a fresh hearing.
It also asked the Bar Council of India to form an expert committee to examine AI’s role in the legal system.
The takeaway?
AI can accelerate research.
But accountability can never be automated.
Human judgment remains the final safeguard.
AI isn’t just transforming businesses. It’s helping save wildlife too.
Indian Railways has deployed Gajraj Suraksha, an AI-powered system that detects elephants up to 200 meters before they reach railway tracks using optical fibre sensors and an Intrusion Detection System.
Key facts:
• 200 elephants were killed in train accidents over the past decade.
• 99.5% detection accuracy.
• Rolling out across 700 km of elephant corridors.
• Uses existing railway fibre infrastructure, making it cost-effective.
A reminder that some of AI’s biggest wins won’t happen on our screens—they’ll happen in the real world.
Bitcoin climbed after weaker-than-expected US jobs data strengthened expectations of Fed rate cuts, boosting sentiment across the crypto market.
Meanwhile, the AI race in crypto is heating up as projects increasingly use AI-powered tools for risk analysis, trading, and smarter token selection.
The next crypto battleground may not just be blockchain—it’s AI.