When Apple Copies, It Is Called Patience

The West has spent decades calling Asian companies copycats. Watch how the story changes when Apple arrives late.

When Apple introduced the iPhone Duo on Sept. 9, it called the phone a "breakthrough foldable design." The phone may well be a fine piece of engineering, thin and durable, with a clever hinge. But it is not a breakthrough. Royole, a Chinese company, sold the first commercial foldable in 2018. Samsung followed a few months later with the Galaxy Fold, which had well-publicized early problems that it spent the next several years fixing in full view of its customers. Huawei and others tried their own designs, and suppliers slowly learned how to make flexible screens that didn't crease and hinges that didn't fail. Apple watched all of that before it made its move.

Waiting was a perfectly sensible thing for Apple to do. What bothers me is how the waiting has been described. Commentators say Apple "let the technology mature," "refused to rush" and "learned from others' mistakes." Each of those descriptions is fair. Now imagine that Samsung had sat back while Apple absorbed the embarrassments of a first-generation product, and then arrived years later with a more polished version. Few people would call that patience. Most would say Samsung had copied Apple, followed it or finally caught up.

That double standard has deep roots. The Western business press has long assumed that American companies are innovators until proven otherwise, and that Asian companies are imitators until they prove otherwise. The same strategy is called learning from the market in one case and copying in the other. A company's passport shouldn't decide which word it gets.

The first mover in any category pays for everyone who comes after it. It finds out which designs fail and which compromises buyers will accept, and it takes the reputational damage when the technology isn't ready. The companies that follow inherit those lessons at no cost. Apple is entitled to that bargain, and it has made it many times before. It did not invent the graphical interface, the smartphone, multi-touch, the tablet or the smartwatch. Its real talent lies in taking ideas that already exist and fitting them into an ecosystem that people find coherent and are glad to pay for. That talent is formidable, and nobody thinks Apple stopped being innovative because others went first. The same courtesy should be extended to Seoul, Tokyo and Shenzhen.

America has been through this before. In the early 1980s, Japanese cars were dismissed as cheap imports, and Washington responded with export restraints instead of admiration. Within a decade, Honda was building Accords in Ohio, and Lexus and Acura were competing at the top of the market. Chinese automakers, whose strength in batteries and software is now plain to see, are following a similar path. Innovation builds on what came before it, and being first has never given anyone a permanent claim on it.

None of this excuses genuine theft. Counterfeiting a trademark or stealing a design is wrong wherever it happens, and the courts are right to punish it. But studying a product that is already on the market and building a better one is simply competition, the same thing American capitalism has praised for generations. It rightly calls competition good for consumers, and that judgment shouldn't become a moral charge just because the competitor happens to be foreign.

So let Apple take its victory lap. If the Duo turns out to be the foldable that finally wins over the mass market, that will be a real achievement, and consumers and shareholders will reward it accordingly. But Samsung and Royole deserve credit for building the road Apple is now driving on. And the next time an Asian company arrives late to a category that an American company pioneered, perhaps the press will describe it the way it describes Apple, as a company that learned from the market.

நீயே என் உயிர்

 

உன் பெயர் ஒன்று உதட்டில் விழுந்தது—

உச்சரித்தாலும் அகலவில்லை;

உலகம் முழுதும் மறக்கச் சொன்னாலும்,

உள்ளம் மட்டும் மறக்கவில்லை.

முதலில் உதட்டின் வாசம் நீயே;

மெல்ல மெல்ல நினைவின் தேசம் நீயே;

இன்று என் நெஞ்சின் நிலவும் நீயே—

இமை மூடும் இரவின் ஒளியும் நீயே.

இரவுக்குள் நிலவாய் வந்தாய்,

இதயத்துள் உயிராய் நின்றாய்;

நான் என்று இருந்த என் நெஞ்சை

நீ என்று மாற்றி சென்றாய்.

இப்போது என்னிடம் கேட்பதென்ன?

இதயம் நீ… உயிரும் நீ…

என் மூச்சின் ஓசை நீயே—

என் மௌனப் பாசம் நீயே.

நினைவுக்கு அப்பால்

 எண்ணாத எண்ணம் எங்கோ எழுந்தாலும்

என்னுள் நீயே வந்து நிறைந்தாலும்

என்னை மறந்து உன்னை நினைத்தாலும்

எட்டாத உலகம் என்னை அழைத்தாலும்

ஒளிமிகு நாளை ஒருமுறை மீட்டாலும்

ஓவிய வானின் நிறங்களைத் தீட்டாலும்

பொன்னொளி பூத்த பொழுதுகள் காட்டினாலும்

போனதன் நிழல்கள் பின்னே தொடர்ந்தாலும்

படித்தும் புரியாப் பக்கங்கள் எத்தனை

பார்த்தும் தெரியாத ரகசியம் எத்தனை

நடந்தும் அறியாத பாதைகள் எத்தனை

நானாக நினைத்த நானும் எத்தனை

சொல்லாத சொல்லும், தெரியாத மொழியும்

சொல்லில் சிக்காத சொல்லாத விழியும்

காணாமல் போன கனவின் ஒளியும்

காலம் கடந்து போகும் நதியும்

மறந்த நினைவும், மறைந்த நொடிகளும்

மௌனமாய் என்னைக் கடந்த தடங்களும்

என்னை அறியாது என்னுள் கடந்ததும்—

எல்லாமே நீயும்… எல்லாமே நானும்.

The voice from within

 The cruelest words were mine alone,

Born in the chambers of my mind;

I carved them deep, then called them stone,

And wondered why no peace I’d find.

With years, that stern and watchful voice

Seemed less my own, yet would remain;

It spoke as though it had the choice

To make each tender thought a pain.

I sought no hatred from the world,

But feared the mirror’s searching gaze;

Yet in the morning light unfurled,

The self I judged still breathed and stayed.

Then, walking where the quiet stream

Ran silver through the meadow grass,

I heard the gentler voice of earth:

“No heart is healed by making war.”

So may I learn, as fields grow green,

To meet myself with kinder eyes.

That quiet heaven

 O, let me find that quiet shore

Where anxious names are known no more;

Where leaves, that fall from autumn boughs,

Bear gently down my earthly vows.

Let Heaven be not a distant sphere,

But some still place where none may hear

The clamour of the troubled mind,

And breath comes freely, unconfined.

As lightly as the butterfly

That wanders through the summer sky,

So let me pass from place to place,

Unburdened by the world's embrace.

My steps, like some meandering stream,

May wander farther than I dream;

Yet let them wander where they will—

Some kindly shore may greet them still.

Give me the river's patient art

To yield, yet never lose the heart;

To follow every bend and gleam,

And trust the current with the dream.

Let senses drink the changing scene—

The silver rain, the fields of green,

The swelling cloud, the amber light,

The hush that gathers into night.

Let care grow faint, and fear depart,

And courage kindle in the heart;

Not fashioned by the world's decree,

But wild, unpractised, bold, and free.

Then, if the road be rough or long,

Let hope be still my pilgrim-song;

For he who walks without despair

May find a heaven everywhere.

And when at last the leaves let go,

As all things must, and softly flow,

May I, as freely as the tree,

Release the world—and simply be.

The Hour that we did not name

 The music drowned the honest word,

And midnight kept its own;

We spoke of trifles, lightly heard,

Yet felt what went unsown.

Your shoulder brushed mine—briefly so—

As though by chance we met;

Yet neither moved, nor chose to know

The thing we might regret.

No questions sought the heart beneath,

No answers crossed the air;

Your name, a little wreath of breath,

Dissolved and vanished there.

Without, the rain-wet pavement shone,

The sleeping city gleamed;

And all the world seemed built alone

To shelter what we dreamed.

You spoke of morning—“What shall be

When this strange hour is past?”

But dawn had scarcely touched the sea,

And night was fading fast.

So let tomorrow keep its claim,

Its reckonings and its light;

Tonight we need not give a name

To what belongs to night.

No histories of former days,

No maps of roads ahead;

The heart has wiser, quieter ways

Than all the words we said.

We stood as strangers might have stood,

Yet neither quite withdrew;

The silence understood us good—

And I, perhaps, understood you.

The room grew still; the hour grew late;

The world withdrew its sound.

And there we lingered, face to face,

While darkness gathered round.

Let reason wait beyond the door,

Let daylight ask its due;

Some moments ask for nothing more

Than being wholly true.

And when the final note had died,

And night gave way to day,

We kept the thing we never tried

To speak, nor name, nor say.

இரு மனமே!

 அவளுக்காக அவனும், அவனுக்காக அவளும் பாடும் தனித் தனி இரு பாடல்கள்!

அவன்:

கோவில் விளக்கே — குளிரும் இரவே,

கோலம் கண்டேனே — கனவின் நிறமே;

கண்ணில் விழுந்தாயே — காதல் தந்தாயே,

காணும் பொழுதெல்லாம் — கானம் ஆனாயே!

மெல்லச் சிரித்தாயே — மேனி சிலிர்த்ததே,

மௌனம் உரைத்தாயே — நெஞ்சம் திறந்ததே;

சொல்லும் முன் நின்றாயே — சொந்தம் ஆனாயே,

சொல்லாத வார்த்தையும் — சொல்லித் தீர்த்தாயே!

மாலை தரவில்லை — மனதைத் தந்தாயே,

மாங்கல்யம் கேட்குமுன் — வாழ்வைத் தந்தாயே;

கைகள் இணைந்தாலே — காலம் மறந்திடும்,

காதல் இணைந்தாலே — ஜென்மம் நிறைந்திடும்!

யமுனை அலையாவாய் — நினைவில் வருவாயே,

இளந்தென்றல் ஆகி — இதயம் தொடுவாயே;

துளசி இலையாக — பாதம் பணிவேனே,

துணையாக நீ வந்தால் — வாழ்ந்தே முடிவேனே!

கோவில் மணியோசை — காதில் ஒலிக்குதே,

கோடி கனவெல்லாம் — கண்ணில் மலருதே;

நீயே என் பாடலே — நீயே என் பாதையே,

நீயிருக்கும் வாழ்வே — நித்தம் என் வாழ்வே!

அவள்:

கோவில் வாசலில் காத்திருந்தேனே,

கோலம் கண்டதும் நாணம் கொண்டேனே;

காணும் விழியில் காதல் கண்டேனே,

காணாத நாளை எண்ணி நின்றேனே.

மெல்ல வந்தாயே — மேகம் போலே,

மௌனம் தந்தாயே — மோகம் போலே;

சொல்லாத சொல்லெல்லாம் விழியில் சொன்னாய்,

சொந்தம் என்றே என் நெஞ்சில் நின்றாய்.

மாலை வேண்டாம் — மனதைத் தந்தால்,

மாங்கல்யம் வேண்டாம் — அருகில் வந்தால்;

கைகளில் கைகள் கலந்திடும் நாளே,

காலங்கள் யாவும் வசந்தம் தானே.

நிலவு தேய்ந்தாலும் நினைவு தேயாதே,

நெஞ்சம் மாறினாலும் நானோ மாறேனே;

துளசி இலையாக உன் பாதம் சேர,

தூய உயிராக உன்னோடு வாழ்வேன்.

கோவில் மணியோசை கூவி அழைக்குதே,

கூடும் நம் காதல் வாழ்த்தி இசைக்குதே;

நீயே என் பாடல், நீயே என் பாவை,

நீயிருக்கும் வாழ்வே — எனக்கொரு காவியம்.

Wall Street's New Job in the Age of AI

For as long as I've watched this business, Wall Street has run on a simple bargain: hire someone bright, teach him to build a spreadsheet, and pay him well for turning a hundred pages of financial statements into a single target price. The spreadsheet became more than a tool. It became a diploma — proof the analyst had done the work.

Artificial intelligence is about to revoke that diploma.

A machine can already read the transcripts, reconcile the filings, and produce a first-draft model before the analyst finishes his coffee, and it will only get better. The real question isn't whether AI will replace analysts. It's whether the profession ever understood which part of its work was worth anything in the first place.

The honest answer: most of what passes for expertise in finance is mechanical execution dressed up in a suit — and mechanical execution is exactly what machines do best.

But there is a difference between transcribing the past and judging the future. A computer can consolidate ten years of statements with perfect consistency, because consolidation is arithmetic. It cannot tell you whether a management team that has never done something before is capable of doing it now. When an industrial company announces it is becoming a software company, the balance sheet is silent on the only question that matters. You are exercising judgement — and judgement does not compile.

The same line separates rules from principles. Rules can be codified and applied instantly, which is a poor foundation for a career. Principles require knowing why the rule exists, so you know what to do once the world stops cooperating with it. There's no historical series for a moat that software can copy overnight. You have to reason it out.

The industry has become remarkably good at debating an 8.5% discount rate versus a 9% one, and remarkably careless about the assumptions sitting on top of it. A half-point there might move a valuation a little; a wrong guess about durability can move it by a mile. We've been polishing the doorknob on a house with a cracked foundation.

None of this means the model stops mattering — it means the model should go back to being arithmetic in service of a story. If you're underwriting 15% revenue growth, there had better be a reason: a market opening up, a product taking share, new pricing power. Name no reason, and you don't have a forecast; you have a cell reference dragged across a spreadsheet because that's what spreadsheets make easy.

Humans bring their own bias to a model — falling for a stock and reverse-engineering assumptions to fit a price already fixed over breakfast. AI won't cure that; given enough compute, it will just produce eight hundred impressive-looking pages defending the same conclusion.

What's worth paying for is building a narrative before the numbers exist — noticing a management team is unusually good, or that customers are quietly changing habits, then working out what that's worth in cash. That's not storytelling in the salesman's sense. It's explaining, in plain English, why tomorrow won't look like yesterday, and making every number answer to that explanation.

Nowhere does this matter more than valuing companies riding the AI boom. A big market and a profitable one aren't the same thing, and plenty of smart people confuse the size of the pie with the size of the slice any one company keeps. If competition is fierce, customers keep the winnings; if infrastructure is expensive, the picks-and-shovels sellers keep a chunk. The question was never how big the market is. It's who gets to keep the money — the oldest question in corporate finance, wearing a new hat.

The same reckoning awaits accounting and appraisal work built on rules of thumb — a standard illiquidity discount here, a small-company premium there. If the job is picking a conventional percentage off a shelf, a machine will do it more cheaply and consistently. Stay useful by asking why the discount exists, and whether it fits the business in front of you.

Business schools should notice this before their graduates do: a machine already knows present value cold; knowing when the calculation doesn't apply can't be taught in an afternoon.

The real danger isn't that AI makes valuation obsolete. It's people handing their judgement to a system they've stopped understanding, because it's fast, tireless, and never asks for a raise. Speed is not wisdom. An analyst who accepts a machine's valuation because the spreadsheet ties out hasn't solved the problem — he's just moved it out of sight.

The right division of labour isn't complicated. Let the machine read the filings and build the base model. Let the person decide which questions matter and which conclusions deserve to be thrown out. Finance has rewarded clever complexity for too long over useful understanding, and complexity has just got very cheap to produce.

Every financial model is a story about the future, whether its builder admits it or not. A machine can help write the numbers. Someone still has to decide whether the story is true. That's not a smaller job than the one Wall Street has been doing.

It's a harder one.

The AI Revolution - Real. It’s Financial Case – on Trial

On November 13, 2025, Michael Burry filed the paperwork to shut down Scion Asset Management, the firm that made him a legend for seeing the 2008 mortgage crisis coming before almost anyone else. He didn't go quietly. In the weeks prior, he had disclosed more than $1 billion in bearish bets against Nvidia and Palantir, built on an unusually specific argument: that America's biggest tech companies were overstating profits — by his estimate, $176 billion worth between 2026 and 2028 — through how they depreciate their GPUs.

On its face, it was a bookkeeping dispute. Nvidia's chips, Burry argued, behave like commodities with two-to-three-year useful lives, not the five- or six-year assets some hyperscalers carry them as. Meta had just stretched its server depreciation schedule to 5.5 years, trimming $2.9 billion off expenses — nearly 4% of pretax profit — in a single stroke. Amazon, staring at the same silicon, went the other way, shortening its schedule and taking a $700 million hit. Two of the most sophisticated finance organizations on earth, looking at identical hardware, reached opposite conclusions about how fast it wears out. That divergence should worry an investor more than any keynote about superintelligence. It suggests nobody actually knows.

That's the real argument to have about AI, and it isn't the one most people are having. That the technology will reshape the economy is no longer seriously contested. Whether the sums being spent to build it will earn back anything like what today's valuations assume is a separate, harder question.

History isn't comforting here. The internet transformed commerce; most companies that raised money on that promise in 1999 no longer exist. Railroads reordered how goods moved across continents while bankrupting the men who financed the track. A technology can be indispensable and still be a poor place to have put your capital. A market can be enormous without the companies selling into it capturing much of that value as profit. The size of the pie says nothing about who gets the slice.

AI has a structural problem neither the internet nor enterprise software faced: it's expensive to serve, not just to build. Traditional software, once written, cost almost nothing to sell again. Every AI query burns real, ongoinghyperscalersower. That's one reason the biggest hyperscalers are on pace to spend $700 billion to $760 billion on AI infrastructure in 2026 alone — capital that has to be serviced with cash flow the technology hasn't yet reliably produced.

Increasingly, that capital comes from debt. Oracle's credit-default-swap spreads — the price of insuring against an Oracle default — hit an 18-year high in July 2026, driven by anxiety over how much of its buildout, including its roughly $300 billion commitment tied to OpenAI, is debt-financed rather than cash-funded. That debt sits inside a tangle of related-party dealing: Nvidia invests in OpenAI, OpenAI commits to buy compute from Oracle and CoreWeave, Oracle buys chips from Nvidia to build that compute — and each transaction gets booked as revenue somewhere in the loop. None of it is illegal. But when the same dollars appear to circulate among a small number of counterparties, it gets harder to tell how much real external demand sits underneath the numbers — the same question examiners were asking about mortgage securitizations in 2007, long before anyone said "crisis."

None of this requires believing the technology doesn't work. It likely does. The trouble is that working and paying for itself aren't the same achievement. A widely cited MIT study found that roughly 95% of generative-AI pilots at large companies failed to show a measurable return in 2025 — not evidence AI is a dead end, but evidence that converting a genuinely useful technology into a bottom-line result is its own unsolved problem. And even where AI does make a bank or a retailer more productive, competition tends to push the gain toward customers as lower prices, not toward the seller as margin. That's been true of nearly every general-purpose technology in economic history. There's no obvious reason AI repeals it.

None of this settles the argument. Inference costs have fallen sharply since 2023 and could keep falling. Enterprises could eventually redesign whole workflows around AI rather than bolting it onto what already exists. If that happens, today's data-center spending will look, in hindsight, like the railroads that did get built profitably.

But that's a thesis, not a conclusion — and the more money committed on the assumption it's already proven, the costlier it gets to discover otherwise. By the time hyperscalers close their books on 2028, the GPUs at the center of Burry's argument will be reaching the end of the shorter working life he insisted was the honest one. Somewhere between his number and theirs sits $176 billion. Whether that gap turns out to be an asterisk in an annual report or the first line of the next one will say more about this era than any amount of enthusiasm for the technology ever could.

ஒருநாள் விடியலிலே

ஒருநாள் விடியலிலே

உறங்காமல் இரவு போச்சே

கண்மூடி கனவு காண

கலைந்தே இரவு போச்சே

மெல்ல மெல்ல வந்தவளே

மேகம் போல நின்றவளே

கையில் வந்த காதல் கொஞ்சம்

காற்றில் போக விட்டவளே

முத்தம் மிச்சம் வைத்திருந்தேன்

மௌனம் மட்டும் பேசியிருந்தேன்

சொல்ல வந்த சொல்லை எல்லாம்

சொல்லாமலே தூங்கிவிட்டேன்

காலை வரும் நேரம் என்று

காத்திருந்தேன் காதல் நெஞ்சே

காண வந்த கண்ணின் ஓரம்

காணாமலே போனதேனோ?

நிலவு நின்ற வானம் கூட

நினைவு சொல்லி நின்றதே

நெஞ்சம் கொண்ட ரகசியத்தை

நட்சத்திரம் கேட்டதே

மெல்ல வீசும் தென்றல் வந்து

மேனி தொட்டு போனதே

மீண்டும் உன்னைப் பார்க்க வேண்டும்

மனசு மட்டும் சொன்னதே

பாடி வைத்த பாடல் ஒன்று

பாதியிலே நின்றதே

பாதி சொல்லி போன காதல்

பாதையிலே நின்றதே

முடிக்காத அந்தப் பாடல்

மூச்சுக்குள்ளே வாழுதே

முடிவென்று நினைத்த இரவும்

மீண்டும் வந்து போகுதே

**ஒருநாள் விடியலிலே

உறங்காமல் இரவு போச்சே

கண்மூடி கனவு காண

கலைந்தே இரவு போச்சே!**

மருதாணி நினைவில்

 மருதாணி வாசம் வந்து

மனமெங்கும் வீசுதடி

மல்லிகைப் பூவின் மீது

மழைத்துளியும் பேசுதடி

முற்றத்து மெல்லக் காற்று

முன்னாளைக் கூறுதடி

முல்லையின் வாசம் போல

முகம் வந்து போகுதடி

மஞ்சளிட்ட மேனி அன்று

மனதுக்குள் மின்னுதடி

மங்காத அந்தக் கண்கள்

மாலைநிலா தூவுதடி

தென்றலிலே தேடுகின்றேன்

தேயாத உன் வாசமே

தூங்கிடாத என் விழியில்

தோன்றும் உன் நேசமே

மறந்திடலாம் என்றாலும்

மனம் கேட்க மாட்டுதடி

மறைந்திடலாம் என்றாலும்

முகம் மறைய மாட்டுதடி

மருதாணி சிவந்த கையில்

மிச்சமான காதலடி

மல்லிகையும் வாடிப் போகும்

மனம் மட்டும் வாடாதடி!

The rise and rise of Luckin' Coffee

 Luckin Coffee. Does this sound a bell or ring a bell to any of you? I bet not. For most people outside of China and select parts of Southeast Asia, the name remains a mystery. Yet, right under our noses, this brand has quietly built a juggernaut waiting to take on the rest of the coffee world. If you haven't crossed paths with them yet, brace yourselves. Watch out, because the global coffee landscape is shifting beneath our feet.

For those who don't know, China is the second largest market for Starbucks. For over two decades, Starbucks completely dominated the scene, teaching China how to drink premium coffee. Its formula was simple: beautiful stores, premium locations, and the famous “third place” experience between home and work. They sold the romance of the cafe. Then came Luckin Coffee, and it flipped the script by asking a radically provocative question: Why do you even need the coffee shop?

Luckin wasn't interested in selling an afternoon escape; it reinvented the economics of selling coffee. Order on your phone, pay digitally, walk in, pick up, and walk out. By stripping away traditional overhead—using tiny stores, minimal seating, lower rents, aggressive digital promotions, and rapidly changing products designed for local tastes—Luckin stripped coffee down to its pure, fast-paced essence.

Then came the spectacular, near-fatal crash in 2020. Luckin collapsed after admitting to massive accounting fraud, resulting in a humiliating Nasdaq delisting. Wall Street and industry experts thought the company was completely finished. But they underestimated its resilience. Luckin is the ultimate Phoenix that arose, not from the ashes, but from coffee powder! Instead of dying, Luckin rebuilt itself with ruthless efficiency, capturing tens of millions of customers, generating massive revenues, and opening thousands of new locations in a single year to completely conquer its home market.

This violent disruption triggered a massive domino effect, culminating in the high-profile firing of Starbucks global CEO Laxman Narasimhan. While corporate critics blamed his lack of operational retail experience, it arguably wasn't entirely his fault. Narasimhan was caught completely off guard by a rival playing by a totally different rulebook. Luckin’s aggressive tech-first, low-cost strategy fundamentally broke the economics in Starbucks’ most critical growth market. When China sales plunged, it dragged down the entire brand, precipitating a global crisis for Starbucks and proving that traditional premium strategies were defenseless against ultra-fast digital scale.

The ultimate proof of this shift came when Starbucks completed a major deal to sell the majority stake of its China retail operation to a private capital firm, retaining only a minority share and the brand name. The American giant now sits completely eclipsed in footprint by Luckin's massive digital network. While Luckin hasn't displaced Starbucks globally just yet, it has fundamentally broken and rewritten the rules of the game. Now, it is taking this battle-tested, high-speed model overseas, and the coffee world will never be the same.

Infographics courtesy: The Internet

पाकर तुझे, हाय मुझे कुछ होने लगा है

 एक मन था मेरे पास, वो खोने लगा है,

पाकर तुझे, हाय मुझे कुछ होने लगा है,

रातों की नींद आँखों से रूठ सी गई,
दिल को तेरी यादों का रोग लगने लगा है।

ज़माने की कोई बंदिश अब रोक न पाएगी,
मेरा हर रास्ता तेरी तरफ मुड़ने लगा है।

मौसम की पहली बारिश ने आग लगा दी,
इश्क़ का ये रंग मुझपे चढ़ने लगा है।

'मनन' ने छोड़ दी दुनिया की परवाह करना,
सजदा अब बस तेरी चौखट पे होने लगा है।

दूर आपसे अब तो नही रहना है

 कहने की नही बात मगर कहना है,

दूर आपसे अब तो नही रहना है,

ज़ुल्म दुनिया के हँस के सहूँगा मगर,
जुदाई का ये सदमा नही सहना है।

काट दी मैंने आधी उम्र तन्हाई में,
बाक़ी सफ़र बस तेरे साथ कटना है।

शाम ढलते ही यादें घेर लेती हैं,
इन धड़कनों को अब तुझमें ही बहना है।

'मनन' माँगता है बस एक दुआ रब्ब से,
ज़िंदगी भर मुझे तेरा साया बनके रहना है।

चुपचाप तू सहती है क्यूँ

 रुप क्यूँ है दमका, रंग क्यूँ है चमका, साँस तेरी महकी है क्यूँ?

मस्ती जो है बहकी, चुनरी तो है ढलकी, ये चाल तेरी बहकी है क्यूँ?

क्यूँ आग से तू तपे, कुछ तो है तेरे मन में, बात कोई अधूरी सी है
है कैसा नशा छाया, किसने तुझे बहकाया, गिरती सम्हलती है क्यूँ?

मौसम ने क्या छेड़ा, कोई ख़्वाब क्या देखा, होश गँवा बैठी है तू
साए से तू डरे, रह-रह के आहें भरे, धूप में पिघलती है क्यूँ?

आईने के आगे खड़ी, ख़ुद से ही बातें बड़ी, राज़ छुपाती है क्या
दीवाने की चाहत में, या चाहत की राहत में, शम्अ सी जलती है क्यूँ?

'मनन' इस दीवाने को, दिल के आशियाने को, तूने आवाज़ ही न दी
अश्कों की ये रवानी, दर्द की ये कहानी, चुपचाप तू सहती है क्यूँ?

கோபாலா!

 மாயூரம் தந்த எழில்விளக்கே – எங்கள்

குடும்பத்தை வழி நடத்திய பெருந்தவமே!

தாயூறும் அன்பைத் தந்து எங்களை

தாங்கிடும் எங்கள் நற்குலமே!

ஐந்து பேரை நீ கரை சேர்த்தாய் – இள

வயதிலேயே சுமை ஏற்றாய்!

பாசத்தில் நீ ஒரு தசரதனாய் – பெரும்

தியாகத்தில் என்றும் ஒரு மகனாய்!

கேரம் போர்டில் நீ ராஜா – உந்தன்

கைவிரல் அசைந்தால் ஜெய பூஜா!

கோரம் மோதும் சோதனையை – உன்

கூர்மதி வெல்லும் சாதனையே!

பணத்தைத் தேடி நீ ஓடவில்லை – உந்தன்

மனதில் என்றும் பேராசையில்லை!

மனிதரைத் தேடியே நீ வாழ்ந்தாய் – இந்த

மண்ணில் எளிமையாய் உதிர்ந்தாய்!

நெருக்கடி நேரும் நேரமெல்லாம் – நீ

நிமிர்த்து நின்றாய் மலைபோல!

சுருக்கமாய் சொன்னால் நீதானே – எங்கள்

சொந்த பந்தத்தின் தலைமகனே!

இன்றுடன் வயது எழுபத்தாறு – உனக்கு

ஈடாய் சொல்ல இங்கு எவருமே யாரு?

என்றும் நீயே எங்கள் ஹீரோ – உன்னை

வணங்கிடுவோம் நாங்கள் நேரோ!

நலம் வாழ வாழ்த்தும் நெஞ்சங்கள் – இந்த

நாளில் பாடும் மங்கலங்கள்!

கோபாலா நீடூழி வாழ்கவே – உந்தன்

புன்னகை என்றும் ஆள்கவே!

Desilting the Veeranam

 Southern India is facing a serious water crisis, TN included. But the problem is not only inadequate rainfall. Every monsoon, enormous volumes of Cauvery floodwater rush downstream and eventually into the sea because we lack sufficient storage to capture it. We then face scarcity when the dry months arrive.

Veeranam Lake is a striking example of this failure.

More than a thousand years ago, the Cholas understood that water security meant storing water when it was abundant. Between 907 and 955 AD, under Prince Rajaditya Chola, they created a vast reservoir extending up to 16 kilometres to capture and store the erratic flows of the Cauvery system. They achieved this without modern machinery or engineering technology.

Today, we are allowing that achievement to deteriorate. Veeranam's original capacity was *1,465 million cubic feet (mcft), and nearly one-third is estimated to have been lost to accumulated silt. Restoring that lost capacity should be treated not as routine maintenance, but as *critical water-security infrastructure.

There is also an opportunity to make the project serve several purposes at once. The millions of tonnes of material removed from the lake could, after appropriate testing and treatment, be used for suitable highway embankment and slope applications in the major road projects underway in Cuddalore and neighbouring districts. This would reduce the need to source earth elsewhere while restoring the lake.

MGNREGA can add another dimension. Alongside mechanical desilting, suitable labour-intensive restoration work could provide employment to thousands of rural workers while rebuilding a vital community asset. Welfare expenditure would thus become productive infrastructure investment.

The opportunity is therefore much bigger than desilting a lake: water security, rural employment and infrastructure development can be addressed through one coordinated programme.

Veeranam has already waited long enough. Parts of it were addressed decades ago under the New Veeranam Project, but continued silt accumulation has steadily reduced its capacity. Every year of delay means less storage and greater risk. The machinery exists. The workforce exists. The road projects and government schemes exist. What is needed is the administrative will to connect them.

The Cholas built Veeranam a thousand years ago because they understood that water security could not be left to chance. We should at least have the wisdom to restore what they built.

The Billionaire’s back-yard retreat

 From rockets to Mars and the deep star-map,

To taking a long afternoon garden nap.

No more Neuralink or satellite beams -

Just selling organic sambar-vada dreams.

The boardrooms are silent, the Twitter-wars cease,

I’m trading my stocks for some "piece of the peace."

Instead of a Tesla that drives on its own,

I’ll drive a slow TVS50 through the harvest zone.

I’ll sing a few songs to a gathering crowd,

While selling my onions and feeling quite proud.

Forget the high tech and the cold Martian soil,

I’ll just watch my pot of filter coffee boil.

The Comfort of Being Poor, Cheaply

 There is a small ritual that plays out at most Indian dinner tables when the conversation turns to the economy. Someone quotes the nominal GDP per capita — a little under $2,700 — and a discomfort settles over the room. Then someone else offers the corrective: in PPP terms it's nearer $11,700. The room relaxes. Someone mentions that a haircut here costs what a coffee costs in London. Conversation moves on to cricket.

I have sat through that ritual more times than I can count, and I have come to think it is one of the more comforting untruths we tell ourselves — not a lie, since both numbers are correct, but a sleight of hand in which we choose the story that flatters us.

PPP was never meant to be a balm. It answers a narrow question: how much can a given income buy inside the country where it is earned? By that yardstick, an autorickshaw ride in Chennai or a plate of idlis at a Udupi restaurant will always look absurdly cheap next to their equivalents in Zurich, because they are priced in Indian wages and consumed entirely within India's borders. None of that is fiction.

The trouble starts the moment an Indian family looks up from the dosa and wants something the world, not the neighbourhood, has to sell them. A laptop. A semester abroad. A cardiac stent made in Minnesota. At that point the comforting PPP number quietly leaves the room, because Lenovo and Boeing do not accept payment in purchasing-power-adjusted rupees. They want dollars, at the going rate — closer to $2,700 a year than $11,700.

I think of an old colleague, an engineer with two decades of solid experience, who once did the arithmetic on sending his daughter to a decent state university in America. By any local measure he was comfortably middle class — the kind of household PPP statistics are designed to flatter. But tuition abroad was priced in the other economy entirely, the one where his rupee bought exactly what a currency converter said and not a paisa more. He didn't complain that India was poor. He simply adjusted his ambitions to what his income, translated honestly into dollars, could reach. Multiply him by several hundred million households making the same silent adjustment, and you have a more honest picture of where the country stands than any PPP table offers.

This isn't an argument against PPP. Economists need it, particularly in an economy where a great many transactions — a haircut, a maid's wage, a bus fare — never cross a border at all. These are non-tradables, priced by local conditions, and it is precisely because India remains a low-wage economy that they stay cheap. The affordability we celebrate and the poverty we'd rather not dwell on are two readings of the same fact.

What we haven't reckoned with is that this affordability has a shelf life on aspiration. It works beautifully right up to the point where middle-class life wants to touch the global economy — a foreign degree, a decent camera, a retirement fund not hostage to the rupee. At that boundary, the far less flattering nominal figure reasserts itself as the only one that matters. Every economy that made the leap from poor to rich — South Korea within a working lifetime — did so not by making imported goods cheap for its citizens, but by making its citizens rich enough in hard currency to afford them at world prices.

Which is the more useful question for India to ask, not at the finance ministry but at the dinner table where this argument usually gets settled in the corrective's favour: not "how much can a hundred rupees buy here," which we already answer too well for our own comfort, but "how many dollars can an Indian actually earn." That number is unglamorous and considerably harder to move than a statistical adjustment.

The dosa, for what it's worth, should stay cheap. There's no case for making ordinary Indian life expensive in the name of national pride. The case is for making sure the people eating it can also buy the laptop, or send the child abroad, without financial vertigo — not because these things got cheaper, but because they got richer. Until then, I'd treat any dinner-table cheer over the $11,700 PPP number the way I treat a currency's forward premium — real enough as a number, but not something you can actually spend.

From a Professional Platform to a Pimping Platform

 

There was a time — not even that long ago — when LinkedIn meant something. It was the one corner of the internet where you could reasonably expect a conversation to stay on the rails: a recommendation from a former manager, a genuine job lead, a thoughtful post about an industry shift. It was boring, sure. But boring in the way a well-run institution is boring. You trusted it precisely because it didn't try too hard to entertain you.

That LinkedIn is gone. What's left is a platform in visible, accelerating decay — and it didn't happen all at once. It happened in stages, each one a little more embarrassing than the last.

Stage One: The Ad Creep

It started innocently enough. Sponsored posts, a "Promoted" tag here and there. Companies pushing their own PR under the guise of "thought leadership." Annoying, but tolerable — every platform monetizes eventually, and at least the ads were roughly adjacent to professional life.

Stage Two: The Personal Brand Industrial Complex

Then came the influencer-ification of professional identity. Suddenly every third post was a humble-brag dressed up as a lesson: "I got fired. Here's what it taught me about leadership." The line-break-heavy, fake-vulnerable, engagement-bait post became its own genre. Recommendations stopped meaning "I worked with this person and they were good" and started meaning "I owe this person a favor and LinkedIn rewards mutual back-scratching."

Stage Three: The Op-Ed Takeover

Somewhere along the way, people decided LinkedIn was also the place to relitigate every social and political controversy of the week — entirely unmoored from any professional context. A platform built on job titles and endorsements became a soapbox for opinions that had nothing to do with anyone's actual work. Professional credibility got diluted into just another algorithm-chasing performance of having takes.

Stage Four: The Culture War Spillover

From there it was a short walk to full-blown political theatre. Posts about elections, ideology, and grievance politics — the exact content people once went to LinkedIn to avoid — now regularly outperform actual industry insight in the feed. The platform's entire value proposition was that it wasn't Twitter. It has since worked very hard to become Twitter, just with worse jokes and a "connect" button.

Stage Five: The Final Indignity — Sponsored Matchmaking

And now, the coup de grâce. LinkedIn InMail — a feature built for recruiters and business development — has become a delivery mechanism for dating and matchmaking pitches. A "Sponsored" message shows up in your professional inbox asking, apologetically dressed in corporate language, whether you're "single and open to finding a life partner." The pretext is thin: someone "came across your profile" and thought your "professional background" made you a good match — not for a job, not for a client, but for a spouse.

It's a small moment, but it's a telling one. It shows a platform that has fully surrendered the one thing that made it distinct: context. LinkedIn no longer seems to care what kind of attention it monetizes, as long as it monetizes something. Your resume, your work history, your professional photo — all of it is now just targeting data for whatever ad category is willing to pay for it that week, romance included.

The Common Thread

Every one of these stages has the same shape: a boundary that used to matter — professional vs. personal, informational vs. promotional, workplace vs. everything else — quietly dissolved because dissolving it was good for engagement and good for revenue. Each individual step looked defensible. The cumulative effect is a platform that has forgotten what it was for.

LinkedIn didn't become a bad product by accident. It became a bad product by optimizing, one small compromise at a time, for attention over trust. The tragedy is that trust was the entire product.