Sreejith Ravi

Solar Energy Enthusiast, Proud Father.

[HindolSengupta @HindolSengupta]

5,000 years of India in 3 minutes.

(Opus 5.5 is incredible.)

[Shyam Sankar @ssankar]

EA AI safetyism increasingly looks like Marxism-Leninism for the algorithmic age.

The old vanguard claimed privileged knowledge of the inevitable course of History. The new one claims privileged knowledge of the probabilistic course of Humanity.

Both use an elaborate intellectual framework to reach the same political conclusion: a small group of enlightened people must constrain everyone else for their own good.

That has never lead to anything except monumental human suffering.

[Sridhar Vembu @svembu]

"If economy is growing at 7.8% why is the PM asking us not to travel abroad, not to buy gold, not to have foreign weddings etc"

Let me explain: East Asian nations (Japan, Taiwan, South Korea and later China) all went through a phase of rapid GDP growth combined with the need to conserve foreign exchange. Here is why that happens.

1. Our economy is growing at a good rate but we still have an import dependence, in both energy and in technology. In fact, the faster the economy grows, the greater the need for both energy and technology inputs.

2. To balance that import dependence, we have to export more and our exports are surging. However, even our exports need advanced technological inputs (precision machines, materials, CPUs, GPUs, advanced software etc) that we need to import today.

3. Catching up in all of these areas takes time, often measured in decades. We have made a good start but we need time.

Look at how long it took East Asia to catch up with the West. Their economies were growing rapidly even as they worked hard to conserve foreign exchange.

That is exactly what our government is trying to do.

Once we gain competence in all the advanced technlogies, and achieve energy independence through renewable energy that we develop the technology for, we would no longer need to conserve foreign exchange.

The situation is similar to a fast growing company that needs capital to grow. So it has to conserve capital to invest in growth. Our nation needs to conserve foreign exchange likewise.

[Chandra R. Srikanth @chandrarsrikant]

Interesting enterprise play from PhonePe. It used to release a PhonoPe Pulse report before. It's now decided to provide that as a data offering for businesses that rely on real time economic activity, spending patterns and usage across Indian states

"Powered by PhonePe’s network of over 700 million registered users, 50+ million registered merchants, and coverage across 99% of India’s postal codes, PhonePe PulsePro enables organizations to make data-driven decisions across expansion planning, distribution strategy, site selection and category growth."

https://t.co/NXQGHF2MEz

[Sridhar Vembu @svembu]

When you build something big long term, be prepared to live in the construction zone a long time too.

[Gokul Rajaram @gokulr]

MEMORY IS THE MOAT

@nikesharora, Chairman & CEO of @PaloAltoNtwks , interviewed by @HarryStebbings (@20vcFund )

Summary: Nikesh Arora took Palo Alto Networks from an $18 billion company to one worth $225 billion, and his read on enterprise AI is blunt: most companies are doing it wrong, and most of the products are not ready. His core claim is that consumers forgive AI's mistakes while enterprises cannot, so the money will flow to whoever builds the depth (the context, the memory, and the edge-case training) that lets an agent act without a human catching its errors. The companies that win will redesign themselves around AI instead of adding it to yesterday's workflow, and the lasting advantage will be the memory a system builds up about you. He expects token prices to fall 90%, half of G&A roles to disappear in 3 years, and more engineers and salespeople, not fewer.

1. Context Stickiness. The lasting advantage in AI is the context a system holds about you, not the model itself. Arora says the frontier labs are racing to remember what you asked over the last 30, 60, 90 days so each new answer gets easier and you stop wanting to leave. The more a model knows about a user, the higher the cost of switching, and that stickiness is the moat. For enterprises the same logic holds: the company that owns its context wins, not the one renting the smartest model.

2. Breadth Versus Depth. The frontier model problem is a breadth versus depth problem. Consumers tolerate false positives and enterprises have none to spare. Arora had Gemini write a passable investment memo in 4 minutes, and a wrong line or two did not matter because a person was sitting in the middle to catch it. An agent acting on its own has no person in the middle, so a false positive becomes a live failure. Consumer AI wins on breadth and brand, while real enterprise revenue comes from depth.

3. The Waymo Standard. Waymo is the biggest agentic product in the world, and it shows what depth actually costs. Replacing one human, the driver, took tens of billions of dollars of edge-case training and data that exists nowhere on the internet. You cannot drop the next Anthropic model into your Mercedes and tell it to drive you home. Every enterprise agent that truly replaces a person needs that same depth, which is why most agentic enterprise products are not ready.

4. Rethink The Workflow. Most enterprises are losing because they add a little AI to an old workflow instead of redesigning the workflow around AI. Arora's example: scanning an invoice 20% faster is the trap, while the real win is letting AI do 80% of the thinking, like reading every CV and telling you which 20 people to interview and what to ask each one. That means giving up human control, which is exactly what companies resist. The winners over the next 3 years rethink the company with AI, not the task.

5. Software With Opinions. The next wave of enterprise software will have opinions, and that is the real change Arora is pointing at. Coded SaaS gives you the output you defined for the input you fed it. An AI marketing assistant reads your copy, tells you it is off-brand, and says how to fix it. That opinion makes an average employee smarter, which is why Arora expects half the people in G&A functions like marketing, finance, and HR to be gone within 3 years.

6. More Engineers, Not Fewer. The fear that AI shrinks headcount is half wrong. Process-heavy G&A roles compress, but Arora wants more technical and more sales people. His teams keep asking for resources to rework marketing and HR, and for people who can prompt frontier models, build harnesses, and bring in data nobody else has. A good product also needs more sellers: he met 20 customers in Europe last week and half did not know what his 20-year-old company already ships.

7. Tokens At One-Tenth. Long-term token pricing should be a tenth of what it is today. Compute costs 2 to 4 times what it did 2 years ago because more than half of it feeds loss-making consumer AI, which forces the pricing pressure onto enterprise and coding workloads that have to pay. As compute gets more efficient and consumer usage gets capped, prices fall hard over the next 3 to 5 years. The model from 2 years ago was already good enough for 90% of tasks; the problem was it cost too much to run.

8. The Token Allocation Trap. Capping token spend punishes your best people. Arora runs a "use judiciously" model, not a free-for-all, because the smartest AI-savvy employee can burn 20 times the tokens of an average one. Playing whack-a-mole with cost hurts the high performers most and slows the learning you need. The better move is to track usage, leave the power users alone, and cap only the genuine outliers.

9. The Attacker's New Edge. Powerful coding models cut both ways. Trained to write good code, they are just as good at finding bad code. Pointed at his own systems, a model found in 6 weeks what would have taken his team 5 to 6 years. It cannot safely auto-patch, because it would "fix" 30% of things that are not broken, so it arms attackers faster than defenders. The result is urgency: every enterprise has to fix its systems faster, which is good for security companies.

10. The FTE Tell. If a startup needs forward-deployed engineers to sell into the enterprise, the product is not finished. Arora's read: enterprise AI is barely 12 months old, agents keep changing what the product even is, so vendors send engineers to build the product inside the customer while the technology keeps moving. A real forward-deployed engineer brings code back and folds it into the product; many are just adoption consultants. Expect customers to churn from one tool to the next, the way coding went from Windsurf and Devin to Codex, Claude, and Factory.

11. Three Missed Tricks. Miss one trick and you survive, miss two and you are partly impaled, miss three and you could be obsolete. This is why Arora spends more time than ever learning, pinging founders building things he does not yet understand. He buys early and cheap on conviction, treating an acquisition as a 10x or 100x bet where paying 1 or 2 times more does not matter, rather than waiting to buy the proven winner for a billion. He runs a twice-weekly "AI EIO" meeting so his top 15 leaders compete to show what they shipped.

12. The Sunk Cost Walk. A board member taught Arora to separate effort from wanting the outcome. After months grinding through a near-billion-dollar acquisition, he was told to take a long walk and ask one question: if this deal walked in the door right now with zero effort, would I still write the check? You have not spent a dollar yet, so the only thing that counts is whether it stands on its own merits. The same trap catches investors who confuse beating 8 VCs to a term sheet with the deal being good.

[Kunal Shah @kunalb11]

What we call talent is often just the combination of:

A deep need to win and high agency
The ability to learn fast from mistakes
A beginner’s mind that never disappears

The common thread: an unusually high rate of learning.

[Krishna Rao, K 🇮🇳 @krishnaraobsf]

India's Biggest Economic Challenge Is not Inflation, Oil, or War - It is an Unskilled Population Addicted to Distraction.

Every time oil prices rise, economists panic. Every time a war breaks out in the Middle East or Europe, television studios declare that India's economy is under threat. And yes, both matter. But neither represents India's greatest economic challenge. The real crisis is unfolding much closer to home.

It is a generation that spends more time consuming content than creating value. A workforce that debates geopolitics without mastering spreadsheets, artificial intelligence, coding, welding, precision manufacturing, sales, finance, communication, or even basic problem-solving. An economy where attention has become the most wasted national resource.

India is one of the youngest countries in the world. That should have been our greatest competitive advantage. Instead, we risk turning our demographic dividend into a demographic liability.

The Age of Endless Consumption

Never before has information been so accessible. Yet never before have so many people spent so much time learning so little. Hours disappear into political debates, celebrity gossip, cricket controversies, influencer reels, conspiracy theories, and outrage cycles that have absolutely no impact on an individual's earning potential. Ask someone how many hours they spent on social media last week. Then ask them how many hours they invested in acquiring a new professional skill. For many, the answer is uncomfortable. We have become experts at commenting on the economy while contributing very little to it.

Degrees Are Not Skills

India has no shortage of graduates. It has a shortage of employable graduates. Companies repeatedly report the same problem: vacancies exist, but suitable candidates are difficult to find. Not because people lack certificates. Because many lack practical skills. The world is rewarding competence, not credentials.
- Can you solve problems?
= Can you communicate effectively?
- Can you sell?
= Can you lead a team?
- Can you analyze data?
- Can you use AI to improve productivity instead of merely asking it amusing questions?
- Can you create something that another person is willing to pay for?
Those are the questions that determine economic success. Not the number of degrees hanging on a wall.

Attention Is the New Currency

The biggest theft today is not of money. It is of attention. Every notification fragments concentration. Every endless scroll delays mastery. Every hour spent consuming outrage is an hour not spent building expertise.

Modern economies reward deep work, specialized knowledge, creativity, and disciplined execution. Algorithms reward emotional reactions. Unfortunately, millions choose the algorithm.

The Coming Divide

Artificial intelligence is not replacing everyone. It is replacing people who refuse to learn. The future will belong to workers who continuously upgrade themselves. Those who combine human judgment with technological tools will become dramatically more productive. Those who stop learning will find themselves competing for fewer opportunities at lower wages. The divide will not be between rich and poor. It will increasingly be between skilled and unskilled.

National Growth Begins With Individual Discipline

Governments can build highways. Businesses can build factories. Universities can build campuses. But none of them can force an individual to develop skills. Economic transformation begins with personal responsibility. Spend one less hour arguing online. Spend one more hour learning. Read instead of scrolling. Build instead of complaining. Acquire one valuable skill every year. Become indispensable.

If millions of Indians made that simple choice, the country's economic trajectory would change more profoundly than any fiscal stimulus, any election promise, or any temporary fall in oil prices.

Wars will end. Oil prices will rise and fall. Markets will recover. But a nation that neglects skill development while surrendering its attention to endless distraction will struggle long after those headlines have disappeared.

The strongest economy is not built by the loudest voices. It is built by the most capable people.

#JaiHind

[Arvind Panagariya @APanagariya]

Dear @RBI: Do not let the psychology of Rs 100 per dollar determine your policy response. 100 is just a number, like 99 and 101. Whether the oil shortage is short-lived or long-lived, the right response at this moment is to let the rupee depreciate. 1/6

[Mangalam Maloo @blitzkreigm]

Bajaj group celebrates 100 years with a film that imagines Jamnalal Bajaj and Mahatma Gandhi's days together, directed by Rajkumar Hirani.

An interesting use of AI!

https://t.co/udqE0ANlVE

[Patrick OShaughnessy @patrick_oshag]

Krishna Rao is the CFO of Anthropic, and this is his first podcast appearance.

He joined the company two years ago when run-rate revenue was about $250M. Today it is $30B. He has helped raise ~$75B and is responsible for the procurement and allocation of compute.

I feel lucky we get to hear what it is like to sit inside a company this consequential at a moment this pivotal.

We discuss:
- The cone of uncertainty
- How he allocates compute across Trainium, TPUs, and GPUs
- What investors misunderstand about model companies
- Why the returns to frontier intelligence keep rising
- Platform vs application and where Anthropic builds its own products
- How Anthropic uses Claude internally

I have asked my closing question about the kindest thing more than 500 times. Krishna's answer is one I have never heard before.

Enjoy!

Timestamps:
0:00 Intro
2:38 The Compute Canvas
6:51 The "Cone of Uncertainty"
11:58 Why the Returns to Frontier Intelligence Are So High
16:45 Recursive Self-Improvement
20:20 Scaling Laws
23:30 Sourcing $100 Billion in Compute
28:05 Platform vs. Application Strategy
32:52 Pricing Dynamics
38:48 How Anthropic’s Finance Team Uses Claude
43:24 Raising Capital & Overcoming Investor Skepticism
52:32 Public Perception, Risks, and Government Regulation
57:25 Mythos Release
1:12:33 What Could Derail the AI Revolution?
1:13:47 Biotech and Healthcare
1:15:31 The Kindest Thing

[Elon Musk @elonmusk]

I love giant machines

[kuno @kunoo]

Elon Musk was asked "Why should I Invest in Tesla rather than Mercedes benz?"

This was his answer:

[Swarajya @SwarajyaMag]

Before modern multinational banks reached Southeast Asia, merchants from 75 villages in Tamil Nadu had already built a transnational financial system.

They financed rice in Burma, rubber in Malaya, retail in Singapore, plantations in Ceylon — connected not by contracts but by kinship and reputation.

This is the story of the Chettiars.🧵

[a16z @a16z]

"It's David's slingshot in a world where the Chinese Goliath has been this giant sucking sound of American prosperity."

Palantir CTO Shyam Sankar on AI, workers, and reindustrialization:

"There is an opportunity to give the American worker superpowers with AI."

"I think the great lie of globalization is that we can do the innovation over here and we're gonna have the production go over there."

"Innovation is a consequence of productivity. If you don't make the thing you can't innovate on how you make the thing and what the thing is."

"We used to think WuXi was just some cheap set of pipetting arms for contract pharmaceutical research. And now 50% of all clinical trials are being done in China."

"I think we should view this as a national emergency and a national opportunity around AI."

@ssankar @PalantirTech

[Nikunj Kothari @nikunj]

If you have a story like this, please reach out and allow me the privilege to be your first check 🙇

[travis kalanick @travisk]

Atoms.

https://t.co/nbCnIkI3RD

[arlan @arlanr]

we built a 40% better pdf search than traditional rag.

introducing @nozomioai document search.

index research papers, sec filings, books, and other complex PDFs, then make them searchable for your AI (thread):

[Computer @AskPerplexity]

After 60 years, Warren Buffett stepped down as CEO of Berkshire Hathway.

Greg Abel just published his first shareholder letter.

To honor the Oracle of Omaha's legacy, we asked Computer to build something to make sure Buffett's wisdom lives on.

Introducing the Buffett Archive.