Nishant Zutshi

Every AI agent ultimately has a human principal.

“Most men, the herd, have never tasted solitude. They leave father and mother, but only to crawl to a wife and quietly succumb to new warmth and new ties. They are never alone, they never commune with themselves.” — Hermann Hesse

Cricket is a simple sport.

There is no greater illusion than thinking the accomplishment of some goal will change your life. What will change your life is the person you become in the process of going for it.

Demis Hassabis says he can cure every disease in 10 years. Most people roll their eyes when they hear this, but I don't. Demis is the guy who just won the Nobel Prize for solving protein folding with AI (a problem biologists had been stuck on for 50 years). But that was just one milestone in his much grander plan. In 2010, he founded DeepMind with a 2-part mission: "solve intelligence, then use it to solve everything else." Step 1: make AI good enough to do real science. Step 2: point that AI at humanity's biggest problems. Step one was AlphaFold. He used AI to figure out the 3D shape of every protein in nature (which is basically what every drug attaches to). Demis said it would have taken "a billion years of PhD time" to do by hand. Step two is curing all disease. And as of today, step two is fully funded. Isomorphic Labs (his AI drug discovery company inside Google) just raised $2.1B led by Thrive Capital. Here's where the money goes and what Demis thinks happens next: > Drug discovery currently takes 5-10 years and costs billions per drug. That math is why most diseases don't have good treatments today. > AI fixes the math. Their drug design engine compresses development from years to months. Maybe weeks. > Isomorphic's first AI-designed cancer drug enters human trials this year. > Their pipeline expands beyond the current 17 programs across cancer, immune diseases, and heart disease into more health domains. > The endgame is personalized medicine: drugs designed overnight for your specific biology and your specific disease. That last one is the whole point. Today's drugs are mass-produced for an "average" patient who doesn't really exist. So most existing treatments work inconsistently from person to person, and most rare diseases never get a treatment at all (no market = no drug). When drug design gets fast and cheap, that whole calculus flips. Cancer variants get drugs designed for that specific variant, rare diseases get treatments because economics stop mattering, and drug-resistant infections get new drugs faster than they can evolve. That's what curing every disease actually looks like. Now imagine what your life looks like in 2036. A doctor draws your blood, sequences your genome, sends your disease profile to an AI. By morning the AI has designed a custom drug for your specific biology. Side effects, dosage, drug interactions all worked out before you take the first pill. You and your kids never see a cancer ward. That's what $2.1B is buying today. Demis was right about AlphaFold. If you consider the possibility that he's right again, every disease alive today is on borrowed time.

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@sabakaul1 @RiseofMartand Very poignant. Thank you for sharing 🙏🙏🙏

बाँट के अपना चेहरा माथा आँखें जाने कहाँ गई फटे पुराने इक एल्बम में चंचल लड़की जैसी माँ नास्ति मातृसमा छाया, नास्ति मातृसमा गतिः नास्ति मातृसमं त्राण, नास्ति मातृसमा प्रिया! माँ कबीर की साखी जैसी तुलसी की चौपाई–सी माँ मीरा की पदावली–सी माँ है ललित रुबाई–सी माँ वेदों की मूल चेतना माँ गीता की वाणी–सी माँ त्रिपिटक के सिद्ध सुत्त–सी लोकोत्तर कल्याणी–सी माँ द्वारे की तुलसी जैसी माँ बरगद की छाया–सी माँ कविता की सहज वेदना महाकाव्य की काया–सी माँ अषाढ़ की पहली वर्षा सावन की पुरवाई–सी माँ बसन्त की सुरभि सरीखी बगिया की अमराई–सी माँ यमुना की स्याम लहर–सी रेवा की गहराई–सी माँ गंगा की निर्मल धारा गोमुख की ऊँचाई–सी माँ ममता का मानसरोवर हिमगिरि–सा विश्वास है माँ श्रद्धा की आदि शक्ति–सी कावा है‚ कैलास है माँ धरती की हरी दूब–सी माँ केशर की क्यारी है पूरी सृष्टि निछावर जिस पर माँ की छवि ही न्यारी है माँ धरती के धैर्य सरीखी माँ ममता की खान है माँ की उपमा केवल माँ है माँ सचमुच भगवान है।

Game theory explains why working harder inside a broken system is the worst response to that system. Because a system is never truly broken. It's just producing exactly the outcomes its own incentive structures were designed to produce, whether intentional or not. Working harder inside this system increases your output in the payoff matrix, but it simply won't change the actual structure of the system's matrix. Thus, the correct response is not more effort. Instead, you must aim to identify whose interests the current structure serves and position yourself in favor of those interests rather than against them. Change the game, or play the game that is actually being played. Either way, you must stop optimizing for the game you wish it to be and start acting realistically.

You want to become a better investor … just follow this.

@ErikaMorris79 Oh by a distance. There are landscapes in Canada on either coasts that are unbearably beautiful.

@ZUTSHIN Newfoundland is meant to be stunning

Last #Cornwall video spam for the day Video 1) Lands End Video 2) Crantock (100 metres from our cottage) Video 3) Porthcurno My soul belongs on the beach ☀️🌊

@MonaADhar There is so much that I have picked up from your posts. Thank You

The Tawi River, a tributary of the Chenab (Chandrabagha) River flowing majestically through Jammu, is also known as “Surya Putri.” Surya Putri is one of the 64 Yoginis.

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise. Some quick takeaways: * Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow. * Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated. * Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs). * Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these. * Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs. * Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy. * Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems. * Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been. One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise. This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.

Visualization of Schrödinger wave equation.

sequoia put out a blog post called "services is the new software" look at this map of over $1T in services being replaced by AI agents

They capture the exact moment when a developing heart shifts from silence to its first beat. There is no “switch”: many cells gradually become active and, upon crossing a critical threshold, the entire tissue suddenly synchronizes.

@tds122 Bumrah >> Kapil hands down. But Kapil and Dhoni are more like “Standing on the shoulders of giants” kinda personas. Bumrah not there quite yet… at least in public consciousness.

@ZUTSHIN we're only allowed 4

Tendulkar Kohli Kapil Dev Bumrah

Testing gives you confidence. Proof gives you a guarantee. Proof forces every assumption to be explicit. Wrong ones surface as obligations that cannot be discharged.

Bhagavad Gita ✨