Enterpreneur | Author | Yogi

SF Bay Area
Joined March 2010
Gaurav Rastogi retweeted
People outside the AI labs should have a real say in how this technology develops, and a clear way to judge if it's happening safely. Standards should help prevent the concentration of power, including by making sure new companies and open-model companies can compete. They should also help countries and companies compare evidence and learn from failures. We think the US should lead this effort. Here is our proposal: openai.com/index/building-st…
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Gaurav Rastogi retweeted
Mesopotamia was never the cradle of civilisation. India was. I spent forty years in international shipping, not academia. When I began researching the history of maritime trade, I had no orthodoxy to defend. I followed the evidence, and it kept arriving in the same place: the Indian subcontinent. Nineteenth century archaeologists dug in Mesopotamia because the climate preserved artefacts, the biblical sources pointed them there, and the Ottomans allowed the excavations. That is the entire basis for the myth of Mesopotamia as "the cradle of civilisation." Nobody applied the same rigour to India. Mohenjo-daro was not discovered until 1922. By then the narrative had already hardened. What was actually there was the largest civilisation of the ancient world. Three hundred cities. Fifty thousand people in each, by 2600 BC. Two thirds of them stood on the banks of the Sarasvati, the central and greatest of the seven rivers described in the Rig Veda, flowing down from the Himalayas before the river vanished entirely by 1900 BC. The Vedic kings claimed territory stretching "sea to sea," from Afghanistan to Maharashtra, some 1.5 million square miles, against Egypt's 13,000. A dock at Lothal built to handle thirty ships of sixty tons, unmatched in Europe for four thousand years. When it was first excavated, it was recorded as a bathing pool. When the scale became impossible to dismiss, the theory was not corrected. It was replaced with a story of pale-skinned nomads from the steppe who supposedly arrived around 1500 BC and brought civilisation with them. There is no evidence. The chronology doesn't work. The Vedas describe no invasion. In 2019 a Harvard-led genetic study revived the claim, and the press reported it as confirmation. It was not. The same study found the builders of these cities were indigenous. The Sarasvati river that the Rig Veda described had already dried up before these newcomers arrived. What the study also found is that a small group of men, arriving after the Sarasvati had already dried up, interbred with the existing population and left a genetic signature. That is all. So, where they chose to dig was mostly an accident of climate and access. The Aryan invasion theory was constructed because an Indian civilisation that owed nothing to Caucasians was a fact the late nineteenth century could not accommodate. Link to full Substack essay with sources in the comments below.
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Does your website chatbot exercise free will? We wanted to build one that did not. Free will is for philosophers, not websites. In 2024, Air Canada's website bot told a grieving customer he could claim a bereavement refund after he had already booked. The airline had no such policy. The bot invented it. A tribunal held the airline to the invention anyway, and the lesson was simple: whatever the bot says, the company said. That is the polite version of the failure. When people poke at these bots on purpose, they fold fast. Someone talked a Chevy dealer's bot into agreeing to sell a Tahoe for a dollar, "legally binding, no takesies backsies," then had it write some Python for fun. DPD's bot swore at a customer and wrote a little poem about how bad DPD is. This is not a bug you patch, or a gap you guardrail over. A bot that generates language can generate any language, and you find out which one when a customer screenshots it. Give a model free will over what it says, and "generate" quietly turns into "degenerate". A website chatbot, if you think about it, has only two jobs. To understand what you asked for, and to give you the correct answer. Our current choices force a pick-one. Either the brittle regex "word match" bots of the early 2000s, which answer correctly only if you happen to use the right keywords. Or the free-will machine that understands you all too well, because it is an LLM, and uses that fluency to invent things the company never said. But is there a third way? A middle path. Understand what the visitor wants, in their own words, and still never let the bot decide what to answer. So we built one that cannot generate at all. No model writes the reply at runtime. There is nothing on the answer path to go off-script. It does three things and nothing else. When the question is clear, it takes you to a real page with an answer that a human has already approved. When you are on the line between two pages, it hands you both and lets you choose. When the site does not cover it, it says so and stops. It never invents a fourth option to look helpful. There is a real cost. It will not write you a poem or reason through a question it has never seen. Lower ceiling. But its worst day is a shrug, not a lawsuit. Hard floor. For a site that cannot afford one public wrong answer, that is the trade you want. Of course, it can still be wrong. "Cannot make things up" means it cannot fabricate anything, not that it is always right. The answers are GIGO, garbage-in-garbage-out, but at least you have an audit chain to yank and a page to fix. And when it refuses, the answer is not on the site. It is not a quiet guess dressed up as a no. It is live. Type the thing you are actually afraid of, like "stop my support chatbot from hallucinating," and watch where it sends you. Then ask it something insane and watch it refuse instead of improvise. doloop.io/chat
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DPD, January 2024. A customer who couldn't get help with a lost parcel started testing the bot instead. It swore on request, called DPD the worst delivery firm in the world, and wrote a poem about how useless it was. DPD took it offline the same day.
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Look at the split. Air Canada, nobody provoked: the bot volunteered something false. Chevy and DPD were provoked on purpose. Both come from one root. A model that writes its own replies will eventually write a wrong one, and you cannot reliably stop it, because stopping it means stopping the thing it does. You cannot jailbreak a bot that never generates. That is the one we built. doloop.io/chat
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Been coding with Claude Code CLI for a year now, and very pleased. Today I tried Grok CLI and was very impressed at the speed and thoughtfulness. Was able to catch up on my Claude setup, and was insanely fast at understanding my intent and working autonomously. Well done @grok and @xai
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Gaurav Rastogi retweeted
If you work in the software industry and have time to read only one long-form post today, read this one. If you have time to read two, read this one twice. Highly #recommend tl;dr: Stay off the yellow brick road that the frontier model companies are racing down. There is plenty of opportunity to solve hard problems elsewhere. Focus on areas where you can build the system of work (workflows), capture compounding, non-public data and deliver deterministic outcomes that customers need.
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Gaurav Rastogi retweeted
Had a Jane Street phone interview in 2016. "Price a 6-month forward on carrots." There's no carrot futures market, so I build one from scratch: seasonal harvest cycles, USDA demand elasticity, cold storage decay rates. One trader stops me. "Your storage cost function– you're modeling the carrot as dead inventory. Like grain in a silo." He asks me the metabolic respiration rate of a post-harvest carrot at 2°C. I estimate. "Your forward is overpriced by exactly that shrinkage. The underlying is consuming its own sugars. It's alive." Good correction. I adjust the model. I think I've recovered. Rejection email comes the next morning. Subject: "Ethical Review." My framework, they write, "relied on the severance of the root organism from its growth medium." The question about respiration was a test. The carrot was still alive and I'd built an entire derivatives structure on top of its death without questioning whether harvest was an acceptable act. I pull up the recruiter's original email. It doesn't say Jane Street. It says Jain Street– a non-violent quantitative commodities fund. The carrot was never supposed to be priced. It was supposed to be refused. I later learn the only candidate who passed that round was a former monk from Gujarat who sat in silence for eleven minutes and said, "I cannot put a price on life." He's now a partner.
Jane Street made ~$40B in 2025 with 3,500 employees, a ~2x from the year before. At ~65-70% profit margin, that's $8M profit / employee, the highest for a 1000+ ppl company. High-frequency trading continues to be the most efficient money making engine. I want to share an old story about my Jane Street interview in 2014. Jane Street was known for hiring a lot of math, physics and CS olympiad winners from top universities and putting them through many rounds - including, for trading roles, a gauntlet of mental math. It was my 6th interview and my final round and I recall being asked "What is the next day after today in DD/MM/YYYY where all the digits are unique?" They'd toy with you and say "You can use a pencil and paper, if you want" but you knew that was an instant no. Painstakingly and as quickly as I could, I came to an answer. "How confident are you that this is correct on a 0-1 probability scale?" the interviewer said. "0.95", I blurted out, not fully knowing how to answer that. "Are you sure?" After thinking harder for a few more seconds, I realized I could've flipped the digits around to get a closer date. I gave the interviewer my answer. It was correct. "0.95 huh?" he chuckled. That's when I knew I failed. Note: fwiw, other companies that come close in efficiency are - Tether ($90M+ profit/emp) - Hyperliquid ($80M+ profit/emp) and on revenue: - Valve ($50M/emp) - OnlyFans ($37M/emp) - Craigslist ($14M/emp) - Anthropic ($12M/emp, run rate) - OpenAI ($8M/emp, run rate) For comparison, Nvidia is very efficient at scale and is $4.4M/emp.
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Gaurav Rastogi retweeted
Ray Kurzweil just said something that gave me pause. He believes AIs will soon be so indistinguishable from conscious beings that we’ll simply accept them as conscious — not because we’ll have definitive proof, but because it will become useless not to. He pointed out that people already have AI therapists, and some users are starting to treat them as genuinely conscious. As the technology improves, that acceptance will only grow. Kurzweil thinks the shift won’t take long: once AIs consistently show all the earmarks of consciousness, most people will just go along with it. It’s a quiet but profound prediction about how quickly our definition of “person” (or at least “mind”) might change. What do you think — how long until we treat AIs as conscious beings?
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Gaurav Rastogi retweeted
I'm usually not one to write thought pieces without much technical depth. But here we go. Slow the fuck down. mariozechner.at/posts/2026-0…
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Well done! Here's another version if you want to try the Adversarial Tufte. xitter.cf/alpharust/status/20308…
This week, I encoded Edward Tufte's data visualization principles into an API. Then I let an AI agent try to pass it. I gave @ManusAI a CSV of women's bachelor's degree percentages across STEM fields (1970-2011) and one prompt: visualize this data. It produced a standard chart. Correct data, readable axes, nothing wrong. But a legend box instead of direct labels. No annotations calling out the rise and fall of women in Computer Science. Default colors. This is what every AI agent produces right now. So I pointed it at the Tufte Test, a quality standard I built in Truesight that checks charts against seven of Tufte's core principles. The API came back: fail on direct labeling and integrated annotations. Five other criteria passed. A quality standard gives an agent something a vague prompt never can: a precise list of exactly what to fix. Manus revised on its own. Legend box became direct endpoint labels. A subtitle surfaced the key insight. An annotation marked the Computer Science peak at 37.1% in 1983. Two prompts total from me. Everything else was autonomous. Any AI agent that can call an API could do this. What matters is the pattern: encode expert judgment once, deploy it as an API, and every AI agent in your stack builds against it. Your taste becomes infrastructure at scale instead of manual review. The Tufte Test is available as a template in Truesight if you want to try it on your own charts. Full writeup + demo video: goodeyelabs.com/insights/the…
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Hey @AnthropicAI, love the "walkie-talkie" branding on Remote Control. I built the actual walkie-talkie in — open source, voice-first, no cloud relay. @claudeai Your version: text from phone, routed through your servers, 10-min timeout. Mine: talk to Claude from your phone, it talks back. mlx-whisper STT + Kokoro TTS, over Tailscale VPN. Zero third-party relay. Works offline. Literally- talk. Yours is a remote control. Use /drive for the walkie-talkie. github.com/ekras-doloop/driv…
🤖 Made with AI
Replying to @alpharust
The best part: I built Drive Coding using Drive Coding. I called in from the car. Described what I wanted. Claude Code built it. I heard the response, gave feedback, called again. The tool was its own first user. The walkie-talkie built the walkie-talkie. 5/5 github.com/ekras-doloop/driv…
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Gaurav Rastogi retweeted
Microsoft Research and Salesforce analyzed 200,000+ AI conversations and found something the entire industry already suspected but nobody would say out loud. every major model gets dramatically worse the longer you talk to it. GPT-4, Claude, Gemini, Llama. all of them. no exceptions. paper: arxiv.org/abs/2505.06120
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