What are the top 10 ranks from IITJEE, the hardest exam in India, 1987 doing today?
1M compete for 10k slots in engineering at IIT. People believe a top rank is a made life. Is it, 37 years on?
4 Profs, 2 founders, 2 financiers, 2 tech and 4 in India and 1 big on X!
🧵
1/13
Rank 1 — Rajesh Gopakumar
One of the best string theorists.
— From Calcutta, did Physics at IITK, not CS
— Princeton PhD, research at Harvard
— Moved back to India to be a Prof at TIFR in Bangalore
— Fun fact: ICTS was funded in large part by Jim Simons of RenTech
2/13
Rank 2 — Alok Sharma
Hedge fund guy.
— Went to IIT and then IIM Ahmedabad
— Spent 19yrs and became an MD at Bank of America
— Got an MBA at Booth in the meanwhile
— Was an MD at Millenium for 10yrs
— 5yrs at a $12B AUM hedge fund in NYC
3/13
Rank 3 — Soumen Chakrabarti
The Professor.
— Also from Calcutta, did CS at IIT Kharagpur and a PhD from Berkeley
— Spent some time at IBM Research
— Returned to India to be a Professor of CS at IIT Bombay
— Wife is rank 16 Sunita Sarawagi who is also a Prof at IITB
4/13
Rank 4 — R Govindarajan
The tech careerist
— IIT Madras and then MS from UMaryland
— 15yrs ar Oracle
— Went to India for 10yrs at various firms most notably Myntra and was a startup CTO
— Came back to be Sr Director at Oracle before being head of LLM at ServiceNow
5/13
Rank 5 — Rajeev Singh
The bootstrapped entrepreneur.
— Went to IIT Kanpur for CS and later did an MBA at Columbia
— Was a PM at a tech firm before starting a supply chain automation business in Delhi, IntelliPlanner.
6/13
Rank 6 — Krishna Kunchithapadam
Mystery man.
— Went to IIT and then seems to have done an MS at UWisconsin for 7yrs
— Spend 18yrs at Oracle
7/13
Rank 7 — Amod Agashe
The Mathematician.
— IIT Bombay, Stanford MS electrical before Berkeley PhD in math
— Hopped around Europe and India to do research
— Professor at various places, eventually FSU!
8/13
Rank 8 — Ravi Sundaram
Tried the industry, back to academia.
— IIT Madras CS and then a PhD from MIT
— Worked at a trading firm before being Director of Engg at Akamai
— Went back to being a Professor at Northeastern
9/13
Rank 9 — Rajaraman Krishnan
The bootstrapped entrepreneur.
— IIT Madras CS and then MS at UNC
— Worked at GE and Fujitsu
— Started and ran Solnet for 25yrs before it seems to have been acquired by Accenture
10/13
Rank 10 — Swami Nathan @avataram
The financier (who loves watches).
— IIT Kanpur CS and an MBA for IIM Bangalore
— Worked at various financial institutions across Mumbai, Dubai, Spain and NYC
— Now an MD at Vega, a hedge fund in the Cayman Islands
11/13
In this year, of the 10 people:
— 4 Profs, 2 founders, 2 financiers, 2 tech
— 4 India, 4 in US, 1 Singapore, 1 Cayman
— All Profs aren't equal. Gopakumar is a legend.
This isn't meant to be a success test, but a peek into how life plays out for those once at the very top!
12/13
Source: I'm using a proprietary dataset from 1985 - 2000. Clearly IIT is a sore topic given the few upset comments "why you posting you didn't even go"
Nonetheless, I've covered '86, '87, '98, '92, '09 and will be posting more in the coming months. Follow if interested!
13/13
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We just dropped a 12 page AI report on how ~500 execs at US enterprises use generative AI.
I read it all so you don't have to. Top 8 takeaways:
Anthropic is the #1 model provider in the enterprise, with 40% of ~$37B spend, with OpenAI dropping to #2.
1/8
On overall AI spend.
Generative AI has captured ~6% of software spend at $37B, growing ~3.2x YoY. Investments are coming to fruition and buyers are seeing results.
2/8
On where the spend goes.
Companies are using off-the-shelf models more than they're training their own. Horizontal AI tools like ChatGPT Enterprise, Claude for Work, Msft Copilot and Glean have exploded. "Departmental" AI like Cursor and Github Copilot also sees a huge boost.
This new DeepMind research shows just how broken vector search is.
Turns out some docs in your index are theoretically incapable of being retrieved by vector search, given a certain dimension count of the embedding.
Plain old BM25 from 1994 outperforms it on recall.
1/4
This result gives me a lot of joy as a search nerd for more than a decade.
Haters will say that the dataset the authors created, LIMIT, is synthetic and unrealistic, but this has been my observation building search systems at Google / Glean.
Vector Search was popularized as an approachable drop-in search since OpenAI embeddings grew in popularity, but has clear limitations in production settings.
Even aside from this result, showing it just misses certain docs constantly, it
– doesn't search for concepts well
– often retrieves similar but unrelated results
– doesn't account for non-content signals of similarity (recency, popularity)
3/4
I'm using GPT5 Pro to find me the best stocks and startup investments.
Asked it to use modern portfolio theory and size investments.
—Top Privates [+9.7%]: Databricks, Stripe, Anthropic, SpaceX
—Top Publics [+14.2%]: Nvidia, TSMC, Microsoft, Meta
Just put $1000 into the stocks!
Prompt: "Check all public / private stock market companies and tell me what I should invest in from first principles reasoning. You have $1000.
Please do deep research and present rationale for each investment. Each one should have a target price and expected value. Use advanced math for trading. Draw research from authoritative sources like research and unbiased pundits. Size my bets properly and use everything you know about portfolio theory. Corroborate each decision with a list of predictions about those companies.
Your goal is to maximize expected value. Make minimum 5 investments. Write it in a table."
This follows my previous experiment on Polymarket, which seemingly had ~2-4x the expected returns!
And yes, I know they’ve always reported on the 477 denominator, but that’s NOT “SWE-Bench verified”, that’s an entirely different metric, it’s “OpenAI’s subset of SWE Bench Verified” and that number can’t be compared
Microsoft just leaked their official compensation bands for engineers.
We often forget that you can be a stable high-performing engineer with
great work-life balance, be a BigTech lifer and comfortably retire with a net worth of ~$15M!