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Deedy Profile picture
@deedydas
Jul 19 • 2 tweets • 1 min read • Read on X
🚨 BREAKING: Detailed list of all 44 people in Meta's Superintelligence team.

— 50% from China
— 75% have PhDs, 70% Researchers
— 40% from OpenAI, 20% DeepMind, 15% Scale
— 20% L8+ level
— 75% 1st gen immigrants

Each of these people are likely getting paid $10-$100M/yr. Image
Source: anonymous Meta employee

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More from @deedydas

Deedy Profile picture
Dec 9
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 Image
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 Image
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.

3/8Image
Read 9 tweets
Deedy Profile picture
Aug 31
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 Image
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.

Source:


2/4alphaxiv.org/pdf/2508.21038
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
Read 4 tweets
Deedy Profile picture
Aug 10
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! Image
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!
Read 4 tweets
Deedy Profile picture
Aug 8
Ridiculous that OpenAI claimed 74.9% on SWE-Bench just to prove they were above Opus 4.1’s 74.5%…

By running it on 477 problems instead of the full 500.

Their system card only says 74% too. Image
Source: cdn.openai.com/pdf/8124a3ce-a… cdn.openai.com/pdf/8124a3ce-a…
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
Read 4 tweets
Deedy Profile picture
Jul 31
🚨Anthropic is at $4.5B annualized revenue and is the fastest growing software company in history!

They just overtook OpenAI to become the market leader in LLM API cost.

We just dropped this and more in our mid year Enteprise AI report:

1/7 Image
Enterprise LLM API spend has exploded from $3.5B to $8.4B by mid year, and that number is already stale!

2/7 Image
Enterprises and startups are choosing closed source models.

Only 11% of enterprises show high open source model usage.

3/7 Image
Read 7 tweets
Deedy Profile picture
Jul 31
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! Image
Source:
archive.is/biMgu
On the top chart:
Blue is base, purple is stock, green is bonus
Read 4 tweets

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