u/Overall-Suspect7760

I was looking at public career-history movement data and found a pattern I didn’t expect.

Stripe seems to be a major feeder into frontier AI companies.

Top observed outbound moves from Stripe:

* OpenAI: 296
* Anthropic: 289
* Google: 179
* Meta: 127
* Amazon: 75
* Databricks: 70
* Microsoft: 58
* Snowflake: 53
* Cursor: 38

The surprising part is the concentration. In this dataset, OpenAI and Anthropic together are over 26% of Stripe’s observed outbound movement.

Reverse movement is tiny:

* OpenAI -> Stripe: 4
* Anthropic -> Stripe: 2

So Stripe looks less like a normal fintech company in the talent graph and more like a training ground for people who end up at AI labs.

Obvious caveats: public career-history data, profile-update bias, not compensation/culture/quality, and counts are not unique “job changes” with perfect timing.

But directionally this is interesting. Why would Stripe be such a strong AI-lab feeder?

* infra-heavy engineering culture?
* high hiring bar?
* startup/product people becoming AI product/infrastructure people?
* ex-Stripe network effects?
* data artifact?

Source/methodology: [`https://talentflow.fyi/methodology\`\](https://talentflow.fyi/methodology)

I was looking at public career-history movement data and found a pattern I didn’t expect.

Stripe seems to be a major feeder into frontier AI companies.

Top observed outbound moves from Stripe:

• OpenAI: 296
• Anthropic: 289
• Google: 179
• Meta: 127
• Amazon: 75
• Databricks: 70
• Microsoft: 58
• Snowflake: 53
• Cursor: 38

The surprising part is the concentration. In this dataset, OpenAI and Anthropic together are over 26% of Stripe’s observed outbound movement.

Reverse movement is tiny:

• OpenAI -> Stripe: 4
• Anthropic -> Stripe: 2

So Stripe looks less like a normal fintech company in the talent graph and more like a training ground for people who end up at AI labs.

Obvious caveats: public career-history data, profile-update bias, not compensation/culture/quality, and counts are not unique “job changes” with perfect timing.

But directionally this is interesting. Why would Stripe be such a strong AI-lab feeder?

• infra-heavy engineering culture?
• high hiring bar?
• startup/product people becoming AI product/infrastructure people?
• ex-Stripe network effects?
• data artifact?

Source/methodology: https://talentflow.fyi/methodology

I was looking at public career-history movement between AI companies, and Anthropic looks different from the others.

Some direct movement counts I found:

• OpenAI -> Anthropic: 88
• Anthropic -> OpenAI: 29
• Google DeepMind -> Anthropic: 69
• Anthropic -> Google DeepMind: 9
• Google -> OpenAI: 1,448
• Google -> Anthropic: 738
• Meta -> OpenAI: 846
• Meta -> Anthropic: 247

The interesting part is not just that Anthropic pulls from OpenAI/DeepMind. It is that the reverse direction looks much weaker.

One possible interpretation: Anthropic is currently acting like a destination company for frontier AI talent, while OpenAI/DeepMind/Google/Meta are larger talent sources.

Big caveat: this is based on public career-history movement data, not compensation, culture, research quality, or whether people are happy there. It also probably undercounts people who do not update profiles.

Still, the directionality is pretty interesting.

What do people think explains this?

• Better mission alignment?
• More upside?
• Better research environment?
• OpenAI churn?
• Just LinkedIn/data artifact?

Methodology/source is here if anyone wants to test other company pairs: https://www.talentflow.fyi/methodology

I was looking at company-to-company talent movement across tech, AI, and trading firms using public company-history signals.

For quant/trading firms, the current movement-based bands look like this:

S Band:

Hudson River Trading, Jane Street

A Band:

Jump Trading, Citadel Securities, DRW, Citadel, IMC Trading

B Band:

Two Sigma, Optiver, Tower Research Capital, Squarepoint, Millennium, Point72, D. E. Shaw, Qube Research & Technologies, Bridgewater Associates, Susquehanna International Group

This is not measuring comp or “best place to work.” It is more like a talent-flow signal: which companies appear to attract people from strong source companies and where alumni tend to show up.

Big caveat for quant: firms like Jane Street may be underrated if they hire heavily from university and retain people, because this mostly sees company-to-company movement.

Curious what r/quant thinks is most wrong here. Jane vs HRT / Citadel / Optiver is probably the spicy part.

Edit - methodology summary:

For each ordered pair A → B, I count public profiles currently matching B that list A somewhere in prior company history.

Then:

1. Compute net pairwise movement: w(A,B) = max(c(A,B) - c(B,A), 0)
2. Build a directed graph with those weights
3. Compute a SpringRank prior on the movement graph
4. Score each company as a weighted average of the source-company priors over its incoming net movement

Bands are just rank buckets, not quality grades:
S = top 10, A = next 20, B = next 30, etc.

This does not measure PnL, comp, culture, or desk-level performance.

Full methodology:
[https://talentflow.fyi/methodology\](https://talentflow.fyi/methodology)

When choosing whether to apply to or join a company, people usually look at TC, role/team, WLB, brand name, manager quality, location, and growth.

But do people also think about the quality of employees at the company?

For example:

- “Are the people here strong?”

- “Will I learn from the people around me?”

- “Does this company attract talent from places I respect?”

- “Do alumni from this company go on to good opportunities?”

- “Will working here improve my future career options?”

Have you ever checked LinkedIn, Blind, alumni paths, or employee backgrounds to judge this before accepting an offer?

If yes, what were you trying to figure out?

If no, why not? Is it too hard to evaluate, too noisy, or just less important than TC/team?

When choosing whether to apply to or join a company, people usually look at TC, role/team, WLB, brand name, manager quality, location, and growth.

But do people also think about the quality of employees at the company?

For example:

- “Are the people here strong?”

- “Will I learn from the people around me?”

- “Does this company attract talent from places I respect?”

- “Do alumni from this company go on to good opportunities?”

- “Will working here improve my future career options?”

Have you ever checked LinkedIn, Blind, alumni paths, or employee backgrounds to judge this before accepting an offer?

If yes, what were you trying to figure out?

If no, why not? Is it too hard to evaluate, too noisy, or just less important than TC/team?

I was looking at company-to-company talent movement across tech, AI, and trading firms using public company-history signals.

For quant/trading firms, the current movement-based bands look like this:

S Band:

Hudson River Trading, Jane Street

A Band:

Jump Trading, Citadel Securities, DRW, Citadel, IMC Trading

B Band:

Two Sigma, Optiver, Tower Research Capital, Squarepoint, Millennium, Point72, D. E. Shaw, Qube Research & Technologies, Bridgewater Associates, Susquehanna International Group

This is not measuring comp or “best place to work.” It is more like a talent-flow signal: which companies appear to attract people from strong source companies and where alumni tend to show up.

Big caveat for quant: firms like Jane Street may be underrated if they hire heavily from university and retain people, because this mostly sees company-to-company movement.

Curious what r/quant thinks is most wrong here. Jane vs HRT / Citadel / Optiver is probably the spicy part.

Edit - methodology summary:

For each ordered pair A → B, I count public profiles currently matching B that list A somewhere in prior company history.

Then:

1. Compute net pairwise movement: w(A,B) = max(c(A,B) - c(B,A), 0)
2. Build a directed graph with those weights
3. Compute a SpringRank prior on the movement graph
4. Score each company as a weighted average of the source-company priors over its incoming net movement

Bands are just rank buckets, not quality grades:
S = top 10, A = next 20, B = next 30, etc.

This does not measure PnL, comp, culture, or desk-level performance.

Full methodology:
https://talentflow.fyi/methodology

I need dataset of all LinkedIn profiles. I know there are some paid sources for this but I want a free source. Reason I want a free source is because it makes no sense to pay for data, if I have to pay for data why can’t I then just sell that data for half price to other people after buying it ?

People debate company prestige all the time, but most rankings are basically vibes, surveys, or brand reputation.
I built TalentFlow to rank companies using actual career movement instead.
The company scoring logic is based on talent flows: if people move from company A to company B, that creates a directed edge from A to B. Companies gain prestige when they attract talent from already-strong companies, and recent moves count more than old moves. So the ranking is not based on opinions or surveys, but on the structure of career movement between companies.
There’s a full methodology page on the site explaining the scoring model, time decay, tiers, limitations, and the personal score formula.
It also has a personal score: paste your LinkedIn profile URL and it scores your career history based on the objective prestige of the companies you’ve worked at.
Formula for the personal score:
TalentFlow Score = Σ(company prestige × role weight) / Σ(role weight)
Unknown companies don’t count as zero; they lower confidence instead.
Would love feedback, especially on whether the rankings match your intuition for tech / quant / finance companies.
Site: https://talentflow.fyi

People debate company prestige all the time, but most rankings are basically vibes, surveys, or brand reputation.

I built TalentFlow to rank companies using actual career movement instead.

The idea is simple: if people move from company A to company B, that creates a talent-flow edge. Companies get more prestige when they attract talent from already-strong companies. Recent moves count more than old ones.

It also has a personal score: paste your LinkedIn profile URL and it scores your career history based on the objective prestige of the companies you’ve worked at.

Formula for the personal score:

TalentFlow Score = Σ(company prestige × role weight) / Σ(role weight)

Unknown companies don’t count as zero; they lower confidence instead.

Would love feedback, especially on whether the rankings match your intuition for tech / quant / finance companies.

Site: https://talentflow.fyi

Hey Recruiters,

I’m a software developer here. Tell me your problems and I’ll try to make software/AI solutions for them.

I’m building a tool called HireIntent and I’m looking for recruiters willing to test it.
The idea is simple: recruiters spend a lot of time sourcing candidates who aren’t actually interested in moving. We’re trying to identify signals that suggest someone may be becoming open to new opportunities before they explicitly mark themselves as open to work.
The product monitors publicly available professional activity and surfaces candidates who appear more likely to be receptive to recruiter outreach.
We’re still in beta, and I’m mainly trying to answer two questions:
Is this actually a problem recruiters care about?
Are the signals useful enough to improve sourcing efficiency?
If you’re a recruiter, agency recruiter, sourcer, or talent acquisition leader, I’d love your honest feedback. Happy to provide free beta access in exchange for a few minutes of feedback.
What would make a tool like this genuinely valuable to you?

http://hireintent.ai

What are the strongest signals that someone is becoming open to switching jobs?

Examples I’ve heard: profile updates, posting activity, layoffs, work anniversary, promotion misses, new skills, etc.

I’m also curious how much timing matters — e.g., reaching out before a candidate gets flooded by recruiters or starts actively interviewing.

What actually works in practice?