What struck me is that Vision Pro’s problem isn’t price or hardware, but mental models. Apple keeps framing spatial computing through TV/movie conventions, when the real power is presence with minimal mediation. At least for me, long takes, fixed viewpoints, fewer edits feel “boring” on TV but transformative here.
Precisely. Vision Pro lets the user mediate POV by controlling what's looked at, the opposite of TV/movies which force you to look at what the director decides.
Cowork feels like a real step toward usable agent AI — letting Claude actually interact with your files rather than just answer questions. But that also means we’ll really learn how robust (and safe) this stuff is once people start trying it on messy, real workflows instead of toy tasks.
yeah, the job title data is pretty wild. 62.8% of these apps are just 'Analyst' or 'Developer.' From a data perspective, using those generic SOC codes lets high-volume firms standardize everything. It meets the 'specialty' degree requirement on paper, but avoids the higher wage floors that a more specific title like 'Machine Learning Engineer' would trigger. Essentially, the system is being used for scale rather than niche talent scarcity, which shows up clearly in that $50k wage gap.
This is excellent feedback. You are absolutely right that a multivariate regression controlling for location, experience, and job family is the rigorous way to isolate the staffing firm coefficient from the raw data. We stuck to descriptive statistics (medians/distributions) for this initial post to keep it accessible to a general audience, but the 'Cap Exempt' comparison you suggested is a brilliant idea for a validity test. I’ll definitely look into and will try to add a 'Cap Exempt' binary variable to our roadmap for Part 2. Thanks for the 2 cents, it’s worth a lot more than that!
The mobility → wages connection is clear in the data. Interesting point
about green card backlogs adding another mobility restriction layer.
I focused on what's measurable: the wage gap and its correlation with
job-switching constraints. Policy intentions are beyond my scope - I'm
just showing what the numbers reveal.
Fair points. Title should specify "employer type" - it's staffing vs product
companies, not all H-1B.
And you're right that I didn't prove concentration causes wage gaps, just
that both exist. Would need to analyze if top 100 employers actually pay
less than smaller sponsors. Geography section had tighter logic.
Lemme appreciate the feedback, this is my first time posting here on HN, and getting such great feedback will definitely pave my way forward. Thank you.
Key points for policymakers:
• Dataset: DOL OFLC LCA Disclosure Data (2,404,784 certified applications)
• Finding: $50,950 median annual wage gap between employer types
• Mechanism: Mobility restrictions create monopsony conditions
• Geographic: 52% concentration in 5 states amplifies effects
We can provide:
- Full methodology documentation
- Cleaned dataset (anonymized, aggregated)
- Detailed methodology documentation
- Raw source file list
Email for detailed briefing materials: theh1brecords@gmail.com
Note: This is empirical analysis of public data, not policy advocacy.
The data patterns are reproducible and verifiable.
The mechanism appears to be mobility restriction creating monopsony conditions.
When workers can't easily change jobs (employer-specific visa, 60-day rule),
employers can offer below-market wages.
Used OFLC disclosure data, filtered to computer occupations (SOC 15-xxxx).
Happy to discuss methodology. Built an interactive calculator for people to
check their specific situation.
It's really shocking how hard it is for a skilled foreigner to get an EAD-like work permit that allows them to freely change employers. It takes 3+ years for most, and I think up to 15+ if you're from India. Of course the ability to suppress wages explains it.
Spot on. That 15+ year backlog turns a 'temporary' visa into a long-term economic trap. Our data actually showed the wage gap widens the longer a worker stays on H-1B (rather than converging with citizen wages), precisely because they are locked out of the free market (EAD) for the prime earning years of their career.
It's kind of an aside, but it also prevents the individual from doing the most economically efficient thing. I mean, suppose an H1B worker wanted to start a startup and hire some Americans to work for their company. They're legally not allowed to do that and create those jobs. Technically they need a business visa, but how would they get one if they can't start the business in the first place?
Exactly. We call this the 'Innovation Tax.' By legally tying workers to a single employer, the system prevents them from becoming employers themselves.
Our data showed 62% of H-1B filings use generic job titles like 'Analyst'—suggesting that even highly specialized founders are forced into 'cogs in the machine' roles. This isn't just a wage loss for the worker; it's a job-creation loss for the U.S. economy. No taking any side, but based on real data analysis quoting this.
The data shows it’s a structural feedback loop. Even comparing identical job titles in the same city, the $50k gap persists. The gap widens because citizen workers can leave staffing firms for raises at any time, while H-1B holders face 'mobility friction' (60-day rule/backlogs) that keeps them locked into lower-paying tiers longer.
Many-analysts results don’t prove bias; they often show the effect is under identified and analyst choices flip sign. Best fix: preregistration + multiverse/sensitivity reporting. Public preferences ≠ measured welfare-support effects.
Python optimized for C-interop (NumPy) just as data science exploded. While Perl won text processing, Python became the universal interface for C libraries. That ecosystem lock-in—not syntax—is why it won. It was the right glue at the right time.
