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Kevin A. Bryan Profile picture
@Afinetheorem
Nov 25, 2025 • 4 tweets • 2 min read • Read on X
Wild set of stories in Canada recently: most famous indigenous author, 2nd most famous writer, most famous singer, most famous director, a federal cabinet minister, a big university president, famous law professor...all turned out to be faking their Native ancestry. 1/3 Image
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In the case of King, he's getting favorable press about how "devastated" he is to have learned his absentee father wasn't actually Native. But online you can find discussions going back a dozen years from Cherokee genealogists noting he ignored them even back then. 2/3
The "why" seems to be "it's a more fun identity". I see younger folks saying "who would pretend to be a member of a downtrodden group?" There's of course been discrimination, but...Native folks have been cool my whole life! Even Dylan pretended to be Native to be "authentic". 3/3
(Take care: huge jump in Native identity on censuses in US/Canada since 2010 should be taken with a massive grain of salt. Also, I love that my 10th great grandmother is Wampanoag, so somehow at 1/4096th Native I beat Buffy. Don't worry, I don't claim it! )history.vineyard.net/daggett.htm#jo…

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More from @Afinetheorem

Kevin A. Bryan Profile picture
Jul 9
I have a fun new paper today w/ @joshgans: what makes an AI valuable? We noodled on this literally since 2024. Answer: AI is used by humans. They can extend, verify, get a second opinion, etc. AI is therefore part of what decision theorists call a "composite experiment". 1/10 Image
Let using AI have some cost, and likewise those other actions also potentially be costly. To make this concrete, assume you can trust an AI prediction, try to verify it as cost, or not use the AI at all. You then take an action if you like, getting H>0 if right, L<0 if wrong 2/10
A given AI has coverage (how often it tries to predict) and accuracy (how often it predict correctly). For a given human who has some cost of verification and some loss if they make a mistake, we can solve for how the human will use the AI. 3/10 Image
Read 10 tweets
Kevin A. Bryan Profile picture
Jun 29
I read Stephen's argument (caveat, he lived downstairs from me in college!) as "US can treat market access like a monopsonist, extract revenue from abroad for access". Surely true just like China can insist on tech transfer in exchange for FDI but Uganda can't. But a problem: 1/4
The world integrating inputs with US economy is *good* for US. Just like Toyota doesn't want to extract all rents from suppliers: many noncontractible fixed cost investments that make both parties better off. Grossman-Hart-Moore plus relational contract logic tells you why. 2/4
The problem with Trump Economics in general is that he and his admin see everything as zero sum where you should use leverage all the time. Not in principle crazy: Ontario uses tax and subsidy policy to deter auto suppliers from leaving province. But many things not zero sum! 3/4
Read 4 tweets
Kevin A. Bryan Profile picture
Jun 1
On PPP, this is good. Theory is pretty clear: take domestic output or income, deflate, look at real GDP pc or labor productivity. US growing ~1% faster than W Europe per year. Converted to current or chained PPP, you don't get this. 1/7
Imagine preferences and product mix identical in US and Europe: say, we only consume nontradable widgets. 100 people in each. Yr 1: 200 widgets consumed in each, 1 Euro=1 USD. Yr 2: 200 in US, 300 in EU, 1 Euro=2 USD. In both years, a widget in US is $1, in Europe is 1 Euro. 2/7
Let exchange rate be $1=€1 in year 1, $1=3€ in year 2. In year 1, nominal GDP is $200 and €200=$200. PPP is 1, GDP at PPP with US as numeraire is $200 and $200. In year 2, nominal is $200 and €300=$100. PPP is 1/3. GDP at PPP is $200 and $300. 3/7
Read 7 tweets
Kevin A. Bryan Profile picture
Mar 29
My read on "normal policymaker & corp. leader on AI": mostly now they don't need to be convinced it is very important (unlike a year ago). But they still see its capabilities as today + epsilon. So just briefly, here is what even "AI is normal tech" folks in the labs believe: 1/8
1) There is basically no evidence internal to the labs that we are seeing a slowdown in capabilities. 2) Demand will grow fast enough that compute + energy will become real caps in short run, though we have ideas on what to do here. 2/8
3) "AI researchers" will start meaningfully contributing to AI development in 1-3 years. 3 is the longest timeline I've ever heard. 4) We have many non-LLM techniques still far from being tapped out, and LLM-based researchers will help there. 3/8
Read 8 tweets
Kevin A. Bryan Profile picture
Jan 10
Another two weeks in various parts of India. I have said before that India feels like China 2005, where I briefly worked, in ambition and growth. However, three major issues need to be solved. 1/5
First, the License Raj mentality is still all over government services. Almost every govt interaction is poor from the first step (joke of an e-visa site, need to scan passport for Wifi, blink your eyes on video to get a SIM, queue at office 1 then hand token to office 2...) 2/5
If govt in India has too many rules, individuals have too few. Anarchy everywhere, from supply chain fraud to pollution control to, of course, the roads. China 2005 was not like this. Relational contracts - that is, trust - are essential for growth. 3/5
Read 6 tweets
Kevin A. Bryan Profile picture
Oct 2, 2025
@joshgans and I did an internal talk on AI for research. Mostly demos, so no slides, but broadly: 1) Research should be efficient, open & replicable. 2) AI helps will all three. 3) Always use the best model. 4) Structure your processes/tools/etc. so you can continue to do 3. 1/15
What I mean by "structure your process/tools/etc" is first that everything you do - code, writing, editing, collabs, slides - should have plain text as the substrate. This means no matter what, you can always use every AI tool now and in the future to interact with it. 2/x
And second, "structure your process/tools/etc" means training yourself on how to complement AI. E.g., if you don't know how to peer review code (or worse yet have never seen a diff b/c you write it all yourself), you are not setting yourself up to use AI in your workflow. 3/x
Read 15 tweets

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