Balaji Srinivasan

The reported dosage is less than half what the Chinese Government recommends (500mg x 2 (BID)). So it's possible that they didn't hit the effective dose. https://medium.com/@balajis/the-official-chinese-government-guide-to-d…

Might be regional differences in Google results. Quick googling from North America yields the following results about 21.co's initial bitcoin chip design & mining operations[1], and then the pivot to Earn.com "social network tokens"[2][3]; also in Balaji Srinivasan's own words[4]. [1] https://techcrunch.com/2015/05/18/what-is-21-co-really-doing-an-excerp… [2] https://techcrunch.com/2017/09/20/21-co-announces-a-token-system-to-ge… [3] https://www.coindesk.com/meet-earn-com-21-rebrands-social-network-shif… [4] https://medium.com/@balajis/the-turnaround-2d145589d814

That preprint [1] is a great example of how far ahead of the skis you can get with motivated reasoning, bad math, and no peer review. They recruited people over Facebook at a time that California's COVID tests were backlogged (with ads not presented in the paper, with links to signup that could, anecdotally, be shared out); in that situation, avoiding selection bias towards people that were sick with something previously and want to know about potential immunity would be rather tough. (Per WTO protocol, they asked about symptoms in their sample but poof for some reason that data isn't in the paper). They got a 1.5% positive rate in their sample with 50 individuals testing positive. They then immediately applied demographic weighing to move that raw number to around 3%. Only then did they contemplate the false positive rate, taking the data of the manufacturer and 30 samples as a point estimate to figure out a 95% CI of positives around that 3%. That's a no-no for correctly propagating uncertainty. The statistically legitimate way to do that would've been to generate the 95% confidence interval for belief about the test's false positive rate and carry that forward in the demographic reweighing. That basic math would've avoided the frankly laughable result that a randomly selected person in Santa Clara county could have a 5% chance of having had COVID-19 while in the same county people seeing a doctor and getting tests through their doctor are only around 10% positive for…

I followed these guys pretty closely back then and this is close to my recollection as well. One addition I had to check on, which confused me at the tilt was how Balaji was so closely involved in 21 yet also a partner at Andreesen Horowitz, which I had read about their daily and weekly routine and was clearly a full time job. Balaji went from founder to "advisor" of 21 pretty quickly, becoming a partner of a16z in the interim. After things went to shit at 21 he went back as full time CEO. Reasons for how that went down are speculative. The results are not though. The exit is real but wow is it questionable. Conflicts of interest seemingly all over the place. I don't know who else is on Coinbase's cap table but I'd love to hear their takes on the acquisition. You can read Balaji's congratulatory take in his own words on what he did here. [0] I also recall Balaji positioning himself for a position in the Trump administration which saw him scrub his entire twitter history. If anyone recalls more details from that episode feel free to add them. [0] https://medium.com/@balajis/the-turnaround-2d145589d814

I’m not looking to downplay the virus - the initial downplay came from Vox and the NYT (journalists, not scientists - but often pretending they’re delivering scientific consensus): https://medium.com/@balajis/citations-for-the-recode-handshake-debunki… They also framed virus fears at the time as racism and complained about border closures. The “masks don’t work” arguments came from scientific authority though. Same with “not transmitted via the air” and dismissing the lab leak hypothesis. I’m not arguing with a partisan position - partisanship is what lead to a lot of dumb positions from scientific authority (and politicians too obviously).

You don't get a gourmet meal by starting with garbage and reprocessing it and adding garnishings. Most notably "But there’s continued concern about the methodology. Volunteers for the test were recruited from Facebook — so its sampling was neither random nor representative of the community as a whole." For an expert review of all the problems please see https://medium.com/@balajis/peer-review-of-covid-19-antibody-seropreva… In the midst of a crisis the poor quality work of the scientists suggests that they either don't take the matter sufficiently seriously or don't possess the necessary skills to deserve our trust. It's a dangerous time to take bad advice especially at a time people are so desperate for good news.

This LA study has one of the same authors as the Stanford study (Neeraj Sood). It uses the same test and so it has the same issues with the false positive rate. An honest person would address those but I didn't see that in the press release. This post describes the issues: https://medium.com/@balajis/peer-review-of-covid-19-antibody-seropreva… See also this thread: https://news.ycombinator.com/item?id=22924118

The Santa Clara study has gotten a fair bit of press and attention but it's not clear whether the results will hold up. The antibody test is new and they're assuming very few false positives, and it's a non-random study (recruitment via Facebook). Here's some more criticism: https://medium.com/@balajis/peer-review-of-covid-19-antibody-seropreva…

Balaji Srinivasan had a medium post about this: https://medium.com/@balajis/peer-review-of-covid-19-antibody-seropreva… A summary of his post: 1. There were 2 false positives in 401 pre-covid samples (known negative). Assuming a normal distribution, 2 sigma confidence can account for up to 80% of the positive results reported in this study. 2. For the virus to have already infected this many people, it should’ve spread so fast that it doesn’t match what we know about similar epidemics. 3. The researchers recruited volunteers via facebook ads. There could be sampling bias here. I encourage everyone to read the article: my summary < The original article.

Sampling uncertainties aside, this is super sensitive to mis-estimating the false positive rate. Which they seem fairly likely to have done, using an estimated test specificity of 100% (with error bars) in one branch of their analysis after getting 30/30 negatives against pre-COVID serum samples. Too small a sample to get that confident in an atypically high specificity, especially if that collection of samples was disproportionately low antibody (e.g. summer blood rather than post cold/flu season blood). That's to my mind the most consequential misestimation of the test characteristics, but Balaji Srinivasan details more: https://medium.com/@balajis/peer-review-of-covid-19-antibody-seropreva…

"21.co had been started as 21E6, a bitcoin mining company with a sizable data center footprint and a monthly bill to match. The company was set up to return mined bitcoin to its shareholders" https://medium.com/@balajis/the-turnaround-2d145589d814

Here is Balaji's own writeup of the company's trajectory: https://medium.com/@balajis/the-turnaround-2d145589d814

It sounds like they indirectly acquired BTC via mining: > And to make matters worse, all the BTC returned to shareholders by the mining operation was in danger of being completely clawed back if the company entered a messy bankruptcy. https://medium.com/@balajis/the-turnaround-2d145589d814

>Why would they be? Almost all of the Ethereum-hosted ERC20 tokens are. Instant liquidity, according to @balajis: https://medium.com/@balajis/thoughts-on-tokens-436109aabcbe >why bother with the added complexity of that, and just make their stock tradable on public markets? It's far less complex to write a basic whitepaper and copy-paste ERC20 into an Ethereum smart contract than it is to list a traditional stock on a public stock exchange. You don't even need revenue, or even a working product. https://theethereum.wiki/w/index.php/ERC20_Token_Standard

Network effects, yes like how BTC is owned by a tiny pool of oligarchs who need other users to transfer real capital in for their zero sum pyramid scheme. One important point: if we actually include all 7 billion people on the earth, most of whom have zero BTC or Ethereum, the Gini coefficient is essentially 0.99+. And if we just include all balances, we include many dust balances which would again put the Gini coefficient at 0.99+. Thus, we need some kind of threshold here. The imperfect threshold we picked was the Gini coefficient among accounts with ≥185 BTC per address, and ≥2477 ETH per address. So this is the distribution of ownership among the Bitcoin and Ethereum rich with $500k as of July 2017. In what kind of situation would a thresholded metric like this be interesting? Perhaps in a scenario similar to the ongoing IRS Coinbase issue, where the IRS is seeking information on all holders with balances >$20,000. Conceptualized in terms of an attack, a high Gini coefficient would mean that a government would only need to round up a few large holders in order to acquire a large percentage of outstanding cryptocurrency — and with it the ability to tank the price. With that said, two points. First, while one would not want a Gini coefficient of exactly 1.0 for BTC or ETH (as then only one person would have all of the digital currency, and no one would have an incentive to help boost the network),…

Sure! Andrew Gelman (Stats at Columbia) had a commonly shared piece: https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-stanf… Also a good dive into the issues: https://medium.com/@balajis/peer-review-of-covid-19-antibody-seropreva… Mercury News also had a good article covering a lot of this: https://www.mercurynews.com/2020/04/20/feud-over-stanford-coronavirus-… And yes, lots of twitter discussions from folks in the field, e.g. Natalie Dean of University of Florida https://twitter.com/nataliexdean/status/1251309217215942656 and Trevor Bedford (Fred Hutchinson) https://twitter.com/trvrb/status/1251332447691628545 and others.