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WhatsApp has >1.5B users (Q2 2018)
Facebook's worldwide ARPU is ~$6.09 (Q3 2018)
Facebook's margins are ~30-40%
And Facebook paid ~$19B for WhatsApp

So, very roughly, about two years of monetization gets $19B in revenue. And five years gets $19B in profit to recoup the purchase.
Key assumption is that WhatsApp will monetize like other Facebook properties. Given that, I think the ballpark estimate holds.

WhatsApp MAUs: techcrunch.com/2018/01/31/wha…
Worldwide ARPU: fourweekmba.com/facebook-arpu/
Margins: wired.com/story/facebook…
Acquisition: techcrunch.com/2014/02/22/cru…
Also, while Facebook's margins are on a downward trend due to the human curators, their ARPU and WhatsApp MAU numbers are likely still trending up.

Those could cancel out, but the roughly 10 year time frame from 2014 acquisition to 2024 breakeven looks right. After that? Profit.
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Balaji S. Srinivasan
@balajis
An on-chain hash will become the new DOI.
h/t @ErinJHolmes who has been thinking about this in the context of academic publishing.

DOI background for those who aren't familiar:
en.wikipedia.org/wiki/Digital_o…
@ErinJHolmes Seems like DOIs and the WayBack Machine would be great use cases for @ensdomains, @blockstack, @unstoppableweb, @hns, @ipfsbot, @axiomorg, and the like.

Solving link rot, rather than censorship resistance per se.
Read 3 tweets
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Balaji S. Srinivasan
@balajis
I wonder if we could articulate a Moore's law for machine learning. Every X months, we see a doubling of Y, where Y represents something intrinsic like transistor count.
zdnet.com/article/google…
The obvious kinds of metrics are "papers" or "funding", but those are easily gamed.

Another idea is a battery of cognitive tests, like an AIQ score, but that's also gameable.

Ideally we want a ratio-scale variable like transistor count. Maybe neural network size?
Maybe we could define an AIQ score after all. Take AI vs human performance on various benchmarks, and plot each of those over time as shown.

I'd refresh a regularly updated dashboard with all benchmarks like this. Maybe rise of the machines dot com?
gigazine.net/gsc_news/en/20…
Read 4 tweets
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Balaji S. Srinivasan
@balajis
Sam technically lost the bet but it was very close. He stacked the deck against himself but nailed two out of three predictions. Only the first missed, and by 15-20%. daniellemorrill.com/2020/01/result…
Another “directionally right, technically wrong” bet was @bhorowitz vs @felixsalmon.

Ben bet that 10% of Americans would use Bitcoin to buy something by 2019. That didn’t happen.

But Felix thought BTC would go to zero. And that really didn’t happen. npr.org/sections/money…
A third example: @bryan_caplan vs @MarkSteynOnline.

Caplan bet that no EU member with a population over 10 million in 2007 would officially exit before Jan 1, 2020. Steyn bet they would.

But if Brexit occurs on Jan 31, 2020, Caplan will have barely won. econlib.org/i-win-my-eu-be…
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