Starting to collect important questions for 2021 and beyond in this thread.

Please send questions you are interested in too!

1. Will demand for compute continue to double every few months? Tons of implications for semiconductor design and manufacture.

(THREAD) Pre-election Russia collusion may take down Trump. It's equally possible the Trumps' pre- and post-election collusion with Saudi Arabia, the UAE, and Israel will do it—a course of collusion also connected to the Kremlin. I explain here. I hope you'll read on and retweet.

1/ I must stress how unbelievably complex the "Grand Bargain" theory of the Trump-Russia case is—a different thing from saying it's not substantiated. It's substantiated in *almost every single particular*—it just *also* happens to be very confusing. Not byzantine—just confusing.

2/ The basics: Saudi Arabia, Israel, and the UAE all view Iran as their chief regional enemy. Iran is propped up by Russia. Therefore the Saudis, Israelis, and Emiratis all need a US government willing to find a way to get the Kremlin to *stop* supporting Iran in the Middle East.

3/ The best way to get Russia to stop supporting Iran—or reduce support—was/is to drop all sanctions on Russia over its 2014 annexation of Crimea, as that'd be worth *trillions* to the Kremlin over the next decade. Everyone knew that Clinton wouldn't do this—and that Trump would.

4/ Per the NYT, on August 3, 2016, Donald Trump Jr. met secretly at Trump Tower with a Saudi and Emirati emissary, George Nader, as well as an Israeli intelligence expert, Joel Zamel, with *significant* ties to both Israeli intelligence *and* Russian oligarchs allied with Putin.

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.