Matt's Thoughts In Between - Issue #52

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The biggest tech/politics news of the week was Amazon’s decision to pull out of its planned New York City HQ following backlash from local politicians (though not, it seems, most New Yorkers). There’s a good argument that economic development subsidies to corporates don’t always work out well (e.g. a particularly egregious case), but the rhetoric from the new Democratic left is much more generally anti-big tech.

I expect the idea of fighting monopoly power to become a much bigger part of left and centre left platforms on both sides of the Atlantic. This is not just about big tech, as this thread shows. There’s a critique - Matt Stoller is the must-read proponent - that this is a much more general problem in the US economy.

This may fundamentally disrupt the politics of Silicon Valley, which are currently reliably Democratic, particularly as views on the monopoly debate increasingly split along partisan lines. Even inside Silicon Valley, we’re starting to see the argument that its pursuit of monopoly as its highest goal is leading the tech ecosystem astray. As long as Trump is in the White House, social and cultural issues are likely to keep the Bay Area blue, but I’m not sure I’d bet that it will last.

OpenAI, the AI research company, released a new unsupervised machine learning model that can generate impressively human-sounding paragraphs of text (good semi-technical discussion here). It’s an interesting contribution if you’re an AI researcher, but what made it mainstream news was OpenAI’s claim that the full model was too dangerous to release publicly.

OpenAI’s CTO shared one example which, perhaps, illustrates the potential for “malicious applications” (see also). Nevertheless, there’s been a good deal of criticism and debate from within and without the AI community. Some prominent researchers have accused OpenAI of whipping up unhelpful hysteria. Zac Lipton wrote a fairly searing critique here, as did Stephen Merity here. This, though, is a useful defence from Joshua Achiam of OpenAI.

I remember when OpenAI launched in December 2015, Scott Alexander published an excellent and pessimistic essay on its risks. The part he was most worried about was the “open” bit:

It’s interesting to see OpenAI change tack on this, as Alexander predicted and hoped. Even if you see this as a publicity stunt, it’s a new milestone in public AI discourse and one worth pondering.

There was an important new study in Nature (or see this profile in the NYT) this week on the differing strengths and capabilities of small and large teams. The authors looked at 65 million (!) papers and patents over a 60 year period and conclude:

Perhaps that’s intuitive - it’s certainly the norm in the startup world and even the tech giants - but it’s the detail that’s interesting. In particular, the study suggests that “most of the effect occurs at the level of the individual, as people move between smaller and larger teams”. That is, more innovative/disruptive individuals select into smaller teams. I’m biased, but I think that effect is starting to play out at the level of macro-talent allocation, as well as within teams.

And what is true of disrupting science may be true of good conversations too: small is beautiful.

Thanks for sharing TiB with so many new people last week. If we add as many readers this week as we did last week, we’ll pass 1,000 subscribers. Please do forward to someone you think would enjoy it - or share it on Twitter or LinkedIn. Replies and feedback always welcome too.

Until next week,

Matt