Longreads
- In Palladium, Omar Shams writes that AI will have an unsustainable warming impact on earth, not strictly because of the emissions generated from powering it, but because of the heat generated by computation itself, which has a theoretical minimum we can't beat. But the piece isn't hand-wringing about the need to slow down growth. Instead, it makes a pragmatic, abundance-flavored argument that we should just tile the moon with solar panels and GPUs. Conveniently, the moon has many of the raw materials we'd need to construct all of this, at least given sufficiently sophisticated robots. So there will be an efficient frontier between the cost of moving kilograms to the moon and the cost of moving kilobits there, versus the delay entailed by bootstrapping lunar industry compared to shipping terrestrial robots, fabs, etc. there. This is obviously a very science fiction-flavored scenario. Then again, the scaling laws it's based on are the ones that mean that, using the device on which you're reading this, you can have a conversation with an AI about how realistic the plan is. So, not that far-fetched after all.
- Philo at MD&A has a great review of the not-so-great book The Man Who Broke Capitalism, a biography of GE's Jack Welch, where he starts with a few factual errors, backs into the author's flawed model of the world—and then turns around and applies the same problem to the subject of the book! One good piece of revisionism here: there's a narrative that Jack Welch was a universally respected CEO during his tenure, but apparently the questions about GE's accounting started in the early 90s, and were widely covered in the media. In something a bit like Greek astronomers rejecting heliocentrism because they checked the data and didn't see the parallax the theory implied, Welch was questioned when he promised 15% annual growth in earnings per share from a cyclical company, but eventually accepted because he delivered for an implausibly long time. Especially in fields involving human behavior, a theory can be correct in the very long term but generate terrible short-term predictions, and vice-versa.
- Nilay Patel interviews Robinhood CEO Vlad Tenev. One of the interesting points that Tenev makes about the distinction between sports prediction markets and sports betting is that prediction markets don't try to select the skilled bettors out, while sportsbooks that are taking the other side of customers' wagers do. There are skilled odds-setters in prediction markets, but they only get to be the house if they make the best bid or offer. One way to look at an equilibrium is that DraftKings and the like will capture more of a customer's initial balance as revenue in year one, but if Robinhood earns revenue proportionate to customer balances, and has some way to increase those balances over time, they may be able to outbid pure gambling platforms by mixing just enough investment with the gambling to make the whole mix positive expected value.
- Ruxandra Teslo on whether edgy heterodox centrists should apologize for the Trump administration. One of the virtues of the rationalist-adjacent community is that they can step back from exciting political issues and ask meta questions, and this is a good example. It's basically a qualified elitist defense of, if not populism, then at least a set of meta issues that currently lead to object-level beliefs that are a bit more populist-friendly. A good contribution to the meta discourse.
- Post-Human Posting has a surreal encounter with a Reddit account that seems to be using AI-generated content to promote a business of AI-enhanced books, and one of its viral comments is about that very topic. Any time a large number of people have a complaint about something, models will be able to reproduce an articulate version of that complaint. And the bot, being the average of many real people's real opinions, can end up being more relatably average than any of them.
- In this week's Capital Gains, a look at why liquidity begets liquidity, from the closing auction to medieval trade fairs.
- This week on The Riff: tariffs and trade policy, the economics of open source AI, and the future of white collar work. Listen with Spotify/Apple/YouTube.
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Books
In the Plex: How Google Thinks, Works, and Shapes Our Lives: it's sometimes instructive to read business profiles a long time after the fact, to see what kinds of outcomes were visible before, what kinds were surprising, and what little details turn out to be significant. This book tells the story of Google's rise from grad school project to massive company, mostly with a focus on the early days.
Part of the fun is that early on, the story is one hack after another, at different levels of abstraction: Google was cadging computers from wherever it could, and assembling rickety-looking distributed systems that could collectively handle more than special-purpose hardware. But this also meant that they were inventing a lot of the science of reliability as they went along, and sometimes would run into problems like being unable to finish their crawler without the whole program crashing and needing to start over. (They clearly fixed this.) Other signs of Google's early compute constraints, no doubt formative for the company culture, included doing a deal where they powered search for Netscape and didn't have enough hardware to support both that and their direct-to-consumer business (they turned off the latter until they could get more machines).
There are some unbelievable stories of Google's early days and early culture. This one, for example, I just have trouble believing:
The need for some oversight became clear as early as 2000, when Brin sent employees an email announcing that Google had a new valuation (meaning the estimate of its market price had gone up) and would soon reprice its employee stock options—from 25 cents to $4.01. Some people didn’t realize that $4.01 was a reference to the calendar and frantically tried to buy up all the shares that they were entitled to before the price went up. They dug into savings and borrowed from their families. Google eventually had to make people whole.
If you take the abstract beats of the story, it kind of makes sense: Google said its stock would go up, so people naturally bought, but Google was joking, so they lost money, and Google had to repay them. But: what did they buy, from whom, and why? There wasn't an active market for Google shares, but employees presumably had options. But if the stock price goes up, the options still allow you to buy it at the lower price at which the options are struck, this being the point of options. If they wanted to exercise early, presumably they had to ask someone in HR how they could do so, which would be a good cue for that HR person to say "There's no reason for you to do that, and why would you want to?" which would settle the whole issue before money changed hands. And, anyway, what loss did Google have to make whole, here? The interest on borrowing money from family members for several days and then paying it back? My guess is that the part where Google played a prank about their valuation did happen as described, but the evocative details don't make sense. (Or there are some less evocative details that could have been relegated to a footnote to make the nitpickers happy. On the nitpicking front, I did decide to forgive the author for talking about a rating—the share of objectives achieved in a given period—expressed as a number between 0 and 1, with 1 referred to as "the integer 1" as the high score. Maybe they did ritually cast it from a float to an int as a celebratory gesture. It is, at least, the kind of thing I can imagine someone arguing about.)
One thing the book foreshadows well is Google's future position in AI: they're talking about it constantly in the narrative, and they even talk about "very, very, very lage language models, much larger than anyone has ever built in the history of mankind." (These were the in modern terms tiny models that powered their translation service.)
Google was also forward-thinking about just how much online behavior they could control. In an interview that almost certainly happened long before Google worried about antitrust, Larry Page said that one reason for the famous I'm Feeling Lucky button was that Google wanted to get people out of the habit of using URLs at all, when they could just search for everything they needed. This is the kind of ambition that's endearing when the founders are still technically taking a break from their PhDs in order to start a little company, but that looks ominously grasping in retrospect. And, of course, it's both: the sunny optimism that leads people to start companies in the first place, and to build the most ambitious version of whatever they were working on, sometimes works out well enough that the people in charge are now running important institutions of the sort that get called to testify before lawmakers and accused of ruining other companies' economics. Executing on the plan implied by I'm Feeling Lucky requires more skill than luck, but the technical and product skill it requires is only weakly related to the political skill necessary to preserve that victory.
Open Thread
- Drop in any links or comments of interest to Diff readers.
- What are some plans out there with a similar level of ambition as turning the moon into a datacenter? (Are they raising?)
Diff Jobs
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- A Google Ventures-backed startup founded by SpaceX engineers that’s building data infrastructure and tooling for hardware companies is looking for a software engineering manager with 7+ years experience building large scale distributed systems. (LA, Hybrid)
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