Byrne Hobart
Longreads
- No Dumb Ideas on what the economics of buying and selling jobs would look like. Many fun threads to pull on here. The piece is correct that this is somewhat similar to a guild system, but it's also similar to the tradition of selling military commissions, which turned out to be incentive-aligned in some historical periods, because it solved two problems: first, it encouraged commanders to actually attack their enemies (since that led to opportunities for looting, which they'd need to pay back the loans to buy commissions) and because it meant that officer pensions were paid as a lump sum by the next generation of officers. It had some obvious problems, too. And it's also worth pointing out that there are some jobs you can basically buy, like "fast food franchise operator" or "laundromat owner." They have a monetary cost of entry, but that spending is a complement to full-time work, not an investment that substitutes for it. Ultimately, the hard part of extending this is that it's an information and matching problem: the job-seller wants to sell to whoever will pay the most, but their employer wants whoever will do the most, and while those might be correlated, it will be imperfect. Still, it's an interesting model—you do have to pay to get jobs, especially good ones, but the way you pay is by accumulating skills and connections and going through an uncertain interview process. Any time something like that is the equilibrium for allocating a scarce resource, rather than cash, it's an interesting signal about the nature of the transaction.
- Brian Potter on who wins Nobels, and when. Highlights include the absolute dominance of Bell Labs among corporate labs where researchers did Nobel-winning work (they had as many Nobels as the next six runners-up combined), Cambridge's similarly skewed performance compared to other European universities, and the growing lag between doing the work that wins the prize and actually collecting it. But this piece is also good as an example of human/AI synthesis: the original dataset was missing some information, which Claude was able to fill in. And it turned out that Claude corrected errors in the original data, too. So this is another instance where backtesting is surprisingly hard, but also an example of how better tools mean that we actually have more historical data to work with than before.
- Wired on Google's race to catch up in AI deployment. What this story does very well is that it answers the question: why should startups spend very little time worrying that a bigger company will launch something and crush them? To be clear, Google has, in fact, shipped many great AI tools in the last few years, and they are crushing it, having also invented new categories, like converting a document into a podcast. But it took a heroic effort on their part, and involved moving people out of other projects, enforcing different corporate culture norms on the AI team, etc. Big companies have more resources than small ones, but deploying those resources effectively has a higher cost, too.
- Bryan Burrough on what it was like to work for Vanity Fair in the Peak Magazine era. If you've ever wondered why there used to be so much great writing in magazines like that, consider that Burrough was, at his peak, being paid just under $500k for three 10,000-word articles a year. And, not that it's especially material at that point, but the expense accounts were generous, too. There are times when business is good enough that it doesn't make sense to be picky about the prices of critical inputs. But these times are brief, and it's a good idea to enjoy them while you can but also save a very large fraction of what you're making.
- The quest to model C. Elegans, a creature with a few hundred neurons that we still can’t quite simulate. It’s striking that there’s been so much progress in building statistical models of intelligence, and so little in taking the simplest natural forms and trying to simulate them and scale them up. As it turns out, convergent evolution can happen even in entirely different substrates, and, just like in the natural kind, it doesn’t always follow the same path.
- In this week's Capital Gains, we look at the concept of economic complements. They come in many varieties: left shoes and right shoes, cans and can openers, energy and just about anything. And understanding which products are complementary to sell and which assets are complementary to own is a major driver of outsize success.
- In this week's issue of The Riff, we're talking about existential risks, political brands, tariffs, and more. Listen with Spotify/Apple/YouTube.
Open Thread
- Drop in any links or comments of interest to Diff readers.
- The Elegans piece, in light of LLMs, is an example of leapfrogging development in the natural world: we didn’t develop artificial worms, just artificial junior software engineers. Are there other cases where it’s easier to build an artificial version of something natural and complex than something natural and simple?
Diff Jobs
Companies in the Diff network are actively looking for talent. See a sampling of current open roles below:
- A well-funded startup that’s building the universal electronic cash system by taking stablecoin adoption from edge cases to the mainstream is looking for a senior full-stack engineer. (Singapore, NYC)
- An OpenAI backed startup that’s applying advanced reasoning techniques to reinvent investment analysis from first principles and build the IDE for financial research is looking for software engineers and a fundamental analyst. Experience at a Tiger Cub a plus. (NYC)
- Well-funded startup is developing a next-gen trading platform that leverages blockchain technology and AI. Experience with iOS or React is a plus. (NYC)
- A company that was using ML/AI to improve software development before it was cool—and is now inflecting fast—is looking for someone who can apply some analytical rigor to their growth model. If you live at the efficient frontier between Excel and Python, and like making quick decisions backed up with data, reach out. (Washington DC area)
- A premier proprietary trading firm is looking for smart generalists to join their investor relations team, working with external investors, rating agencies, and the internal finance team. Investment banking and/or investor relations experience preferred. Quantitative background and technical aptitude a plus. (NYC)
Even if you don't see an exact match for your skills and interests right now, we're happy to talk early so we can let you know if a good opportunity comes up.
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