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We have a new @camo_hku paper out (with Jin Li and @chang_sun_econ): "Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design." We show that AI may not just replace jobs; it may make the jobs that remain worse.
Matthias Fahn
@FahnMatthias
Associate Professor @HKUFBE, Associate Director of @CAMO_HKU (camo.hku.hk), co-organizer of @RelConWorkshop.
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Exactly! Instead of automating jobs, AI may cause a shift towards "bad jobs." The paper by @JonSteinsson and co-authors provides one very sensible microfoundation, our paper (with Jin Li and Chang Sun) another, incentive-based: matthiasfahn.net/wp-content/upl…
The dominant model people use to think about AI's effect on the labor market is automation. But perhaps commoditization is a better model. Automation implies job apocalypse. Commoditization implies plentiful jobs with low wages. 1/View quoted post on X
Interesting case that supports @alexolegimas' prediction of the rise of the relational sector in the age of AI. I am a bit skeptical regarding its eventual importance, but am sure we will see more examples like this.
A newly released animated film called “Niu Lai” (牛来) is going viral for all the wrong reasons in China.
People are calling the visuals “disaster-level” , the entire production team was basically just two people: the director and the screenwriter. After 9 days in theaters, the box office still hasn’t even crossed 10,000 RMB.
However, over the last day or two, Niu Lai's reputation has been turning around a bit.
Many people feel that in the age of AI, having filmmakers who still stick to "handcrafted" animation and genuine effort is both honest and far more powerful than polished yet empty productions.
Today, its box office sales grew by 500,000RMB! a massive single-day jump from previous days！
Let's wait and see how the box office plays out!View quoted post on X
I think this makes more sense👇, in particular given the vast evidence that competition generally is good for innovation. Would be giod to see a model tailored to this specific situation.
I like Dean Ball's writing and respect him, he's been a thoughtful voice, but I disagree with the logic in this piece really forcefully. Given that he's now one of the leading policy people at a trillion dollar frontier lab, I think these arguments warrant extra scrutiny.View quoted post on X
"Open-weight models are inherently decelerationist"
This is simply not true. If OSS models are good and there is demand for them, this benefits the neoclouds at the expense of the labs' margins -- but margins aren't capex!! The GPUs still get bought to serve the tokens. My company is putting more inference through providers like @baseten and @FireworksAI_HQ and less through the labs lately, and we're using MORE tokens than before while delivering better value to our customers. How is this bearish for progress? Linux didn't decelerate computing -- it commoditized the OS layer and built the cloud, which is now the hyperscalers' most profitable business. Competition is good. If and when the labs deliver price-performant models, we will move more inference back to them.
"AI communism"
The implied chain (I think) is: open weights win -> model production isn't a viable business -> only states can fund frontier training -> state-provided AI -> communism. This strikes me as so fanciful it’s barely worth refuting, but: open ecosystems have historically been funded by commercial complements, not states: Linux by IBM and Google, Android, Chromium, Llama by Meta's ad business. Roads, GPS, and TCP/IP are public infrastructure and nobody calls them a dystopian hellscape. The chain only holds if you assume near-term AGI makes human intelligence irrelevant, a premise doing a lot of unstated work here. If you view AI as anything like normal technology, having it cheaper and more competition around producing it is strictly good for innovation and progress and the consumer and the economy. It's really that simple.
regulatory FUD
Dean frames this as a prediction, but he also calls it the administration's "best strategy" and sketches the playbook in some detail, including the observation that "it needn't be that well justified." Regulation by vague threat is how things work in banking and fintech, and it's precisely why those spaces are barren wastelands for innovation. Importing that model into AI and software generally would be disastrous.
This is an important point: (applied) economic theory can play a valuable role in anticipating the potential consequences of AI by combining models of trade-offs we already know matter with emerging facts about AI.
A stray meta thought: this was my first time writing a paper without any intention of publishing it in a journal. Econ academia rewards papers that are pretty, high tech, and surprising. Such papers are valuable! I love consuming them and also try to produce. But there’s a different kind of value in applying basic and careful econ reasoning to urgent questions. I wish more people felt like they could do so without worrying about whether it’ll be published in journal X. Maybe it won’t; so what? All the worse for academia. Many of us got into economics because it’s a powerful language for understanding the world… and understanding takes many formsView quoted post on X
