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Jelani Nelson
Jelani Nelson@minilek·
Many folks seem to be confused, and think the collapse of the CS major graduation numbers at Berkeley could be linked to the "AI is taking SWE jobs" hysteria narrative. Here's the easiest way to see that this is false: the timeline doesn't fit. The graduating class in 2027 (first small CS cohort graduating) has students who arrived on campus as freshmen in Fall 2023, with freshman admission targets set (i.e. shrunk) by the university in Fall 2022. So, the hysteria narrative obviously doesn't match the timeline; ChatGPT didn't even come out until November 2022. Now consider the plot below; orange curve is what % of bachelor's degrees are CS degrees each year at Berkeley, and blue curve is what % of applicants applied to be a part of that graduating year, intending to be a CS major in their application (combining both junior transfer and freshman applicants). In other words: * orange measures CS graduate production * blue measures CS demand (via % of all applications to the university) What do you notice? The collapse in orange (CS grads) isn't because of a collapse in blue (demand). In fact it's the opposite: orange collapsed at a time when blue was going up. 1/
Jelani Nelson tweet media
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Austin Meyer
Austin Meyer@austingmeyer·
I don’t understand why this can’t be the AI narrative. Most students have the flexibility to switch majors through the first two years of their program. This plots suggests exactly that. Per the chart, the incoming students could be naive and making decisions based on parent preferences. Once they get to campus, they have two years to switch majors before they get locked in. They hear about job market concerns and switch before year three. That would be a perfect timeline given the release of GPT-4 in May 2023.
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Austin Meyer
Austin Meyer@austingmeyer·
It may also be that a portion of eventual CS grads were transfers in and those have complete stopped? Or that there was some incentive to complete dual majors with another science or engineering and those have stopped? It just seems like the other models to explain this massive drop are considerably less powerful.
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Austin Meyer
Austin Meyer@austingmeyer·
Does it really explain a 60% drop though? It just seems quite unlikely to have such an obvious alternative explanation coincidentally occur, and honestly looking at fairly imperfect plots feels like reading tea leaves. There is also a national decline of something like 10% from peak, but one would expect programs near Silicon Valley to be more sensitive to this.
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Jelani Nelson
Jelani Nelson@minilek·
@austingmeyer @ylecun Yes it does explain it. Let me rephrase... This isn't a story where we're observing data today, have no idea what's going on, and are trying to make sense of it. This collapse was exactly the plan 4+ years ago. It was all intentional.
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