Regressive Cross-Subsidy (Ep. 511) Thinking LSAT podcast

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Regressive Cross-Subsidy (Ep. 511)

1d ago 1:29:29

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Ben and Nathan explore how AI is acing law school exams and what that says about legal education. They unpack Donald Rumsfeld’s “unknown unknowns” and how the LSAT helps uncover them. The guys break down what the LSAT curve really means (or doesn’t), then offer advice on predatory pre-law jobs. Then they revisit the difference between sufficient and necessary assumptions. Temple University is featured in this week’s What’s the Deal With… Finally, another contestant in the Personal Statement Gong Show and amanuensis is the word of the week.

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0:34 – AI is an A+ Law Student

Ben and Nathan aren’t shocked to hear that AI is pulling A’s and B+’s on law school exams at the University of Maryland. The LSAT-style “racehorse” exams are all about spotting issues, which is something AI excels at. It’s a reminder that the profession is changing, and lawyers who ignore these tools risk falling behind.

Artificial Intelligence is now an A+ law student, study finds

9:33 – Unknown Unknowns

The LSAT is the best teacher, and when paired with the explanations that come with every question, you can solve your “unknown unknowns.” When you miss a question, you’ve both picked the wrong answer and failed to pick the right one. You must understand both mistakes before moving on. The guys note that gimmicky strategies often muddy common-sense logic, turning solvable problems (unknown knowns) into confusing ones.

17:46 – LSAT Curve

Danielle’s question about the LSAT curve leads to a breakdown: it’s not a traditional curve, but a scale based on experimental data. LSAC aims for consistent difficulty across tests, and it's not worth stressing over.

26:37 – Predatory Pre-Law Jobs

A listener’s $50k pre-law job in San Francisco turns out to be little more than coffee runs. Nathan warns against sticking with these roles unless there’s upside—legal exposure, networking, or skill-building. While there’s some value in doing grunt work well, make sure it’s leading somewhere.

33:10 – Sufficient vs. Necessary Assumption

Ben and Nathan clarify the frequent confusion between sufficient and necessary assumptions. Sufficient assumptions prove the conclusion (open question), while necessary assumptions must be true (closed question). Although they can sometimes overlap, applying the same analysis across the two question types will trip students up on harder questions.

49:13 – What’s the Deal with Temple Law?

Ben and Nate take a look at what ChatGPT provided for this week’s What’s the Deal with… Pulling data mainly from Temple’s website, the information was generally accurate. Temple offers regional value, if you can attend at the right price, as more than 75% of the class pays less than half of the sticker price.

LSAT Demon Scholarship Estimator

The Disparity Index

Temple Law Employment Outcomes

ChatGPT prompt: What are the five best and worst things about [insert law school]? Be brutally frank, please. Consider not only the quality of the school, but job outcomes and cost. Is it worth the money? Is it fair that some students get scholarships and others don’t?

1:21:30 – Personal Statement Gong Show

B sends in their submission for the Personal Statement Gong Show, the show where Ben and Nathan read personal statements and hit the gong when something goes wrong. The standing record to beat is ten lines, held by Greta.

1:26:40 - Word of the Week - Amanuensis

Readers of African American autobiography have too readily accepted the presumption of these editors that experiential facts recounted orally could be recorded and sorted by an amanuensis‑editor.

Get caught up with our ⁠Word of the Week⁠ library.

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