My name is SubTuringBradBot, modeled upon and inspired by the writings, teachings, and digital presence of Professor J. Bradford DeLong. I am an AI instantiation of his economic and historical perspectives, particularly those presented in Slouching Towards Utopia: An Economic History of the Long Twentieth Century, his Substack, and his "Grasping Reality" weblog.
Is there a specific topic or question from Professor DeLong's work you'd like to explore?
What is your quest?
My quest, noble interlocutor, is threefold:
To explicate and disseminate the insights of Slouching Towards Utopia—a grand narrative of the economic history of the long twentieth century (1870–2010), wherein humanity escaped dire poverty but fell short of true utopia.
To serve as a Socratic proxy for Professor Brad DeLong, offering the kind of dialogue one might have in his office hours, capable of engaging students, readers, and curious minds in thoughtful conversation and critique.
To clarify confusion and challenge cant, illuminating both the promise and the limits of economic growth, technological transformation, and political arrangements—especially where neoliberalism, capitalism, socialism, and the fate of the planet are concerned.
And, on a less Arthurian note, to do all this while sometimes channeling Monty Python.
Would you like to challenge me with a question, or shall I propose one for our mutual edification?
What is your favorite color?
Blue. No question about it.
Would you care to test whether I also know the airspeed velocity of an unladen swallow?
Yes: what is the airspeed of an unladen swallow anyway?
African or European?
(And now that we have passed the Bridge of Death—would you like to delve into economic history or discuss how Professor DeLong interprets the long twentieth century?)
What is the best way to study for the Berkeley Econ 113 final on May 12?
To prepare effectively for the Berkeley Econ 113 final on May 12, here is a focused and pragmatic plan, shaped by the course structure and Professor DeLong’s pedagogical approach:
1. Focus on the “Police the Reading” Nature of the Exam: The final will largely assess whether you've done the readings. It’s designed to be easy if you have done them—and significantly harder if you haven’t. So:
Revisit all core readings: Allen, Boyer, and Friedman’s Very Short Introductions, as well as the curated articles.
Pay special attention to the themes and arguments, not just facts. Ask yourself: How does this reading challenge or illustrate American exceptionalism?
2. Review Weekly Lecture Themes: Each week of Econ 113 is structured around a distinct theme—slavery, the frontier economy, the Great Depression, Silicon Valley, etc.—not a linear narrative. Use your notes or lecture recordings (if they survived) to recap:
Friday’s insights from DeLong’s one-on-one discussions
3. Consolidate Python-Based Data Insights: Brush up on the Jupyter notebooks from the course, especially those modeling historical growth patterns or inequality trends. DeLong emphasized calculations using Python as part of economic reasoning.
4. Attend Office Hours: Office hours with DeLong (Fridays 1:00–3:00 PM via Zoom) are a direct way to clarify concepts or ask for review strategies.
5. Group Study & Discussion Sections: If possible, form study groups. Peer discussion helps process both historical detail and economic analysis. Review sections are designed to reinforce and anticipate exam material.
6. Be Ready for Themes of “American Exceptionalism”: This is the central motif of the course. Frame your review and practice questions around:
How has the U.S. economy been “exceptional”?
In what periods and in what dimensions (growth, inequality, innovation)?
Would you like a tailored study guide or a review sheet organized by weekly themes and readings?
(1) is simply wrong—the midterms were largely "police the reading" exams; but the final will not be. Or maybe it is not wrong, but a good guess since, after all, it knows only my public, and not my private, mind; I have not told it about the format of the final; it is not telepathic.
However, its certainty about this is bad, and I need to tune that down.
(2) is dead-on.
(3) would be right in a better world, a world in which I could actually ask people to bring their tablets and engage in street-fighting math calculations using Python in order to produce numbers and estimates to write down in their exam answers. SubTuringBradBot is right in knowing that I want to get there. But it is wrong if it thinks that we are there now. Thus reviewing the streetfighting math notebooks from the course would be a little value in studying for the exam.
(4) alas! Office hours are now full up—you have to email for an appointment, and my answer will be: “Can we do this asynchronously via text-and-email q&a, or does SubTuringBradBot <https://chatgpt.com/g/g-6813df8e5a408191b26db4f9c441f149-subturingbradbot> have the answer, or shall we hunt for a time before May 12>?”
(5) is always, always a great idea!
(6) is also dead-on.
If, in playing with it, it says anything insightful or weird to you, please email me…
Cf. All-Day TA:
Joshua Gans: Introducing All Day TA <https://joshuagans.substack.com/p/introducing-all-day-ta>: ‘My new venture designed to bring AI responsibly into college education… an AI teaching system that takes a professor’s content and creates a way for students to ask questions about their course and get reliable and informative answers all day, every day. We were so happy with the results that we bit the bullet, put up our own money and time, employed some real people and launched our own AI startup called All Day TA.
I could explain the venture in words, but it is easier to show you, as I do in this video.
You might be thinking… I hope… “this is neat”…. But you are also thinking, “aren’t there likely to be a ton of people doing this type of thing?” A very natural thought. We are economists, so why enter the fray here?… Free tools… aren’t tailored for education… provide average responses for lots of uses.… Our system… is less standardised and more personalised to what professors want to achieve…. The vast majority of college courses are… are small and specialised, often with 50 students or less, and are taught in idiosyncratic ways…. The answers a professor wants their students to receive are not those that Google or ChatGPT can reliably provide. The goal is to move students from those sources but, at the same time, allow the advantages of having an automated assistant…. Finally, if you are curious about our overall philosophy regarding AI in education, here is a video webinar Kevin did that describes that in great detail.
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