When Better Models Widen the Gap: AI's Cost Divide in Higher Ed (AI Goes to College, Ep. 36)

What happens to students when the best AI models cost ten times more than the basic ones? That is the question Craig and Rob keep circling in this episode, prompted by Anthropic's brief and strange release of Fable 5.

Fable 5 arrived as a guardrailed version of Mythos, a model so good at exposing software vulnerabilities that Anthropic had restricted it to a small set of secure organizations. For about a week it was freely available to paid users; then federal import controls landed and Anthropic pulled it, with no clear word on when, or whether, it returns. The hosts use that whiplash to get at the questions that actually matter for higher ed: who can afford the most capable tools, what that does to learning, and why none of it changes the deeper problem with how we assess students. They also dig into a large new study on student AI use, the agents Rob is building for faculty this summer, and a 70-page course handbook Craig generated in an afternoon.

What you'll hear

The cost gap, in real numbers. Craig walks through Anthropic's tiers (Haiku, Sonnet, Opus, Fable) and what they cost to run: a task that runs free under his Opus subscription would have cost roughly $50 in Fable 5, while Haiku sits around $5. His worry is that this turns into an SAT-prep dynamic on steroids, where score gaps come from resource access rather than ability.

Rob's counterintuitive flip. Rob raises the possibility that students stuck on weaker models might actually learn more, because they cannot offload as much of the cognitive work and have to stay involved in it. Neither host claims to know; they treat it as a real open question.

A large study on student AI use. The hosts dig into a Science paper covering more than 95,000 students across 20 major U.S. public research universities. About two-thirds reported using generative AI in the prior year; roughly 9% of those users said they turned in AI-generated work knowing it wasn't allowed. The inappropriate-use rates run higher in non-STEM fields even though adoption there is lower.

Faculty tools built over the summer. Rob describes agents his student interns are building: a syllabus-comparison tool that flags where a faculty member's syllabus diverges from the new template, an active-learning brainstorming assistant, and an AI-resilience checker for assignments and assessments.

A textbook-grade handbook in an afternoon. Craig recounts handing OpenAI's Codex a couple of syllabi and one-shotting a 70-page course handbook for a freshman business course, then refining the activities. He pledges to release the finished version under a Creative Commons license.

Why the gap is the real story

The Fable 5 saga is good copy, but the hosts keep pulling it back toward something more durable. When the most capable models cost an order of magnitude more than the entry-level ones, the divide isn't only between rich and poor institutions; it reaches into a single classroom, where one student on a free model and another paying for the frontier model are turning in work that no longer means the same thing.

Craig's answer isn't to chase the frontier. It's to teach students to match the model to the task; you don't pay for the expensive employee to do routine work, and you don't burn Fable 5 on something Haiku can handle. Rob extends the point to policy: banning AI outright is folly, both because it's nearly impossible to detect without introducing bias and because it leaves you with a classroom where you have no idea who learned what. Craig demonstrates the detection problem directly, running lightly edited AI text through Pangram and getting a "100% human" verdict. The shared conclusion is one they've made before and make again here: the urgent work is assessment reform, because a graded artifact is no longer a trustworthy signal of what a student actually knows.

Episode highlights

(12:11) Rob on weaker models and learning: "I wonder if the people who aren't using these highly capable models might actually learn more, because they're not going to be able to cognitively offload as much of the things that they're doing, and they'll need to be more involved in it."
(15:51) Craig on Anthropic's rollout: "Anthropic really came off like a drug dealer that gives you a little taste before they try to get you hooked."
(20:01) Craig on the study's central finding: "About 9% of those users turned in AI-generated work knowing it wasn't allowed."
(26:31) Craig on why assessment has to change: "It's no longer a trustworthy signal of what they know if we keep doing things the way we've been doing them."
(37:21) Craig on the AI-built handbook: "It was a 70-page handbook with learning activities; good, not great, in about 20 minutes."
(45:31) Craig's top tip: "Handoff documents and memos will make your life so much easier when it comes to AI."

References mentioned

Anthropic's Fable 5 and Mythos models. Discussed as a guardrailed public release (Fable 5) built on the restricted Mythos family, later pulled following federal import controls. (Link to Anthropic announcement / status page: see "What I need from you.")
Study on student generative-AI use in Science (data collected 2024). (Full citation and DOI to be provided: see "What I need from you.")
Pangram: AI-text detector Craig used to test a lightly edited, skill-generated draft. (Link: see "What I need from you.")
OpenAI Codex: GPT-based coding agent Craig used to generate the course handbook.
Skills in Claude / Cowork and Microsoft Copilot: discussed as reusable, callable tools for tasks like generating active-learning activities.
Van Slyke & Crossler textbook (with co-author Franz Boulanger): referenced as a point of comparison for AI-assisted content creation. (Title / link if you want to include it: see "What I need from you.")

Questions to consider

If two students submit work produced with very different models, are you assessing their learning or their access?
Where in your own courses is a graded artifact still a trustworthy signal of what a student knows?
What's one tedious faculty task this summer that an agent could take off your plate?

AI Goes to College is a podcast for higher education professionals trying to make sense of artificial intelligence in their classrooms, their research, and their institutions. Co-hosted by Craig Van Slyke and Rob Crossler, the show focuses on practical, evidence-based perspectives on AI in higher education without the hype.

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Links

Science article

Chirikov, I., Smirnov, I., & Kizilcec, R. F. (2026). Generative AI use and misuse call for assessment reform in higher education. Science, 392(6800), 818-820.

https://www.science.org/doi/10.1126/science.aec5115

Anthropic Fable 5/Mythos announcement

https://www.anthropic.com/news/fable-mythos-access

Pangram (AI "detector")

https://www.pangram.com/

Information Systems for Business: An Experiential Approach (Belanger, Van Slyke & Crossler)

https://www.prospectpressvt.com/textbooks/b%C3%A9langer-information-systems-for-business-an-experiential-approach-5-0

Fable 5's Cost Gap, AI "Cheating" at Scale, and a 70-Page Handbook in 20 Minutes

AI Goes to College

Fable 5's Cost Gap, AI "Cheating" at Scale, and a 70-Page Handbook in 20 Minutes