Tom Pepinsky

Author: tompepinsky

  • Technology is not the Solution to the Crisis of Confidence in Higher Education

    Technology is not the Solution to the Crisis of Confidence in Higher Education

    A recent report from Gallup shows that Americans’ confidence in institutions continues to be near its all-time low. Examining public opinion data from the past four decades, we see that confidence in all institutions has declined steadily since the mid-2000s.

    Visual inspection suggests that the decline dates roughly to the second Gulf War in (2003-2004 or so), but interocular tests are notoriously unreliable. There is surely more work to be done to decompose this series into its trends, intercept-shifts, and noise.

    As Mark Copelovitch has noted on Bluesky, these data are important background context for the widely-noted “crisis of confidence in higher education” (see also here and here and here and so on and so forth). It is one thing if higher education is facing a lack of public confidence but other American institutions remain popular. It is quite another thing if all American institutions are facing a crisis of confidence, and higher education is just one among many.

    Buried in the links of the Gallup report are data on two institutions of particular interest to me: higher education and large technology companies. As a higher education professional, I have good reasons to follow public perceptions of this sector, and I am also quite attuned to the cacophony of voices who insist that technology is going to revolutionize—or undermine, or fix, or transform, or displace—the sector entirely. Happily, Gallup makes its raw data avaiable to the public, which allows me to put together this figure.

    The sector-specific results mirror the overall trends identified above: public confidence in higher education is declining rapidly, surely a worrisome trend if you are someone in, to quote Tom Lehrer, the “ed biz.” But American have even less confidence in large technology companies. And that gap appears to be widening in the most recent surveys.

    These results should lead higher education leaders and administrators to think carefully about what the crisis in public confidence in higher education really means and how best to respond to it. If you think that the crisis in confidence in higher education requires a solution, then it is hard to argue that embracing technology is an effective response to that crisis, because the companies that manufacture and market that technology are even less popular than higher education is. It is foolish to embrace EdTech in 2026 if the goal is to regain the public’s trust.

    Speaking in my individual capacity, my own view is that the crisis of confidence in higher education does not have an immediate solution. The problems are systemic, they are general features of American public life that cannot be solved by committees, reports, or public relations campaigns. The “solution” to the crisis in public confidence in higher education is to focus on principles, values, and the mission of higher education. I will have more to say on that in the coming months, but my cantankerous take is that it’s time to put mission ahead of metrics. Become unmeasureable, and never apologize for it.

    But for now, as always, a good rule of thumb is: there are no technological solutions to political problems. Anyone who insists there is a technological solution to something like a public crisis of confidence in higher education is probably trying to sell you that technology. And Americans have grown weary of the promises of the technology companies, both in the higher education space and more broadly.

    One wonders why the pundits and higher education commentariat have so thoroughly missed the vibe shift. Is it because they, too, are trying to sell us something?

  • AI Generated Maps of Southeast Asia Are Here

    AI Generated Maps of Southeast Asia Are Here

    Last summer, I tried to use generative AI models to create interesting maps of Southeast Asia. My results were disappointing on the whole, and led me to the skeptical conclusion that commercially available generative AI models were not great at fact-based spatial reasoning (even if it is very good at other things).

    But that was 2025. Now it’s 2026.

    So less than a year later, I’m back to considering whether generative AI can create maps of interesting things in Southeast Asia. Inspired by my own continuing obsession with the the region’s linguistic complexity—so many language families, such interesting spatial variation both across space and by altitude—we return to the case of drawing a map of the world’s major language families in Southeast Asia, something which probably exists but does not exist online and certainly not in the format which I need it.

    In August 2025, just ten months ago, the best I could obtain was this figure, which is hilarious and wrong in dozens of different ways. It cost US$20 and was generated by manually prompting o4-mini-high for about half an hour.

    Generative AI has changed a lot in the intervening year. The advances are pretty staggering. I didn’t know what “agentic AI” meant in June 2025; in June 2026 I work with Claude Code in my terminal, and read endless commentary on how to herd my agents. But most critically, the most advanced and expensive AI tools that were available to me in June 2025 have been surpassed by several new generations of models.

    In June 2026, for the same US$20, I am able to work with Claude Fable 5, which Anthropic bills as its most powerful and advanced consumer-facing product:

    Fable 5’s capabilities exceed those of any model we’ve ever made generally available. It is state-of-the-art on nearly all tested benchmarks of AI capability, showing exceptional performance in software engineering, knowledge work, vision, scientific research, and many other areas. The longer and more complex the task, the larger Fable 5’s lead over our other models.

    In true Silicon Valley fashion, Anthropic also teases us a little bit by warning us that Fable 5 might be a little bit dangerous!

    Releasing a model this capable comes with risks. Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage.

    I don’t think my light usage will endanger national security or the survival of the human species, but I guess it’s a waiting game right now.

    So now for the big reveal. Here is what I can now make using Claude’s most advanced AI model ever:

    This is good. It is, in fact, really good. The colors group linguistic areas by major language family, every label is legible and spelled correctly, and the shaded areas correspond to the locations in which these languages are spoken with a pretty high degree of precision. This is impressive. I can, and will, use this when I teach Southeast Asian politics, and that was the purpose of this exercise in the first place.

    With that said, the details and the process of generating this map may be of some interest. First, the details:

    • Fees: To make this map, I had to pay US$20 for my Claude Fable 5 subscription. Free versions of Claude, like other AI models, are still wholly unable to create something like this.
    • Time: It took about 24 hours to make this map. Partially this can be explained by limits on Claude messages; I could have made this in less time had I paid even more for a the most premium subscription.

    The costs associated with creating such a map—in terms of money and time—will decline over time as ever more powerful AI models replace current ones. In a year, I bet I will be able to do this for free.*

    The process is perhaps more interesting. Put very directly, the first attempts at this map contained major errors. Among the list of things that I had to manually explain through repeated prompting:

    • Rhade, Jarai, and several others are large minority languages in Vietnam’s Central Highlands.
    • The northern provinces of Vietnam are home to speakers of Kra-Dai languages, like the Tày and Nùng.
    • Assamese does not extend into Myanmar/Burma (an error in earlier versions).
    • Within China, Austroasiatic languages are not spoken in most of coastal Guangxi (an error in earlier draft versions), but they are widely spoken in some parts of Yunnan.
    • Mon languages in Myanmar/Burma are located primarily in Mon State and northern regions of Tanintharyi Region, not in the south (as in earlier draft versions).
    • There are speakers of Dravidian languages in Bhutan and Assam.
    • Tsat is an Austronesian language on Hainan.

    Moreover, I had to manually supply information on groups and their geographic locations:

    • Maps of the Hmong-Mien and Austroasiatic language families.
    • Lists and geographic locations for ethnic groups and minority language communities in Bangladesh, Cambodia, China, India, Indonesia, Laos, Myanmar, and Vietnam. These came from Wikipedia and Ethnologue.

    Other tweaks included explaining that Papuan and Australian are not language families, but rather geographic designations for non-Austronesian languages in the eastern and southern reaches of the map; replacing “Tai-Kadai” with “Kra-Dai” in the label, and removing extraneous labels and text with errors.

    All of that tells me, and should tell you, that the latest generative AI models are incredibly powerful. But they are also still meaningfully limited. They need guidance for factual accuracy and fidelity in representation. Because maps are models, not territories, an exercise such as this is bound to be imprecise, implying a tradeoff in clarity versus precision. If the goal is to show roughly where major language families are in Southeast Asia, advanced AI models can do it. If the goal is to illustrate the quirks and the details, you must supervise. It remains way too easy for the results produced by AI to be authoritatively wrong.

    It remains an open question whether I could have printed out a blank map, colored it in with help from Wikipedia, and scanned it to produce roughly the same result more quickly and cheaply. But make no mistake: Claude’s map is very accurate, and it is also beautiful.

    NOTES

    * I will set myself a reminder for 12 months from now, and will report back.