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Lee Vincl (1:27)

Specialoffer welcome to the New Books Network.

Lee Vincl (1:34)

Welcome to Peoples and Things where we explore human life with technology. I'm Lee Vinc.

Lee Vincl (1:58)

My God, our current AI bubble. What is there to say? Well, it's incredible. We can say that for starters, it's much larger than the dot com bubble which led to the dot com bust. Some recent articles say our current bubble is more than 17 times larger than that earlier one. And it's been so textbook. In this bubble, it's like everyone is playing from a well known standardized, generic basic bitch textbook. I mean the way that people have become irrationally exuberant around generative AI and argue that it's going to change everything. But also the way crit hypey critics, including lots of people in the fields I work in, the critics have come along and made outlandish claims about the potential and real negative effects of this technology, including about its impacts on the environment, on work and wages and employment, on critical thinking, on the degree to which people have a writerly or poetic voice, and so on and so forth. My God, the textbook nature of it all. Both the hype and the criti hype have been so, so disappointing and to be frank, to be real with you, it has broken a part of my soul. A breaking that has at once been very painful, but also ultimately profoundly liberating. When this bubble is over, what's gonna be left? Well, first off, let's just hope the air goes out of it slowly, huh? Not in a pop, or else we could be in a pile of trouble, really. I mean, AI capital expenditure accounted for something like half of US GDP this past year. If that goes all of a sudden, along with a mountain of value in folks retirement accounts and such, well, things could get pretty ugly indeed. But what else will be left over from this bubble? The reality is that none of us know. It will take years for the meso organizational and macroeconomic effects of generative AI adoption to become clear, if they ever do. And it could be, as many are arguing today, that the kinds of irrational infrastructural investments in data centers and such could have unforeseen benefits down the road, as some argue happened with telecom infrastructural investments during the dot com moment. But in the meantime, so many folks will have gone along with the powerful narratives about the promises of this technology. They will have gone along with the marketing, both the marketing of these new technologies and the marketing of critics books about the dangers of these new technologies. Because let me tell you, holy crud. There is a wave of Genii criticism books being published and on the way. Oh my God. I get asked to review and blurb them on a weekly basis. My belly tells me. Well, it tells me that most of them will hold up as well in a decade as podcast bro Sam Altman's ridiculous statements about the existential risk of AGI or whatever the fuck. But what if someone came along and told you that in a deep and non trivial sense, the very concept of AI has always been about marketing? That's what eminent computer historian Thomas Haig does in his forthcoming book, tentatively titled Artificial Intelligence the History of a Brand. Haig, who is a professor of history and affiliated faculty of Computer Science at University of Wisconsin, Milwaukee, has written a lot, including co authored books on ENIAC and the general history of modern computing, which you should look up. In his forthcoming book, Tom examines the history of modern AI going back to its roots in the 1950s, and examines how and why different groups found it useful to slot their technologies, which often had very little do with one another, under the term AI. Let me tell you folks, I love this little concise book. It packs a punch. One of the things I most appreciated about it is that Tom is able to deftly track both the real history of different developments in hardware and software and the history of what people have said about these things over time, which is what we sometimes call historiography. It's very rare for historians to be able to do these things so nicely and compactly together. And the book is fun and ironic with this kind of British understated humor. It had me in stitches. I am very excited to give you a little preview of this book, which you're going to want to buy. You know, there's a famous, in these parts, at least, famous military marching band here at Virginia Tech called the Heidy Tighties, who sometimes play outside my office window here on campus. And I say, thank you, lasses and lads, I know you came here to play just to inspire me, but sometimes I wish, what if they could just accompany me when I was introducing other people's works? My God, they could play a little fanfare.

Summary

Podcast Summary: Thomas Haigh on the History of “AI” as a Brand

Podcast: New Books Network, “Peoples & Things”
Host: Lee Vinsel
Guest: Thomas Haigh, Professor of History, University of Wisconsin-Milwaukee
Date: December 8, 2025

Overview: Main Theme and Purpose

This episode explores Thomas Haigh's forthcoming book, Artificial Intelligence: The History of a Brand, which investigates the conceptual and institutional evolution of “AI” not simply as a field of research, but as a brand—a flexible label strategically applied to a multitude of sometimes unrelated technologies. Haigh and host Lee Vinsel discuss how the narrative, hype, and identity surrounding AI have been constructed, sold, challenged, and continuously rebranded over nearly seventy years.

Key Discussion Points and Insights

1. The “Brand” of AI – Why This Lens?

Defining AI as a Brand
Haigh describes his work as a “concise history” that deliberately starts with the coining of “AI” in the 1950s, instead of ancient precedents (08:38).

“Brands gather together a bunch of things that have no particular inherent connection to each other and make it seem like they have a coherent set of qualities… The AI brand does [that], imputing a connection to cognition… even when the work itself… may be about developing algorithms to optimize search.”
(Thomas Haigh, 10:27)
Brand Evolution and Aspirational Naming
Haigh likens “AI” to aspirational brands like “universal prosperity” for economics—naming the field after an unfulfilled promise (13:05).
Survival through Hype and Crisis
The AI brand’s resilience, even after cycles of disappointment (“AI winters”), echoes consumer brands weathering fads or association crises (14:41).

2. Haigh’s Path to the Project

From Computer Science to History
Haigh’s undergraduate and early grad training in AI during the 90s (16:28), and later his experiences writing broad histories of computing that struggled to include AI, led him to question AI’s absence in practical computing narratives, and its persistent presence in CS identity (17:57).
Intellectual Genesis
The book started as a historiographical essay critiquing “long histories” of AI, and evolved into a hybrid of narrative history and meta-analysis (21:14–24:13).

3. The AI Bubble and Present-Day Hype

Parallels with Past Tech Bubbles
Vinsel and Haigh contextualize the current AI frenzy—especially since ChatGPT—in relation to previous tech manias like the dot-com or blockchain booms (01:57, 32:14).

“It’s textbook … the way that people have become irrationally exuberant around generative AI … and the way critic-critics have made outlandish claims about the potential and real negative effects…”
(Lee Vinsel, 01:57)
Historical Continuities in AI Hype
“Some credible people like Nobel Prize and Turing Award Winner Herb Simon in 1960 said, within the next decade… Marvin Minsky predicted all this great stuff within the next three to eight years… None of those previous things have come remotely close to coming true.”
(Thomas Haigh, 35:40)

4. Regulation, Mystification, and Terminological “Creep”

The Danger of the AI Label
The broad and mutable definition of “AI” complicates regulation and meaningful discussion:

“Every time we use the term AI, we're kind of adding to the mystification… we're better off talking about specific applications.”
(Lee Vinsel citing John Lindsay, 37:25)

Haigh agrees, drawing on conversations with EU and Austrian policymakers:

“When you talk about AI, it's not a stable thing. A few years ago… self-driving vehicles and face recognition. Now… ChatGPT and deepfakes… Very few ways in which trying to approach this at the level of let’s come up with regulations for whatever happens to be called AI this week [is productive].”
(Thomas Haigh, 39:49–41:01)

5. The Origins of “Artificial Intelligence”

Dartmouth Workshop and Proposal
McCarthy’s 1955 coinage of “artificial intelligence” captured aspirational ambitions and, deliberately, sidestepped established brands like cybernetics (41:33–43:50):

“He just drops it in perfectly naturally… to get money. It’s literally invented to sell a proposal… McCarthy wrote that later—why not call it cybernetics? He didn’t want to have to deal with Norbert Wiener.”
(Thomas Haigh, 41:53; 43:50)
What “Intelligence” Meant Then
Early AI focused on automating “high culture” problem areas—chess, theorem proving, and composing music—a reflection of its founders’ technocratic, meritocratic backgrounds (47:54–51:49).

6. The Institutionalization of AI in Computer Science

AI as a Disciplinary Core
Haigh notes how AI shaped CS department prestige, even as practical impact lagged far behind:

“AI in the 20th century existed almost entirely within the emerging discipline of computer science… writing synthetic history, I realized I could ground AI as part of the history of CS better than most actual historians of science.”
(54:57–59:20)
Neglected Histories
Few comprehensive histories of computer science or its subfields exist, making AI’s story unusually reliant on internal myth and branding (57:12–60:28).

7. Cold War Funding and Misconceptions of Military Influence

“Inspired Boondogglers,” Not War-Driven Automatons
Haigh counters narratives that see the military-industrial complex as shaping AI’s values and research aims:

“I see the founders of AI as less as militaristic imperialists and more as inspired boondogglers who diverted a few buckets of money from a tsunami of Cold War spending to advance their quirky personal obsessions.”
(Thomas Haigh, 61:38)

The field’s actual budget was miniscule in comparison to military staples, and researchers’ motives more idiosyncratic than mission-driven (67:17).

8. The Expert Systems Era and the AI “Winters”

Expert Systems: Hype and Retreat (1980s)
The “expert systems” brand (distinct from AGI) briefly overtook general AI—selling practical, if modest, applications and raising investment, but ultimately generating disappointment and industry collapse (70:51–75:21).
Dubious “AI Winters”
Haigh debunks popular accounts of a severe 1970s “AI winter,” instead showing evidence for steady growth in membership and discourse; the only real “winter” was the post-expert-systems crash in the mid-80s (76:23–80:29).

9. AI and Automation, circa 2010s–Present

Recent Hype Cycles
“Overhyped flop after overhype flop” characterizes adjacent innovations—VR, blockchain, self-driving, voice assistants—and sets context for skepticism of generative AI’s transformative claims (82:35–85:23).
Persistence of Automation Narratives
Automation of labor isn’t new:

“Ninety percent of the [Early US] population worked in agriculture… now it’s 1.6%. If anything, [generative AI] is just more of the same that’s been happening to other kinds of workers for centuries.”
(Thomas Haigh, 90:53–93:02)

10. The Sociological Power and Limits of the “AI” Brand

Adoption, Hype, and Corporate Use Haigh is ambivalent about the world-changing claims of current generative AI, noting the persistence of enthusiasm from major players—even when business cases are unproven. He is open to real, as-yet-unforeseen impacts, but frames this within cycles of inflated expectations and ambiguous practical gains (87:18–90:41).
The Key Analytical Intervention
Haigh’s book aims to show that the “AI brand” has survived by morphing repeatedly, absorbing both technological breakthroughs and criticisms, and that long histories of AI (back to antiquity, for instance) often project present-day aspirations onto the past. His short history is a corrective to these totalizing narratives (98:31).

“To write any long history [of AI], you're taking some particular thing that AI has been to someone over the last 70 years… My book’s existence will help people see where the kind of ‘AI’ they take as their definition fits within this short, brand-driven history…”
(Thomas Haigh, 98:31)

Notable Quotes & Memorable Moments (with Timestamps)

On the tissue-thin unity of AI:

“The technologies now gestured towards when you say artificial intelligence have absolutely nothing in common with the technologies … in the 20th century under the same category.” (09:39)
Brand aspiration and naming:

“AI is really unusual as a field because it's named after its aspirational objective that has not as yet been reached. So it's kind of like if… Economics was named universal prosperity.” (13:05)
On regulating “AI”:

“When you talk about AI, it's not a stable thing… let’s come up with regulations for whatever happens to be called AI this week versus let’s come up with regulations for specific actually existing deployed technologies…” (39:49)
On failed prophecies:

“People have consistently been making promises…essentially identical with what people like Sam Altman are promising today …none of those previous things have come remotely close to coming true. Now that doesn't mean they won't come true this time.” (35:40)
On expert systems:

“Expert systems in some ways is a retreat from this dream of what we’d now call AGI… Economically valuable right now, regardless of [generalized intelligence].” (71:58–74:04)
On Cold War funding:

“I see the founders of AI as less as militaristic imperialists and more as inspired boondogglers who diverted a few buckets of money from a tsunami of Cold War spending to advance their quirky personal obsessions.” (61:38)
On automation’s historical continuity:

“There is absolutely nothing new about automation… Technological change, eliminating jobs on a massive scale, is not really a new thing.” (90:53)
On the utility of the brand lens:

“Highlighting the brand-ness of AI is more useful in terms of understanding what it does… it's even hard to understand what's gone on historically without taking the brand-like qualities seriously.” (14:41, condensed)

Important Segments (Timestamps)

01:57 – Vinsel’s framing of the AI hype bubble
08:38 – Haigh’s “elevator pitch” for the book
09:39 – AI as a brand and what this lens reveals
16:28 – Haigh’s computer science background and intellectual path
32:14 – Hype and the “un-sustainability” of the current moment
39:49 – Regulation & the shifting terrain of “AI”
41:33–43:50 – Origin of the term “Artificial Intelligence”
54:57 – Institutionalization of AI in CS
61:38 – Cold War funding and the myth of militarized origins
70:51 – The expert systems episode and first AI winter
76:23 – Debunking the “AI winter” of the 1970s
82:35–85:23 – Recent bubbles and continuity of hype
90:53 – Automation and labor history
98:31 – Concluding reflections on the brand’s analytic work and scholarly interventions

Tone and Closing Impressions

The tone throughout the episode is intelligent, self-aware, and gently ironic, often punctuated by Haigh’s quick wit and skepticism about tech hype—mirrored in Vinsel’s enthusiasm for history as a tool for demystification. Together, they champion careful, empirically grounded history and puncture the mystifications of AI boosters and critics alike. The episode is especially valuable for listeners seeking to understand why “AI” keeps coming back, and what the cyclical inflation and repurposing of the term tells us about technology, institutions, and collective memory.

End of summary.