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.