In this conversation, we trace one thread that runs through Rubel’s dissertation: the recurring belief that each new dating technology — a 1960s IBM mainframe, a 1970s home video camera, a 1980s telephone chat line, today’s matching algorithm — is finally the rational, honest alternative to the mess of ordinary courtship. We talk about the “omniscient” mainframes that launched computer dating on college campuses, the fraud scandals and consumer-protection laws that followed when that trust collapsed, the industry’s decades-long split between “marriage-minded” businesses and the rest, and how the internet quietly dissolved that divide.

That bet collided almost immediately with a public fight: NYC Mayor Michael Bloomberg declared flatly that you can’t teach a coal miner to code, while Mark Zuckerberg insisted you could. Thompson’s reporting shows both were partly right — coding did open up as a route to the middle class, but it also split into castes, with boot-camp grads and career-changers doing lower-paid “pink-collar” front-end work while an elite, credentialed tier captures the real money in machine learning and infrastructure.

In this episode, we go deep on that single argument and push it into the present. Now that AI coding tools can generate exactly the kind of templated, entry-level code that was supposed to be the on-ramp for people like the Kentucky miners, is the blue-collar coding promise still alive, or did AI just kick away the bottom rung of the ladder? Thompson helps us find out.

In this conversation, we trace one of the sharpest and most unsettling arguments in The Computer Boys Take Over: that the familiar stereotype of the antisocial, mathematically-gifted male programmer wasn't simply discovered by the computer industry — it was manufactured, through a decades-long feedback loop of unvalidated hiring tests and personality profiles. We talk about the 1960s programmer shortage that made mass psychometric testing feel necessary, the strange science behind instruments like the IBM Programmer Aptitude Test, how a hiring apparatus quietly became a gender filter, and why that history still echoes in the whiteboard and take-home coding tests of today. ATT #003 — Aryan Talks Tech

In 1965, a group of Harvard undergraduates launched Operation Match, one of the first computer dating services in the United States.[1] The premise was simple, yet novel, filling a ripe market. See, at the time, most universities in the area were not fully co-ed. At Harvard, for instance, all women were enrolled at Radcliffe College, lived in separate buildings, and were restricted from using certain Harvard facilities.[2] To meet girls, male students would have to engage in blind dates and mixers, both of which were inconvenient and ineffective.[3]

Thus, juniors Jeff Tarr and Vaugh Morrill started Operation Match, and with it, Compatibility Research, Inc.[1][4] College students in the Boston region would fill out a questionnaire, mail it in with a $3 fee, and, within a few days, receive a list of compatible matches generated by an IBM 7090.[1][5] From 1965 to 1967, after appearing on CBS and other nationally aired programs, Operation Match paired over 100,000 undergraduates.[1][4] Some of those couples got married, and many are still together today.

From the present’s perspective, Operation Match was an early attempt to turn human identity into data.[6] Statistical and quantitative methods now grip our social lives, but it wasn’t always like this. Participants were sorted by religion, race, politics, smoking habits, income, SAT scores, and self-described attractiveness.[1] But, perhaps most strikingly, the survey tried to quantify the unquantifiable: love.[1][6]

Endnotes

[1] Operation Match booklet and questionnaire, Computer History Museum archive, accessed May 1, 2026. This primary source documents the founders, questionnaire, $3 fee, IBM 7090 processing, and the categories used by the service.

[2] Alicia M. Chen, “Operation Match,” The Harvard Crimson, February 16, 2018. On the Harvard-Radcliffe social setting and separation.

[3] “Operation Match,” The Harvard Crimson, 1965 coverage describing the “irrationality” of blind dates and mixers as the problem the founders set out to solve.

[4] “Operation Match,” The Harvard Crimson, November 3, 1965.

[5] Computer History Museum, “Making Your Match,” February 11, 2021.

[6] Eli J. Finkel et al., “Online Dating: A Critical Analysis From the Perspective of Psychological Science,” Psychological Science in the Public Interest 13, no. 1 (2012): 3–66.

He is the author or co-author of numerous books, including A History of Modern Computing — long a standard text in the field — Internet Alley: High Technology in Tysons Corner, on the overlooked Washington-area tech corridor; Computing: A Concise History; Time and Navigation: The Untold Story of Getting from Here to There, a companion volume to the Smithsonian exhibition he co-curated; and, most recently, A New History of Modern Computing, co-written with Thomas Haigh. He also curated or co-curated major NASM exhibitions, including Beyond the Limits: Flight Enters the Computer Age and Time and Navigation.

This episode centers on his 2018 book GPS — a sharp history of how a satellite system born from a genuine rivalry between the Navy and the Air Force, tested in secret, and deliberately degraded for military security, became something a billion people depend on without a second thought. We trace that transformation from Cold War submarine tracking to the phone in your pocket, and use it as a case study for a larger question: what does the history of one piece of invisible infrastructure tell us about the digital infrastructure — AI included — being built today, under strikingly similar institutional conditions.

In this conversation, we discuss why the human computing workforce never translated its labor into prestige for the field it built, where computing's intellectual authority actually came from, and John von Neumann's outsized role in giving computing its identity as a theoretical discipline.

To find that part of the story, one must travel back to March of 1975. This was before Apple existed, and when the concept of computing was largely relegated to geeks and computer hobbyists. Yet, an enthusiastic group of them decided to start gathering as the Homebrew Computing Club in Menlo Park, CA. There was no business plan or agenda, but just nerds nerding out. Members would bring parts, schematics, and half-finished designs, all trying to imagine some distant future where computers were in every household. Steve Wozniak was one of these nerds. [2]

The Apple I and II were designed strictly on a hobby, for-fun basis, not to be a product for a company. They were meant to bring down to the club and put on the table during the random access period and demonstrate: Look at this, it uses very few chips. It’s got a video screen. You can type stuff on it.

— Steve Wozniak [3]

Wozniak had no intention to sell his circuit board. Rather, he was deeply inspired by the collaborative and intellectual culture of the club, one in which curiosity was prioritized over commercialization. In fact, Woz produced the initial design as a way of “showing off” to the people at the club. [4] He loved elegance, and with its affordability and built-in video display, the club loved his build. With this environment, the Apple I received a unique kind of development.

“[It was] shown off every two weeks at the club meeting … We’d get some positive feedback going and turn people on. It’s very motivating for a creator to be able to show what’s being created as it goes on,” he described. Through this space, Wozniak was able to gather steady feedback from similar minds and make constant tweaks for improvement. Further, he never had to worry about “a million people trying to steal it”—these people weren’t greedy businessmen, but just passionate technologists. [3]

It was also this excitement around Woz’s design that sparked an idea in the mind of an onlooker, Steve Jobs. With a sharp acumen for sales, Jobs speculated that demand for such a product might exist beyond this group of geeks. [4] From there, he spoke to Paul Terrell, owner of the local Byte Shop, who placed an order for 50 (how touching). From there, Apple commercialized, and the rest is history … in-brief.

Perhaps this story is most important not as a myth-busting tidbit, but a reminder that technology is very rarely created in a vacuum. The Apple I was a product of Menlo Park’s culture of technological curiosity, one that was institutionalized via Homebrew. These institutions are centered around nerds like Wozniak, many of whom are willing to spend years in R&D, but occasionally strike gold. Now that’s what I call thinking different. So, Apple, here’s to 50. Happy birthday.

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Endnotes

1. Apple, “50 Years of Thinking Different,” accessed April 3, 2026.

   https://www.apple.com/50-years-of-thinking-different/

2. Computer History Museum, “Apple Timeline,” accessed April 3, 2026.

   https://computerhistory.org/apple-timeline/

3. Steve Wozniak, “Homebrew and How the Apple Came to Be,” The Digital Deli, accessed April 3, 2026.

   https://www.atariarchives.org/deli/homebrew_and_how_the_apple.php

4. Computer History Museum, “Steve Jobs,” accessed April 3, 2026.

   https://computerhistory.org/blog/steve-jobs/

It was April 2018, and Facebook was in trouble. Throughout the 2010s, a British consulting firm called Cambridge Analytica (CA) had been scraping data from the social media giant for political advertising. CA had also played a significant role in Donald Trump’s 2016 campaign by psychologically profiling users. [2] Of course, like most data scraping, it was without informed consent. An estimated 87 million people were impacted. [3] So, when whistleblower Christopher Wylie (former CA employee) leaked documents to the press, Mark Zuckerberg needed to respond.

Called to testify before the Senate on April 10, Zuck’s testimony was meant to address issues of data privacy, platform responsibility, and the growing power of social media. What it more clearly revealed was a gap in understanding. [4]

Of course, it was a small exchange. In fairness, the viral clip was somewhat taken out of context. [5] But, when I—like millions across the country—first saw it happen, I felt deeply concerned. With an aging group of policymakers, but an increasingly rapid pace of innovation in technology, how can we expect Capitol Hill to keep up? They struggle to grasp the basic mechanics of the systems they are tasked with fixing.

What happened in 2018 was somewhat embarrassing. Each Senator was allowed only five minutes, and what were supposed to be “hard questions” just made the group look ill-prepared. [4] Moments like this have become increasingly common, too. With the onset of artificial intelligence, corporations are pleading for regulation, yet Congress just can’t give it to them like they want. The two groups—technologists and policymakers—often fail to understand one another.

And that’s important. It’s not just those on the Hill, but the technologists too. Executives as well as their engineers will focus on capability, efficiency, scale, and performance, but then forget the metric of human consequence. Whether those at Instagram make their algorithms as addictive as possible, or the rapid development of AI inextricably causes labor displacement, they are all symptoms of the same underlying problem. [6]

The divide isn’t new, though. In fact, it was most explicitly identified more than half a century ago by C. P. Snow. Born in Leicester, England, Snow was uniquely positioned to identify a problem with the modern West. With an educational background in the sciences, receiving his physics doctorate from Cambridge in 1930, yet a professional background in the literary arts as a successful novelist and public intellectual, he had seen both sides of the story. [7]

So, when he was invited to deliver the prestigious Rede Lecture at Cambridge in 1959, he took a stab at it.

“I believe the intellectual life of the whole of western society is increasingly being split into two polar groups,” Snow stated. “Literary intellectuals at one pole—at the other scientists … Between the two a gulf of mutual incomprehension—sometimes (particularly among the young) hostility and dislike.” [8]

I couldn’t put it better myself. In fact, I see this phenomenon all the time. Look at the modern education system: students, as early as their adolescence, are pushed into one of two groups. Either you study “STEM” or the humanities, and no matter what, you will come to condemn the other side. Entire cliques are formed over this “mutual incomprehension.” For those who only love the sciences, literature and philosophy are the kinds of subjects where students frolic in gardens and add no societal value. Those with knowledge just in the literary arts stereotype “computer science nerds” as chronically online, but themselves suffer from staying behind in a world where technology keeps moving forward.

It is basic economic logic that people should specialize to maximize output. What arises is a negative externality, though, insofar as there is a social element to the side you choose to join. For example, Snow noted, “The scientific culture really is a culture, not only in an intellectual but also in an anthropological sense … there are common attitudes, common approaches and assumptions.” [8] He continued, citing the general rise of atheism among scientists as compared to the rest of the Western world. “Statistically also, slightly more scientists are on the Left in open politics.”

Of course, none of this is a dig at scientists. These trends follow from two main ideas. Firstly, when faced with the choice between the sciences and the literary arts, there are many social factors at play. One’s family, economic status, and geographic location all influence that “decision,” and thus, you get many people who come from similar backgrounds in the same room. Secondly, though, the practice of science follows certain analytical and data-driven practices (the scientific method), which themselves mold the way in which scientists approach social issues.

Similar trends exist with the literary intellectuals, and they tend to themselves. It is a classic analysis of polarization: the speed at which two disparate groups culturally separate is exponential, because echo chambers become louder and, eventually, the social divide becomes institutionalized. “This polarisation is a sheer loss to us all,” Snow lamented. [8]

What was identified as an intellectual divide has, six decades later, manifested into something far more consequential. The technologies that currently shape our everyday lives—social media, the internet, and digital infrastructure—as well as those being developed—artificial intelligence, green energy, and quantum computing—are being built in one culture but used and governed in another.

We see lawmakers struggling to regulate innovations that they don’t fully understand. More concerningly, it is not always clear that they are incentivized to do so. Meanwhile, technologists create products that don’t fully account for social externalities, mostly because there is a lack of intelligent regulation, but also because they themselves don’t have guardrails.

The mistake that this polarized society can make is to treat technology as something purely technical. It is not, and we see that every day. From autonomous drones being used in Middle Eastern warfare to geopolitical tensions in the Taiwanese Strait because of semiconductors, technology is shaped and shapes governments, markets, and cultural values. To understand said technology, then, is to not only understand how it works, but how it develops. Those who fund technology, who build it, who guide it, and the consequences that follow from it are all critical when leveraging and optimizing it. Without that broader perspective, we are left with fragments—unchecked technical knowledge or abstract social critique.

Technology is not slowing down. We are moving towards a series of years that will determine a long-term future for humanity. To set the momentum correctly, we must move beyond this divide. That is my goal for Aryan Talks Tech—and what I want to articulate here. I hope that each piece informs us on how technology, as a system shaped by policy, institutions, and markets, has developed over the past, and what it means for our future. Snow warned of a world divided into two cultures, and we see it happening in real time. The challenge is no longer to simply recognize this gap, but to bridge it.

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Endnotes

1. Pew Research Center, “Nearly All Members of the 118th Congress Have a Bachelor’s Degree…,” Feb. 2, 2023.

2. Matthew Rosenberg et al., “How Trump Consultants Exploited the Facebook Data of Millions,” The New York Times, March 17, 2018.

3. Christopher Wylie interview, Business Insider, Oct. 2019.

4. NPR, “Mark Zuckerberg Senate Testimony,” April 10, 2018.

5. “Zuckerberg Senate Transcript,” Wikisource, 2018.

6. Stanford HAI, “Social Media Algorithms and Mental Health,” 2023.

7. American Physical Society, “C. P. Snow and the Two Cultures,” 2017.

8. C. P. Snow, The Two Cultures, Rede Lecture (Cambridge: Cambridge University Press, 1959).