In Response to “What A.I. Is Actually Doing to the Economy”pt.2

Another thing that bothers me a lot about this episode can be essentially summarized in the following questions:

What should kids do? Will colleges still be necessary in this AI era?

This episode is not alone in raising those questions.But this IS PRECISELY the time to go to college.

You can even major in computer science and get a great job—as long as you focus on learning about and building AI.

Go to college, pick a major, pour everything you’ve got into it, and build domain knowledge and skills. Along the way, explore and use AI tools in a way that enhances your learning and professional skills. Become more mature and well-rounded. No future is more certain than another. To be prepared for uncertainty, do not overlook your liberal arts education (often called general education).

Colleges are not static—they, too, are adapting to AI to properly prepare students for the AI future.

Also remember, the knowledge economy won’t regress back to a labor-intensive economy. Labor-intensive jobs are the prime targets of AI and its associated robotics.

In Response to “What A.I. Is Actually Doing to the Economy”

I like The Daily. Its episodes are informative, polished, and clearly require substantial effort and resources to produce. But episodes like today’s also make me question the reliability of its coverage on subjects I know less about, such as law, medicine, or public policy.

One thing digital technology has done well for humanity is that it allows the public to hear directly from scholars who study technology. So, as a technology scholar, let me offer a different perspective.

The subtitle of the episode asks: “Some data suggest that artificial intelligence is causing job losses. Other sources show the opposite. Why is it so hard to figure out what’s going on?” The answer is not especially mysterious. Researchers—whether in academia, industry, or government—use different datasets, variables, analytical methods, assumptions, and time periods. These choices can produce different findings.

Equally important, researchers are human. We have biases, whether intentional or unintentional, and limited cognitive abilities. We do not know everything. We misremember things, overlook evidence, and sometimes see what we expect to see. Social science does not usually produce objective truth in the way many people imagine. It produces provisional explanations that become credible through evidence, criticism, replication, and, eventually, some degree of scholarly consensus. Disagreement is not necessarily evidence that research has failed. It is often how research works.

My larger concern with the episode, however, is its fearmongering tone. One of its central claims was that the Internet revolution of the 1990s unfolded gradually. Technology entered workplaces slowly enough for people to adjust. Layoffs happened over time, and some workers were even able to finish their careers before the full effects arrived.

The episode then avoided making a direct prediction about the AI era. But the implication was difficult to miss: this time will be different. AI will spread too quickly. Workers will not have enough time to adapt. Large-scale job losses may arrive suddenly.

Really? What is the evidence? Is it the rapid adoption of GenAI tools? News stories about AI-related layoffs? The apparent intelligence of systems such as ChatGPT and Claude? The speed at which these systems seem to be improving?

Not so fast. Much of what we are seeing is familiar. AI is surrounded by enormous hype, as many previous technologies were. Premature technologies are being aggressively promoted by vendors and amplified by the media, as they have been before. Many organizations are adopting so-called AI technologies partly because other organizations are doing so, as they have before.

Even thoughtful companies and capable employees need considerable time to determine how a new technology can actually create value. Decades of research in information systems (IS) and management suggest that technological change is rarely determined by technical capability alone. Organizations are constrained by politics, institutions, routines, regulations, incentives, skills, budgets, and ordinary human resistance.

Technology may advance quickly, but organizations usually do not. That distinction matters.

Yes, AI will contribute to layoffs. New technologies often do. Yes, some professions will be affected more severely than others. That is also not new. But those observations do not justify the claim that workers will have no time to adjust or that sudden, widespread firings are inevitable in the next few years.

I do not see convincing evidence that what people describe as AI is fundamentally different from previous waves of technological change.

Let us not mistake fear for insight.

Critical Thinking Is Overrated

I hear the importance of critical thinking invoked repeatedly—both within higher education and in broader public discourse. Over time, I grew weary of this and decided to engage in some “critical thinking” about the importance of critical thinking itself.

Let’s begin with the definition. Critical thinking means thinking critically about what is presented to you. Then, how does one develop this ability? In many cases, all it requires is a willingness to question what one encounters. It really is that simple.

Yet, college courses often bend over backward to demonstrate how they explicitly “incorporate critical thinking.” If I present a piece of information or knowledge to you, can you assess whether it is true or false? Just question it. The moment you do so, you are exercising your capacity for critical thinking. So, if we want to instill critical thinking in future leaders, our task is surprisingly simple: create environments in which students feel free to question anything.

But there is another, more demanding case where genuine critical thinking is actually required. If you truly want to determine whether something is true or false, questioning alone is not enough. You need domain knowledge. In other words, you need to learn.

Let’s help our future leaders learn deeply and independently.

Perhaps we should place less emphasis on the abstract, catch-all notion of “critical thinking” and more on cultivating independent learning. After all, that is often what people really mean when they invoke the C-word.

Painful Loss but Overall Good Season

My Oklahoma Sooners were eliminated by Alabama this past Friday in the first round of the College Football Playoff. Being a Sooner through and through, the game was hard to watch, and the loss was heartbreaking. I am still mildly depressed. But looking back, it was a great season overall. I am so proud of my Sooners. I am so much looking forward to 2026.

On Calling AI a “General Use Technology”

One of my favorite podcasts is The Journal by The Wall Street Journal. But in today’s episode—“China and the U.S. Are in a Race for AI Supremacy”—I heard something that genuinely me.

The reporter stated, “This [referring to AI] is the first general use technology we've seen come along since the internet, and so it affects potentially everything.”

Comments like this—coming from a reporter for a major newspaper—spread misunderstanding about AI and mislead a wide range of listeners. AI is not a “general use technology.” It is not one coherent thing. What we commonly call AI is a sprawling family of techniques, applications, and systems that vary dramatically in purpose, capability, and design.

The episode’s underlying narrative is that the U.S. and China are locked in a race to build “artificial general intelligence” (AGI). But is AGI a general-use technology? Only if we define AGI as a system that “knows everything deeply.” Under that definition, a hypothetical future ChatGPT could be called AGI. But such a technology is neither necessary nor even desirable. In many fields—medicine, law, finance—specialized systems already surpass (or will soon surpass) human expertise in narrow domains. What good reason do we have to combine all such domain-specific “intelligent” systems into a single, unified AGI? None.

And are those domain-specific intelligent systems “general use technologies”? Again, no. They are built to solve specific problems, operate under specific constraints, and serve specific communities of practice.

So here is my plea: When we talk about AI, we must define—at least conceptually—the specific technology at hand. You don’t need to list a concrete product or application, but you do need to specify what kind of system you’re referring to. Otherwise, we are speaking in vague abstractions that obscure far more than they reveal.

Precision matters. Without it, public discourse on AI will continue to drift toward confusion—rather than understanding.

Teacher, Entertainer, Babysitter: The Hidden Triple Threat in Academia

In a recent episode of This IS Research Podcast, one of the co-hosts argued that a “superstar” professor in any field should not be teaching freshman classes. According to him, teaching first-year students requires nothing more than a blend of teacher, entertainer, and babysitter—hardly a good use of a superstar’s time.

Perhaps society does benefit when its academic superstars devote most of their energy to research. Even if we grant that possibility, isn’t that supposedly “simple” blend—a teacher, an entertainer, and a babysitter—actually a remarkable combination? I have rarely met individuals who genuinely possess this mix of skill, charisma, and patience.

If you are one of them, you bring tremendous value—especially now, as colleges confront the so-called demographic cliff. Your work with first-year students is not trivial; it is indispensable.

Why “Different” Is the Default, Not the Insight

I recently read “ Agentic AI at Scale: Redefining Management for a Superhuman Workforce ” in MIT Sloan Management Review. The article notes that nearly 70% of surveyed experts claim that agentic AI accountability demands entirely new management approaches, while 25% disagree. I applaud the minority.

It is remarkably easy to label every new invention as fundamentally different. We do it because:

1. Novelty is socially inherited, not independently concluded.
   Experts and observers often believe a technology is new because they heard others say it is. Repetition manufactures inevitability.
2. Agreement is cognitively and reputationally cheap.
   Conforming to the majority is easier than resisting it.
3. Continuity requires proof; difference requires none.
   Declaring “difference”—the popular choice—invites little challenge. Declaring continuity flips the burden: you must defend it. In a low-attention, high-velocity world, most avoid the cost.

Difference is often not insight—it’s echo, convenience, and safety in disguise. That alone makes it worth interrogating.

Doing Slow Work in a Fast Digital World

Much of my research looks at very fast phenomena—tweets, platform launches, market reactions. Yet the work itself is painfully slow. Data collection, coding, theorizing, and revising a paper over many years can feel out of sync with the speed of digital life.

I have come to see this tension as a feature, not a bug. Slowness gives us room to notice patterns that are invisible in the moment and to question “obvious” narratives. It is one way academia can add value in a world flooded with instant commentary.

First-Generation Paths and Paying It Forward

As a first-generation college graduate, I still remember how opaque universities felt when I was a student. Many unwritten rules were confusing, and chances to “get involved” or “network” did not feel designed with students like me in mind.

That memory shapes how I advise and teach. I try to make expectations explicit, open doors to research and projects, and remind students that their background is not a deficit but a source of insight. Education works best when it makes more future paths visible, not fewer.