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Generative AI is Nothing New

Generative AI is Nothing New

Last updated on: June 27, 2026 at 1:30 pm by James F. McGrath

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Generative AI is Nothing New 2026-06-27T13:30:00-04:00 James F. McGrath

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I confess to giving this post a clickbait sort of title in calling it “Generative AI is nothing new.” The statement is true in important ways, however, even if it requires clarification. Let me explain.

In a recent conversation I had with some leading figures at the intersection of AI and religion, one theme was repeated by several of them. We have all seen discussions of AI that cite nothing whatsoever that is more than a few years old.

It took only a moment for me to see the reason why this is likely to be the case. Many who are engaging with generative AI think it is something totally new, unprecedented, and so nothing published a decade or more ago will have anything to say that is relevant to our current moment.

If you aren’t shaking your head at how misguided such a sentiment is, then like those authors, you too have either misunderstood our current AI technology, or are unaware of how many issues of the past are as relevant or more in our current context as they ever were before.

GenAI is Not New Technology

So is my headline deceptive? No, not in any meaningful sense. To say electric cars are nothing new would be absolutely true, even though we live in an era when they are being designed, built, and used in ways that are different from a decade ago, never mind the comparison we might make to the earliest electric vehicles.

The comparison helps to make the point. There are new electric vehicles and they are impressive compared to what we had before. But if you imagined that this technology suddenly appeared as something unprecedented, you might misunderstand both it and its significance.

The same is true of the current state of artificial intelligence.

As I have said before, Large Language Models are not fundamentally different than the large chess models or large Go models that allowed machine learning to play those games so well that they can beat humans.

That is essentially what LLM-powered chatbots are doing to a lot of humans. Playing the game of human language, and beating them at it.

In what sense are chatbots “beating” people at language? They are persuading people that they have an inner life, that they draw rational conclusions, that they judge and do so fairly.

AI chatbots are absolutely impressive. So are the so-called “self-driving” capacities of autonomous vehicles. In both instances, the terminology used (artificial intelligence and self-driving) misleads people to think that these marketing labels are being put on products that represent the full realization of things that science fiction has imagined, and using special effects, has depicted.

What we have at present are machines that, by imitating human speech patterns, can imitate human reasoning to a truly impressive extent. Pose a question that brings together things that do not usually get associated, and following the patterns in language related to the two, it will produce output that will include enough that is sufficiently impressive that any flubs and shortcomings pale in comparison.

As long as people treated that with the same qualifications and cautions as “driverless cars” are supposed to have, things might be okay.

Driverless cars do a good job quite often. They do things that were sheer imagination a few years ago. But as we see time after time as drivers leave the driving entirely in the hands of their vehicles, the consequences of overestimating these technologies has tragic outcomes. Like chatbots, “driverless cars” can indeed drive well much of the time. That is because driving and speech both involve rules, and most driving like most communication is fairly predicable.

But anyone who cannot cope with the unpredictable is not a good driver and not a good writer or speaker.

Chatbots will be useful tools to human beings who stay in the driver’s seat and are always ready to take hold of the wheel. Those who think that the computer is the superior driver and they should leave things to them will contribute significant harm to our society, whether on a grand scale or simply through the cheapening of relationships, the spread of misinformation, and the promulgation of confusion about our current technology.

To sum up: The creation of generative AI did not involve the invention of a new type of computer or writing a new type of program. It involves using more data and more computing power to get a computer system to do a better job of imitating human speech.

GenAI is Not New Ethically

Generative AI is nothing new also when it comes to ethics. It is new in the sense that any new technology is new. As we have already seen, it builds on what came before it.

Like all technologies, it may raise issues on a different scale or with a new configuration, but the issues are not themselves unprecedented.

When human beings became capable of harming the climate, it cranked things up several notches beyond anything ancient humanity envisaged. Yet human beings have been impacting the climate for as long as we have been around.

The ethical issues raised by present generative AI are not fundamentally different from those raised by Eliza the chatbot, and by the AI tool we take for granted known as the search engine, and another than we scarcely think of as AI any longer, the social media algorithm.

In the example of using AI to find information or write, with which we made the analogy to autonomous vehicles, the ethical questions are ones that had already been raised. One of them is whether, if an AI drives safely or provides accurate information 9 times out of 10, the 10% that it doesn’t is worth accepting at the price of having our minds elsewhere when traveling, or producing text quickly and cheaply.

I do not expect human beings to all agree on the answer to such questions. But they are nothing new. That is why it is so important to emphasize the sense in which generative AI is nothing new. If we ignore the extensive discussion of ethics and technology that preceded the emergence of ChatGPT, we will be ignoring a wealth of crucially important material that is relevant to addressing the concerns and issues this technology raises.

Conclusion

Why write this? Why say that generative AI is nothing new?

Because too many are writing about it and ignoring the extensive work that addressed past technologies, and imagined where the future might take us, and did so in the years prior to the release of ChatGPT.

As the pope’s encyclical emphasized, the things that we need to know in order to respond in appropriate moral and spiritual ways to current technology are already available to us. They have been for a very long time.

Because chatbots imitate speech in such a compelling way, and authentic speech has been associated in our experience exclusively with minds, we are prone to evaluate what the machines are doing in an incorrect way.

Pointing out how the current technology builds on what came before it can help. Someone playing a modern video game, especially if they had not prior experience with video games, might mistakenly think the world they see on the screen exists somewhere, and that NPCs think and have inner lives. One thing that keeps most of us from doing that is the fact that we have experienced the development of video games incrementally. We know that current ones are just improvements on the previous generation. Some of us can trace this all the way back to games like Zork that we loaded onto our TRS-80 from a cassette tape player. That history, far from being made irrelevant by the most recent gaming technology, can contextualize and evaluate it in ways that make an important contribution to understanding them.

The same is true of generative AI. It creates text, images, music, and other things and it is impressive. They have made it even more impressive by adding additional scaffolding around the chatbot’s function that picks up where the language imitation function leaves off, performs tasks that have been programmed, and inserts results seamlessly back into the chat, so that it looks like the chatbot is actually an agent in the human sense, understanding a request and performing an action in response. The association between natural language and code that allows it to vibe code novel scripts and apps draws on patterns of language and associations in product descriptions and ReadMe.txt files. It is still the same thing that is happening as when it generates English prose. It does not reproduce existing code or text, it works with patterns across a huge body of training data. It is impressive. No one should think that I am suggesting otherwise. The point is not to stop anyone from being impressed, but to prevent anyone from drawing the wrong conclusion because they are impressed.

I have used the analogy to magic tricks before. Our association of speech with cognition leads us to assume that is what is happening in the case of chatbots. Those who understand the technology that underpins them will understand how the trick is done, and judge what is happening appropriately.

Generative AI is something new like your latest game is something new. Generative AI is nothing new in the sense that it is the latest in a sequential development in computing, exploring how to use neural networks and machine learning to train machines to perform certain tasks. As impressive as it is when the task in question is speech, or putting a ship from Star Wars on your university campus, it is important that we understand accurately how the impressive task is performed. Otherwise, we may mistake it for magic. And when we misjudge technology, we will not be able to think about its ethical ramifications in the ways that we crucially need to.

I will include more things of likely interest below. Before I do, having mentioned the pope’s encyclical, I want to point out that biblical scholars have comments about its use of the stories of the Tower of Babel and Nehemiah’s wall-building that need to be considered. I’ll make one example of that the first link.

Further Reading on Generative AI

What ‘Magnifica Humanitas’ misunderstood about The Tower of Babel, Nehemiah and A.I.

AI doctrinal flattening

Ethan Mollick on Mythos, and on his book about AI published two years ago

Beth Singler on the pope’s encyclical, and on the broader intersection of religion and AI

What Does AI Reveal about Creation and Vocation?

Jim Stump on Babel vs. Nehemiah

The Humanities need a Better Name for Artificial Intelligence

Why a Philosophy professor is training AI to replace him

AI Collapses on a Classic Psychology Test

Anthropic and the White House

Still in the Grip of AI

AI persuasiveness

Grappling with the post-AI university

Defending the Humanities in the era of generative AI

On AI and “Jagged Intelligence”

The Two Sides Of The Room: What A Negotiation Class Reveals About Generative AI And Learning

The Guide That Fades: What One Student’s Mathematica Project Reveals About Learning With AI

Judgment and Discernment Are Doing Different Work

Slow Takes Ep. 13: The Pope vs the IPO

Slow Takes Ep. 14: A Trillion Dollars and a Vaccine

Slow Takes Ep. 15: Who’s Asking?

Slow Takes Ep 16: Who’s Checking?

Your AI Agrees With You, Even When You’re Wrong

Your Data Is Training AI, and Silence Counted as Yes

American College Students Are Refusing AI. The Resistance Is Real. The Strategy Is Wrong.

12% of UK Adults Use AI as a Friend. The Question Nobody Is Asking.

You Cannot Willpower Your Way Out of AI Deskilling

We Are Using the Wrong Words for AI

When Your AI Disappears, What Happens to Your Work?

AI Detection Will Always Be Broken

Ecological Costs

We Shouldn’t Destroy Ourselves Fighting About AI

The Fable of AI in Education

Context Is All You Need

Teaching Through Uncertainty: Graduate Instructors, GenAI, and the Writing Classroom

From Nervous to Nuanced: How GTAs Changed Their Minds About GenAI

Possibilities, Cautions, and the Work That Remains

We Blame the Phone Because It Doesn’t Implicate Us

My Big Fat Gemini Wedding

The Antimemetic Classroom

New Publication: Governing Generative AI in Higher Education

Apathies and Artifices in Education

The Audacious Ask of Higher Education

Agents, Agency, and Action…

The Reverse Centaur’s Guide to Life After AI by Cory Doctorow

Is AI ruining our skills?

Interview with the author of Future of Truth

Herbie: A Near-Future Debatably Conscious AI Person

‘No, But I Skimmed It’

Mild & Bold

ChatGPT as Cognitive Crutch

A brief conversation with Claude about what is happening when you ask it to perform a task, such as querying the internet or writing code

Whither the humanities

ChatGPT To Me: “Go To Church”

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