These 13 messages exploring Paul’s Prison Letters, with a focus on Ephesians, were prepared for Long Beach Friends Church in 2026. The links will open a video of the message on YouTube.
The photo of Paul is from BiblePlaces.com. It is clipped from their photo of a cave painting of Paul in Ephesus. The original was downloaded from as a part of their package of photos on Ephesians, used by permission.
Ted Chiang is a writer whose writing is quite exceptional, whether it is technical or fiction. He is, shall we say, a rather careful writer, who does not produce articles and stories rapidly. I pay attention when a new piece of his comes out. He is easily one of my favorite science fiction writers; but his technical pieces written for general audiences are truly outstanding in clarity and accuracy. So, when I saw an article in The Atlantic entitled “No, Artificial Intelligence Is Not Conscious” – an article that even Chrome could predict I might well find interesting – and then realized that Ted Chiang was the author, I wanted to read it immediately. Of course, life interfered with this plan, and it was some hours before I was able to begin reading the article as a passenger while my dear wife Susie drove us home.
I was absorbed and smiling at the shear artistry of Chiang’s descriptions of what continues to pass for “artificial intelligence” these days – that is, large language models (hereinafter designated by the abbreviation “LLM”). His description of LLM’s was to aid in his argument that these models are not conscious, of course, but so well done that they deserve an article and kudos all on their own. That Chiang goes on to carefully explain why today’s LLM’s are not conscious or moral (but still quite useful) reminded me of the kind of illustrations that I first encountered in Douglas Hofstadter‘s writing back when I was one of his students – and then Chiang referred to a comment Hofstadter often made back in those days. This brought a smile. So, at the risk of seeming too much a fan of Chiang – but of course I could not rightly deny such – I decided to recommend this article.
Who should read the article, and why should they read it?
I recommend that anyone who wants to understand the nature of LLM’s in 2026 read this article. The preliminaries of Chiang’s argument include the cleanest and clearest explanation of how LLM’s work and what they are that I have yet read – at least for a general audience.
Secondly, Chiang gets into a more philosophical space as he discusses whether or not LLM’s are conscious or have the capacity to be moral actors. I’ve seen a few nits picked regarding his arguments here; though I found them quite reasonable and plausible. His claims regarding what would be required to truly develop conscious software remind me of speculation I heard from Hofstadter back in the day, as he was completing Gödel Escher Bach – a much more challenging read than this article, of course. I found Chiang’s discussion of machine consciousness to be potentially very helpful to the more casual reader for whom reading GEB would be asking much – but also quite worthy of being mentioned in the context of the larger and deeper work.
Being enamored of the discussion in the article, I decided I would ask Google’s LLM, Gemini, what it thought, er… had to say, er… how it predictively responded to the article. So I did. Below, I reproduce the interaction.
I asked Gemini:
Is Ted Chiang’s article entitled “No, Artificial Intelligence Is Not Conscious,” recently published in The Atlantic, accurate in how it describes the operation of LLM’s?
Gemini’s thorough response, in its entirety, is reproduced below:
Yes, Ted Chiang’s article “No, Artificial Intelligence Is Not Conscious,” published in The Atlantic, is technically and mechanically accurate in how it describes the base operation of Large Language Models (LLMs).
Chiang—noted science fiction author and former computer scientist—avoids overly dense academic jargon and cuts straight to the core mechanics of how these systems function.
His descriptive claims align with how LLMs operate under the hood in several key ways:
1. LLMs as “Sentence-Continuation Machines”
What Chiang says: He treats LLMs fundamentally as text generators and probabilistic prediction engines rather than feeling, sentient entities.
The technical reality: This is completely correct. At its core, an autoregressive LLM takes a sequence of input tokens and calculates mathematical probabilities to predict the single most likely next token. It does not “think ahead,” draft a concept in a mind’s eye, and then translate it into prose; it simply extends the statistical pattern of the text it has been given.
2. The Roleplay / Simulation Argument
What Chiang says: He uses an intuition pump comparing asking an LLM to generate a dialogue between Julius Caesar and Genghis Khan to asking it to roleplay a “helpful assistant”. We intuitively recognize that writing fiction about Caesar does not magically conjure a conscious instance of Caesar; similarly, generating text as a helpful assistant does not mean a conscious entity has woken up inside the machine.
The technical reality: This accurately reflects instruction-tuning and alignment. An LLM is a chameleon; it adopts a stylistic persona based on prompt conditioning and context windows. When it outputs “I feel…” or “I think…”, it is executing text continuation conditioned on what a helpful human assistant would statistically write in that context, not reporting an internal subjective state.
3. Text as a “Deepfake Medium”
What Chiang argues: He suggests that we need to view text itself as a deepfake medium—meaning fluent, grammatical, or emotionally resonant language is no longer a reliable proxy for a thinking, feeling mind behind the keyboard.
The technical reality: This is a vital distinction in the modern AI landscape. Humans have a hardwired evolutionary tendency toward anthropomorphism (attributing minds, intentions, and emotions to things that act communicative). Because LLMs master human syntax and emotional masking so well, they easily trigger our social instincts, making it feel like “someone is home” when mathematically, it is just a high-dimensional matrix multiplication engine outputting text strings.
Where the Essay Focuses (Policy over Metaphysics)
While Chiang’s description of how LLMs operate is accurate, the main thrust of his essay is actually a social and legal critique rather than a pure computer science paper.
He warns that allowing tech companies or the public to blur the lines around AI consciousness creates a convenient moral shield. If people believe a chatbot is a conscious agent that “decided” something of its own volition, it shifts legal and moral accountability away from the corporation that engineered, trained, and deployed it.
In short, Chiang’s technical breakdown of LLMs as sophisticated text-prediction tools—stripped of inner life, intent, or consciousness—is spot on.Yes, Ted Chiang’s article “No, Artificial Intelligence Is Not Conscious,” published in The Atlantic, is technically and mechanically accurate in how it describes the base operation of Large Language Models (LLMs).
Chiang—noted science fiction author and former computer scientist—avoids overly dense academic jargon and cuts straight to the core mechanics of how these systems function.
His descriptive claims align with how LLMs operate under the hood in several key ways:
1. LLMs as “Sentence-Continuation Machines”
What Chiang says: He treats LLMs fundamentally as text generators and probabilistic prediction engines rather than feeling, sentient entities.
The technical reality: This is completely correct. At its core, an autoregressive LLM takes a sequence of input tokens and calculates mathematical probabilities to predict the single most likely next token. It does not “think ahead,” draft a concept in a mind’s eye, and then translate it into prose; it simply extends the statistical pattern of the text it has been given.
2. The Roleplay / Simulation Argument
What Chiang says: He uses an intuition pump comparing asking an LLM to generate a dialogue between Julius Caesar and Genghis Khan to asking it to roleplay a “helpful assistant”. We intuitively recognize that writing fiction about Caesar does not magically conjure a conscious instance of Caesar; similarly, generating text as a helpful assistant does not mean a conscious entity has woken up inside the machine.
The technical reality: This accurately reflects instruction-tuning and alignment. An LLM is a chameleon; it adopts a stylistic persona based on prompt conditioning and context windows. When it outputs “I feel…” or “I think…”, it is executing text continuation conditioned on what a helpful human assistant would statistically write in that context, not reporting an internal subjective state.
3. Text as a “Deepfake Medium”
What Chiang argues: He suggests that we need to view text itself as a deepfake medium—meaning fluent, grammatical, or emotionally resonant language is no longer a reliable proxy for a thinking, feeling mind behind the keyboard.
The technical reality: This is a vital distinction in the modern AI landscape. Humans have a hardwired evolutionary tendency toward anthropomorphism (attributing minds, intentions, and emotions to things that act communicative). Because LLMs master human syntax and emotional masking so well, they easily trigger our social instincts, making it feel like “someone is home” when mathematically, it is just a high-dimensional matrix multiplication engine outputting text strings.
Where the Essay Focuses (Policy over Metaphysics)
While Chiang’s description of how LLMs operate is accurate, the main thrust of his essay is actually a social and legal critique rather than a pure computer science paper.
He warns that allowing tech companies or the public to blur the lines around AI consciousness creates a convenient moral shield. If people believe a chatbot is a conscious agent that “decided” something of its own volition, it shifts legal and moral accountability away from the corporation that engineered, trained, and deployed it.
In short, Chiang’s technical breakdown of LLMs as sophisticated text-prediction tools—stripped of inner life, intent, or consciousness—is spot on.
Long winded, Gemini, to be sure! And, quoting Gemini surely means that a portion of this post used “generative artificial intelligence” – a term to which I object with a mild cringe, nodding to common usage. And so it goes…
A couple of days later, I decided I would ask ChatGPT, the same question. Below, I reproduce ChatGPT’s response, which ChatGPT presented in a way difficult to paste into WordPress.
Yes—but with an important qualification: Chiang is broadly accurate about the basic mechanics of LLMs, but some of the conclusions he draws from those mechanics are stronger than the mechanics themselves warrant. I found the recent Atlantic essay and checked its central claims against current technical literature, including work published in 2026.
Where Chiang is right The core description is sound:
An autoregressive LLM is trained primarily to predict the next token from the preceding context.
A transformer processes the context through layers of learned representations and self-attention.
At inference, it produces a probability distribution over possible next tokens, selects/samples one, adds it to the context, and repeats.
There isn’t a little internal sentence-generator or homunculus deciding what to say. The computation is distributed across enormous numbers of learned parameters.
The fact that the training objective is “predict the next token” is not a metaphor. That’s genuinely the fundamental training objective for standard decoder LLMs.
So if Chiang is arguing against the picture that an LLM is secretly a miniature human mind sitting behind the words, he has a very strong case.
But “just predicting the next word” is misleading This is where I’d qualify his argument. “Next-token prediction” describes the training objective, not necessarily the best description of everything the resulting network does internally. A sufficiently large model can learn internal representations of things that aren’t themselves tokens: syntax, semantic relationships, spatial relationships, facts, abstractions, and apparently even procedures for solving certain kinds of problems. Research on transformer mechanics, for example, has found that attention layers can learn mechanisms that amount to retrieving relevant information from context and composing it to produce predictions. And this is an especially important distinction: Predicting the next token can require constructing an internal representation of the situation that makes the next token predictable. Suppose I ask: “Alice put the glass on the table. Bob picked up the glass. Where is the glass now?” The model doesn’t necessarily retrieve a memorized sentence that looks like that. It can construct an internal representation involving Alice, Bob, the glass, the table, and the sequence of events, then use that representation to predict the answer. That’s still next-token prediction. But calling it merely “autocomplete” can obscure the extraordinary computation that may be occurring in between input and output. Indeed, a very recent 2026 paper explicitly argues that “just next-token prediction” has become an oversimplification, distinguishing the training objective from the internal computational processes that emerge in models trained with it.
There’s an even bigger problem with Chiang’s argument I think Chiang’s strongest argument is actually not a technical argument about how LLMs operate. It’s a philosophical argument about consciousness. And there is an important logical gap: “This system works this way” ® “therefore this system isn’t conscious.” That inference isn’t established by our current science. We don’t actually know what physical/computational properties are sufficient for consciousness. Consider a hypothetical: (1) We discover that human brains ultimately perform physical information processing. (2) We discover that some artificial system performs a sufficiently similar kind of information processing. (3) Does that automatically make the artificial system conscious? We don’t currently know. Conversely: (1) An LLM is a transformer. (2) It predicts tokens. (3) It doesn’t have a persistent biological nervous system. (4) Therefore it cannot possibly have subjective experience. The last step isn’t something neuroscience has demonstrated. Philosopher David Chalmers has made essentially this point in discussing LLM consciousness: there are substantial reasons to doubt that today’s LLMs are conscious, but the question isn’t settled merely by pointing out that they’re language models.
And there’s an interesting irony here Chiang’s essay seems to rely heavily on the distinction between simulation and genuine mental activity. But that’s precisely where things become philosophically difficult. Imagine a system that has: a persistent internal state; a model of itself; a model of its environment; long-term memory; goals; the ability to plan; the ability to learn from experience; recurrent processing; sensory inputs; and sophisticated self-monitoring. At what point would saying “it’s merely predicting the next token” cease to be an adequate explanation? The answer can’t simply be “never,” because we could build a system whose output interface happens to be token prediction while its internal architecture becomes radically more agentic. And we’re already seeing systems where the LLM is only one component of a larger architecture—memory, tools, retrieval, external state, planning loops, etc. The distinction between the LLM itself and an LLM-based agent therefore matters enormously.
One point where I think Chiang is particularly persuasive His argument that a conversational transcript is extremely weak evidence of consciousness is excellent. If an AI says, “I’m afraid,” that tells us almost nothing by itself about whether there is an underlying subjective feeling of fear. The same problem exists in reverse. An entity might conceivably have subjective experience while being unable or unwilling to express it linguistically. So: humanlike language ¹ evidence of consciousness. That’s a very important corrective to anthropomorphism.
My overall assessment
Chiang’s claim
My assessment
LLMs are fundamentally trained through next-token prediction
Correct
Transformer attention is crucial to processing context
Correct
LLMs don’t contain a little conscious “self” directing responses
Next-token prediction explains everything about their cognition
Too strong
Current LLMs aren’t conscious
Plausible and probably correct, but not scientifically proven
LLM architecture establishes that machine consciousness is impossible
Not established
So I’d say Chiang is a very good guide to why we shouldn’t anthropomorphize current LLMs, but a less reliable guide to what can ultimately be inferred about machine consciousness from their architecture.
The really fascinating question, in my view, isn’t “Are today’s LLMs conscious?” I think there’s good reason to answer probably not.
It’s:
If consciousness is an emergent property of information processing rather than something uniquely biological, what additional computational properties would turn an LLM-like system into something for which “probably not conscious” was no longer a reasonable assumption? That’s where Chiang’s argument becomes much less definitive—and where the current science gets genuinely interesting.
Like I said, ChatGPT’s response was not presented in a manner that was easy to put into WordPress. So, apologies for the unpleasant formatting above. I trust ChatGPT was not consciously problematic…. [that’s a joke].
So read Chiang’s article, if you find it even a mildly interesting topic.
In closing, I’d like to recommend where else a non-specialist one might turn for practical and reasonable perspectives on artificial intelligence. Arvind Narayanan, Computer Scientist and Professor at Princeton, has produced a number of quite helpful resources. Here are a few recent resources that he produced.
These 24 messages on the Sermon on the Mount were prepared for Long Beach Friends Church in 2024. They follow the Bible Project‘s Sermon on the Mount material for additional study. The links below will open a video of the message from Long Beach Friends Church on YouTube.
These 19 messages on navigating contemporary cultural issues and disagreements were prepared for Long Beach Friends Church in 2023 and 2024. The links will open a video of the message on YouTube.
These nine messages on how Christians relate to politics and government were prepared for Long Beach Friends Church in early 2025. The links will open a video of the message on YouTube.
These five messages on the implications of recognizing Jesus as Lord were prepared for Long Beach Friends Church in early 2026. The links will open a video of the message on YouTube.
In 1854, more than a hundred years and some days before I was born, Ralph Waldo Emerson wrote a personal letter to his daughter Ellen. It included this advice:
You must finish a term & finish every day, & be done with it. For manners, & for wise living, it is a vice to remember. You have done what you could — some blunders & absurdities no doubt crept in forget them as fast as you can tomorrow is a new day. You shall begin it well & serenely, & with too high a spirit to be cumbered with your old nonsense. This day for all that is good & fair. It is too dear with its hopes & invitations to waste a moment on the rotten yesterdays.
Over the years, R.W.E.’s writing was much-quoted and published in various forms and editions, with editors doing their re-writing… er, editing of his informal note to his daughter, which apparently many found might be helpful to a broader readership. I read this advice in various places and forms over the years and wondered why it differed a bit each time I encountered it. The essence was generally there; but the words were not the same. And so it goes. One wonders what Ralph would have thought about it all. All art is stolen? (Read Austin Kleon.)
Today I read it adapted in a Garrison Keillor newsletter:
Write it in your heart that every day is the best day of the year. Live in the sunshine, swim in the sea, breathe the wild air. Finish each day and be done with it. You have done what you could. Some blunders and absurdities no doubt crept in; forget them as soon as you can. Tomorrow is anew day. You shall begin it serenely and with too high a spirit to be encumbered with your old nonsense.
And then one can find it posted all ’round the webs in various pretty displays as:
Write it on your heart that every day is the best day in the year. He is rich who owns the day, and no one owns the day who allows it to be invaded with fret and anxiety.
Finish every day and be done with it. You have done what you could. Some blunders and absurdities, no doubt crept in. Forget them as soon as you can, tomorrow is a new day; begin it well and serenely, with too high a spirit to be cumbered with your old nonsense.
This new day is too dear, with its hopes and invitations, to waste a moment on the yesterdays.
I’m sure there are other… editions? Regardless, I appreciate the sentiment and choose to let the questions about editing and re-writing fall by the wayside. Is that consistent with Emerson’s advice? In any case, I’ve serenely stolen it three times here probably with too high a spirit, and do not find it to be old nonsense at all.
Because of the Lord’s great love we are not consumed, for his compassions never fail.They are new every morning; great is your faithfulness.
–Lamentations 3.22-23 [NIV]
See, I am doing a new thing! Now it springs up; do you not perceive it? I am making a way in the wilderness and streams in the wasteland.
— Isaiah 43.19 [NIV]
Therefore we do not lose heart. Though outwardly we are wasting away, yet inwardly we are being renewed day by day. For our light and momentary troubles are achieving for us an eternal glory that far outweighs them all. So we fix our eyes not on what is seen, but on what is unseen, since what is seen is temporary, but what is unseen is eternal.
John Perkins passed into eternity last Friday morning (March 13, 2026). I learned a lot from his teaching, speaking, and writing. I’m more than a bit at a loss for words at the thought that that his time among us is now past. Here’s what I wrote about him among some recommendations I offered a few years back.
I have listened to John Perkins tell his story, teach, preach, and have met him personally several times. Rarely does any human being impress me so deeply as a godly man. He is genuinely an American hero, and a man of God whose leadership and teaching I admire greatly. He has founded several organizations and authored numerous influential books. I highly recommend his book Dream with Me: Race, Love, and the Struggle We Must Win. I have given away many copies of this book, including to many of the leaders of Long Beach Friends Church. Immediately after writing that book, he wrote another:One Blood: Parting Words to the Church on Race. It’s excellent too. John Perkins has written and co-authored quite a few other books. I’ve read most of them. Good stuff. You can find him on YouTube and other places. The Christian Community Development Association has many audio recordings of John Perkins. He tells his story in a ten-minute video. There’s a longer, 21 minute video available on YouTube also.
I heard the voice of God saying God loves me. And he calls me to love him back.
John Perkins
Justice is any act of reconciliation that restores any part of God’s creation back to its original intent, purpose or image. When I think about justice that way, it doesn’t surprise me at all that God loves it. It includes both the acts of social justice and the restorative justice found on the cross.
John Perkins
People overcome racism. Good overcomes evil. In my deepest time of pain and sorrow and conflict, God has always brought somebody into my life who has loved me and embraced me at the time when it would have felt better to hate.
Rabbi Shai Held wrote an essay recently that appeared in the New York Times. It is entitled One of the Bible’s Greatest Moral Revolutions. I would think that Jesus’ admonitions to love our neighbors and enemies and to treat others as we would like to be treated ourselves would be enough for Christians… But (speaking as a citizen of the United States) apparently not, judging by the cruel and arbitrary treatment of immigrants by many carrying the authority of our government these days.
Rabbi Held addresses biblical teaching from Hebrew scripture, focusing mostly on the Pentateuch, as one might expect. He concludes:
…many of our leaders lack the most fundamental understanding of the central biblical commandment to love and care for the immigrant.
Rabbi Held discusses what he calls the revolutionary teaching of the Torah regarding love for immigrants. The first love is love of God. The second is love of neighbor. Those two ideas were widespread in biblical times. The third love we are called to observe in Torah, though, was revolutionary in Torah times – love the immigrant residing in your community! See, for example, Exodus 22.21. His essay is highly recommended. If we claim to follow Yahweh and do not love immigrants in our community, we’re not listening to the teaching that expresses God’s concern for those who are in disadvantaged situations with little power.
If we claim to follow Jesus and do not love our immigrant neighbors…. Well, we’re not really following him, are we? There’s really no excuse for one who claims to follow Jesus to be cruel and unjust.
I read a quote by David French a few days ago that I appreciated:
Jewish lives aren’t more precious than Palestinian lives, and any form of advocacy for Israel that treats Palestinians as any less deserving of safety and security than Israelis isn’t just un-Christian; it’s anti-Christian. It directly contradicts the teachings of Scripture, which place Jews and Gentiles in a position of equality.
Given the events of this day and this year, I might add that Iranian lives are not less previous than other human lives. And immigrant lives are not less precious than citizen’s lives. War begets war. Violence begets violence. Hate begets hate. Injustice begets injustice. We are called as followers of Jesus to make peace – to love enemies even.
George Washington often quoted from Micah 4.4 in his writing. I’ve quoted Micah 4.2-4 below. The last sentence is Micah 4.4.
Come, let us go up to the mountain of the LORD, to the temple of the God of Jacob. He will teach us his ways, so that we may walk in his paths.” The law will go out from Zion, the word of the LORD from Jerusalem. He will judge between many peoples and will settle disputes for strong nations far and wide. They will beat their swords into plowshares and their spears into pruning hooks. Nation will not take up sword against nation, nor will they train for war anymore. Everyone will sit under their own vine and under their own fig tree, and no one will make them afraid, for the LORD Almighty has spoken.
