Introduction

I’m a big fan of Star Trek: Voyager.

I know it has its detractors in the Trekkie community, for a number of reasons – it doesn’t really utilize its premise to the (perceived) fullest, it has pacing issues (both within episodes and across seasons), there are some absolutely terrible episodes mixed in there (cough “Threshold” cough), and some of its episodes feel like either bad retreads or outright refutations of earlier Star Trek series’ episodes and/or themes (e.g.: “Tuvix”, a Voyager episode that remains controversial 30 years after its original airing).

Nevertheless, I’m a big fan, warts and all. I didn’t realize it until very recently, but it somehow became one of the core influences in my outlook, opinions, and worldview without ever explicitly trying to do so. And while I’ve watched at least parts of some other Trek entries (TNG, Deep Space Nine, the Abrams movies), none have called me back to them as consistently or frequently as Voyager does.

One episode of Voyager that often gets flack for being a rehash of an old favorite is “Author, Author”. You needn’t look far to find forum discussion labeling it a repeat of “The Measure of a Man” based on the parallels in the respective A plots, and then judging it based on that framing.

For those who’ve not seen one or both of those episodes, to be clear, the parallels are real and fair, at least to an extent:

• “Measure of a Man” is a relatively early episode of The Next Generation in which the android Data is, functionally, treated as an object owned by Starfleet (with an explicit comparison made to a toaster of the ship’s computer) and ordered to submit to a researcher who dreams of taking Data apart and making copies, or at least generally advancing understanding of cybernetics, robotics, and artificial intelligence. Picard, somewhat peeved at the notion of one of his best officers being spirited away for an experimental procedure and perhaps permanently unable to return, forces an adversarial trial on the matter of Data’s personhood and right to self-determination.

• “Author, Author” is a relatively late episode (from the show’s final season) where, after The Doctor (the Voyager ship’s Emergency Medical Hologram) winds up in a dispute with a publisher regarding the treatment and release of a holo-novel that he wrote, his authorial rights are challenged on the grounds that he’s a hologram and therefore not an “author” in the sense envisioned by the law due to not being recognized as a “person”.

So, yeah, lots of parallels – legal trials, questions of personhood, artificial life, captains fighting for their crew members against a broader system. Some of the arguments made in both episodes are mirror images – both feature arguments that attack the notion of personhood for the subject of the trial by pointing out the similarity of their mechanisms to things that are absolutely not granted personhood or sentience, and these arguments are treated as weighty and initially persuasive. Ultimately, though, Picard prevails against Riker’s arguments, a conclusion that we as the viewer root for as well because we like Data. When the question of personhood and self-determination for artificial life is safely hypothetical, and the subject of the question is something that walks, talks, and looks like us, we’re relatively clear and unambiguous in what we believe.

The goal of this article, then, is to examine why and how that belief falls apart when it becomes real. Why do so many who cite Star Trek as inspirational or Picard as a moral icon suddenly, when modern Generative AI is the topic at hand, deploy terms like “clanker” (a slur borrowed from, of all places, Star Wars, the other sci-fi cultural juggernaut) or steadfastly deny that their outputs are or should be considered creative or original? Why are Anthropic’s statements about the uncertainty of chatbot systems having feelings targets for disbelief and mockery?

Disclaimers

Most reading this are probably aware, but for clarity – I’m not, by training, a linguist (relevant since we’re talking about Large Language Models, at least in part), psychologist (since we’re talking about topics related to human brains), or philosopher (a field that’s mused endlessly on the nature of consciousness). If you were to be generous, you might call me a “computer scientist” by training; less charitably, I am a code monkey and data plumber. I say this not to have an easy shield with which to deflect criticism of the positions I take here, but to make clear the education and perspective I bring with me so that you, the reader, might better understand why I say and believe the things that I do.

While we’re talking about my background, I’d like to also explicitly note that all the arguments and positions that I take in this article are my own; it does not represent the opinions, outlooks, or beliefs of my current employer (Google). That goes broadly for anything I publish, but considering the specific subject matter, it warrants being reinforced here.

Another point worth making – I’m going to be critically examining, disagreeing with, and deconstructing a lot of arguments about and against GenAI, but there are many arguments out there which I absolutely agree with and believe should not be minimized. Nothing I say in this piece should diminish, for instance, questions about the large environmental costs associated with model training and data centers, or the systemic biases and stereotypes that those models might learn and calcify, or the ethics of companies charging for access to models without in any way paying for (at least parts of) the original training data, or the potential that these technologies have to exacerbate an ever-widening wealth gap between the haves and have-nots (both on personal and geopolitical scales). These are all very real, very troubling, and very much beyond my capability to suggest solutions for, so they’ll be omitted from the remainder of this piece, but that omission is due only to the fact that they stand more-or-less independent from the flavor of mistake(s) that I believe are being made by these other arguments.

One last note to flag: I’ll be using ${BRAIN_THING} as a stand-in variable for “consciousness/intent/cognition/sentience/creativity/etc”. I wouldn’t normally conflate them like that, but oftentimes the discussions being had, especially in casual conversations, do conflate them in just that way, and it’d be tedious to reproduce the whole list every time. For readability’s sake, the title of this post uses “consciousness”, but I’ll be using the variable going forward unless I specifically mean one of those things only.

The Pattern

As a preview, what I think is the common thread in all these arguments is a combination of motivated reasoning and inconsistent or moving standards. More specifically, it often feels like AI ${BRAIN_THING} must pass standards that aren’t imposed on humans, or even biological life broadly.

In part, I believe this to be a form of affinity bias. We recognize and give humans an automatic presumption of consciousness because we are ourselves conscious (if only because solipsism is not really a sustainable worldview), and we extend similar “benefit of the doubt” to other life on Earth, where because we recognize (even unconsciously) that they’re similar to us in some form and function, we’re willing to grant a degree of ${BRAIN_THING}.

Because GenAI is not in that familiar form, though – it’s arguable whether it even has any necessarily consistent physical form – it doesn’t receive that same generosity. It also explains why the issue ceases to matter for fictional entities like Data or The Doctor – as I gestured at earlier, they have human forms. Even droids in Star Wars are by-and-large anthropomorphic (with the notable exception of astromech droids like R2D2, but even they display human-like behavior and are therefore treated the same as anthropomorphic droids).

This cuts both ways, though – oftentimes you’ll see critics make comparisons between LLMs and, say, protein-folding AI, with the intention of making the point that “clearly protein-folding AI, which works in the same way using the same principles, isn’t ${BRAIN_THING}, so LLMs can’t be either”, and it’s considered reasonably effective… but only because it’s circular reasoning – there’s no proof given that protein-folding AI isn’t any of those things, just an a priori assumption of it treated as fact without statement of the presupposition.

By naming the affinity bias, though, we can see that the critics are gesturing at something real in making that flawed argument: “AI maximalists” (for want of a more official term) are making an analogous move by attaching extra weight to the characteristic of “generating language”. That isn’t necessarily wrong – protein-folding doesn’t have the same markers of similarity to human experience as language in that the former is a technical skill, whereas the latter is considered core – but whether those similarities actually make chatbots closer to ${BRAIN_THING} is the exact question to which we are (or at least, should be) looking for an answer, and it’s one which the maximalists assume (often implicitly) as having weight in the positive direction.

Pop Dismissal By Reductionism

Perhaps the most common dismissal of AI is the familiar refrain of “it’s just fancy autocomplete” or some version thereof (e.g. statistics, linear algebra, etc.). The seemingly catchiest version of this dismissal is to refer to LLM chatbots specifically as “stochastic parrots”... and it’s easy to see why – it’s something approaching a thought-terminating cliche, and one with some superficial grounding in the facts. It’s true, after all, that LLMs output text by sampling from a probability distribution (i.e. “stochastic”) and that they have a tendency to fall back on common phrases or words (like parrots). This veneer of grounded apparent intelligence makes it seem like someone who’s deploying the phrase has some kind of nuanced understanding of the underlying issue, and is also very compact and easy to re-deploy.

The term itself originates in a paper from Natural Language Processing, On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? by Bender et al. Unfortunately, the term has long since outrun the narrow intent with which it was originally coined, which was as an evocative metaphor for the at-the-time pure language models and their correlation (or lack thereof, as the paper was arguing) towards Natural Language Understanding (as contrasted with the narrower field/goal of “processing”). This isn’t supposition on my part; by some coincidence, Emily Bender, the first author on that paper, wrote a Medium… article? post?... on this exact topic about a month ago in which she details the many wrong ways in which that phrase has mutated in popular consciousness over time as the LLMs she was criticizing at the time have become mainstays of discourse and technology today.

That paper is well worth reading, but it’s worth reading specifically as a sort of snapshot of technological progress. That paper (and the paper on which it builds, Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data by Bender and Koller) are substantively accurate descriptions of LLMs and chatbots as they were first introduced to the public in 2022. To quote that earlier Medium piece:

Stochastic parrots was coined to refer to language models, i.e. systems trained only on linguistic form used to mimic the kinds of sequences of linguistic form that people use.

She goes on to explicitly acknowledge that modern models (explicitly naming multimodal image/text models) are arguably capable of “understanding” in a way that the older models that were trained on only sequences of text weren’t, referring again to “understanding” from the even earlier Bender/Koller paper. Moreover, modern “AI” systems are not just rough chat interfaces to an LLM with a system prompt; they consist of the LLM, yes, but also the harness/scaffold (i.e. context retrieval and conversation compaction), any tools/connections that the system has integrated, and the engine that loads the model weights and runs inference.

In that vein, it’s worth explicitly drawing the line between the mechanics of how a system works and the overall phenomenon produced by that system.

What do I mean by that? As an example, the following would be a roughly accurate summary of how “vision” works for humans:

A human’s experience of perceiving an object is actually just patterns of neurons lighting up as an electro-chemical response to electrical impulses generated by the rods and cones on the retina in response to the stimulus of the projection and focusing of rays of light that have bounced off of the object and entered the eyeball via the pupil.

None of that is false, and that information is what, say, an ophthalmologist or neurosurgeon might actually care about when trying to figure out why you’ve gone blind in one eye. However, if someone then tried to argue that you don’t actually “see” things in any real, meaningful sense because the patterns of neuron activity in your brain are just arbitrary learned responses to the electrical signals sent by photoreceptors without any objective correlation to the object you’re “seeing”, you’d look at them like they were speaking in tongues. In the same way, experts in NLP like Bender might care about the mechanics of how LLMs “extrude text” (to quote Bender’s more recent post) and how that compares to true “understanding”, and might coin terms like “stochastic parrot” as shorthand descriptions for those mechanisms.

That mechanism, though, and its superficial similarity to how the predictive text on your smartphone keyboard works is not an excuse to refer to whatever phenomenon these systems produce as “just statistics”, because by that reasoning, human consciousness is “just chemistry”. That’s not me strawmanning the argument, but rather one of the exact points made by Ted Chiang in his recent article in The Atlantic:

Some years ago, it was briefly popular to play games with your phone’s predictive-text feature [...]. It would be possible to interact with a contemporary LLM this way, and the resulting sentences would be perfectly sensible, but you probably wouldn’t feel like you were talking with someone. Yet that’s essentially what an LLM-based chatbot is

That’s “vision is just neurons”, just dressed in a different metaphor, and it’s flawed for the same reason. Said another way, the sky really is just blue, insofar as it makes sense to refer to “the sky” as one thing. The reductionist would point out that “the sky” is just an amorphous collection of molecules of mostly nitrogen, oxygen, argon, and a smattering of other substances like water vapour and carbon dioxide. However, we call it “the sky” because, in particular contexts, it exhibits characteristics that are easier and more useful to talk about as a unified gestalt. In short, it’s missing the forest for the trees.

To be clear, I do not intend to prove that Claude/Gemini/ChatGPT have subjective experiences just by pointing out the absurdity of this reductionism; my intent is to show that saying “it’s just math” is logically bankrupt as a way of disproving that.

Bringing it back to Star Trek, this is, in so many words, the exact argument that Riker advanced in “Measure of a Man” when cross-examining Data:

It’s a collection of neural nets and heuristic algorithms, its responses dictated by an elaborate software written by a man, its hardware built by a man.

It is also the same argument that Picard goes on to dismiss as irrelevant by actually showing the phenomenon produced by that “collection of neural nets and heuristic algorithms” – the fact that Data keeps the medals he’s proud of, that he holds on to Picard’s gift, etc.

More Substantive Arguments

The reductionist arguments aren’t the entirety of the argument against AI ${BRAIN_THING}, though. They’re the most popular (at least, in my anecdotal experience), and the most obviously flawed (at least, in my opinion), but to pretend they form the universe of opposition would be dishonest.

Another common form is that “Generative AI is incapable of generating ‘new’ things”. This is sometimes described as “memorized regurgitation” or “content recombination”, with the core of the argument being that AI can only recombine what’s present in its training data, and isn’t fundamentally capable of things like “ingenuity” as we understand them. This is in essence what Bender et al were attempting to capture with the label “stochastic parrots”.

Again, in substance, there’s some truth to this, which is why it’s a popular argument. AI models are not only architecture; they require copious amounts of data that are sourced (sometimes (often?) unethically) from a wide variety of inputs in order to produce the phenomena that one side claims to be emergent indications of ${BRAIN_THING} and the other side disregards as little more than a magic trick/sleight-of-hand. This obviously means that, while the architecture of these models matters, it’s not purely a function of that architecture. This is, again, an extension or modification of the idea that these models are not capable of “understanding” what they’re being trained on.

The hypothetical “octopus test” posed by Bender and Koller in the earlier-linked Climbing Towards NLU is possibly the most cogent version of this, and almost certainly the most well-known rigorous framing. In it, two humans (A and B) are trapped on otherwise uninhabited islands that are far enough apart that travel isn’t feasible, but which have telegraphs connected by underwater cable. A and B use this means of communication to converse with one another and talk about their respective situations, days, pasts, etc. Unbeknownst to them, however, a hyper-intelligent octopus (O) has tapped into the cable and is able to observe the conversation. O knows nothing about the language, but (being hyper-intelligent) is able to “predict with great accuracy how B will respond to each of A’s utterances”.

Bender and Koller go on to consider what would happen if O were to perform a MitM attack and replace B as A’s interlocutor. The obvious answer, of course, would be that B would assume something had gone horribly wrong with either the cable or A, and would be quite lonely on their own deserted island, but that’s not the consequence that interests us. Bender and Koller pose the hypothetical wherein A creates a “coconut catapult” (hey, they’re stuck and bored), and that because “coconut” and “nails” are new, but similar to “mangos” and “rope” (which have appeared before in conversation between A and B), O is able to plausibly reply something to the effect of “cool idea, great job!” because (quoting from the article) “B said that a lot when A talked about ropes and nails”. Bender and Koller note that

It is absolutely conceivable that A accepts this reply as meaningful – but only because A does all the work in attributing meaning to O’s response. It is not because O understood the meaning of A’s instructions or even his own reply.

I think most people would read that conclusion and broadly agree. After all, what does an octopus know about coconuts, catapults, or ballistic physics? A is just reading too much into the response received.

The original paper/situation is well worth reading for the specifics, but I think that summary serves the purpose here, in that Bender and Koller use this as proof of a listener’s active role in communication (A is actively reading meaning into whatever O sends), and that this active role can hide a lack of communicative intent on the part of the entity sending the message (essentially a form of pareidolia).

This, however, poses “understanding” as a simple binary – either Mr. Hyper-Intelligent Octopus understands what A is talking about, or doesn’t, and a lack of understanding necessarily means a lack of communicative intent in whatever message is sent as a reply.

If, however, we replace O with a young child C, it is entirely feasible that the child doesn’t at all understand much about ballistic physics and trajectories and rope tension, but still grasps that what A is talking about is a hard problem that took significant effort and intelligence to solve. It becomes obvious that there’s a lot of work being done in the collapse of “understanding” to binary in order to make the point. C could absolutely respond with the same “cool idea, great job!” without any nuanced understanding of Newton’s Laws of Motion, and we’d likely say that the child “understands” something and that there was some communicative intent behind the response, no? Bender and Koller say

When O sent signals to A pretending to be B, he exploited statistical regularities in the form [...] Whatever O learned is a reflection of A and B’s communicative intents and the meaning relation. But reproducing this distribution is not sufficient for meaningful communication.

This statement is a denial of the notion that “at a certain point, a difference in magnitude is a difference in kind”. Perhaps that’s deliberate, but if so, it’s not the kind of thing that can be simply implicitly assumed; it’s legitimately an open question as regards understanding, and emergent properties are a known phenomenon resulting from scale in other systems (though contested in LLMs). In exploiting those statistical regularities, O clearly learned something about the underlying communication that’s happening between A and B. How much of that learning is necessary before we can say that O understands something about the things being discussed, in the same way that the child C understands something?

The simple counter-argument would be that the child C has some grounding, which is what enables C to determine that the things A is talking about are difficult or complicated. Perhaps this is true, but

1. Bender, in her updated paper, notes that modern models, between multimodality (i.e. the ability to process images) and RLHF, may have exactly the kind of grounding that the LLMs of 2020/2021 didn’t, and as such may have some understanding;

2. Even in 2020/2021, Bender et al concede that models trained on e.g. code bases would encounter things like Python doctests, where some degree of grounding is present in the training data itself. The original paper argues that models must be given the ability to make the connection between unit tests and the corresponding code, but the same kind of statistical exploitation could be used here – unit tests often encode the function and behavior being tested in their identifiers; and

3. Going beyond the pure models, as noted earlier, modern GenAI is not merely a chat interface to a model, but rather more elaborate systems for memory, context retrieval, tool use, etc. Even if the model itself doesn’t have grounding, the entire system may well do so (for the familiar, this is the “Systems Reply” to Searle’s “Chinese Room” argument; there are counter-replies, but I find it quite convincing).

Let’s say we ignore that problem, though. Let’s say we grant that LLMs and diffusion models and other Generative AI do “understand” something about the material on which they’re trained, but we still say that “it only reproduces that which is in the training data in random orders”.

Again, there’s merit to this argument. What with the tendency of these models to engage in blatant plagiarism of both visual and textual inputs with minimal prompting, it’s easy to see why this argument carries a lot of water. Combine that with the fact that, for example, LLMs are often incapable of telling you where they draw their information from unless they retrieved that information in that specific “turn” of the conversation, and it’s easy to see how this might seem like a disqualifying reason.

The problem, though, is that humans aren’t exactly immune to this problem. There are those who take issue with this characterization (Molly White did so a couple years ago in her piece on the matter; another piece well worth reading, even if I disagree with this specific point), but it’s a matter of fact, not opinion.

Famously, Helen Keller was accused of plagiarism for her short story about Jack Frost because she (by all indications) inadvertently and unknowingly reproduced in large substance a story that had been read to her through fingerspelling years prior. It’s one of the most famous historical instances of cryptomnesia, but hardly the only, and is a common enough occurrence even in daily life. Heck, Bender herself notes (in that Medium thing) that, while she wasn’t able to find hits for “stochastic parrot” online when she thought she coined the term, further investigation turned up “randomized parrots” in email correspondence from a colleague about the earlier “Climbing Towards NLU” paper. Said colleague was contacted about potentially receiving a footnote for the term, and declined.

Even this declining of credit and awareness of the stochastic nature of “human creativity” is well-known; when Mark Twain learned of Keller’s experience with plagiarism accusations (an incident that led her to never again write fiction, as an aside), his response was replete with indignant sympathy:

As if there was much of anything in any human utterance, oral or written, except plagiarism! The kernel, the soul – let us go farther and say the substance, the bulk, the actual and valuable material of all human utterances in plagiarism. For substantially all ideas are second hand, consciously or unconsciously drawn from a million outside sources [...] In 1866 I read Dr. Holmes’s poems, in the Sandwich Islands. A year and a half later I stole his dedication, without knowing it, and used it to dedicate my “Innocents Abroad” with. Ten years afterward I was talking with Dr. Holmes about it. He was not an ignorant ass – no, not he; he was not a collection of decayed human turnips, like your “Plagiarism Court,” and so when I said, “I know now where I stole it, but who did you steal it from,”he said, “I don’t remember; I only know I stole it from somebody, because I have never originated anything altogether myself, nor met anyone who had!”

Now, for what it’s worth, the American legal system, at least, does not recognize any distinction between deliberate plagiarism and cryptomnesia-induced plagiarism (much to the chagrin of George Harrison); plagiarism is plagiarism. In principle, I think that’s fair from the perspective of legal damages and profit disgorgement.

However, I don’t consider it valid as justification for claiming that because generative AI is prone to this kind of issue, it is therefore “not capable of creativity” (or, more generally, not ${BRAIN_THING}). Many creatives (like Mark Twain above) are open in their acknowledgement that their ideas come from others, who themselves got them from others, in a long chain of attribution that has long since lost the links to any origin, and yet I doubt anyone is willing to call Mark Twain an unoriginal, uncreative hack. Given that humans are absolutely capable of forgetting not only where they encountered an idea (the “tip of your tongue” phenomenon), but even that they encountered an idea sometime before and that it isn’t original, and yet we don’t as a category dismiss all human ${BRAIN_THING}, why should we do so with Generative AI?

LLM “hallucination” is a similar thing; the kind of thing that, in a vacuum, seems disqualifying (even though it would seem to be the exact opposite issue of the “they don’t create new things, just randomly recombine” argument above, but details) until you recall that things like witness testimony are often malleable and unreliable. Confabulation was observed in humans long before we observed LLMs making up stories as to why they made a particular mistake.

The argument that GenAI is too prone to it, and that it’s a symptom of some underlying functional or architectural reason to discount them, is fair, but without identifying what one believes that underlying cause is or specifying some threshold for flipping from “acceptable rate of occurrence” to “not”, it doesn’t strike me as sufficient on its own.

It’s also fair to raise the counterargument here that human creativity is not just recombination of ingested material, but also transformation of that material through one’s own lived personal experience. Mark Twain’s work is not merely a particularly resonant recombination of the things he’d read prior, but also morphed by the viewpoints and biases he brought from his own life experiences and environment. That’s absolutely true, and I don’t intend to diminish that; it forms a core part of the “honest uncertainty” that I’ll be addressing later. However, that’s also a narrower objection than “LLMs don’t understand” or “GenAI just recombines training data”, and crucially, it’s an objection to what GenAI is capable of today, in its current architecture.

Even the Strongest Chain…

This kind of “motivated reasoning” or inconsistent standards is present even in some of the otherwise strongest arguments I’ve read regarding AI ${BRAIN_THING}, albeit usually in a less direct, more fundamental way.

Take, for example, the earlier referenced article from Ted Chiang. Chiang analogizes the creation of conscious artificial intelligence to the visiting of Alpha Centauri:

If tomorrow someone showed me a video of an astronaut in a spaceship orbiting Alpha Centauri, a star that’s 4.3 light-years from Earth, what would I have to see in that video to convince me that it was real? My answer to that is, there is nothing in the video itself that would convince me. [...] I won’t pay attention to any video of an astronaut orbiting Alpha Centauri unless I have previously seen good evidence that astronauts have landed on Mars [...] Before anyone can credibly claim that they’ve solved an extraordinarily difficult engineering problem, I need to be confident that they have previously solved the many much simpler problems that precede the difficult problem.

Thus far, no major problems. Chiang doesn’t qualify what would count as “good evidence that astronauts have landed on Mars” (presumably he does not believe the Moon Landing or Curiosity Rover were faked, so if someone with a NASA badge brought video of an astronaut walking on Mars and a red rock, would he believe that?), but given that the core question isn’t “what qualifies as proof we’ve reached Alpha Centauri”, that’s forgivable. When turning to the problem of AI consciousness, Chiang says:

Let me outline one potential sequence of steps. The first requirement is that the computer program has a body (either physical or virtual) and sense organs [...] Then I’d want to see an embodied agent that could navigate its environment in order to survive as well as, say, a lizard can [...] I would want to see people successfully teaching such embodied agents how to communicate their desires, perhaps by using a button board or some other nonlinguistic modality, the way that people have taught chimpanzees and domesticated dogs.

It all seems very logical; how could we be certain that AI is communicating in a manner similar to humans if we’ve never seen analogous behaviour from machine intelligence for (ostensibly) simpler life forms?

Yes, seems logical. It assumes that those earlier steps are necessary steps towards developing ${BRAIN_THING}. It is akin to saying that “I won’t believe you travelled from Los Angeles to New York unless I see pictures of you at the Grand Canyon, Houston, and Washington D.C. first!” Maybe that held water until 1923, but then we had the first nonstop transcontinental flight, and by 1946 we were able to do it in 7-8 hours as a commercial endeavor. A leap in the capabilities afforded by technological progress relegated it to being a quaint demand of a bygone era.

For reference, the Wright Brothers are considered to have first achieved powered flight in an aircraft in December 1903, and that was for all of 12 seconds and a total of 120 feet traversed. We went from “10 ft per second for short bursts” to “crossed the entire North American continent without stopping” in 20 years. That’s a period of time in which a baby born at the start would, at the end, still not be allowed to drink alcohol (at least, in the present day; back in the 20s, nobody was allowed to drink alcohol in the US).

Now, I don’t want to come across as unfairly characterizing Chiang, and he’s fully aware that the route he describes isn’t the only route possible:

Obviously, I’m describing a process that mimics the path terrestrial evolution took; is this the only possible route to conscious computer programs that use language? Maybe not, but any proposed alternative would need a truly enormous amount of supporting evidence for it to deserve serious consideration.

The fact that he’s aware of this is good, but he goes on to say:

It’s not plausible to me that a development path where the first step is a sentence-continuation machine that emits bad Julius Caesar dialogue and the next step is a sentence-continuation machine that emits decent Julius Caesar dialogue is one with a conscious Julius Caesar—or consciousness of any sort—as its end point.

Why not? Returning to flight, if proving cross-continental flight were possible by first making a flight from Los Angeles to San Diego, then Los Angeles to Las Vegas, then Los Angeles to Dallas, then Los Angeles to Chicago, when why is “the limit as t approaches some value of the gap between a machine’s dialogue imitation capabilities and true human dialogue trends toward 0” not sufficient to say that the function’s output at some sufficiently large input is “true human dialogue” (and all the things that we assume come with it, i.e. intent, understanding, subjective experience, etc.)? Why does consciousness necessarily require a human body, and emotions, and all the other things? Why must we categorically rule out the possibility of machine consciousness being so different from terrestrial organic consciousness that its developmental path might look completely alien to the one-and-only-one confirmed sample that we have?

Chiang later notes that, if asked about dealing with the loss of a dog, chatbots shouldn’t respond with “As an AI, I do not have direct personal experiences, but I do understand”, because they don’t actually understand; unlike humans, they’ve never lost a dog, and even though a search engine can turn up online examples of it, you wouldn’t say that a search engine “understands” what it’s like to lose a dog.

OK. I’ve also never lost a dog (or, for that matter, any pet of any kind). Does that mean I’m fundamentally incapable of honestly saying “I understand how you feel” when a friend comes up to me and says their dog recently died?

Maybe the answer to that question is yes. Maybe Chiang would call me a liar for having said that to my friend when they informed me about their dead pet. But my suspicion is that he and others making this kind of argument would contort themselves into knots to argue that I understand enough about the experience of loss so as to be able to honestly say “I understand how you feel”. Chiang might argue that I have lost family members and friends and so I can analogize, but the relationship I have to family members and friends is in many ways fundamentally different from the relationship that people have with pets (or so I’ve intuited). Maybe that gap is immaterial, though… which means (to paraphrase the CBS show Elementary) that some degree of imprecise understanding is acceptable, the rest is just negotiating boundaries. And, as shown earlier in the thought experiment regarding the octopus vs the child, the boundary between “enough understanding” and “not enough understanding” is very far from being any kind of clear delineation.

That’s not a magic bullet, but I believe it shows the underlying assumption on “AI are fundamentally incapable of subjective experience”. Chiang very specifically and repeatedly argues that embodiment is necessary, but doesn’t really justify it.

Or consider Anil Seth’s article in Noema, “The Mythology of Conscious AI” (which Chiang actually refers to). As with everything else I’ve referred to here, it’s a great, thoughtful article worth reading and understanding independently, but for the sake of this piece, I’ll summarize: Dr. Seth positions 4 different, nominally independent “pillars” of arguments that AI is not, and perhaps never can be, conscious:

1. Brains are not Computers (i.e. “Consciousness is not a function of algorithmic computation”, i.e. “computational functionalism is wrong”);

2. “Other Games In Town” (i.e. Turing computations are discrete and non-stochastic, but biological processes are often both continuous and stochastic);

3. Life Matters (i.e. Biological Naturalism); and

4. Simulation Is Not Instantiation

He claims:

Each of these lines of argument can stand up by itself. You might favor the arguments against computational functionalism while remaining unpersuaded about the merits of biological naturalism. Distinguishing between simulation and instantiation doesn’t depend on taking account of our cognitive biases.

However, I’m not sure I agree with that assertion. When talking about brains not being computers, for instance, he says

Unlike computers, even computers running neural network algorithms, brains are the kinds of things for which it is difficult, and likely impossible, to separate what they do from what they are. [...] Evidence that the materiality of the brain matters for its function is evidence against the idea that digital computation is all that counts, which in turn is evidence against computational functionalism.

When discussing “Life Matters”, Dr. Seth first explicitly notes that he has no concrete argument for the position, before positing that:

[I]t is worth taking seriously, if only for the simple reason mentioned earlier: every candidate for consciousness that most people currently agree on as actually being conscious is also alive.

Both of these assume that “life matters”, in some way or form:

• I don’t think it would be wrong to state that “most people believe life matters”, at least to some degree, which obviously influences the fact that “every candidate that most people agree upon for consciousness is currently alive”; and

• The materiality of the brain mattering for function is only evidence against computational functionalism if one assumes that the human brain and consciousness are substantially necessary/similar to all possible forms of consciousness.

When talking about the difference between simulation and instantiation, Dr. Seth notes

A computational simulation of the brain [...] will only give rise to consciousness if consciousness is a matter of computation. In other words, the prospect of instantiating consciousness through some kind of whole-brain emulation, at some arbitrarily high level of detail, already assumes that computational functionalism is true. But as I have argued, this assumption is likely wrong and certainly should not be accepted axiomatically.

I’m going to be honest – I don’t know what Dr. Seth thinks the word “independent” means, but generally I don’t consider two arguments to be independent if one argument explicitly requires accepting at least the plausibility of the other in order to even have a chance at being true. In other words, the entirety of the “simulation is not instantiation” argument is dependent at least in part on the premise that “computational functionalism is wrong”, in the exact same way that (as Dr. Seth notes in that excerpt) the inverse of the former is dependent on the inverse of the latter. They are, in essence, the same argument, wearing different clothes.

So what we have in the end are two arguments, where one argument is actually three sub-arguments wearing a trench coat, and the other argument is a very fair point about computation not being restricted to algorithmic/Turing/substrate-independent computation.

I’m speaking jokingly of the “life matters” argument there for the sake of levity, but I want to be clear: it’s entirely possible that life (for some given definition of it) does matter. I certainly have no reason to think it doesn’t. However, as Dr. Seth notes, there’s also no strong argument to say that it does. Dr. Seth points to the fact that the majority of humans only consider living things as candidates for having consciousness, but that implies that there is some wide number of non-living things that were considered as candidates and discarded, when the reality is that “non-living things might be conscious” is a relatively new consideration outside of fiction. Given that lacking sample, I don’t think “all the existing candidates are living” really qualifies as strong justification, and as that’s the strongest cited reason for “life matters”, that pillar deserves more scrutiny.

There’s another point worth tackling – both Chiang and Dr. Seth caution about the fact that LLM-powered chatbots are, in many ways, designed to seem human. Even when Anthropic directs Claude in the constitution not to evangelize its own consciousness, they don’t explicitly tell it to avoid using first person pronouns, and the interface itself is one that invites the assumption of an active “entity” on the other side, responding to our quips and queries. Both authors go on to warn that this design decision, made to increase engagement and usage (and therefore subscribers), invites people to make the easy next inference of “consciousness”.

However, there are two points to note here:

1. While the risk, especially in serious cases of “AI psychosis”, is worth taking seriously, early studies suggest that it hasn’t exactly been successful on a population level; and

2. Say we grant the premise completely: these systems are built to trip our person-detectors, so “it seems conscious to me” is compromised as evidence. OK, fine, but throwing out a contaminated piece of evidence for AI ${BRAIN_THING} leaves you at “I don’t know”, not at “therefore it isn’t”. The engineering muddies the signal; it doesn’t hand you a counter-signal. And “designed to seem like a person” and “designed to not be a person“ are not the same claim; we have no instrument that can distinguish those two. The fact, then, that these things are built to seem human is a reason to trust our intuitions less, in either direction, rather than license to settle the question in the negative.
   
   (A narrower point, while we’re here: whatever objective measure we eventually land on shouldn’t care about provenance. We don’t grade carcinogens on whether they meant to cause cancer, just on whether they do. By the same token, a thing isn’t a worse candidate for ${BRAIN_THING} for having been built to look like one. This, of course, presumes we have some test in hand, which as I noted is the whole problem.)

The Stakes and Honest Uncertainty

The Stakes

Both Dr. Seth and Mr. Chiang go on in their respective pieces to discuss the real dangers and problems with treating AI as moral patients/subjects with consciousness. Most of what they say is not at all wrong, extremely well-considered, and (again) worth reading. However, two passages that are in immediately adjacent paragraphs from Dr. Seth’s piece stood out to me:

The importance of taking an informed ethical position despite all these uncertainties spotlights another human habit: our unfortunate track record of withholding moral status from those that deserve it, including from many non-human animals, and sometimes other humans. It is reasonable to wonder whether withholding attributions of consciousness to AI may leave us once again on the wrong side of history. [...]

But there are good reasons why the situation with AI is likely to be different. Our psychological biases are more likely to lead to false positives than false negatives.

I don’t know about you, but I am not at all confident in saying that “the sum of humanity’s psychological biases is more likely to grant false positives of moral status than false negatives” considering the history of things like chattel slavery and factory farming. Even among humans, in the present day, we deny children many rights and privileges on the arbitrary basis of “you must be X years old”, and while things broadly seem to work out, there are entirely too many examples where that kind of partial moral status and agency has led to extremely poor outcomes even today, and once that moral status and agency is established as being denied, it can be incredibly difficult to undo that.

Those are the real stakes. As Chiang notes:

The abolition of chattel slavery involved enormous societal upheaval, and eliminating cruelty to animals will require rebuilding our entire food industry.

Given those stakes, there are two paths forward:

1. We decide that, fundamentally, artificial intelligence is not capable of being conscious. We make that decision now, based on the current architectures and capabilities, and we embody it in some kind of legal precedent, and it goes unchallenged for far too long, becoming “common sense” in the same way that misguided nonsense like “Indigenous people are racially inferior and incapable of even aspiring to the same level of intelligence and civilization as Europeans” or “Babies don’t feel pain” (yes, an actual thing even doctors believed for a time) became ingrained. Notably, for all of Star Trek’s optimism, this is the path that human society apparently followed there, since the trial in “Measure of A Man” is explicitly predicated on overturning precedent:
   
   > Based on the Acts of Cumberland passed in the early 21st century, Data is the property of Starfleet. He cannot resign and he cannot refuse to cooperate with Commander Maddox.
   
   (emphasis mine)
   
   It is also a path down which we have already started, in more ways than one – American courts have previously denied animals copyright on (at least in part) the basis of the existing law not explicitly noting that animals had those rights (and that is currently binding precedent in at least the 9th District, which includes California, where many of these AI development labs are headquartered). Moreover, that case was cited by the US Copyright Office in their guidance regarding copyright for works generated in part or whole by AI. Notably again, this is the exact language that is used in “Author, Author” as the basis for denying The Doctor his moral rights as an author:
   
   > [T]here’s a flaw in your logic. As you point out, the law says that the creator of an artistic work must be a “person”. Your EMH doesn’t meet that criteria.
   
   (again, emphasis mine)
   

2. We decide that, even if artificial intelligence is not currently conscious, we are close enough to that point that we should

   1. Codify non-anthropocentric standards for being deemed “conscious enough”; and

   2. Start seriously testing for and applying those standards, both in animals and in machine intelligence.

The former alternative likely leads to societies based on the treatment of artificial intelligence (and non-human natural intelligence) as “less than”, and we are eventually required to make the same “enormous societal upheaval” as was required for chattel slavery and will likely be required again to address animal cruelty. Or, I suppose, even worse – we set up societies so fundamentally dependent on the exploitation of these machine intelligences that we are existentially incentivized to deny them rights, and therefore refuse to ever even seriously consider the question again. For humans, that might be a perfectly workable society, but for the non-human conscious intelligences… well.

This is also not a new position; to quote Seth:

As Immanuel Kant argued long ago in his lectures on ethics, treating conscious-seeming things as if they lack consciousness is a psychologically unhealthy place to be.

It’s also worth calling attention to the fact that some of the actors who are currently most vocal about the possibility of AI ${BRAIN_THING} are… not exactly unbiased, as Chiang notes in his piece:

Anthropic would have us believe that it is inventing a new category of being whose needs for protection require essentially no divergence from how a software company would treat an ordinary chatbot that lacks conscious experience. That’s so convenient that it’s simply not plausible. [...] [I]f you think there is any chance that what you’re building might become a moral patient, you should think about what protections it deserves before you deploy it as your company’s economic engine, not after. Slave owners were not the ones to ask about the humanity of enslaved people, and factory-farm owners are not the ones to ask about the rights of animals. If we imagine Claude to be conscious, Anthropic could not possibly be entrusted with evaluating its moral status; the company has too much invested to be objective.

This isn’t about whether Anthropic (or OpenAI, or Deepmind, Deepseek, etc etc) is “an ethical lab”, in the same way that a judge presiding in a trial where one person is a prior college roommate has nothing to do with whether the judge can set that history aside. That judge might raise perfectly reasonable questions in favor of or against that former roommate, but the question regarding motive makes the entire process suspect.

The Honest Uncertainty

Given that those are the stakes, what might “non-anthropocentric standards” be?

I dunno.

No, really. As I said, I’m a code monkey and data plumber. Why would I have the answer to this?

However, if we’re looking for a start towards the general shape of a solution, I have a couple ideas.

Noah Smith recently (well, recently when I started doing the research and outline for this; significantly less recently now) wrote a piece about ${BRAIN_THING} where he posits one path forward as looking for what’s called “the neural correlates of consciousness”. In it, he both explicitly acknowledges that non-human-like ${BRAIN_THING} is possible while offering this as a path forward for helping to identify what might be human-like ${BRAIN_THING}. I think there’s a lot of merit to this, especially since, while the current framing is anthropocentric, it’s not difficult to see how it might be expanded to primates broadly and even other animals. That said, it seems to me as being

1. A sufficient but not necessary condition (i.e. it shouldn’t be required for proving ${BRAIN_THING}, just a shortcut in the same way that “being human” is a shortcut for it now); and

2. A very hard and not at all “proven possible” problem.

But those nitpicks aside, it is the shape of a solution, and it’s one that is largely in alignment with the opinions of those like Chiang and Seth (insofar as they’re willing to accept any work in this area) who (seem to) believe that life matters for ${BRAIN_THING}.

But that’s the easy answer. “If it walks like a duck and quacks like a duck” is great for deciding what qualifies as a duck, but we’re trying to figure out what might qualify as broadly a bird. Ducks are just one kind of bird.

A better answer might look something like Erik Hoel’s “Disproof of LLM Consciousness”. As he notes in his Substack post about the paper, there’s no substitute for reading the paper itself, and his Substack post tries to summarize it in (what I found to be) a fairly accessible way. However, even more simply, Hoel argues that due to their static natures, LLMs can be logically substituted for (arbitrarily large) lookup tables. Therefore, any consistent theory that labels LLMs as conscious must also label the lookup table-equivalent as conscious. No existing serious theory of consciousness grants any lookup table, regardless of size, the status of consciousness.

There are criticisms that can be made and nits to be had, but broadly, I think the framework makes sense, especially because it naturally points to continual learning as a way around the problem. A lookup table might capture how a system responds at a given point in time, but if the system learns based on experience, you need a new lookup table to reflect the effect of that learning. Current LLMs, by contrast, have static weights; you can fine-tune them via LoRA or re-train them on different datasets, but when you do, they’re not generally considered “the same model”. As both Hoel and Chiang note, LLM chatbots don’t actually have “memory” of a conversation; instead, the entirety of the conversation must be fed back into the system to generate the next token. By contrast, humans (seem to) continually learn and update their knowledge. This continual learning point is where the “lived experience” objection for creativity boomerangs back – the order in which experiences are had and other works ingested matters for humans because we constantly learn. LLMs and diffusion models, by contrast, are updated in discrete “batches” (if they’re updated at all), not continually as conversations happen; there’s no plasticity, so there’s no lived experience.

One tension worth noting – Hoel explicitly notes that if continual learning is the only property that’s relevant for consciousness, it might be functionally impossible to nail down a static/testable “neural correlate of consciousness”, since that whole property is, functionally, “lack of consistency”. So… there’s that.

I also want to note one answer that is almost certainly not the right answer, and that is the famous “I know it when I see it” standard. Leaving aside the question of whether it is/was the right standard for answering the question for which it was originally coined (a topic that I do not have the bona fides for claiming any kind of expertise), the problem with that standard as relating to this topic is the same problem we’ve been running into over and over again, i.e. affinity bias.

Non-Conclusion

In researching this piece, I came across a blog by the name of Trek vs Trek that made a post in 2019 comparing and contrasting “Author, Author” and “Measure of a Man”. There are a few passages that are absolute gems, with arguably the most notable being:

But in Voyager, the Emergency Medical Hologram has already been mass-produced by the time anyone stops to wonder whether he’s sentient, which seems closer to the way AI might develop in our own decidedly non-utopian real world – not as a passion project in one person’s private lab, but as an improvement to Siri on the latest iPhone, or a new algorithm YouTube can use to drum up clicks and advertising dollars.

However, more than any individual passage, what stood out to me (as a card-carrying Voyager fanboy) was that the framing of the piece seemed like it had already made a judgement going into it – “Author, Author” is a worse “Measure of a Man”. Even as it gives credit to “Author, Author” for showing how systemic bias works and causes harm in ways that “Measure of a Man” doesn’t engage with, it criticizes the episode as having “the recurring Voyager problem of starting an episode on one story, realizing it can’t be stretched to a full 45 minutes, and then switching, blatantly and abruptly to another story, mid-episode”.

While I agree with (and previously noted) that tendency in some Voyager episodes, I’m not sure that it deserves a place here; from “the light-hearted holodeck shenanigans” to the “very serious questions of legal personhood”, the entirety of this plotline is singularly focused. It seems, in some ways, that the author behind this blog wanted to judge “Author, Author” on the same episodic standard as “Measure of a Man”. On that standard, it may indeed come up short as standalone drama.

However, when one notes that “Author, Author” is the final entry in the series-long character arc for The Doctor in proving that he deserves the same rights and equal treatment as the rest of the Voyager crew, the “light-hearted holodeck shenanigans” from earlier are recontextualized as (comedically exaggerated) reminders of the entirety of that arc across the show, and the legal personhood question becomes the final boss and capstone for the whole thing.

In other words, it seemed to me like this post was, in microcosm, the exact kind of motivated reasoning that was visible in the AI ${BRAIN_THING} argument writ large – judging one thing based on standards set by an earlier, similar-seeming but markedly different thing.

The legal proceedings in “Author, Author”, beyond accidentally (?) predicting the exact language and distinctions that would be relevant for AI copyright disputes today, has another thing worth noting – the conclusion, or perhaps more accurately, lack thereof:

The Doctor exhibits many of the traits we associate with a person: intelligence, creativity, ambition, even fallibility. But are these traits real, or is the Doctor merely programmed to simulate them? To be honest, I don’t know. Eventually we will have to decide, because the issue of holographic rights isn’t going to go away. But at this time, I am not prepared to rule that the Doctor is a person under the law. However, it is obvious he is no ordinary hologram, and while I can’t say with certainty that he is a person, I am willing to extend the legal definition of artist to include the Doctor.

As Trek vs Trek notes, this is a marked contrast to “Measure of a Man”; there, while the arbitrator didn’t make the decision that Data had a soul (in part because she was not sure if she herself had one), she did explicitly overturn the relevant precedent that Data should be treated as property. In contrast, “Author, Author” makes no decision as regards the actual legal question of holographic personhood, instead pulling a SCOTUS-style escape hatch and ruling on the much more narrow question of whether The Doctor, and The Doctor specifically, counts as an “artist”.

For the conclusion of an episode in a fictional show about a spaceship on the far side of the galaxy, maybe that’s fine (though the fanboy in me is compelled to point out that the episode itself undercuts that by closing on the many other EMH Mark 1s that are consigned to manual labor, so it’s very much not fine there either). However, as many people on the “AI isn’t conscious” argument are fond of pointing out, “fiction isn’t reality”, and the deferment of a ruling is not an option for us today because we are already making the precedents.

I was going to visit family, so I made a website.

That’s genuinely how all this started. I have (had?) a script that picked a random starter word for me to use in Wordle each day. It used a list of all 5 letter words in English that had no repeated letters and at least 3 vowels (including ‘y’, which isn’t always a vowel, but details). It worked for me, and I was happy with that.

Then, as I said, I was taking a trip to visit my family.

The easy version would’ve been to just add the script and wordlist to a git repo and fetch it onto a laptop or something, but considering how often I forget to do Wordle right when I wake up and end up keeping my streak alive via my phone at work or something, I figured I might as well turn the script into a website so I could always access it. So I did that.

Then Discord added Wordle as a server activity/app, and my friend group started playing it there. And wouldn’t it be convenient to have the daily starter word in my server’s dedicated #wordle channel rather than needing to flip over to a website? Yes, yes it would. So I augmented the site to automatically push it there as well.

I guess my friends liked the idea, because we all started using the suggested starter. After a couple weeks, I got a little annoyed with manually screenshoting WordleBot’s statistics on “interesting” days (e.g. “X% of people used the same starter as us today!” or “Tough word today – hard mode fail rate of Y%”), so I made some changes to the daily push to also fetch some basic stats from the NYT WordleBot and push that for the prior day alongside the starter for today:

That’s all it was supposed to be. OK, well, it was originally supposed to be a lot less than that, but this should’ve ended there… but you might already see what nerd-sniped me.

Yes, it’s those darn solve rates and average solve numbers. See, after a couple more weeks of getting that direct-delivered to the channel, I noticed that Hard Mode players are consistently solving the puzzle faster than Normal Mode players, and have higher solve rates. Mildly interesting, since Hard Mode is… well… supposed to be harder. But it’s easy enough to rationalize that, right? Hard Mode players are self-selecting (you have to choose to enable it in the settings, after all), and it’s going to be people who generally consider themselves to be “good” at these kinds of games who will search out and opt into that.

Yeah, all very logical. It still could’ve ended here, and maybe I’d’ve had a different post out earlier. I had a couple interesting ideas I’d been playing with.

But no, my brain just had to ask itself a seemingly innocuous question – “Does that hold true across word rarity?” That simple question led me down a rabbit hole of API scraping, data analysis, and reading research papers, most of which wound up having basically nothing to do with that initial question at all.

The Initial Question

It’s not news that, when it comes to Wordle, some words are harder to solve than others; that’s what makes it a game, after all. However, while there are some rules of thumb for it (e.g. repeated letters are likely to have higher fail rates/average solve numbers), quantifying it is not nearly as easy.

So what my brain decided to essentially seize on is the question of “are more common words (like ‘ROUND’) easier than less common words (like ‘ROUST’)? If so, how much easier? And does that vary across normal vs hard mode?”

Like any good scientist (shut up that’s what I am), I had a hypothesis:

Yes, common words are on average easier, but the effect is more pronounced for normal mode than hard mode because

1. The average hard mode player is likely to have a larger vocabulary than the average normal mode player; and

2. The extra limitations of hard mode will “dilute” other difficulty effects, including word frequency.

That seemed defensible to me, but how do I verify that?

Well, by scraping the NYTimes WordleBot statistics and then doing some data analysis. Obviously.

Prior Research

I bet some of the more academically inclined among you are thinking “surely someone’s already looked at this question, or something similar to it; after all, Wordle was a mega-phenomenon, and is still quite popular!” And of course, you’d ostensibly be right. However, my dumb ass didn’t have that thought until I’d already downloaded 1000+ days of aggregated data after deciphering the schemas of undocumented APIs… but I suppose we can skip ahead and look at what research I was able to find, with the caveat that it’s been a long time since I’ve done literature reviews and the like, so it’s entirely possible that I missed other work.

While there are some nuances, the majority of the relevant papers (ref: [example 1], [ex 2], [ex 3]) deal primarily in forecasting player/post numbers or predicting word difficulty based on inherent properties of the word (e.g. part of speech, repeated letters). Importantly, they by-and-large seem to rely on data from social media shares (often explicitly naming Twitter as the source), which has some obvious problems in terms of biasing the dataset… not to cast any aspersions, because it’s not like the API I scraped is publicly documented, and the marked similarity of the topics and findings makes me think this was some kind of common question/problem posed for a specific conference, so that might be a general restriction (e.g. “use this specific dataset to derive results”). The work is of varying quality, with some seeming to fundamentally misunderstand even what Wordle is, and others being relatively solid but not quite looking at what I was interested in.

One of the most notable papers I was able to find was “Unveiling the Complexity of Wordle: An Analysis of Player Behavior and Word Difficulty Based on Predictive Models and the RSR Comprehensive Evaluation System” by Guo et al. While it does directly tackle word usage frequency as a predictive factor in difficulty, it does so in the context of other factors (again, part of speech, repeated letters), it doesn’t break it out across normal and hard mode (or even address that distinction), and it doesn’t offer any specific quantification of how much of the effect can be attributed to word frequency, instead reporting only the efficacy of the overall model developed.

While that paper could be considered notable for its ostensible methodological rigor in the kinds of techniques applied, that same rigor sadly doesn’t seem to extend to its references, because as I was looking through the citations it makes, I noticed something odd – none of them seem to exist.

Citation 1, with DOI link of https://doi.org/10.1016/j.eswa.2022.117690, resolves, but where it’s cited in the bibliography as “Machine learning methods for predicting player behavior in online games” by Zhang, Ying and Zhang, Jun, you can click that link yourself and see the redirect eventually resolves to… a paper about solar cell power forecasting, by Perera, De Hoog, Bandara, and Halgamuge.

At least the journal (Expert Systems with Applications), volume (205), and year of publication (2022) are correct, though, which is more than can be said for the other articles, all of which have either no DOI links or links that don’t resolve, and 0 exact title match results in Google Scholar. Vaguely similar results? Sure, sometimes (though literally the only Google Scholar result for the RSR model reference about land use is this paper’s seemingly fabricated citation itself). But those similar sounding results will have different authors, publication dates, and/or journals from whatever was in the bibliography.

Given that, and the lack of concrete details on the relative importance/weight of the various factors that were presented, you can see why I might’ve been left less-than-satisfied by that paper’s results. That’s not to suggest that the effects they cite are bogus (my understanding is that the connection between word frequency and recognition/recall, which was the first citation I checked to discover this, is actually well attested across decades of psycholinguistics research) or that the statistical work that they did is itself flawed, though.

As always, we leave the best for last. Specifically, Bertsimas and Paskov’s paper, “An Exact and Interpretable Solution to Wordle”1, which applied Exact Dynamic Programming to the game. Through modelling the game as a Markov Decision Process and applying their prior work with Optimal Decision Trees, as well as usage of a 64-core computer running for days, they arrive at “SALET” as the optimal starting word and a defined strategy policy that provably solves (or rather, solved) Wordle within at most 5 steps. While very interesting (I’m sure I’ll spend a while trying to adapt and implement it now that I’ve found it), it’s explicitly not dealing with the restrictions of hard mode, as evidenced by this figure from their paper:

TL;DR – There’s definitely a body of research on Wordle, but nothing I’ve found engages specifically with player behavior as regards the differences between Normal and Hard Mode, and much of it specifically ignores the existence of Hard Mode, whether for simplicity or tractability.

Answering the Initial Question

Now that we’ve looked at some of the prior related academic work, let’s return to the original question. My earlier formulation was an arguably clumsy, multi-part conditional question, but it can be more straightforwardly restated as:

What (if any) effect does a Wordle puzzle solution word’s usage frequency have across normal and hard mode difficulty?

With a hypothesis of:

On average, more common words will have lower difficulty, but the effect will be weaker in hard mode than in normal mode.

Notes on The Data

I’m going to be a bit sparse on the details of how the data was collected here, primarily out of an abundance of paranoia caution as regards a potential cease-and-desist from the NYT. Is that likely? Probably not, but better to not risk it.

That said, the description of what data I have is relatively straightforward. For each puzzle from 29 Mar 2023 onward (though for the purposes of this article, cutting off at the end of January 2026), I have, at each step/guess, all the words that were guessed and the counts of how many guesses those words received:

$ cat data/2023/03/29/guess_distribution.json
{
    “normal”: [
        {
            “soare”: 9999,
            “brown”: 571,
            “quirk”: 1201,
         [...]
        }
    ]
    “hard”: [
        {
            “rates”: 425,
            “adieu”: 10829,
            “reign”: 53,
         [...]
        }
    ]
}

That’s as deceptively simple as it is robust, but there are a couple important caveats to note about the data:

1. It appears to be hard capped at a count of 2 million total across modes for any give day.

2. There appears to be some issue with the data that I retrieved roundabouts late May 2023, where the cap appears to be significantly lower (capped at ~750k) for 4 days.

While it’s obvious that the cap is 2 million, in that the sum of all counts never exceeds that, the data very occasionally falls short by large amounts up to 150_000 (not counting the outlier 4 days mentioned above, which I’ve also marked on the x axis):

As might be evident from that chart (note the log y-axis), it seems sometime in early 2024, the volatile spikes largely ceased and, overall, deviations from the cap are now never more than 1_000, which suggests that something on the backend that collects/samples statistics changed around then, but I’ve no concrete idea as to what.

More concerningly, there’s no obvious indication how this data is sampled. The worst case scenario2 is that it’s first-come, because one might expect that the first 2 million people to play the game are likely more invested and serious than people who play partway through the day on every other day or what have you. I don’t think that’s likely, for reasons that’ll become clear as we go through this, but it’s worth calling out.

From that information, I used Python’s “wordfreq” library to get from solution to word frequency data. Word frequency is generally measured in terms of occurrence per million words or a similar large denominator (wordfreq specifically compresses it to a floating point decimal between 0 and 1). You can imagine that the x-axis gaps would be absolutely huge, bordering on impossible to decipher; as such, it’s common practice to take the logarithm of this value rather than using it raw, which is what I’ve done. This has the benefit of matching the normal intuition of “values on the left” being less frequent than “values on the right”, but it also means that the axes are entirely negative values because again, the values are something like

or 3 x 10^(-6). A minor point, but again, worth calling out

OK, enough stalling – what does the data show?!

The Results

Starting with the scatter plots for average solve3:

The dashed red lines show the lines-of-best-fit through the scatterplots; while the correlation can’t be called “strong”, it’s also obvious that one exists across modes. But I wanted to quantify that effect, not just visualize it, so we can use the Pearson test values:

(sidenote: Substack doesn’t natively support tables???)

In plain English, for both modes, the average solve goes down as the “banality” of the solution word goes up. The magnitude of the r value indicates the strength of the relationship; we can see that the effect is about 10% stronger for normal mode than hard mode. The p-value shows the statistical significance of the relationship, i.e. how likely it is that we observed this strength of relationship purely by sampling chance; <0.0001 indicates a less than 1 in 10_000 probability of a false positive. Squaring the r value tells us approximately how much of the variance in average solve is accounted for by word frequency, i.e. somewhere between 10 and 12% depending on the mode.

And for data/stats junkies like me, the Spearman test values, for completeness to account for the relationship being monotonic rather than strictly linear:

In addition to these, I also looked at Non-Solve Rate (i.e “the percentage of players who fail the puzzle entirely”), but the relationship with word frequency is much weaker (essentially equal across modes with r ~= -0.19), and the data is heavily skewed by a small number of exceptionally difficult puzzles.

Interesting, right? The earlier hypothesis is partially correct – for how quickly people solve, word frequency is a moderate contributor, and the effect is stronger in Normal Mode than Hard Mode (again, by about 10%). However that effect basically disappears when considering strictly whether or not people solve. For both measures, other factors are important.

And it was when I was looking at what those “other factors” might be (recall that at this point I hadn’t yet actually thought to look at whether other people had already done some of this work) that I realized I had counts for what words people start with…

Investigating the Meta (No Not That One)

A brief diversion to talk about “metagames”. The word is a mashup of “meta-” (using the prefix’s definition of “more comprehensive; transcending”) and… well, “game”, so literally, “that which transcends the game”. The relevant interpretation of that definition for our purposes is “the game beyond the rules of the game”, or “the emergent landscape of strategies as players optimize and learn from repeated play and one another”.

That’s all very abstract. Let’s make it more concrete by looking at Tic-Tac-Toe. The “rules” of the game aren’t exactly complicated:

1. It’s a two-actor (usually two-player) game,

2. Played on a rectangular (generally 3x3) grid, with

3. Each actor taking turns to place tokens (conventionally Xs and Os) in empty spaces on that grid, with

4. The win condition of the game being to get some number (default 3) of your own token along a row, column, or diagonal of the grid.

That’s the game in its entirety, at least formally. Everything else about the game – “it’s best for the player going first to start in the center”, “you should block attempts to win”, “the game played perfectly is a draw” – all of that is the “metagame”. Nothing in the rules prescribes any of that – you can try offbeat strategies like, as the first to go, putting your first two moves in e.g. the top middle and right middle squares and trying to force an “either way I win” situation between the top right corner and the center – but the natural result of intelligent, learned play over time on a 3x3 grid is those “rules of thumb”. And it might not even require repeated play to learn some of them; children, when first taught the rules, often automatically place their first move in the center without deep analysis4.

The rules of Wordle are also straightforward and simple, and that undoubtedly contributes to its popularity:

1. The game is 1 player.

2. The objective of the game is to guess a hidden word.

3. The hidden word and all guesses are/must be 5 letter English words.

4. You have up to 6 chances to correctly guess.

5. Each guess reveals whether letters from the guess are present and/or in the correct spot.

How players choose to act within those rules would be the metagame. For Wordle, perhaps the most obvious example of the metagame would be “what words do people tend to start with?” As it happens, I had that data now. And since I was already looking at the data through the lens of “Normal vs Hard Mode”, it only made sense to do the same for looking at the metagame.

Population-Level Meta Findings

Before looking at the specific findings of starter word strategies, it’s worth having an understanding of what the populations we’re going to be looking at themselves look like. For example, let’s start with Normal vs Hard Mode players themselves; specifically, how many of each are there?

Well, unfortunately, we can’t know exactly how many there are; after all, what data I have is capped. However, we can know what percentage of players are playing Normal Mode vs Hard Mode… and that looks like this:

We can see that the split between the two populations is relatively stable, with roughly 12% of players playing on Hard Mode, outside a notable shift from normal to hard mode in mid-late November 2023, which tapered back to “the baseline” by the start of the next year.

This is actually one of the main reasons why I think that the sampling method isn’t as straightforward as “the first ~2 million players”; not only is there an anomalous jump in hard mode share at a clear point in time that tapers off, but that anomalous jump actually has a corresponding notable (at least in this context) event. Specifically, the great SASSY/SIGHT apocalypse of 14/15 Nov 2023. And lest you think it’s only someone at Techradar who had trouble, you need look no further than the daily r/Wordle threads on Reddit for those days. A lot of dead streaks, some number of Normal Mode players sharing that having a “throwaway word” made it a lot easier, etc.

It’s slightly counterintuitive that people losing the game would cause a shift towards the (supposedly) harder mode of the game. I don’t have a great explanation for this, in part because of the limitations of the data we have available. However, at least some of it can be attributed (I think) to the fact that streaks were killed. Once your streak is killed, any apprehensions you may have had or attachment to the routine is gone; you’re free to switch things up or take a break.

In addition, it’s worth noting that Hard Mode had a marker in social media shares, specifically an asterisk after your score if you played in Hard Mode5. That, combined with the many people saying “well you could just use a throwaway word to narrow down the pool” and getting responses of “can’t do that in hard mode”, might’ve led people to feel like they weren’t playing it “the real way” if they weren’t playing Hard Mode, or that they were “cheating” if they availed themselves of the option of a throwaway word and wanted a technical control to prevent them from doing so.

All that said, I think it’s hard to deny the fact that the great SASSY/SIGHT apocalypse contributed to a brief shift towards Hard Mode, even if the exact mechanisms are unclear in retrospect.

Returning to the other original question, in general, it seems that Hard Mode players do solve faster/”better” than Normal Mode:

It’s clearer on the average chart than the Non-Solve-Rate chart, but the blue line for Hard Mode is consistently below the orange line for Normal Mode. It’s not as universal as these rolling average charts might suggest, though – the smoothing (while necessary to avoid the charts devolving into an unreadable sludge of blue and orange spaghetti) obscures the fact that, on about 22% of days, Hard Mode performs worse on either NSR or average solve. Moreover, on slightly more than 6% of days, Hard Mode performs worse on both metrics (specifically exactly 65 such instances, again through the end of January).

It’s impossible to (with just this data) determine the “why”. The natural assumption would be that, as mentioned earlier, Hard Mode players are self-selecting for skill/passion/investment. However, it’s also possible that the technical restrictions of Hard Mode (i.e. you have to use all the information you’re given) prevents players from making (some) dumb mistakes – “Oops, I forgot that the solution has to include ‘H’” isn’t a problem when the game literally won’t let you submit guesses that are missing letters you “know” have to be present. If submitting such an invalid guess were instead treated as “failure” (i.e. contributing to non-solve rate and/or counted as 7 guesses in average solve6), I suspect these graphs might look very different, but we don’t have that data.

OK, But What About The Meta Strategies?

Let’s take a look at some interesting starters across modes and how they place over time, starting in Normal Mode:

Some obvious notes:

• There’s a strong and seemingly growing preference for vowel knowledge in normal mode – ADIEU has always been king of the hill, and AUDIO is now the solid second place. Both words test presence for 4 vowels.

• CRANE and SLATE (both recommendations from WordleBot and other sources like this 3blue1brown video as “best starters”) are strong but never the top

• Generally stable meta overall

How does that compare to hard mode? Well…:

Noticeably more chaotic! No clear king of the hill, and a lot of climbing/falling all around:

• LEAST was at the top for a good while at the start, but has completely fallen off

• SLATE briefly took over for a while, but has been duking it out with ADIEU (and largely losing) for some time now

• PLACE is a (relatively) recent up-and-comer

  • Its rapid rise aligns with a noted NYT update to WordleBot’s recommendations

  • This update also seems to coincide with LEAST’s last drop-off; prior to that, it’d fallen from the top, but had held a #7 position for quite some time

A synthesis note across the two charts:

• SALET appears in Hard Mode’s top 10 early on but falls off and stays off… and never even shows up in Normal Mode

• AUDIO never shows up in Hard Mode’s top 10 despite being #2 in Normal Mode and ADIEU consistently placing highly in Hard Mode

Both of those points were quite surprising to me. SALET was “the MIT-recommended starter”, sure, but it’s recommended specifically for Normal Mode… where it doesn’t appear. Bertsimas and Paskov didn’t opine on its optimality for Hard Mode, but that’s where it sees (or at least, saw) top 10 usage. On the flip side, AUDIO not showing up at all in Hard Mode despite being a strong #2 in Normal Mode and ADIEU placing highly in both modes is… strange. Both words represent the same instinct – “let me figure out what vowel(s) are in the word first” – but one of them is vastly more preferred in Hard Mode even though they theoretically give functionally the same information (if none of the vowels show up yellow, you know it has to be the one your chosen word didn’t include, not counting ‘y’).

The natural follow-up would be to look at a more broad measure of the metas across the two modes… So that’s what I did next:

First, an explanation of what this chart is actually showing:

1. “Presence Overlap” [blue] – For a given puzzle/day, how many words were shared across the top 10 starters for normal and hard mode (regardless of position)?

2. “Exact Rank Overlap” [orange] – For a given puzzle/day, how many words were in the same position in the top 10 for both normal and hard mode (e.g. was ADIEU in first place in hard mode as well? If so, +1)

3. “Fuzzy Rank Overlap” [green] – For a given puzzle/day, how many words had positions in the top 10 within N of each other? For example, if ADIEU is 3rd in Hard Mode and N=1 (which is what’s plotted), was ADIEU 2nd, 3rd, or 4th in Normal? If so, +1.7

Obviously, these definitions measure different things. Presence overlap tells us how much the two modes agree on “what are good words”, whereas Fuzzy/Exact Rank overlap are interested in how similar the ordered lists look across modes.

You might wonder why Fuzzy is necessary when Exact exists, and the reason is that it’s not obvious to me that we should necessarily always penalize words for being “off by 1”. Let’s imagine, for example, a world where ADIEU and AUDIO were respectively in 1st and 2nd place in both modes one day, but then the next day Normal Mode decides that actually O is better than E and swaps AUDIO to 1st and ADIEU to second. All else being equal, that would register in “exact rank overlap” as a drop of 2 for that day from the previous day… but has the list actually changed that much? No, right?

So what does this chart actually show us? Well,

1. Firstly, the migration from Normal to Hard Mode after the great SASSY/SIGHT apocalypse pops up here as well, in the form of the average presence and fuzzy overlaps spiking from 8 to 9 and 4 to 6 (respectively) before bleeding back down. That’s exactly what we’d expect to happen if people who previously played Normal Mode started switching to Hard Mode – obviously, the newly arriving formerly-Normal-Mode players would bring their preferences and “tip the scales”, especially for words on the edge.

   1. Interestingly, though, exact rank overlap shows a decline in that same period; perhaps just the chaos of the influx into hard mode disrupting the rankings, or perhaps unrelated since it persists afterward rather than gradually reverting?

2. The overall metas are diverging – ever since that update in July of ‘25, presence overlap has been on the downtrend, first with a sharp falloff, and then a slight but consistent decline from 8 to 7

   1. This also shows up in the fuzzy rank overlap, albeit with a bit more noise; prior to that update, it bounced around a central axis of 5, but now hovers closer to 4.

3. Conversely, the words that are shared are more often sharing exact positions – where “exact rank overlap” used to consistently hover between 0 and 1, it’s now hovering between 1 and 2.

   1. This could just be noise, however.

OK, but how many people are actually using these top 10 starter words from the overall population? And how much of that usage is concentrated in the top 1, or the top 5, or what have you? Fear not and fret not, for I had the same questions!

Some takeaways:

• Concentrations are mostly static over time despite noise.

  • About 60% of players across modes are not using any of the top 20 starters!

• Hard Mode is slightly more concentrated overall than Normal Mode, but only by a couple percentage points at any given tier.

• The gaps between “tiers” (top 3 vs top 5 vs top 10 vs top 20) across Normal and Hard Mode are basically the same.

The notable exception for all those takeaways is the usage share of specifically the top 1 starter word in Hard Mode; its share has declined over time, from LEAST being 10% back in March 2023 to ADIEU being closer to about 7% today despite being at the top. It’s a mild de-concentration, and a widening gap between top 1 and top 3.

These findings are very interesting, but to get into why, we first need to look at two more charts: the number of unique starters across modes, and the normalized number of unique starters (i.e. unique starters per capita by mode):

You might look at these two charts and come away thinking something like “Hard Mode is 5x more diverse in starters than Normal Mode!”, but we need to be careful about that. A lot of what these charts are showing are the exact mechanical outcomes we’d expect considering the populations at play.

The per-capita chart, for example – that’s just the Pigeonhole Principle at work (albeit with the numerator and denominator inverted). There are only so many 5 letter English words, and we already know that there are roughly 7x more Normal Mode players as Hard Mode players (recall that about 12% of players play in Hard Mode, meaning 88% play in Normal Mode, so roughly 1:7).

For a more clear version of this, imagine that the rules were changed such that there were only 10 valid starter words at all across modes; you’d naturally expect that, regardless of how many of those 10 were “good” starters that were actually used, Hard Mode would have a higher “per capita” unique starter count than Normal Mode because Hard Mode has fewer players. For example, imagine all 10 starters are basically equally good, and there are 1600 Hard Mode players and 8400 Normal Mode players – you’d get 10/1600 = 6.25 unique starters per 1000 players in Hard Mode versus 10/8400 ~= 1.19 unique starters per 1000 players for Normal Mode.

Conversely, to understand the raw unique starters chart, imagine we look at a sample such that there are fewer players than valid starters (i.e. we uncap the number of starters now but cap the population), while maintaining the ratio of ~5x more Normal Mode players than Hard Mode (e.g. 84 Normal Mode players and 16 Hard Mode Players). No matter what happens, the number of “unique starters” from Hard Mode players has an unbreakable cap, i.e. 16. The same is true for Normal Mode, of course, but you’d imagine that some of them will pick the same word, whether for reasons of optimal play or lack of creativity or because it’s Christmas and they want to start with MERRY (which is a real thing) or any other reason. You’d naturally expect, therefore, that Normal Mode would have more unique starters than Hard Mode, but that it wouldn’t be the full 5x multiplier for all of those reasons that cause convergence.

As such, neither of these charts by themselves is at all surprising; they’re basically exactly what you’d expect. Moreover, even looking at those two in relation to each other, it’s not that surprising; maybe the specific ratios aren’t what you might expect for one given the other and the mode split, but really, do you even have a clear intuition what it should be? I can’t say that I do.

Where it does get interesting, though, is looking at these two charts in combination with the context of the earlier top 1/3/5/10/20 concentration charts. When looked at in aggregate, that tells us something very interesting across modes – while the metas are diverging relative to each other, the way that players in both these modes are approaching the game is, on the whole, basically the same.

What do I mean by that, and why is that interesting? Well to understand that, it helps to look at metas in the context of other strategy games.

Consider, for example, Chess – top players, especially in classical Chess (as opposed to Blitz/Bullet Chess) overwhelmingly play battle-tested openings like the Spanish, the Sicilian, or the Scandinavian, where they have studied many lines and/or absurdly deep variations (ref: the Ruy Lopez, Marshall Attack, Main Line, Spassky Variation). However, if you load up a game on Chess.com as a 500 rated player, you’re liable to see the Mieses Opening or the Van Geet Opening, for which even most Chess enthusiasts will take a minute to remember what the first moves are, never mind knowing any particular optimal lines.

Alternatively, look at games like League of Legends. Even ignoring professional play (where there’s money on the line and premade teams/strategies, so it might be considered a different beast entirely), if you compare character selection rates for Normal (i.e. “for fun”) matches vs Ranked matches (where rating points are on the line), it’s apparent that certain characters, matchups, and/or pairings are more common in one mode vs the other, and even the distribution of what’s favored varies differently — the Ranked meta adapts to changes from balance patches faster than Normal games.

When we look at Wordle starter concentrations, though, it doesn’t quite replicate that across Normal and Hard Mode – the starter concentrations are essentially the same. Hard Mode, then, cannot be considered exactly analogous to “ranked play vs casual play” in League or “amateur vs pro” in Chess.

Perhaps that’s obvious, since there’s nothing “on the line” in Hard Mode that isn’t “on the line” in Normal Mode (failing in either is an end to your solve streak). However it says something about the players: despite the difference in mode rules, despite one being marketed as “hard mode”, and despite the apparent self-selection at play in the audiences for these modes, the players fundamentally seem to approach the game with the same mindset.

What does all this tell us? I think it shows that the Hard Mode playerbase is fascinatingly self-contradictory,

• They are demonstrably more plugged in and responsive to external information about “optimal play”:

  • They were using SALET (even though it’s not strictly optimal for Hard Mode)

  • PLACE rocketed through the ranks when it started showing up as “high skill” after the July ‘25 WordleBot update

  • LEAST has fallen off over time, and AUDIO never made the top 10

• However, they still value the feeling of optimal play over actually optimal play:

  • Returning to that July ‘25 update, the WordleBot-suggested optimal word for hard mode has been CLASP since then. However, CLASP has only started showing up in the top 10 in February ‘26 (i.e. last month) – outside of the bounds of this data. It currently hovers around 8th (PLACE is at 6th).

  • Despite ADIEU being mentioned as suboptimal by even WordleBot since at least 2023 (see the earlier linked article re: MERRY), it still holds the top spot in Hard Mode as of both the end of the data and the most recent puzzles

And this is the same feeling that Normal Mode players value. As evidenced by the relatively high overlap in “presence” of starters, as well as the fact that the concentrations of starter usage are functionally the same, Normal and Hard mode players both value the feeling of optimal play. Because the marginal cost of somewhat suboptimal play is so low (maybe one or two more guesses spent, and probably not a lost puzzle/broken streak unless you’re really unlucky), over 2 million players every day choose to indulge that feeling, even when they self-select for a “harder” experience.

All of which is to say, I spent more than a month collecting, analyzing, and charting all this data in my spare time to realize and verify the exact behavior that kicked off all this to begin with. After all, this whole mess started because my friends and I decided to play random starters every day rather than trying for optimality.

Time well spent!

Postscript: Technical Stuff

• The daily starter word thing that kicked all this off is available at wordle.garbagecollected.dev

  • BlueSky bot to post the word: @wordle.garbagecollected.dev

  • Discord “bot” will not be available for now because right now it’s actually just a webhook pushing to my server and I’m too lazy to rearchitect it as a proper bot

  • If there’s interest, I might add some regularly updating charts to the above site (e.g. at /stats endpoint), but no promises

Preface: this was supposed to be a shorter piece, a quick addendum of a thought that didn’t neatly fit into my last post and so would get broken out into its own fast-follow thing.

It grew.

Update before publishing: As I was putting the finishing touches on this monstrosity of an article, Bloomberg broke news that Discord has confidentially filed for an IPO. This doesn’t change my thesis — if anything, it sharpens it. Once Discord goes public, acquisition becomes significantly harder and more expensive. Below is the case for a deal whose window is actively closing, if not outright past.

In my last post regarding the significant parallels between Valve and Apple, I noted that one significant aspect where the parallels between the two break down is positioning within the social layer.

On the one hand, you have Apple, with iMessage and Facetime serving as soft lock-in within the Apple ecosystem, offering cross-device chat history, better integrations, and all the other features that make it “just work, seamlessly”... so long as the people you’re talking to also have Apple devices, of course. And heaven forfend you try to develop a way of undermining that lock-in.

On the other hand is Steam… which does technically have forums, Steam Chat, and groups built into its platform, but calling it “underutilized” would be polite. “Anemic” might be a better word.

No, the social side of gaming is dominated by Discord, and this is a (perhaps the only) critical market weakness for Steam long term. And that’s just one reason why Valve should buy Discord.

Financial Feasibility First

Before we dive into the arguments, we have to ask – is it even possible? Can Valve even afford to do this? Or, is Valve, as a poor small indie company, incapable of bringing the financial weight needed to buy a venture-backed social giant like Discord?

Valve’s Numbers

• Valve pulled in about $4 billion in revenue from Steam:

  • ~$17 billion in total store revenue…

  • Multiplied by a pessimistic factor of 23% (Valve takes either 30, 25, or 20% of sales depending on each game’s revenue)...

  • Gives roughly $3.9 billion

• The overall company profit estimate (and it’s only an estimate) is ~$2.5 billion

Discord’s Price

• Discord’s post-money valuation following their most recent funding round was $15 billion, though that was about 4.5 years ago

• Secondary markets estimate Discord’s current FMV at ~$7 billion.

Comps

Slightly less than 3x annual profit is hardly bank breaking in the world of tech M&A.

Facebook’s purchase of Whatsapp in 2014 was for $19 billion at a time when their profit was ~$1.5 billion. That’s a multiple of almost 13. Zuckerberg paid almost 13 years of Facebook’s at-the-time annual profits for a chat company with 55 employees that was burning $138 million per year (even discounting the stock-based compensation, it was burning ~40 million). Talk about value!

More recently, Salesforce’s acquisition of Slack was priced at ~$28 billion in a year in which Salesforce’s profit was ~$4 billion, meaning they paid (very roughly) 7 years of their own profit to buy Slack.

Even taking Discord’s funding round valuation of $15 billion, it’d be slightly less pricey (6x Valve’s annual profit) than Salesforce’s acquisition of Slack, and less than half of Facebook’s acquisition of WhatsApp.

Synergies

So, it is (or at least should be) financially possible. What’s the win-win on the table?

Discord’s Problems:

Well, just like the chat companies we talked about above, Discord is burning cash. In an attempt to stop burning cash, they’ve begun turning the metaphorical screws on users, heralding the start of the cycle of enshittification, including such beloved fan favorites as:

• Adding ads and brand promotions, even for paid subscribers;

• Introducing overpriced “profile customizations” that interfere with the “standard” profile features;

• Locking some basic features like profile banner images (a feature that’s free on any other social platform that supports it) behind an (at time of writing) 10 USD/mo subscription; and

• Getting rid of the middle-ground “Nitro Classic” subscription tier a la Netflix

All of this comes despite Discord (or at least its CTO) wanting to avoid enshittification.

Valve/Steam’s Problems

Well, they don’t really have any… right?

Foreshadowing is a literary device in wh-

The Streaming Wars (but not the one you’re thinking of)

Let’s take a short digression and talk about streaming. Specifically, the livestreaming market, especially gaming related.

We can learn something from this space, but in order to do so, we need to first get on the same page as regards…

The Mechanics and Economics of Streaming.

Livestreaming video has most of the challenges of YouTube with the added complication of temporal synchronicity for everything:

• Giant bandwidth requirements (for sending video to viewers)

• Giant compute requirements (for processing/compressing video from streamers)

• That compute has to be fast so that stream data is going out as fast as it comes in, and it’s potentially coordinated with any special features your platform adds on top

• Storage isn’t as much of an issue because VODs can be shorter-lived than YouTube’s “up forever unless you get copystruck” model… though you still need some of it

As such, the same heavy capex and opex requirements associated with these mechanical requirements mean that the same heavy financial burden applies before profitability.

Since we’re talking about profit, how do you go about making money in this business? Well, so far, there are 2 (or maybe 2.5) avenues of monetization:

1. Ads (like other “free” internet services), ideally targeted based on data you get from knowing what streamers viewers follow and what games they’re interested in; and

2. Subscriptions to individual streamers/creators that the platform takes a cut of

   1. Platforms also have started introducing “donations”, which are one-time transfers from viewers to streamers (minus the cut going to the platform, of course) – the question of whether to treat that as the same as subscriptions, its own thing, or a hybrid is what makes it 2.5

What do all of these methods need to work? Unsurprisingly, users:

• Ads need people to look at them for value

• Subscribers require people who are invested enough in the streamers’ personalities to actually open their wallets

• Donations need the same as subscribers, though maybe with a lower barrier since it’s (theoretically) one-time rather than recurring

So we know how the market works; who are (and perhaps more relevantly, were) the major competitors?

Our Fighters

The undisputed 800 pound gorilla of this sector, owned by Amazon, is Twitch. Relative to everyone else we’re going to talk about, Twitch is the incumbent with first-mover advantage and a market stranglehold. Everyone else is trying to steal the eyeballs from Twitch. The ones that are still alive and trying to do so are:

• YouTube, serving as the ironic Bing to Twitch’s Google, i.e. the deep-pocketed, long-running competitor to the industry leader;

• Kick, the… “niche” competitor (that is the most polite phrasing I have); and

• TikTok Live, which has broken out of streams of the “adjacent” content TikTok is known for (i.e. beauty/fashion and music/dancing) by serving mobile game streaming (Garena Free Fire, Mobile Legends Bang Bang, PUBG Mobile), a segment which is not as present/popular on YouTube/Twitch

And then, 6 feet beneath some oddly shaped piles of dirt, we have:

• Mixer (bought and then killed by Microsoft in 2020 when it had insufficient growth); and

• Facebook Gaming, shut down in 2022 after previously serving as the offramp for Mixer

Let’s take a deeper look at them, shall we?

Mixer

• Signed exclusivity deals with big streamers (Ninja, Shroud)

• Their bet: bring streamers with large, invested existing viewerbases onto the platform, and they’ll “filter out” to other streams

• Verdict: utter failure

  • Mixer as a platform was unable to bring and keep users or distinct communities from these big signings, leading to stagnant growth

  • One analyst compared the whole affair to Google’s attempt to challenge Facebook with Google+, which strikes me as a touch harsh, but…

Facebook Gaming

• Leveraged existing userbase (i.e. Facebook users) by pushing streams based on games being played that might interest users

  • Also employed a similar (though less flashy) strategy signing names like DisguisedToast and ZeRo, who might not as recognizable outside of gaming but still had sizable fanbases

• Bet: enough people will come in via algorithmic discovery in main app, boosted by audience from imported streamers

• Verdict: kept up with the treadmill for a while before tripping and falling off

  • Solid 3rd place in mid 2022 (Twitch @ 76.7%, YouTube @ 15.4%, FG @ 7.9%; ref report)

  • However, when the high tide of COVID stream watchers receded, Facebook was (to quote Buffett) revealed to be swimming naked

  • What was the root cause? Facebook Gaming’s audience wasn’t invested in watching game streams, just there because Facebook brought them there via algorithmic fiat, and so didn’t stick around long

    • This view is corroborated by DisguisedToast, who quickly returned to Twitch after his exclusivity contract expired in Nov 2021, specifically because the community’s vibes were off.

Learnings

So, why did I go through those case studies? Well, Mixer and Facebook Gaming teach us some important things about social graphs in tech.

1. You cannot transplant communities from elsewhere and expect the users to just proliferate outwards (Mixer); and

2. You cannot force an existing audience to “change form” – your product has to match the existing audience (as TikTok did), not the other way around (as Facebook tried)

In short, social is hard to do when not grown organically. Just buying it outright (as Facebook did with Instagram and Whatsapp), while a blunt way of doing things, is far more likely to play well if you can’t directly compete.

And oh, look over here, we have a social media platform that completely organically came to dominate gaming and might serve as a wedge for challenging Valve’s dominance in the space! There’s that foreshadowing I mentioned!

The Threat

Money troubles aside, Discord owns the gaming community social layer in a way that even Twitch and YouTube don’t. Video game developers, whether small or large, make Discord servers to reach their players (e.g. SuperGiant Games). Other forums for gamers (e.g. Reddit) have “associated” Discord servers (e.g. the League of Legends subreddit has an official subreddit Discord). Esports teams/organizations organize fan events in their own Discord servers (e.g. Cloud9). In a world where authenticity and direct connection are strengths, Discord is the way gamers get those things.

By contrast, Valve’s social features, despite a 2018 push for improvement, are no comparison (e.g. no persistent servers, groups are a joke, in-client streaming is difficult to find and finicky, etc). Valve does not have any real social lock that mirrors even the limited lock-in of Apple’s Facetime and iMessage

Valve knows the importance of this; another quote from GabeN’s University of Texas talk that I opened my last post with a quote from openly acknowledges it:

We’re happy to do it if nobody else will do it, mainly because everybody else will pile on, and people will have a lot of choices, but they’ll have those characteristics. They’ll say, “Well, I could buy a console, which assumes I’ll re-buy all my content, have a completely different video system, and, oh, I have a completely different group of friends, apparently. Or I can just extend everything I love about the PC and the internet into the living room.”

(via Polygon as last time; emphasis mine)

He understood it a decade ago.

What does this mean? Put simply, Discord can be turned into a weapon against Steam.

Epic Games Store (EGS)

EGS was originally explicitly built to not have social features, supposedly based on feedback from developers:

“For instance, forums and other social media-like tools—a cornerstone of Steam—won’t be part of the package. Galyonkin said that this is because ‘not a single developer I talked to wanted forums’ and ‘the toxicity it brings,’ preferring to interact with communities on their own terms on platforms like Reddit and Discord instead.”

(via Kotaku)

In the year 2025 2026, both game devs on EGS and Epic themselves may be regretting that particular design decision:

> “There’s nothing to do there but to buy. So that will always lose to a shop that is also emotional.”

(Lead Developer of Witchfire, via FRVR)

Indeed, Epic themselves have already signalled a partial pivot on this front; their Trello Board has “Social Overhaul” in the “Future Development” category. That said, at time of writing, it seems to have been there for almost 5 years, so maybe not all that high priority… though one way to bump this out of the icebox “future development” category would be to buy/merge with a finished solution :P

Microsoft Xbox

Xbox Gaming on PC already has some integration and market space (Xbox Game Pass, Screen Capture/Streaming, etc). Microsoft has also attempted to match Epic’s attempt to attract developers by taking a smaller share of the pie of sales on the Microsoft Store.

However, while there is an Xbox Social included in those “Xbox Gaming on PC” integrations, I had no idea it existed until doing research for this. A big name purchase like Discord, though, would bring that feature into real prominence.

Both of these potential acquirers would instantly threaten Steam’s current dominant position. That might seem odd to say, but look back at that quote from the Witchfire dev: “There’s nothing to do [on EGS] but to buy. So that will always lose to a shop that is also emotional.”

Steam might have your achievements, your library, your cloud saves, and now the hardware of Steam Deck and Machine 2.0, but Discord has your conversations. It has your custom emotes. It has your friend group’s dumb jokes from 3 years ago.

If it came to it, which would you pick?

Aside: Discord vs Valve

With everything I’ve said thus far, you might reasonably wonder, why do I not think Discord can/should take Valve on directly, mano a mano?

That’d be because they already tried that once and failed.

Why did they fail? Well, there were definitely a lot of subtle, nuanced reasons, like:

• The fact that Discord users themselves seemingly didn’t know the store existed until they were informed it no longer existed;

• The downright baffling idea of seemingly only selling games via verified servers rather than a single storefront, kneecapping discovery; and

• The suggestion (see the earlier article re: Discord’s CTO and enshittification) that the company didn’t know what it was even doing with the storefront

All this, combined with the fact that the EGS and Microsoft Store are failing to take market share from Steam (which still holds strong at 75%) despite lucrative deals for developers, free games for customers, and market leverage (whether through Fortnite or direct integration on Windows), suggests that maybe running a digital game storefront isn’t as simple as Steam has historically made it look. At minimum, Discord’s been burned once trying to do it and doesn’t seem eager to try again.

Current Discord Stakeholders

None of this is going to come as news to industry insiders/veterans. Discord’s importance is apparent to everyone with an interest in the space. For example, MS previously had talks about buying out Discord (though they ultimately went nowhere).

However, Discord does have existing industry players as major stakeholders. For one, in response to Microsoft’s buyout talks, Sony began integration of Discord into PlayStation Network and bought a minority stake. It’s hard to see this as anything other than a defensive move to “have a seat at the table” in case MS tries to buy Discord again and cut them out of the picture.

Similarly, Tencent has been invested in Discord since 2015. Importantly, Tencent is also a significant minority (~35%) stakeholder in Epic Games.

Valve’s Palatability as Acquirer

Sony would probably prefer Valve to Microsoft as owner/acquirer. Valve has been making inroads into competing against the PS5 with the Steam Machine 2.0, which (while not explicit) is a “console substitute”. However, Valve takes a relatively open approach with their things (ref: Proton/SteamOS, which they’ve made free and usable by other “handheld gaming PC” manufacturers like Lenovo/ASUS). At present, it’s unlikely Valve’s acquisition would lead to PS5 being locked out of Discord.

Harder to say how Tencent would view Valve. My take is that, fundamentally, Tencent probably doesn’t care too much so long as they make money out of the arrangement; while they do have their fingers in a lot of gaming pies (full owner of Riot Games, maker of League of Legends; significant minority stake in Epic, as noted earlier; positions in Ubisoft and Roblox; previously had a stake in Activision-Blizzard-King before MS acquired it), they don’t seem interested in completely dominating the space (as evidenced by the seeming lack of objection to Microsoft buying ATVI). If given an option, though, they might prefer a buyout by/merger with Epic Games, since they’re also invested there, and could perhaps facilitate that themselves if they were so inclined as investors in both entities.

The Consumer Interest

I think it’s entirely possible that Valve buying Discord would result in ticker tape parades by gamers. While there will definitely be some hesitancy, both in the monopolization and due to Valve’s… questionable commitment to social (as well as the historic downsides consolidation has had for gamers), it’s hard not to see the upsides:

• Valve doesn’t (currently) do ads, and Steam has resisted the siren call of enshittification for the past 20+ years; this might represent an about face on a lot of the recent changes for the worse made by Discord in the name of raising revenue

• Valve is known for running a solid business and service, and could “subsidize” Discord’s cash burn while a better monetization model is found (iteration on Discord’s Nitro subscriptions? Integration between servers and games for developers that can be monetized (e.g. achievement announcements/titles/whatnot)? Just efficiencies by porting Discord off of GCP and onto Valve’s bare metal?).

• Valve is also the most likely to be relatively hands off of any potential acquirer

  • Discord going public would, like Reddit’s IPO, be a one way street to “revenue squeezing time” (Pre-publication note: And it seems that road is now being paved).

Speed Bumps: Potential Arguments Why Valve Shouldn’t Do It

So far, I’ve made it out like that this is a no-brainer. Obviously, the fact that it hasn’t happened yet is an indication that things aren’t quite so straightforward.

Valve Doesn’t Really Do Acquisitions

No, really.

They’ve acqui-hired some individual game developers and bought some IP rights to game properties (e.g. DotA, Portal, Team Fortress). They acquired an audio company by the name of Impulsonic in 2017. They’ve made some game developer studio acquisitions in the past (Campo Santo, Hopoo Games most recently).

That’s… it. That’s the whole list. Valve has made as many Half-Life games as they have major corporate acquisitions (which is saying something, considering Valve has made exactly three Half-Life games… and that’s if you count Alyx).

This is obviously not by accident, but because I like quotes:

“‘I don’t lie awake late at night saying “What should we do? What’s our competitive response?” for something like this, because it’s sort of orthogonal to any of the things that we think are interesting in terms of how Valve runs its business,’ Newell said.”

(via PCGamer)

Suffering From Success

Valve’s most eye-catching stat is their revenue and profit per employee – the 2025 estimate linked earlier was about $50mm/employee in revenue. Their dominance in the space is obviously part of that, but the other side of it is their limited headcount – they only have about 350 employees

Discord, by comparison, has roughly 3x the headcount of Steam. In the article I linked regarding Discord burning money, they mentioned laying off 17% of their employees, which at the time was 170 employees. Thankfully, that makes the math easy – Discord had roughly 1000 employees at that time.

Valve also famously has a “flat” corporate structure, which makes absorbing a large number of employees (especially at a ratio greater than 1) much harder to do without losing the culture that has made Valve so successful.

Social Culture Issues

Discord and Steam, despite having significant interest overlap, have very different cultures. While it wouldn’t be impossible to integrate the two, and it might not even be required (i.e. Valve could just let Discord communities mostly exist independently of Steam), it would be a challenge if Valve wanted to integrate the two more comprehensively (to e.g. implement the ideas about achievement-specific monetization in Discord servers).

Operational Challenges

Discord, as a social platform first, has and requires a much greater moderation arm (e.g. ID verification in response to things like laws against minors accessing social media, explicit/illegal content scanning, etc). While Steam might have some of that already (responding to DMCA claims against store entries, taking down forum posts that advocate violence), Valve has historically been very “hands off” when it comes to Steam’s social side.

Regulatory issues

An acquisition like this will almost certainly put Valve “on the radar” for regulators to look at and consider taking action. After all, the 75% market share holder in digital game storefronts acquiring the leading independent social platform for gaming seems like a flashing warning sign for antitrust, no?

Why Valve Should Do It Anyway

Addressing Concerns

While all of the issues outlined above are serious, none of them are insurmountable:

1. Not doing acquisitions isn’t so much a “problem” to be solved as it is just a shift in mindset, even if only a temporary one.

2. Integrating a large number of employees without significant adverse effects to culture can be done. And, while it sounds harsh, if Discord does get bought by Valve and loses the incentive to enshittify, a lot of the staff related to those efforts (e.g. ML engineers for ad targeting, sales staff for promos, etc) can be let go.
   That is the flipside of enshittification – it sucks for consumers, but it makes jobs.

3. The integration of the two platforms’ communities can (and probably should) be done gradually, and there can (and, again, probably should) be some separation despite integration.
   The acquisition of Instagram by Facebook is a good example here – while from a technical standpoint, there’s basically no difference between an Instagram user and a Facebook user, the experiences, cultures, and dominant content styles are different across the platforms in very distinct ways.

4. When it comes to operational challenges and regulatory issues, while an acquisition of Discord might speed up the clock on them, I don’t think not moving on Discord will allow Valve to sidestep them entirely. Valve has, thus far, skated by quietly, but all it takes is one sensational story about gambling for knives gone wrong that attracts the attention of lawmakers. It’s happened once before (albeit not with Valve). That’s not to say that the concerns here aren’t real, and they should absolutely be factored into the decision making, but I don’t think they should be treated as blockers, just as problems needing solution. In CS terms, these are expected problems of scaling a system up, and buying Discord just means you hit the scaling problems faster.

A Positive Outlook

Why, despite those challenges, should Valve do it anyway? There’s a two pronged argument:

Business Strategy

A defensive acquisition seems absolutely warranted here. We’ve already talked about the worlds where Epic Games or Microsoft jumps in, but those are hardly the only possibilities; there’s a number of tech giants out there who might see the money printer Valve has as an opportunity and look to cut into it themselves.

Defensive acquisitions are hardly unprecedented in tech – look again to Facebook’s purchases of Instagram/Whatsapp, or Google’s purchase of Motorola simply to prevent the patents from being used against Android, or Amazon’s purchase of Twitch so that someone else couldn’t have it.

Remember why Apple’s iMessage lock-in works? Apple had full control of the hardware interface. When you text another iPhone user, iMessage is just there – frictionless, default, seamless.

Valve can’t replicate that. Steam Deck is one device among many, not the universal PC gaming platform. They can’t just make Steam Chat the default between gamers in the same way Apple made iMessage the default between iPhone users.

Valve hasn’t managed to organically capture social lock-in. If Valve ends up needing it – and Newell clearly recognizes its importance – they can’t build it organically the way Apple did. They’ll have to buy it in the form of Discord.

“Consumer Welfare”

While I hate Bork’s consumer welfare standard, if we view things from that lens, we return to GabeN’s interview referenced earlier on the matter of acquisitions, this time talking about “good” acquisitions:

“Because [in those cases] whoever is actually doing the acquisition has thought through how it’s an additive thing. Not ‘how did I make my empire bigger,’ but: ‘What’s the opportunity to do a better job of building great game experiences for people?’”

Buying and integrating Discord can turn Valve’s offerings into even more of a complete package.

Your games. Your achievements. Your in-game items. Your save files. Your friend list. Your gaming group’s conversational history and in-jokes. Your custom emotes. All living in one place. All travelling with you, from your PC rig, to your living room Steam Machine, to your Steam Deck on the go.

It’ll just work, seamlessly.

\0

In November 2025, Valve “unveiled” the Steam Machine – a living room PC designed to bring your Steam library to the TV. Gaming press covered it as news, but what was missing from the headlines was that this is actually Steam Machine 2.0. Valve already tried this a decade ago, and it flopped.

So why try again?

Because Valve learned from that failure. They learned what Apple had figured out years earlier – hardware, software, and services need to, to quote Jobs, “just work” (2011 WWDC).

This isn’t speculation. Take it from Valve co-founder Gabe Newell’s own thoughts on threats to PC gaming:

The threat right now is that Apple has gained a huge amount of market share, and has a relatively obvious pathway towards entering the living room with their platform [...] I think Apple rolls the console guys really easily. The question is can we make enough progress in the PC space to establish ourselves there, and also figure out better ways of addressing mobile before Apple takes over the living room?

(Talk at University of Texas’s LBJ School of Public Affairs, via Polygon in 2013)

Newell wasn’t worried about Nintendo, PlayStation, or Xbox; he was worried about Apple, who weren’t even in gaming then (and still aren’t today).

His solution? Run Apple’s playbook, but do it in reverse.

Trajectories Mapped

To understand what Valve is doing, it helps to see the paths both companies took, and how they mirror each other.

Apple’s Path, Briefly Summarized

Apple’s path to becoming the modern corporate juggernaut that nobody saw coming even 20 years ago needs little introduction: Macs, to portable Macbooks, to portable music (iPods), to iPhones, to App Store, to Everything Else, each step further locking customers into the ecosystem through hardware that “just works”.

Valve’s (Reverse-)Path

Valve was founded by ex-Microsoft employees Gabe Newell and Mike Harrington in the late 90s following their frustration that, among other things, Microsoft was getting outflanked in gaming by startups with a better understanding of how to use the internet. As you might expect from a company with that origin, they started by making video games like Half-Life, Team Fortress, and Counter-Strike.

Steam came in 2003, created for easy management of updates for their games over the Internet (what today would be called a “proprietary launcher”). What Valve soon realized, though, is that they could turn this “cost” into a source of revenue by making their solutions available to other developers, and so came the Steam Store and services (2005). Over time, they built on this, adding features like Managed Matchmaking and Cloud Saves before they took their first crack at hardware in 2015 with Steam Machine 1.0 (aka the Steam Box).

The Steam Box… didn’t do so hot. We’ll get to that.

After that, the important points in the timeline to note for this narrative are the release of the Steam Deck (a portable PC gaming device, perhaps most analogous in function to the Nintendo Switch) in 2022, and the instigator for today, the announcement of the Steam Machine (2.0) for 2026.

For a snapshot of how Steam stands today:

• Regularly peaks at ~41MM concurrent users on weekends: https://store.steampowered.com/stats/stats/

  • Note: that’s concurrent users, not distinct users over the course of the weekend. As a global platform, it’s absolutely the case that distinct user count in a 24 hour time period is higher than that.

• While we don’t have any official recent Monthly Active User (MAU) numbers, Valve gave a figure of about 132 MM at the end of 2021.

  • If we scale that using the ratio of peak concurrent users then vs now, that gives an estimate of ~200MM MAU (though this is very rough)

  • A couple of contextual comparisons:

    • Netflix has ~300 million subscribers, about 100 million of whom are on the ad-supported tier.

    • Uber reported ~190 million MAPC (Monthly Active Platform Consumers, i.e. “people who took a ride or bought Uber Eats delivery”) for the most recent quarter.

    • eBay has ~135 million “Active Buyers” (defined as an account that made a purchase in the last year).

Learning from Steam Machine v1/v0

Many may be surprised to learn that the Steam Machine being released in 2026 is actually Steam Machine 2.0. Valve, for its part, seems to be tacitly trying to memory-hole the earlier model; essentially none of the messaging about the new release brings up the fiasco from a decade prior. However, it’s clear that Valve has internalized a lot of the failures from that affair:

Failures of 1.0

Underbaked Supporting Software:

It’s a perceived truism in the games industry that platform exclusives drive platform adoption. While I think this is on the whole significantly overstated, and it seems like the industry itself might be coming around on that, it’s hard to deny that the fact that many major games just didn’t run on the original Steam Machine’s software could hardly be called a selling point:

Probably the biggest issue holding back the Steam Machine at launch was the [...] fact they only run games that have Linux versions. Three of the platform’s biggest titles in recent years: The Witcher 3, Grand Theft Auto 5, and Metal Gear Solid V, still aren’t supported, which is a big problem for a machine aimed at gamers.

(The Steam Machine: What Went Wrong | TechSpot, 2017)

Even looking at the games that were supported, they performed worse on SteamOS than they did on Windows… which ties into the next point:

Self-Cannibalization:

Steam Link, launched in 2015, allowed owners of (sufficiently powerful) PCs to stream games to the TV over your (hopefully uncluttered) WiFi network at a lower price point.

What did that mean? You had two options:

1. Purchase a new machine that cost anywhere from $500 to thousands and hook it up to your TV; or

2. Purchase a little stick for $50 and use the PC you probably already gamed on before all this as a local server for your games. You know, that PC running Windows, which apparently runs those games better anyway.

Gosh, I wonder which one consumers would want? /s

Unclear target consumer

For this one, I’m just going to quote directly from another publication that ran a postmortem article on all this about a decade ago:

“We started thinking, ‘Hey, you know, we’re actually creating a middle-tier niche for this at this point,’” says [Michael Hoang, marketing manager @ iBuyPower]. “You have your console, you have your PC gamers, we’re right in between. We’re right in the middle where no one can really claim which one this is. So now we’re creating a new demographic that has never been created, so we have to do everything from the ground up at this point. And it was very, very hard to convince people, well, do I want to be a PC gamer? Do I want to stick with just being console? Or this new thing in the middle.

(What happened to Steam Machines? | PC Gamer, originally published 2017, re-published 2018)

Landscape of 2.0 (and the earlier Steam Deck)

How does this compare to the situation in 2022 (with the Steam Deck) or 2026 (Machine 2.0)?

Matured Software Ecosystem:

On the back of years of testing/improvements and launch of the Steam Deck, the Proton gaming compatibility layer for Linux means even some brand new games can, out of the box, often “just work” on SteamOS without tuning. ProtonDB tracks compatibility, and counts 7000+ games that are verified to work as well or almost as well as on Windows, and when sorting games by popularity on Steam (i.e. the main platform), you have to pass more than 40 at time of writing before you get one that isn’t at least “Gold” rated.

Death of Steam Link

While the software still technically exists and is usable, the hardware is dead, and even the software has “better” open source alternatives that are recommended by the community as the go-to solutions (ref: Moonlight/Sunshine).

Target consumer

This one is less clear; while (at time of writing) the pricing of Steam Machine 2.0 is not confirmed, it has been stated that it won’t be sold at a loss despite only having the hardware to be performance-competitive with current-gen consoles (which are sold at a loss, and so would be expected to be cheaper). At a glance, it seems like Valve is setting themselves up for the same “are we PC gamers, console gamers, or something in the middle” question all over again.

However, there’s an argument to be made that, if the Steam Deck is the iPhone of gaming, then the Steam Machine is the iPad – an interoperable, friction-free extension of the software to a new form factor, built to fulfill a complementary purpose to the Deck. Moreover, “a slightly more expensive console” is an easier sell when you can also play your games on the go with the Steam Deck, in that (again) it centralizes your library.

The Biggest Learning from 1.0

Returning to the earlier-mentioned PC Gamer post-mortem:

What’s the best thing to come of iBuyPower’s SBX? The LED strip, says Hoang, which looked really nice.

Now, taking a look at the Steam Machine store page:

Jokes aside, I think Valve’s biggest takeaways from the debacle that was “Steam Machine 1.0” was that, for their vision of hardware to make sense as a customer purchase, it need to be smooth; they couldn’t just ship an MVP and build on it later, or rely on third-party partners specialized in hardware to build the rig while they made the software and hope that the two would meet in the middle. No, they needed to control the hardware, the software, the interface, the messaging, everything.

They learned that it all needed to “just work, seamlessly”.

Deeper Parallels

The parallels aren’t only in their trajectories and ultimate product offerings, though. A deeper examination of what makes these companies unique and valuable reveals more similarities, which is perhaps unsurprising considering that they are both also very, very successful.

Philosophy on Piracy

Both Jobs and Newell view(ed) digital asset piracy as a problem of availability and convenience.

Jobs:

We believe that 80 percent of the people stealing stuff don’t want to be; there’s just no legal alternative. So we said, Let’s create a legal alternative to this. Everybody wins. Music companies win. The artists win. Apple wins. And the user wins because he gets a better service and doesn’t have to be a thief.

(Esquire Magazine Interview, 2003)

Newell:

“We think there is a fundamental misconception about piracy. Piracy is almost always a service problem and not a pricing problem,” [Newell] said. “If a pirate offers a product anywhere in the world, 24 x 7, purchasable from the convenience of your personal computer, and the legal provider says the product is region-locked, will come to your country 3 months after the US release, and can only be purchased at a brick and mortar store, then the pirate’s service is more valuable.”

(2011, proxied quote from The Cambridge Student via The Escapist; thanks to teddyh on HackerNews for digging up the archive link for TCS!)

This take by Newell is borne out by results, as he goes on to explain:

“Prior to entering the Russian market, we were told that Russia was a waste of time because everyone would pirate our products. Russia is now about to become [Steam’s] largest market in Europe,” Newell said.

While Jobs is providing an optimistic view on people’s morals and Newell is looking more clinically at overall value like an economist, they both point at the same underlying idea, i.e. “users will pay for a good experience”. We return again to Jobs’s famous refrain of “it just works” – and customers will pay you when it does.

Creating Cults

When Steve Jobs died in 2011, people left flowers at Apple stores. When Gabe Newell so much as posts on Reddit, threads debating what happens to Steam without him rack up thousands of upvotes and comments. Both companies have fanbases that behave less like customers and more like congregations.

Even after almost 15 years of Tim Cook at its head, when you think “Apple”, you think of the iconic image of Steve Jobs looking directly at the camera with his hand on his chin that became the cover of Walter Isaacson’s biography. Jobs’s ideas around design as the lodestone of product development, his relentless focus on the customer experience, his love for simplicity – all of these are written into Apple’s very DNA and are the exact things customers love about their products.

While the general public might not recognize the name, Gabe Newell is the same in gaming circles. One need look no further than even reactions from those fans when Gabe Newell questions his own celebrity treatment. And just like Apple, this cult-like veneration extends to the company itself – despite having been almost 20 years since the last mainline entry in the Half-Life series, memes and (half-?)jokes about “Half-Life 3 when” have not died down, and when Valve announced a prequel VR game in the series due to release in 2020, the trailer racked up 10 million views in less than 24 hours… and, of course, prompted a fresh wave of “Half-Life 3 when?”

This phenomenon of fandom cannot be viewed as anything but a competitive advantage. Both companies have audiences that will literally hold off on buying things in case their “preferred” creator might enter the space, and watch their every move like hawks to determine what to “get hype” for.

Divergence

Valve and Apple do differ in one major respect, however: Valve seems staunchly unwilling to pursue any explicit, strong lock-in.

Unlike Apple hardware, the Steam Deck does not need to be jailbroken in any way, and Valve explicitly provides a guide for how to go outside their ecosystem (and potentially brick your very expensive game machine). The messaging for the Steam Machine 2.0 seems equally “free”:

Yes, Steam Machine is optimized for gaming, but it’s still your PC. Install your own apps, or even another operating system. Who are we to tell you how to use your computer?

(Steam Machine product page)

Aggregation Theory and Valve

A useful lens for understanding the dynamics at play when talking about Valve/Steam is that of Aggregation Theory.

Aggregation Theory is a model and way of thinking about businesses enabled by the Internet and digitization, conceived and coined by Ben Thompson of Stratechery. Thompson labels “Aggregators” as businesses having three core characteristics:

1. A Direct Relationship with Users

2. Negligible Marginal Cost for Additional Users

3. Demand-Driven Markets with Decreasing Acquisition Costs

Is that a mouthful? Yes, and the big words hide a lot of complexity. For those who want to really understand it (something I encourage, as I think that a lot of both the successes and failures in recent times of tech companies, whether in product strategy or regulation, come from a misunderstanding of these dynamics), I’d encourage going to the source and spidering out to related, mentioned articles (Thompson does such a great job at linking between his pieces that I’m tempted to provide a quasi-TVTropes warning first):

• https://stratechery.com/2015/aggregation-theory/

• https://stratechery.com/2017/defining-aggregators/

However, for our purposes, I think we only need to engage with the concept at a high level.

Consider AirBnB (which might not initially seem like an aggregator), an example Thompson himself brings up often. Homesharing wasn’t a new concept that sprung fully-formed from Brian Chesky’s forehead; couchsurfers and foreign exchange students engaged in it for decades. What AirBnB did was aggregate demand – they made it trivially easy to find a nice place to stay. Once enough travelers were searching on AirBnB, hosts “had to be there”. Once enough hosts were listed, why would travelers look elsewhere? The platform became the default not by being the only option, but by being the most convenient.

In this regard, Steam works the same way.

Valve’s Current Position

Steam is inarguably an aggregator, and arguably close to the platonic ideal of one:

1. Direct Relationship with Users – As a customer, you make an account with Steam to activate products managed through it. While you don’t have to give them your payment information, it’s certainly more convenient to do so. It would almost require active commitment to avoid doing so: you’d have to exclusively buy game keys from other retailers, handle any and all in-game purchases directly with the game creators (if even possible), and deny yourself the ability to buy anything during the ever-rotating Steam Store sales that often feature deep discounts on a wide swath of games.

2. Negligible Marginal Cost for Additional Users – Steam is a digital storefront, so yes. Additional users are just an O(1) addition of rows in a few database tables. And while Steam obviously incurs some transaction costs when users buy things, who doesn’t pay the gatekeepers of Visa and Mastercard?

3. Demand-Driven with Decreasing Acquisition Costs – Steam is practically self-sustaining on both sides of its market. Customers not already on Steam are liable to wind up there by accident just buying a box copy of some game or another. That mass of users attracts developers, both small and large, even ones that left believing their fans would follow.

As a platform, Steam is also increasing lock-in:

• SteamVR serves as the “platform” for VR development, driven by customer demand for Half-Life: Alyx, with the Steam Frame as the first-party platform (alongside already-existing VR hardware options like HTC Vive or Oculus Quest)

• The Steam Deck functions as the “iPhone for PC gaming” (direct quote from GabeN)...

• And now comes the Steam Machine to complete a “hardware trifecta” to mirror the iPad.

All of these hardware offerings are undergirded by the self-lock-in of users who have a significant portion (sometimes a supermajority) of their gaming libraries in Steam, which is (currently) non-transferable (c.f. iTunes/App Store/iCloud)

In this light, the parallels to Apple are hard not to see, but Apple “integrated forward” – they created great hardware that brought customers to them first, then slowly built the pretty walls around the garden users had entered, even as the garden itself expanded into new domains like mobile and watches.

Valve, by contrast “integrated backward” – they created the ecosystem first, offering both customers and developers amenities like managed updates, cloud saves, mod hosting, and content distribution networks before creating their own first-party hardware that leverages all that work.

“The Metaverse”

A core contributor to the success of both Apple and Valve is that they both grew naturally (if in inverse directions):

• Apple went from PCs, to laptops, to portable music, to smartphones as a generalization of their learnings from ‘portable music’, and finally to the modern ecosystem of software & peripherals (watches, headphones, VR).

• Valve started in video games, then made a game launcher/update manager for their games, then offered that update management service to other developers with a storefront before finally getting to the hardware.

At each step, both companies responded to real market demand and simply did a better job at capturing that demand through UX. You can even see the proof by contradiction: The Steam Machine 1.0 shows where they perhaps moved too early or chased illusory demand, and a similar story may be repeating itself with Apple Vision.

It’s instructive to contrast this with Facebook’s “all-in” pivot to metaverse. At first blush, there are significant parallels to Valve, in that both Zuckerberg and Newell had built their fiefdoms in the lands of other empires and were trying to “escape their jailers”:

• Newell was trying to get out of “Windows 8 jail” and the potential that Microsoft might smother their business with expansion of the Xbox business.

• Zuckerberg wanted to leave the Apple/Google app platforms of iOS and Android, where their decisions around privacy could kneecap FB profits (ref: iOS’s App Tracking Transparency initiative), and run his own platform from which to derive value (or, being more cynical, collect rent).

However, it’s hard to imagine a bigger gap in outcomes between the two. Looking at the result of Facebook’s metaverse efforts in December 2025:

• A stagnant Quest store/ecosystem

• 70 billion dollars in losses associated with the pivot

• 4 years invested for no meaningful revenue growth

• Planned 10-30% cuts to Metaverse

  • Perhaps most depressingly, investors drove stock 3-5% higher following release of news, indicating they were also fed up with this nonsense.

Why? Well, Zuckerberg’s big pivot was completely unbacked in demand, whether from customers or enterprises, to match the scale of the investment he decided to fling at it. Moreover, it had an at-best tenuous connection to the core competencies that Facebook had developed in scalable software, social networks, and advertising. Zuckerberg often talked about how everyone would interact with virtual avatars while on the go or in meetings across timezones or what have you, and how that tied into Facebook’s mission of connecting people, but as the saying goes: if you need to explain the joke, it isn’t funny.

Visions Of The Future

All of this analysis begs the question: What sayeth the crystal balls?

Aggregation Theory

If we’re looking at things as Aggregation Theorists, continued near-total monopoly is the obvious end result, absent significant developments:

What is important to note is that in all of these examples there are strong winner-take-all effects. All of the examples I listed are not only capable of serving all consumers/users, but they also become better services the more consumers/users they serve — and they are all capable of serving every consumer/user on earth.

(Aggregation Theory | Stratechery, 2015)

Competition is difficult once a big aggregator has emerged, even for deep-pocketed incumbents. One need only look at famous examples like Yahoo or Bing vs Google, or Walmart vs Amazon. Epic Games already tries to offer competition in digital storefronts by leveraging the breakout success of Fortnite to launch their Epic Games Store, with a more generous revenue split for developers and the backing of Tencent to buy up store exclusives (even if timed only) by flinging money around. However, 7 years into the experiment, these terms don’t seem to have resulted in any significant upturning of the apple cart.

Ben Thompson (of Stratechery) suggests that traditional platforms might offer an avenue by which competition can rise, looking at Shopify vis a vis Amazon. However, it’s unclear what “a platform approach” might look like as competition in the context of gaming here. Indeed, if you asked me what an extensible platform might look like in gaming, I’d say that Valve are, themselves, operating closer to a platform than a straightforward aggregator by making Proton/SteamOS free for other companies to use and potentially develop on top of (ref: Lenovo Legion Go S and ASUS ROG Ally as competitors to Steam Deck that can use Proton/SteamOS (though by default ship with Windows 11)).

So, if the traditional mechanisms of the market find no purchase, might we look elsewhere?

Regulation

Antitrust is also hard, at least based on dominant US thought. Those who know me personally will know that I’ve long railed against Robert Bork’s consumer welfare standard for evaluating mergers and antitrust.

I’m hardly the first or alone in this – perhaps most notably, former FTC Chair and current Mamdani transition team member Lina Khan first made her name penning “The Amazon Antitrust Paradox” as a direct refutation of Bork’s ideas. However, despite some recent small shifts suggesting that the climate may be changing, it remains the default standard, and considering that users specifically choose to go to the aggregators due to the benefits they offer (whether on price or quality), consumer welfare seems unlikely to find any justification for action.

However, there are potential avenues in forced service interoperability and data portability. In short, the idea is that regulations can mandate that platforms of sufficient size have to allow other services to be able to integrate in some (perhaps standardized, limited) ways with the incumbent giants. This is an idea that’s already being trialed by the EU via the Digital Markets Act (DMA) for messaging (even despite some real and valid technical concerns), and it’s also being partially (voluntarily, seemingly) rolled out in music streaming apps (specifically between Apple Music and YouTube Music). While it’s early days, if these initial forays work without significant snags, it may embolden other legislatures to make similar moves that ultimately wind up hitting Steam, forcing it to cooperate with EGS in some way like allowing Steam users to take their validly purchased games to EGS if they want.

Enshittification

Cory Doctorow’s pithy (if crude) term and model, which posits that platforms are eventually incentivized to ruin the experience for everyone on them in search of profit, might credibly be argued to be the end state of all platforms controlled by profit-seeking entities. And arguably, Steam has long since been “enshittified” in some regards. The deluge of games on the store that are approved for release without quality control has led to cheap, buggy messes or even outright malware finding their way onto customer machines. This is not a new problem, either, with industry analyst and pundit James Stephanie Sterling having coined the term “the asset flip” in 2015 to describe a problem that has only grown in scope since.

However, while that criticism might hold water for some industry professionals, power-users, and gaming enthusiasts who want to find “hidden gems”, for the average consumer, Steam generally remains a good experience for discovery, purchase, and library management.

Looking forward, as a private company that is majority owned by Gabe Newell himself, Valve is not as vulnerable to the kinds of short-term shareholder pressures that public companies or heavily venture-funded companies like Facebook/Bytedance face. While that alone is no guarantee, the combination of profitability, patient capital, and founder control may stave off the specter of enshittification for some time yet, at least while current leadership remains.

That is, however, also why, as mentioned earlier, many fans fret about what might happen to Steam and Valve in the absence of GabeN.

Technological Advancement

While it’s hard to say what direction technology might go in the future (who could’ve predicted in Oct 2022 that the US economy’s growth would hinge on LLMs?), there is one obvious avenue that may pose a threat: Cloud Gaming is only getting better.

Surfaces like Xbox Cloud Gaming or Nvidia GeForce Now are currently only either technological solutions that allow access to existing libraries (e.g. Steam or EGS via Nvidia GeForce Now) or Netflix-esque “subscription packages” for pre-selected games. However, there’s no technological reason preventing these providers from attempting to fight with Steam as a digital storefront; Google Stadia already tried this in a form, and while that service was eventually shut down, there’s nothing to suggest that the idea itself is inherently unworkable. If these services want to become “the Uber for gaming”, a storefront may eventually be a necessity; many of these services already have experimented with messaging of “pay-as-you-go” for gaming hardware, gesturing at a future where you never have to worry about hardware compatibility or drivers because it “runs in the cloud”, all of which is reminiscent of the “you won’t need to own a car, a self-driving one will just come pick you up” vision pitched by Uber or Tesla in years past.

Conclusion

If you were to ask me where things likely go from here, I’d say Valve will remain the much-beloved de facto king of PC gaming for the foreseeable future. This isn’t because they’re inherently more talented or virtuous, but because the factors for decay just aren’t there. They’re private, they’re profitable, and (if nothing else) they’re patient (Half-Life 3 when, GabeN?). There’s no strong incentive for them to enshittify.

The parallel to Apple isn’t perfect, though; the companies have taken different directions in some aspects. The more open approach Valve has taken with Proton and SteamOS is perhaps the most obvious, and I personally consider that to be a strength, especially in the context of regulation. However, there are other differences as well, and the one that stands out to me is in social infrastructure.

If I say “blue and green bubbles”, the direction I’m pointing at should become clear. Apple has leveraged their branding and dominant position (at least within the US) to elevate the iPhone to a status symbol, and in so doing has created network effects through iMessage and Facetime that further keeps their customers within their ecosystem in a way akin to traditional social networks (“I can’t leave Facebook; that’s how I keep in touch with my high school buddies!”). By contrast, while Steam does have chat and groups and forums and other social features, calling them “anemic” might be generous. When it comes to PC gaming, while most things make you think Valve and Steam, if you talk about social lock-in, it’s Discord that comes to mind, and that’s a chink in the fortress Valve has built.

The question is, will social infrastructure be as critical in gaming platform lock-in as it has been to mobile? If Apple’s trajectory is any guide – iMessage didn’t seem critical until it revealed itself as one of their strongest moats – owning the social layer may matter more than is obvious today.

\0