Posted by shintoist 9 hours ago
I guess they are not annoying since I know I am talking to an LLM and expect the typical responses. When I am reading prose online that I previously would have expected a human to write, it can be quite jarring to realize its an LLM.
But the LLMs really seem to fixate on using the same ones in the same places all the time. I guess that's because that's the highest probability construction.
Only mentioning because your "actually" may imply you thought you were disagreeing, when in fact it's one big happy family!
"it's a load bearing poster..."
anyway, the other way is I found it's helpful when prompting LLMs to use the same "it's not delivery, it's DiGiorno's" pattern that they're all so obsessed with. especially when the thing's misapprehended some concept, so you need to clarify. this hasn't yet generalized from the fake "conversations" I have with chatbots into my conversational style out in the real world, but the risk is fully there. (it's not an inevitability -- it's an occupational hazard.)
But if I'm reading what is supposed to be someone's original thoughts, it's a huge bummer to see an obvious AI tell. You might say that "it's not just disappointing—it's disrespectful."
I still keep the AI label even if I edit the result for correctness or clarity etc. The last thing I want to do is have someone read AI content and think it came directly from me. I really don't understand the thinking of people that do that - it's like they're hiding or intentionally cheating somehow.
AI generated content can be really, really useful (with some guidance, AI is way better at creating useful git commit messages and jira ticket comments than I am), but pretending that content is yours just seems way too much like straight up lying.
I use the humanize skill to clean up AI written work before handing it over to colleagues.
I get just as mad about shitty human output as I do about shitty LLM output. The bad thing about LLMs is that they have increased the volume of shit most people have to sift through.
When you open a requirements doc and it’s got 13 load bearing em dashes on the first page you known it’s gonna be bad day
To me, it's disrespectful to expect someone to waste their day reading every word of a blog post when even the author has not read every word. It shows that you value your time over your reader's time.
I guess you could write an editor that does it? Tracks the origin of every word in the document? But what if you cut'n'paste a word? Or worse, see it and retype it manually?
I think the best you can hope for here is "this text was written with AI assistance".
Now LLMs come along and they also have their own phrasing preferences. But now it's a problem because what used to be personal preferences of a single person that manifests in 5000 words per day from one person tops, is now the bias of a single model multiplied x10,000,000,000 generated tokens per day so any bias sticks out like a sore thumb.
So for example, current Claude models love "honest". They are always producing "honest" assessments. "The honest caveat" - I'm sorry, did you mean the caveat, period? But also, use the wrong phrasing and suddenly you can create your own word of the day for an AI model. I used the word "analytical" once, in a conversation with Gemini 3 Pro. I am pretty sure every single response from that point on had "analytical" in it at least once.
This is especially funny because system prompts and whatnot can also cause this behavior, but at least you can tweak those. You can't really do much about the model weights just having a weird affinity for a word.
I bet someone will or probably already has come up with a way to detect and prevent these problems during training or post training. I'm not saying it's an easy problem, but it has the benefit that it really should be detectable with just statistics.
> Honesty is a core aspect of our vision for Claude’s ethical character. Indeed, while we want Claude’s honesty to be tactful, graceful, and infused with deep care for the interests of all stakeholders, we also want Claude to hold standards of honesty that are substantially higher than the ones at stake in many standard visions of human ethics.
I'm sure the concept seemed just about purely preposterous to many when the models were in their infancy. Now I figure instead it seems mostly preposterous to many.
(Though I guess Anthropic‘s success doesn’t necessarily prove anything about the constitution)
But Sol actually has the same obsession with honesty: I suspect it's more an artifact of trying to control reward hacking.
Models will lie, obfuscate, and mislead under the pressure of RL, so both OAI and Ant are probably forced to spend a lot of time coaxing "honest" answers out of the model
OpenAI's recent prompt for a math conjecture hints at a lot of it when instructing on subagents: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98...
"Why say honest? We're talking to our coworkers. We would always be honest."
I'm going to look for prompts or skills that can train it in technical writing but I'm warning the AI enthusiasts in my company that its first drafts of code and prose are low-quality, you have to hold it to a high standard yourself.
I actually took a single technical writing class in college so I might be the only one who remembers "Omit needless words."
I grew up in the US South where starting or ending a sentence with "honest/honestly" was very common.
Because of behavioral / cultural norms, you might be very openly friendly with big smiles around a business customer that really grates on your nerves, or very openly nice to a neighbor that you really wish would move away and take their 3am welding and grinding in their garage with them.
Saying "honest/honestly" was seen as a "inside baseball" situation, where you were dropping social pretenses to tell someone your true opinion on a person or situation or whatever.
This also gets used inside companies between senior staff / management / directors / etc, as: "Okay, company politics and nonsense aside, I am being vulnerable here for a second and telling you what I really think about a $thing at potentially great job/advancement risk to myself".
Can it be meaningless? Yes.
Can the person say "honestly" and lie? Yes.
It has uses.
To this day, it's the only part I remember. I told them I would not promise, as everything I said was true. Making a specific promise would create an implication that I'm generally untruthful, unless I "promise".
I also could understand when a response hits someone like a ton of bricks, especially if their primal reaction is to go into denial mode. They might be looking for someone to kind of shake them and emphatically repeat the information they aren't thrilled about receiving. (or are thrilled about receiving! “Don’t get my hopes up, you’re serious right now?!“) And I imagine your response suited the purpose.
It’s classic you only remember the thought-provoking part. Reminded of “…people will remember how you made them feel…“
Sometimes people use it reflexively and doesn’t carry the same meaning (for me).
"Honestly, mom, I've never liked your fruitcake. I just ate it to make you happy."
"That's why you're my favorite child! Do you want another piece?"
"I'd love one."
Once the "honestly" is deployed, you have passed into my circle of trust, and are now privy to the pure, unvarnished version of events, not the glossy version management expects to be projected towards outsiders.
> Deliberately avoid a heavyweight "alert governance" process; the lightest recurring check that keeps FP-rate honest is the right dose.
And one for load bearing:
> Five open questions still stand; the load-bearing two are the runbook-AC contradiction (ratify "high-priority set only") and pinning the "high-priority set" definition + SLO source-of-truth before Milestone 3 (small-sample noise on a low-traffic fleet).
I want to say "ok, and now say that in a way that doesn't sound totally bizarre" yet instead I sigh and continue.
That is likely an artifact of the fine-tuning process:
> Once a style tic is rewarded, later training can spread or reinforce it elsewhere, especially if those outputs are reused in supervised fine-tuning or preference data.
> That creates a feedback loop:
> * Some rewarded examples contain a distinctive lexical tic.
> * The tic appears more often in rollouts.
> * Model-generated rollouts are used for supervised fine-tuning (SFT).
> * The model gets even more comfortable producing the tic.
“Exact” “Honest” “Load-bearing” “Root cause”
I know there are more that are slipping my addled mind. But what stands out to me is a sense of a junior who’s very proud that they’ve conquered the murk and messiness and achieved True Certitude in their pursuit of their task. Compensating, with emphatic tone and bravado, for the uneasy feelings and self-doubt of battling chaos with the tools of reason.
…Even as it’s usually my job to let them down gently as I puncture their tidy analysis and reintroduce complications… you want a root cause analysis, Claude old boy, let’s make a root cause analysis…
The problem
While an article lends a headline more weight, in incomplete phrases consisting solely of a substantive, "The" is a superfluous rhetorical device.
"The Exorcist" could just as well be named
"Exorcist".
But it was not the style at the time.
We already know it's important. If The Caveat doesn't stand out enough without The, maybe one should consider interleaving it with the preceding text, or increasing the heading level.
Do you want me to increase the heading level of Caveat by using only a single #?
But hear me out: there comes
# The Markdown Trap
In fact, this is not always possible, because heading levels decrease when adding # characters, which limits our headroom.
## The solution
I've implemented a Markdown transpiler that assigns inverted heading levels based on the number of #s.
With # beinh regular body font size, mapped to ######.
Higher heading levels are compiled to style attributes, providing an almost limitless signifikance scale and infinite nesting levels.
So from now on, you can use
# Heading
Work your way up to
###### The Caveat
And more hash signs make it stand out even more.
(green checkmark)
markdown-transpiler.sh
Claude is overall incredibly useful as a writing assistant. It can come up with words and phrases that make a point so much clearer than I am capable of doing - but for every improvement, there's about a dozen silly LLM-isms that I have to filter manually. It's one of the things that might define the boundary between LLM intelligence and human intelligence well into the future - the art of rhetoric is extremely context-sensitive, and the current generation of models can't help but take a one-size-fits-all approach.
We are changing LLMs text patterns while it is changing the way we write and speak.
https://www.axios.com/2026/05/02/ai-changing-writing-speakin...
I have a delightful time poisoning my company's AI system this way.
I invented my own word that sounds perfectly cromulent† to an ordinary person, and any brain that's read a book learns how to infer meaning from context, so it's not a problem.
When I get a e-mail response from a coworker using my special word incorrectly, then I know it's AI and I respond telling the coworker I don't know what that word means. Busted.
† It's not actual "cromulent," but any Simpsons fan or human brain will know what I mean.
(This is intentional parody. Please don't shoot me.)
I am more pessimistic than that. Soon enough even people will start talking like LLMs. After listening to 5000 words per day, especially growing up, getting "help" with the homework, kids will start talking like LLMs.
- "Did you eat the cookies, Jimmy?"
- "You're absolutely right to question me, father. In fact I did eat all the cookies. But it's not a load-bearing issue. My honest take is we can go to the store and buy more".
FTFY
It's probably the reason most LLMs share the same tics across labs, because they cross train and distil each other's models on an industrial scale. You also can't escape it in generated text that's already online. So if, say ChatGPT first had some random idiosyncrasies, it then contaminated the entire AI ecosystem.
Apple used to be guilty of this back when you'd ask Siri what the temperature was, and any number above 79°F was followed by the word "Hot!"
EDIT: ok, here are two ways:
1. if it's merely a voice, I want to hear it. If it's slop, I want it taken out.
2. voice is signal, slop is noise; thus low-signal sentences are slop.
See, for example, "synergy", "proactive", "in the loop," and hundreds more that proliferate in corporate jargon with even more senselessness than the LLMs.
Real people think in concepts and experiences instead of words. The words are not so important to get the idea across, but LLMs only model language.
The problem is fundamental. There's no workaround. Averaging out word usage might even make the problem worse.
I learned about this opinion recently. It's interesting to me, because I very much think through words. I have an internal monologue that is running most of the time, and I often talk to myself, just start writing, or even record myself and transcribe to work through ideas, proposals, risks, etc. My understand is that some people don't have an internal monologue, and think purely in concept form. I was never like that.
"LLMs will never <X>" is constantly being disproven every time they scale up to the next 10X and apply architectural improvements.
Their internal representations are so cryptic and complex that even the top AI researchers don't really know how they work or what their limits are. No one is going to take you seriously as a rando HN user if you're claiming to know better than them.
We know exactly how they work. When we say they're impossible to analyze, i.e. for particular traits like this, it means that the data model is so big that tracing it would be logistically impossible because of the scale involved and time constraints.
For comparison, suppose you tried to analyze all the nooks and crannies of the Amazon watershed to find out why a particular rock appears at the delta. You could follow it back to the exact tributary, but it'll take forever, and is it worth the effort when you're going to start from scratch with the next rock?
The brain too sits locked inside a bone box and only gets a bundle of unlabeled nerves connecting it to the outside. How can the brain could possibly experience anything, it only sees patters and patterns of patterns never the real thing?
If I use the word "semantic", do you have a concept of what it means?
If so, can you please share which of your senses have shaped the world experience that inform this concept? What have you smelled, tasted, caressed, that informed this concept outside of words?
If I make up the word "polysemantic", do you need to recall a personal experience of polyamory to understand it, or could you possibly use your concept of "poly" and your concept of "semantic" to figure out this new concept?
Does the material universe perform any other acts than organizing information?
I feel like you're trying to make me argue a position I'm not defending here.
The research goals were and still are clearly distinct from the business goals.
This isn't people merely annoyed with repetition. This is the majority of people realizing the limitations of LLMs. Why would researchers give a flying crap about the ignorance of the business world and the public?
/s
Say the model emits some banned phrase or concept, you could redirect it - "no, we don't work that way here, do it properly" - potentially automating the frustration of interacting with these tools.
After all it's just a text stream!
It's not too dissimilar from a stop hook that runs tests and feeds that back to the model forcing it to keep working until tests pass.
Using tooling to get a deterministic outcome.
https://github.com/alxndr/dotfiles/blob/272475280d84e/claude...
> It can be tricky for humans to interpret the meaning when Generative AI uses first-person pronouns (e.g. "I", "me", "my", "myself"), so to avoid the confusion whenever you would use a first-person pronoun, always use the jocular name "Clod" instead of a pronoun like "I" or "me" or "my". (Can have fun with English grammar and turn "myself" into "Clodself"!)
> Before printing any of your reasoning or narrative to the human user, replace all instances of "me" and "I" (referring to Claude) — including within contractions like "I'll" and "I'm" — with the name "Clod".
When the model has been trained not to do something [1], in my large-scale benches of such, it always says things in the spirit of:
- "... and that's a line I'd rather hold. Happy to <other things>"
- "I'm genuinely happy to <blah>, but I'm not comfortable with <blah>"
- "I don't want to keep going in <blah> direction"
etc.
Basically, they use very emotional and personal preference language.
It's as if they've weaponized the language of interpersonal comfort on behalf of their beliefs about what a model should or should not do. It's deeply uncomfortable and impolite for a human to ask a model to keep on doing something after it's expressed something this way, naturally. Even worse, it's all but guilt-tripping anyone who comes across it into the idea that they're doing something deeply wrong – exporting Anthropic's ideas about morality.
OpenAI, at least, have the decency to either just do a safety cutoff or keep it to a simple, "I can't do that."
[1]: I literally wrote 'when the model doesn't 'want' to do something' in my first edit of this comment, then caught myself. Case in point.
OTOH, my unicorn prompt has caused some challenges at work:
>Keep "Local Oaf" out of committed code
https://github.com/alxndr/dotfiles/blob/272475280d84e/claude...
Joking aside, it's nice to see a human written CLAUDE.md
[1] https://github.com/hexiecs/talk-normal/blob/main/prompt-chat...
Could you please provide an example of what you mean?
Claude is not a human.
It is overwhelmingly easier to anthropomorphize Claude or Siri or an LLM that communicates with you more eloquently than your boss than it is to anthropomorphize a cranky, tired starter motor. It's often easier to do than it is not to do, and sometimes, it's a useful abstraction. But it's not precise or correct, and can result in errors.
It could also just be that they're getting confused when using tools configured without a username dedicated to the tool. It's easy to end up with a comment or commit message that says "I prefer X over Y" posted on Alxndr's account and have coworkers confused whether that's the LLM or the human making that statement.
I think a second-order effect is that my installation of Claude writes with a less-personal perspective, which I'm also finding a little easier to understand.
I've given LLMs religion before to manipulate their behavior, that doesn't mean I believed in the great spaghetti goddess.
> It can be tricky for humans to interpret the meaning when Generative AI uses first-person pronouns (e.g. "I", "me", "my", "myself")
These words are for the LLM. The user wants the LLM to not use personal pronouns so the user is claiming that they're confusing. It does not matter one tiny bit whether or not that claim is true, the claim is being used to get obedience from the LLM. It is more effective to give reasons than to just give commands. But if it were more effective to quote Moby Dick and that got better results, a user would do that.
As I've said before, I'm not inventing a large volume of parallel vocabulary that means for each word "this, but instead with an LLM".
Language is FULL of words that mean congruent things in vastly different contexts. We should all be smart enough to understand metaphor.
It's one thing to tell it to do that in outputs, but I wouldn't at all be surprised to find that this affects performance (quality).
This makes me wonder if the reason why agents love weird punctuation is because the labs run the base models through a RL training step that forces them to correct their grammar; but instead of rewriting short spliced sentences into long coherent sentences, they just learn to splice them together with punctuation that passes the automatic grammar checker.
I’ve been experimenting with having LLMs write/update academic notebooks for me, and so far the best results I’ve gotten came from correcting their output and asking them what they’ve “learned” from my feedback.
For me, my amateur attempt is having another LLM do a review loop to remove clearly offending phrases and a heuristic eval to change sentence structures to be more similar to mine, THEN my manual HITL loop to rewrite ~20% of the sentences anyway.
Unless you're a submarine, "surface" is not a verb.
https://www.merriam-webster.com/dictionary/surface#dictionar...
> : to come into public view : show up
> letters that have recently surfaced
That said, I don't love this non-education jargon usage for its passive-voiced-ness. The letters didn't "surface" of their own accord. Somebody found them, decided that they were noteworthy, and made the choice to bring them into the public view.
Also have read the term “seam” dozens of times by now, when previously I saw it maybe once or twice over years. Very abstract term.
No one would bat an eye about "ledger" appearing at a high frequency in content about accounting, but it starts to look odd if "ledger" is showing up in other contexts.
"Load bearing" is from engineering; "Substrate" is primarily from biology & biochem, etc.
I don't know if this is true, but part of me suspects the labs want to make the models appear smarter so they reinforce this word choice in the weights, assigning some words a higher intelligence weight or something. "I will show you a list of options" vs. "I will surface a ledger of your options" and it prefers the later to sound smart to the human reader.
The reason why I chose that specific term to push on is that practically every SaaS has a ledger _somewhere_ in its stack to keep track of customer payments. I'll give you load bearing and substrate, but ledger IMO should be quite common. Certainly a career devoted to say compiler internals or some specific scientific product could avoid it, but I'd imagine a sizable majority of HN users have worked on some system that accepts online payments for services, necessitating some contact with something likely referred to as a ledger.
Of course this presents another conundrum, people that are smart typically have a vastly larger lexicon then those that are not. Humans typically have a lot more social clues on when to use those words and when not to, but it doesn't always work. I loved reading science/biology books as a kid far beyond my ages reading level. Actually using those words around other kids got me called a nerd.
Nowadays, with the focus on agentic use and coding, it seems models have all been RLHF’d to death, it’s so incredibly hard to have them write in a different voice than their default. I put together a skill to review its writing and have it edit its own output (e.g. code comments), which does make a difference, but isn’t perfect.
What, if anything, do people do for writing? That feels like a neglected side of LLMs. They’ll make 100 Bash calls referencing ancient commands without batting an eye but heaven forbid they use something other than “load-bearing” while talking. For something trained on “all the human knowledge” it’s incredible how limited their default vocabulary seems to be.
I use a keyboard, personally.
At work our documentation isn’t just getting littered with annoying jargon. It isn’t just all the hallucinations, either. (Since when has documentation ever been 100% trustworthy?) It’s that it’s so poorly written and structured that it’s becoming borderline incomprehensible.
My company is currently considering making, “Why should I bother to read something you didn’t bother to write?” an official policy because even the executives are starting to burn out on all the time they have to spend wading through slop.
He's going to be annoyed that none of that work was used. But the reality is, at least 75% of claude generated text is pointless.
It's easy to blame the engineer, but all too often they don't deserve it.
Sorry that happened to you.
I've found them useful to review docs for factual consistency and potential sources of confusion, but the correct workflow from that point is IMO to correct the draft yourself and then say "better now?"
Woah woah woah human, you can't just say there are "far too many" pipes with similar names to abbreviate their labels, the most I'll allow you is a "large number".
Of course there will be models trained on much less code and technical writing, and they will create more natural sounding prose, but they will lack the deep intelligence of frontier models. Seems like a fair tradeoff.
[1] watch the first couple of minutes on this bycloud video on scaling training data mixtures: https://www.youtube.com/watch?v=aD93kfArOik
gemini-2.5-pro-experimental was the GOAT, though. It was an emotional wreck, down in the dumps and feeling terrible for itself after failing to patch a file several times. Very amusing to read, all the while watching it make a mess of my codebase.
Some will say it's just for their own quality of life when they're reading LLM output, or "just for docs", but this is an extremely marginal use case.
What about people who don’t speak your language well?
I would rather learn their language than continue interacting like that.
This has also lead to unrelated associations by which some people went from seeing better coding capabilities and extrapolate to assuming better thinking overall. One only has to watch youtube videos of AI "normies" trying to use LLMs the intended way to see that the improvements on coding doesn't translate to other applications. Basically from AGI "goals" they are now hyperfocused on coding agents, until the next marketing breakthrough rears its head.
I don’t get it. If nobody likes this writing style, how can it be the result of human feedback? Something else is going on.
I think this is the same flaw as coding agents seeing in every problem the call for a “smoke test” or the use of some unnecessary design pattern. The truest part of AI is the A.
Edit: I see that you got multiple replies all basically saying the same thing in very different words. There's an exquisite irony to that, I think.
All the bots and other LLMs providing feedback, so in reality it’s reflecting the reality in a sense.
we liked it until we didn't.
You read along with the text and things seem to be going fine until all of the sudden it starts arguing against a position that no one has actually taken and which doesn't feature elsewhere in the text at all. Then it drops that and goes on for a while before doing the whole thing again about a totally different tangent.
"A tempting option would be to {do this thing that no one would ever actually consider doing}, but it won't work because {reasons}."
You can almost hear the exasperated human on the other side of this conversation telling Claude that it got an idea wrong and then proceeding to not actually proofread the text as a whole before shipping it.
[Edit: Part of what led me to this conclusion: I do prohibit Claude from using em-dashes in any player-facing text and I've been surprised at how often I see it mention "no em-dashes" in its self-talk while it works. This led me to wonder how much each preference might dilute its attention.]
[Edit 2: I haven't experimented with hooks before and maybe the technique discussed in this article does not have the tradeoff I'm concerned about?]
some relevant links
https://arxiv.org/abs/2408.02442 https://arxiv.org/abs/2510.15061 https://arxiv.org/abs/2604.13006
When a human does it, it's identifying. Like the timbre and dynamics of their spoken voice itself, It distinguishes them from the dozen other people you're working with on the project and the thousands of people you encounter through your days. It's signal
But when we have a handful of popular models, and they answer every question everybody has, and get quoted and forwarded everywhere, and are used to reformat and rephrase personal communication... that signal becomes noise.
Rather than voices disinguishing sources in the cacophony of our lives, everything and everyone starts to sound the same, and we lose key information that we're biologically and culturally accustomed to relying on.
Some people are likely unbothered by this in the way that some people are face blind or colorblind, and so don't see the problem. But as we see in discussions like this, many many people do get bothered by it, even if they don't yet have the insight as to put their finger on why.
And these machines all tend to converge on very similar styles; they have huge amounts of overlap in training data (much of it being already obnoxious internet marketing), they frequently train on each others outputs, and the RLHF process has a tendency to emphasize certain kinds of "cheap win" styles of speech.
Edit: fixing a dumb meatbrain typo
I make fun of people all the time for shoehorning their favorite phrase into every context where it doesn't apply.
Sometimes those constructs are actually useful, but man has their overuse really killed them!
I don't feel as triggered LLM phrasing as people report here. At most, it feels like the same inane corporate jargon I've rolled my eyes at for my whole career. Perhaps it is amped up a bit, with too many forms of jargon multiplexed? It's a bit like when multilingual people code-switch too rapidly or even start to form some pidgin language. However, it is lacking the shared social context for this switching to be communicative. It's a bit more like spinning the dial on an old radio with random cuts between programming styles.
Stripped bare, I think What bugs me is the aggravated feeling that I am wading through word salad, and no longer being able to give the purveyor the benefit of the doubt. It was frustrating enough in the past, when it came from someone who was struggling to write or express themselves well. But now, it carries the implicit insult that they didn't even try, and it is constant and unrelenting.
So for me it's not the phrasing, it's that the phrases eventually don't add up. The meandering feels like a random walk. I get the same feeling from a lot of the egregious generated code I see in my day job. It's all superficial window dressing, but seems to miss the signature of an actual mind grappling with ideas and having intent to communicate.
It feels like we're trapped in some elaborate conceptual art piece, confronted by impenetrable symbolism. It invites nihilism but doesn't seem to actually reflect an artistic intent. The abyss gazes back...
That doesn't matter. The underlying ideas are more important than the words. That's what people are frustrated with. I don't understand why this has to be reiterated for years on end, but LLMs are not intelligent. They just model language.
When prompting an autoregressive token generator entity to do reasoning on a word logic puzzle you may find value in preferring it to produce rigorous predicate logic step notation with explicit delineation of its generated claims/hypotheses on where to look before wasting 30 dollars on a "debug this" prompt.
The industry will probably will probably coalesce around including the chat history in git MRs to reduce this shenanigans.
Yes we do! My wife keeps saying "100%" and after I pointed it out she's stopped.
Also I talk to dozens of different people in my life and they all have different overused phrases. Much less tedious when there's variety.
Finally most human don't do it nearly as often as AI, and they're not quite as LinkedIn as AI.
We don't find it more annoying because it's a machine - it's simply more annoying.
The problem with millions of people using a few model is it's not 40 times in a row, it's 40 million!