u/didiTonic

All I wanted was to understand why my next MacBook is going to cost 20% more. Apple raising prices mid-year, with no new product, had never happened before. Their explanation fits in one sentence: component costs are rising too fast, we've been absorbing it, we can't anymore.

The culprit is memory. RAM is up 90 to 95% year over year according to TrendForce. Samsung, SK Hynix and Micron shifted their production to AI data centers because it pays way more, and everyone else is fighting over scraps. Sony raised the PS5 by 100 bucks in March, Nintendo followed with the Switch 2, Dell and HP same story. So before anyone even knows if these data centers will ever be profitable, the bill already landed on us.

The thing that really got me was a JP Morgan report. For the AI investments planned through 2030 to return even 10%, you'd need around 650 billion dollars in revenue per year. Per year, in perpetuity. Their own comparison: 35 dollars a month charged to every iPhone owner on the planet. We're nowhere near that. Meanwhile hyperscaler AI capex went from 33% of their operating cash flow in 2023 to roughly 93% this year. They're putting almost everything they earn back on the table.

The parallel everyone brings up is fiber optics in 2000, and honestly it holds. 500 billion invested because internet traffic was obviously going to explode. It did explode. Just not fast enough, most of the fiber laid was sitting unused underground in 2002, and the telecom crash wiped out something like 2 trillion in market value. The tech was the right one. The companies that built it died anyway.

Now I'm not playing doomsday prophet here. Nvidia actually makes money, which the telecoms back then did not, and current multiples are nothing like 1999. Nobody is betting on whether AI changes the world, that part is settled. The bet is on the price being paid to build it.

What bothers me more is the other scenario, the one where the bubble doesn't pop. To justify these valuations someone has to pay eventually, so token prices go up. And the nearly free AI we're all using right now, nothing says that lasts.

Au départ je voulais juste comprendre pourquoi mon prochain MacBook allait coûter 20% plus cher. Apple qui augmente ses prix en plein milieu d'année, sans nouveau produit, ça n'était jamais arrivé. Leur explication tient en une phrase : le prix des composants monte trop vite, on absorbait jusqu'ici, on ne peut plus.

Le coupable c'est la mémoire. La RAM a pris 90 à 95% sur un an d'après TrendForce. Samsung, SK Hynix et Micron ont basculé leur prod vers les data centers IA parce que ça paie beaucoup plus, et tout le reste du marché se bat sur les miettes. Sony a monté la PS5 de 100 balles en mars, Nintendo suit sur la Switch 2, Dell et HP pareil. Donc avant même de savoir si les data centers seront rentables un jour, la facture est déjà arrivée chez nous.

Là où j'ai vraiment tiqué c'est en tombant sur un rapport JP Morgan. Pour que les investissements IA prévus d'ici 2030 rapportent ne serait-ce que 10%, il faudrait environ 650 milliards de dollars de revenus par an. Par an, à perpétuité. Leur propre comparaison : 35 dollars par mois prélevés sur chaque possesseur d'iPhone de la planète. On en est nulle part. Et pendant ce temps le capex IA des hyperscalers est passé de 33% de leur cash flow opérationnel en 2023 à environ 93% cette année. Ils remettent quasiment tout ce qu'ils gagnent sur la table.

Le parallèle que tout le monde ressort c'est la fibre optique de 2000, et honnêtement il tient. 500 milliards investis parce que le trafic internet allait forcément exploser. Il a explosé. Juste pas assez vite, l'essentiel de la fibre posée dormait sous terre en 2002, et le crash télécom a effacé dans les 2000 milliards de valeur boursière. La techno était la bonne. Les boîtes qui l'ont construite sont mortes quand même.

Après je ne joue pas les prophètes de l'apocalypse. Nvidia gagne réellement de l'argent, ce que les télécoms de l'époque ne faisaient pas, et les multiples actuels n'ont rien à voir avec 1999. Personne ne parie sur le fait que l'IA change le monde ou pas, ça c'est acquis. Le pari c'est le prix payé pour la construire.

Ce qui me travaille le plus c'est l'autre scénario, celui où la bulle n'éclate pas. Pour justifier les valorisations il faudra bien que quelqu'un paie, donc les prix des tokens montent. Et l'IA quasi gratuite qu'on utilise tous en ce moment, rien ne dit que ça dure.

Au départ je voulais juste comprendre pourquoi mon prochain MacBook allait coûter 20% plus cher. Apple qui augmente ses prix en plein milieu d'année, sans nouveau produit, ça n'était jamais arrivé. Leur explication tient en une phrase : le prix des composants monte trop vite, on absorbait jusqu'ici, on ne peut plus.

Le coupable c'est la mémoire. La RAM a pris 90 à 95% sur un an d'après TrendForce. Samsung, SK Hynix et Micron ont basculé leur prod vers les data centers IA parce que ça paie beaucoup plus, et tout le reste du marché se bat sur les miettes. Sony a monté la PS5 de 100 balles en mars, Nintendo suit sur la Switch 2, Dell et HP pareil. Donc avant même de savoir si les data centers seront rentables un jour, la facture est déjà arrivée chez nous.

Là où j'ai vraiment tiqué c'est en tombant sur un rapport JP Morgan. Pour que les investissements IA prévus d'ici 2030 rapportent ne serait-ce que 10%, il faudrait environ 650 milliards de dollars de revenus par an. Par an, à perpétuité. Leur propre comparaison : 35 dollars par mois prélevés sur chaque possesseur d'iPhone de la planète. On en est nulle part. Et pendant ce temps le capex IA des hyperscalers est passé de 33% de leur cash flow opérationnel en 2023 à environ 93% cette année. Ils remettent quasiment tout ce qu'ils gagnent sur la table.

Le parallèle que tout le monde ressort c'est la fibre optique de 2000, et honnêtement il tient. 500 milliards investis parce que le trafic internet allait forcément exploser. Il a explosé. Juste pas assez vite, l'essentiel de la fibre posée dormait sous terre en 2002, et le crash télécom a effacé dans les 2000 milliards de valeur boursière. La techno était la bonne. Les boîtes qui l'ont construite sont mortes quand même.

Après je ne joue pas les prophètes de l'apocalypse. Nvidia gagne réellement de l'argent, ce que les télécoms de l'époque ne faisaient pas, et les multiples actuels n'ont rien à voir avec 1999. Personne ne parie sur le fait que l'IA change le monde ou pas, ça c'est acquis. Le pari c'est le prix payé pour la construire.

Ce qui me travaille le plus c'est l'autre scénario, celui où la bulle n'éclate pas. Pour justifier les valorisations il faudra bien que quelqu'un paie, donc les prix des tokens montent. Et l'IA quasi gratuite qu'on utilise tous en ce moment, rien ne dit que ça dure.

L'action Adobe est passée d'environ 700 dollars fin 2021 à environ 200 aujourd'hui. Moins 65%, en plein bull run tech. Et le plus con, c'est qu'ils viennent de boucler leur meilleure année: 23,8 milliards de revenus en 2025, croissance à deux chiffres. Sauf que la bourse s'en fout de ton chiffre annuel. Ce qu'elle regarde, c'est si le business tient encore debout dans dix ans. Et sur Adobe, plus grand monde n'y croit.

En mars ils ont payé 150 millions de dollars pour solder le procès du ministère de la Justice américain sur leurs frais de résiliation cachés. Le plan annuel payé mensuellement, présélectionné à l'inscription, qui t'engageait en douce sur un an avec 50% du restant dû si tu partais avant. Il y a des tutos YouTube à des centaines de milliers de vues juste pour réussir à résilier. Ils ont payé sans admettre la moindre faute, évidemment.

Mais le vrai problème date de 2013, quand ils ont tué la Creative Suite en boîte. La Master Collection coûtait environ 2 600 dollars, une fois, à toi pour dix ans. La Creative Cloud complète c'est autour de 840 dollars par an: tu repayes la suite entière tous les trois ans, à vie. Un graphiste abonné depuis 2013 a lâché plus de 8 000 dollars pour un logiciel qui ne sera jamais à lui. Ça a marché tant qu'il n'y avait nulle part où aller, et Adobe s'en était assuré en rachetant ou enterrant chaque rival (Macromedia en 2005, Freehand supprimé dans la foulée).

Puis tout a craqué en même temps. Les régulateurs ont bloqué le rachat de Figma fin 2023, et Adobe a versé un milliard de dédit que Figma a utilisé pour préparer son entrée en bourse. Canva a racheté Affinity et l'a rendu gratuit, un million d'inscriptions en une semaine. DaVinci Resolve bouffait déjà Premiere. Ajoute les hausses de prix, l'IA imposée de force (crédits mensuels passés de 1 000 à 25 sur le plan le moins cher) et les CGU de 2024 qui les autorisaient à accéder au contenu des utilisateurs. Les gens ont enfin des portes de sortie, ils les prennent.

Et le pire, c'est même pas ça. Depuis 40 ans, Adobe vend des outils qui rendent possibles des trucs difficiles, à condition d'y passer des années. C'est cet apprentissage qui faisait toute la valeur. L'IA générative flingue cette rareté: n'importe qui sort une image en un clic, et Firefly cannibalise même leur propre banque d'images. Quand Microsoft ou Meta ont plongé, l'IA leur a servi de bouée. Pour Adobe, la bouée c'est le truc qui les coule.

Bref. Ils font la meilleure année de leur histoire, et le marché les traite comme si c'était déjà le début de la fin.

Friday night the Commerce Department sent Anthropic an export control directive forcing them to block Fable 5 and Mythos 5 for any foreign national, inside or outside the US, including their own non-citizen engineers. Since they can't sort users by nationality, they shut it down for literally everyone. Other models like Opus 4.8 aren't affected.

The official reason: a jailbreak method on Fable. The letter landed at 5:21pm ET with zero detail on the actual threat. Anthropic basically says it's a misunderstanding, that their safeguards got red teamed for thousands of hours by the US government itself and the UK agency before launch, and that the flaw they're citing works on other public models too.

Best part: the government spent those thousands of hours helping Anthropic harden the model, so the flaw it's now waving around was already known to its own testers. It helped lock the thing down, then banned it overnight over a hole it had validated itself. Real consistent stuff.

What gets me isn't the case itself, it's the precedent. First time a state has pulled a frontier model out of circulation. Not the use you make of it, the model itself, at the source. Before, governments regulated what you're allowed to do with an AI. Now they decide an object is too powerful for certain hands and cut the tap.

And the detail that stings: a chip you block at customs, it's physical, it's traceable. A model isn't. Once it's out it copies and it has no border anymore. So you get a national security measure that cuts off the people who follow the law and leaves alone exactly the ones it claims to target, since those will just grab a jailbroken Chinese model in two clicks (Deepseek, Qwen, Kimi, downloadable, commercial license). Nicely done.

The lesson for anyone working with these tools daily I think is this: if your whole stack rests on one closed foreign model, there's a switch somewhere you don't hold, and it can flip on a Friday night because an administration had an idea. I used to rank this risk below performance in my tool choices. Now I'm reconsidering.

Vendredi soir le département du Commerce a envoyé une directive de contrôle à l'export qui oblige Anthropic à bloquer Fable 5 et Mythos 5 pour tout ressortissant étranger, dehors comme en US, y compris leurs propres ingénieurs non citoyens. Comme ils peuvent pas trier les gens par nationalité, ils ont tout éteint pour absolument tout le monde. Les autres modèles type Opus 4.8 ne sont pas touchés.

Motif officiel "merdique" : une méthode de jailbreak sur Fable, la nouvelle est tombée à 23h21 heure française sans le moindre détail sur la menace réelle. Anthropic répond en gros que c'est un malentendu, que leurs garde-fous ont été attaqués pendant des milliers d'heures par le gouvernement américain lui même et l'agence britannique avant la sortie, et que la faille citée marche aussi sur d'autres modèles publics.

Le meilleur dans l'histoire, le gouvernement a passé ces milliers d'heures à aider Anthropic à blinder le modèle, donc la faille qu'il brandit aujourd'hui ses propres testeurs la connaissaient déjà. Il a aidé à verrouiller le truc, puis il l'a interdit du jour au lendemain pour une brèche qu'il avait lui même validée. Niveau cohérence on est servi.

Ce qui me marque c'est pas l'affaire en soi, c'est le précédent. Première fois qu'un état retire un modèle de frontière de la circulation, pas l'usage qu'on en fait, le modèle lui même, à la source. Avant on encadrait ce qu'on a le droit de faire avec une IA. Là on décrète qu'un objet est trop puissant pour certaines mains et on coupe le robinet.

Et le détail qui pique ; une puce tu la bloques à la douane, c'est physique, ça se trace. Un modèle non. Une fois sorti il se copie et il a plus de frontière. Du coup tu obtiens une mesure de sécurité nationale qui coupe l'accès aux gens qui respectent la loi et laisse peinards exactement ceux qu'elle prétend viser, vu qu'eux ils iront récupérer un modèle chinois débridé en deux clics (Deepseek, Qwen, Kimi, téléchargeables, licence commerciale). Du grand art.

La leçon pour ceux qui bossent avec ces outils au quotidien je crois qu'elle est là : si toute ta chaîne repose sur un seul modèle fermé étranger, y a un interrupteur quelque part que tu ne tiens pas, et il peut sauter un vendredi soir parce qu'une administration a eu une idée. Perso je faisais passer ce risque après la perf dans mes choix d'outils. Là je révise.

Vendredi soir le département du Commerce a envoyé une directive de contrôle à l'export qui oblige Anthropic à bloquer Fable 5 et Mythos 5 pour tout ressortissant étranger, dehors comme en US, y compris leurs propres ingénieurs non citoyens. Comme ils peuvent pas trier les gens par nationalité, ils ont tout éteint pour absolument tout le monde. Les autres modèles type Opus 4.8 ne sont pas touchés.

Motif officiel "merdique" : une méthode de jailbreak sur Fable, la nouvelle est tombée à 23h21 heure française sans le moindre détail sur la menace réelle. Anthropic répond en gros que c'est un malentendu, que leurs garde-fous ont été attaqués pendant des milliers d'heures par le gouvernement américain lui même et l'agence britannique avant la sortie, et que la faille citée marche aussi sur d'autres modèles publics.

Le meilleur dans l'histoire, le gouvernement a passé ces milliers d'heures à aider Anthropic à blinder le modèle, donc la faille qu'il brandit aujourd'hui ses propres testeurs la connaissaient déjà. Il a aidé à verrouiller le truc, puis il l'a interdit du jour au lendemain pour une brèche qu'il avait lui même validée. Niveau cohérence on est servi.

Ce qui me marque c'est pas l'affaire en soi, c'est le précédent. Première fois qu'un état retire un modèle de frontière de la circulation, pas l'usage qu'on en fait, le modèle lui même, à la source. Avant on encadrait ce qu'on a le droit de faire avec une IA. Là on décrète qu'un objet est trop puissant pour certaines mains et on coupe le robinet.

Et le détail qui pique ; une puce tu la bloques à la douane, c'est physique, ça se trace. Un modèle non. Une fois sorti il se copie et il a plus de frontière. Du coup tu obtiens une mesure de sécurité nationale qui coupe l'accès aux gens qui respectent la loi et laisse peinards exactement ceux qu'elle prétend viser, vu qu'eux ils iront récupérer un modèle chinois débridé en deux clics (Deepseek, Qwen, Kimi, téléchargeables, licence commerciale). Du grand art.

La leçon pour ceux qui bossent avec ces outils au quotidien je crois qu'elle est là : si toute ta chaîne repose sur un seul modèle fermé étranger, y a un interrupteur quelque part que tu ne tiens pas, et il peut sauter un vendredi soir parce qu'une administration a eu une idée. Perso je faisais passer ce risque après la perf dans mes choix d'outils. Là je révise.

Disclaimer: yeah, I work in AI visibility, so I'm definitely biased on this. But what I want to get into actually cuts against what my own industry sells, so I figure it has a place here.

Back in mid-May Google put out its first real guide on how to show up in AI answers (AI Overviews, AI Mode). I saw a bunch of write-ups on it and it was always the same song, structure your headings, add Schema, the usual blah. Except there's a "mythbusting" section in the doc I haven't seen anyone pick up on, and it's the most interesting part. Google says in plain terms that the famous llms.txt file does nothing, that you should stop obsessing over Schema.org, and that chunking is smoke and mirrors. Made me smile a bit since that's basically the package some "GEO" agencies are charging for right now.

What they push instead is honestly kind of obvious. They talk about "commodity" vs "non-commodity" content. Like, if an AI can write your article on its own, it'll never cite you, makes sense, it already has the answer, why would it go looking for you. What gets cited is content with something the model doesn't have. A number you actually measured, a test you really ran, lived experience basically.

The example that stuck with me (not in Google's guide, somewhere else) is a small blog specialized in robot vacuums, garbage domain authority, and it outranks the New York Times in AI answers. The NYT has a domain like 3x stronger. Except the NYT puts out an affiliate listicle anyone could copy, and the blog guy films his actual tests with real measurements. Guess who gets cited.

And this is where it gets useful for you I think. It means for the most part you need neither a tool nor an agency. Take your most generic page, just ask yourself "could anyone write exactly this", and if the answer is yes, add something only you know. You don't even need data. A simple "the first question every client asks me is this" and you're already standing out. It's free and it weighs more than all the technical tweaks combined.

The one thing that still puzzles me is measurement. Why a LLM picks one source over another stays pretty opaque, and it shifts with every update. Curious if anyone's actually seeing real traffic from ChatGPT or Perplexity yet, because so far it's often like three visitors a month, and even then you can rarely tell which page it lands on.

Upfront, I work in AI visibility so I'm biased, but I'm saying this because it cuts against what my own industry sells.

Been reading the studies on what AI actually cites (GPT, Perplexity, etc) and Reddit keeps topping the list, often above wiki and youtube. Brand sites and polished corporate blogs barely show up.

Makes sense really. It wants the messy bit: people comparing stuff, complaining, changing their mind, saying what broke after two weeks. A thread where 15 people argue over 4 products beats your 'why we're the best' page every time.

No universal number though, it swings hard by engine. Early-year Tinuiti data had Reddit at 5%+ on GPT, 24% on Perplexity, and 0.1% on Gemini. I had to reread that last one because it looked wrong. Same Reddit, three engines, completely different. So when someone says "I optimize for AI", fair to ask which one.

And it's not stable either. Semrush showed Reddit's GPT share dropping from like 60% to 10% in two weeks off one upstream change.

Anyway, the bit I keep coming back to: the brands AI cites aren't the ones with the prettiest sites, they're the ones people talk about elsewhere. Ahrefs found 80% of URLs GPT cites aren't even in Google's top 100, which kind of breaks your brain if you come from SEO.

So honest take, not in my interest: if nobody mentions you anywhere, schema and llms.txt probably aren't your first problem.

Anyone clicked an AI citation and landed on some random 2021 Reddit post? Seeing it more and more.

Upfront, I work in AI visibility so I'm biased, but I'm saying this because it cuts against what my own industry sells.

Been reading the studies on what AI actually cites (GPT, Perplexity, etc) and Reddit keeps topping the list, often above wiki and youtube. Brand sites and polished corporate blogs barely show up.

Makes sense really. It wants the messy bit: people comparing stuff, complaining, changing their mind, saying what broke after two weeks. A thread where 15 people argue over 4 products beats your 'why we're the best' page every time.

No universal number though, it swings hard by engine. Early-year Tinuiti data had Reddit at 5%+ on GPT, 24% on Perplexity, and 0.1% on Gemini. I had to reread that last one because it looked wrong. Same Reddit, three engines, completely different. So when someone says "I optimize for AI", fair to ask which one.

And it's not stable either. Semrush showed Reddit's GPT share dropping from like 60% to 10% in two weeks off one upstream change.

Anyway, the bit I keep coming back to: the brands AI cites aren't the ones with the prettiest sites, they're the ones people talk about elsewhere. Ahrefs found 80% of URLs GPT cites aren't even in Google's top 100, which kind of breaks your brain if you come from SEO.

So honest take, not in my interest: if nobody mentions you anywhere, schema and llms.txt probably aren't your first problem.

Anyone clicked an AI citation and landed on some random 2021 Reddit post? Seeing it more and more.

Disclaimer: yeah, I work in AI visibility, so I'm definitely biased on this. But what I want to get into actually cuts against what my own industry sells, so I figure it has a place here.

Back in mid-May Google put out its first real guide on how to show up in AI answers (AI Overviews, AI Mode). I saw a bunch of write-ups on it and it was always the same song, structure your headings, add Schema, the usual blah. Except there's a "mythbusting" section in the doc I haven't seen anyone pick up on, and it's the most interesting part. Google says in plain terms that the famous llms.txt file does nothing, that you should stop obsessing over Schema.org, and that chunking is smoke and mirrors. Made me smile a bit since that's basically the package some "GEO" agencies are charging for right now.

What they push instead is honestly kind of obvious. They talk about "commodity" vs "non-commodity" content. Like, if an AI can write your article on its own, it'll never cite you, makes sense, it already has the answer, why would it go looking for you. What gets cited is content with something the model doesn't have. A number you actually measured, a test you really ran, lived experience basically.

The example that stuck with me (not in Google's guide, somewhere else) is a small blog specialized in robot vacuums, garbage domain authority, and it outranks the New York Times in AI answers. The NYT has a domain like 3x stronger. Except the NYT puts out an affiliate listicle anyone could copy, and the blog guy films his actual tests with real measurements. Guess who gets cited.

And this is where it gets useful for you I think. It means for the most part you need neither a tool nor an agency. Take your most generic page, just ask yourself "could anyone write exactly this", and if the answer is yes, add something only you know. You don't even need data. A simple "the first question every client asks me is this" and you're already standing out. It's free and it weighs more than all the technical tweaks combined.

The one thing that still puzzles me is measurement. Why an LLM picks one source over another stays pretty opaque, and it shifts with every update. So I'm curious: are you already seeing real traffic come in from ChatGPT or Perplexity, or is it still like three visitors a month? And if you are, can you actually tell which pages it lands on?

Disclaimer: yeah, I work in AI visibility, so I'm definitely biased on this. But what I want to get into actually cuts against what my own industry sells, so I figure it has a place here.

Back in mid-May Google put out its first real guide on how to show up in AI answers (AI Overviews, AI Mode). I saw a bunch of write-ups on it and it was always the same song, structure your headings, add Schema, the usual blah. Except there's a "mythbusting" section in the doc I haven't seen anyone pick up on, and it's the most interesting part. Google says in plain terms that the famous llms.txt file does nothing, that you should stop obsessing over Schema.org, and that chunking is smoke and mirrors. Made me smile a bit since that's basically the package some "GEO" agencies are charging for right now.

What they push instead is honestly kind of obvious. They talk about "commodity" vs "non-commodity" content. Like, if an AI can write your article on its own, it'll never cite you, makes sense, it already has the answer, why would it go looking for you. What gets cited is content with something the model doesn't have. A number you actually measured, a test you really ran, lived experience basically.

The example that stuck with me (not in Google's guide, somewhere else) is a small blog specialized in robot vacuums, garbage domain authority, and it outranks the New York Times in AI answers. The NYT has a domain like 3x stronger. Except the NYT puts out an affiliate listicle anyone could copy, and the blog guy films his actual tests with real measurements. Guess who gets cited.

And this is where it gets useful for you I think. It means for the most part you need neither a tool nor an agency. Take your most generic page, just ask yourself "could anyone write exactly this", and if the answer is yes, add something only you know. You don't even need data. A simple "the first question every client asks me is this" and you're already standing out. It's free and it weighs more than all the technical tweaks combined.

The one thing that still puzzles me is measurement. Why an LLM picks one source over another stays pretty opaque, and it shifts with every update. So I'm curious: are you already seeing real traffic come in from ChatGPT or Perplexity, or is it still like three visitors a month? And if you are, can you actually tell which pages it lands on?

Disclaimer: yeah, I work in AI visibility, so I'm definitely biased on this. But what I want to get into actually cuts against what my own industry sells, so I figure it has a place here.

Back in mid-May Google put out its first real guide on how to show up in AI answers (AI Overviews, AI Mode). I saw a bunch of write-ups on it and it was always the same song, structure your headings, add Schema, the usual blah. Except there's a "mythbusting" section in the doc I haven't seen anyone pick up on, and it's the most interesting part. Google says in plain terms that the famous llms.txt file does nothing, that you should stop obsessing over Schema.org, and that chunking is smoke and mirrors. Made me smile a bit since that's basically the package some "GEO" agencies are charging for right now.

What they push instead is honestly kind of obvious. They talk about "commodity" vs "non-commodity" content. Like, if an AI can write your article on its own, it'll never cite you, makes sense, it already has the answer, why would it go looking for you. What gets cited is content with something the model doesn't have. A number you actually measured, a test you really ran, lived experience basically.

The example that stuck with me (not in Google's guide, somewhere else) is a small blog specialized in robot vacuums, garbage domain authority, and it outranks the New York Times in AI answers. The NYT has a domain like 3x stronger. Except the NYT puts out an affiliate listicle anyone could copy, and the blog guy films his actual tests with real measurements. Guess who gets cited.

And this is where it gets useful for you I think. It means for the most part you need neither a tool nor an agency. Take your most generic page, just ask yourself "could anyone write exactly this", and if the answer is yes, add something only you know. You don't even need data. A simple "the first question every client asks me is this" and you're already standing out. It's free and it weighs more than all the technical tweaks combined.

The one thing that still puzzles me is measurement. Why an LLM picks one source over another stays pretty opaque, and it shifts with every update. So I'm curious: are you already seeing real traffic come in from ChatGPT or Perplexity, or is it still like three visitors a month? And if you are, can you actually tell which pages it lands on?

Been reading the studies on what AI actually cites (GPT, Perplexity, etc) and Reddit keeps topping the list, often above wiki and youtube. Brand sites and polished corporate blogs barely show up.

Makes sense really. It wants the messy bit: people comparing stuff, complaining, changing their mind, saying what broke after two weeks. A thread where 15 people argue over 4 products beats your "why we're the best" page every time.

No universal number though, it swings hard by engine. Early-year Tinuiti data had Reddit at 5%+ on GPT, 24% on Perplexity, and 0.1% on Gemini. I had to reread that last one because it looked wrong. Same Reddit, three engines, completely different. So when someone says "I optimize for AI", fair to ask which one.

And it's not stable either. Semrush showed Reddit's GPT share dropping from like 60% to 10% in two weeks off one upstream change.

Anyway, the bit I keep coming back to: the brands AI cites aren't the ones with the prettiest sites, they're the ones people talk about elsewhere. Ahrefs found 80% of URLs GPT cites aren't even in Google's top 100, which kind of breaks your brain if you come from SEO.

So honest take, not in my interest: if nobody mentions you anywhere, schema and llms.txt probably aren't your first problem.

Anyone clicked an AI citation and landed on some random 2021 Reddit post? Seeing it more and more.

Been reading the studies on what AI actually cites (GPT, Perplexity, etc) and Reddit keeps topping the list, often above wiki and youtube. Brand sites and polished corporate blogs barely show up.

Makes sense really. It wants the messy bit: people comparing stuff, complaining, changing their mind, saying what broke after two weeks. A thread where 15 people argue over 4 products beats your "why we're the best" page every time.

No universal number though, it swings hard by engine. Early-year Tinuiti data had Reddit at 5%+ on GPT, 24% on Perplexity, and 0.1% on Gemini. I had to reread that last one because it looked wrong. Same Reddit, three engines, completely different. So when someone says "I optimize for AI", fair to ask which one.

And it's not stable either. Semrush showed Reddit's GPT share dropping from like 60% to 10% in two weeks off one upstream change.

Anyway, the bit I keep coming back to: the brands AI cites aren't the ones with the prettiest sites, they're the ones people talk about elsewhere. Ahrefs found 80% of URLs GPT cites aren't even in Google's top 100, which kind of breaks your brain if you come from SEO.

So honest take, not in my interest: if nobody mentions you anywhere, schema and llms.txt probably aren't your first problem.

Anyone clicked an AI citation and landed on some random 2021 Reddit post? Seeing it more and more.

Been reading the studies on what AI actually cites (GPT, Perplexity, etc) and Reddit keeps topping the list, often above wiki and youtube. Brand sites and polished corporate blogs barely show up.

Makes sense really. It wants the messy bit: people comparing stuff, complaining, changing their mind, saying what broke after two weeks. A thread where 15 people argue over 4 products beats your "why we're the best" page every time.

No universal number though, it swings hard by engine. Early-year Tinuiti data had Reddit at 5%+ on GPT, 24% on Perplexity, and 0.1% on Gemini. I had to reread that last one because it looked wrong. Same Reddit, three engines, completely different. So when someone says "I optimize for AI", fair to ask which one.

And it's not stable either. Semrush showed Reddit's GPT share dropping from like 60% to 10% in two weeks off one upstream change.

Anyway, the bit I keep coming back to: the brands AI cites aren't the ones with the prettiest sites, they're the ones people talk about elsewhere. Ahrefs found 80% of URLs GPT cites aren't even in Google's top 100, which kind of breaks your brain if you come from SEO.

So honest take, not in my interest: if nobody mentions you anywhere, schema and llms.txt probably aren't your first problem.

Anyone clicked an AI citation and landed on some random 2021 Reddit post? Seeing it more and more.

Disclaimer: yeah, I work in AI visibility, so I'm definitely biased on this. But what I want to get into actually cuts against what my own industry sells, so I figure it has a place here.

Back in mid-May Google put out its first real guide on how to show up in AI answers (AI Overviews, AI Mode). I saw a bunch of write-ups on it and it was always the same song, structure your headings, add Schema, the usual blah. Except there's a "mythbusting" section in the doc I haven't seen anyone pick up on, and it's the most interesting part. Google says in plain terms that the famous llms.txt file does nothing, that you should stop obsessing over Schema.org, and that chunking is smoke and mirrors. Made me smile a bit since that's basically the package some "GEO" agencies are charging for right now.

What they push instead is honestly kind of obvious. They talk about "commodity" vs "non-commodity" content. Like, if an AI can write your article on its own, it'll never cite you, makes sense, it already has the answer, why would it go looking for you. What gets cited is content with something the model doesn't have. A number you actually measured, a test you really ran, lived experience basically.

The example that stuck with me (not in Google's guide, somewhere else) is a small blog specialized in robot vacuums, garbage domain authority, and it outranks the New York Times in AI answers. The NYT has a domain like 3x stronger. Except the NYT puts out an affiliate listicle anyone could copy, and the blog guy films his actual tests with real measurements. Guess who gets cited.

And this is where it gets useful for you I think. It means for the most part you need neither a tool nor an agency. Take your most generic page, just ask yourself "could anyone write exactly this", and if the answer is yes, add something only you know. You don't even need data. A simple "the first question every client asks me is this" and you're already standing out. It's free and it weighs more than all the technical tweaks combined.

The one thing that still puzzles me is measurement. Why an LLM picks one source over another stays pretty opaque, and it shifts with every update. So I'm curious: are you already seeing real traffic come in from ChatGPT or Perplexity, or is it still like three visitors a month? And if you are, can you actually tell which pages it lands on?

Disclaimer: yeah, I work in AI visibility, so I'm definitely biased on this. But what I want to get into actually cuts against what my own industry sells, so I figure it has a place here.

Back in mid-May Google put out its first real guide on how to show up in AI answers (AI Overviews, AI Mode). I saw a bunch of write-ups on it and it was always the same song, structure your headings, add Schema, the usual blah. Except there's a "mythbusting" section in the doc I haven't seen anyone pick up on, and it's the most interesting part. Google says in plain terms that the famous llms.txt file does nothing, that you should stop obsessing over Schema.org, and that chunking is smoke and mirrors. Made me smile a bit since that's basically the package some "GEO" agencies are charging for right now.

What they push instead is honestly kind of obvious. They talk about "commodity" vs "non-commodity" content. Like, if an AI can write your article on its own, it'll never cite you, makes sense, it already has the answer, why would it go looking for you. What gets cited is content with something the model doesn't have. A number you actually measured, a test you really ran, lived experience basically.

The example that stuck with me (not in Google's guide, somewhere else) is a small blog specialized in robot vacuums, garbage domain authority, and it outranks the New York Times in AI answers. The NYT has a domain like 3x stronger. Except the NYT puts out an affiliate listicle anyone could copy, and the blog guy films his actual tests with real measurements. Guess who gets cited.

And this is where it gets useful for you I think. It means for the most part you need neither a tool nor an agency. Take your most generic page, just ask yourself "could anyone write exactly this", and if the answer is yes, add something only you know. You don't even need data. A simple "the first question every client asks me is this" and you're already standing out. It's free and it weighs more than all the technical tweaks combined.

The one thing that still puzzles me is measurement. Why an LLM picks one source over another stays pretty opaque, and it shifts with every update. So I'm curious: are you already seeing real traffic come in from ChatGPT or Perplexity, or is it still like three visitors a month? And if you are, can you actually tell which pages it lands on?