Purpl3Nurpl3

@zephyr_z9 2031 is the latest estimate for AGI

Guess the easter egg, anon?? Hint: Why is there a huge jump in density from 2030 to 2031

@nebiustf built a dashboard to track ai related bottlenecks: aibottlenecks.app

@demian_ai @nebiustf Beautifully done, thanks!

Inference got a hundred times cheaper this year. The compute bill went up anyway. If you understand why those two sentences are both true at the same time, you understand the most important thing happening in AI right now. I work on inference for a living, at @nebiustf, where we run open-source managed inference at scale. Most of what follows is what I'm seeing from inside the bill. 12 months ago, the cost of 1M tokens of frontier-class reasoning was somewhere on the order of $60. Today, an equivalent quality of output costs roughly $0.50. Price /token of o1-level intelligence has dropped about a 128x in a year. Price of GPT-4-level output has dropped roughly 100x since the original GPT-4 shipped. By any normal reading of a technology cost curve, this should be deflationary. It should be saving customers money. The opposite has happened. The total compute bill at every hyperscaler is going up, not down. Anthropic just signed multi-year capacity deals with both XAI and Amazon. Microsoft's Azure capex guide for 2026 starts with an eight. OpenAI is reportedly spending more on compute every quarter than it did in all of 2023. Nvidia paid roughly twenty billion dollars to acquire Groq, an inference-specialist company that did not exist as a serious commercial entity three years ago. The cost curve and the demand curve crossed, and then the demand curve lapped the cost curve. Here is what happened underneath. A reasoning model burns roughly 10x the output tokens of a non-reasoning model on the same task, because it spends most of its tokens thinking out loud before answering. An agentic workflow chains roughly twenty times the requests of a single-shot completion, because it loops, calls tools, plans, retries, and synthesizes. A modern deep-research query (the kind a research analyst can fire off in fifteen seconds and then walk away from for ten minutes) costs more compute than 10 original GPT-4 queries combined. We made every individual token a hundred times cheaper, and then we built a generation of products that consume ten thousand times more tokens. This is the Jevons paradox playing out at trillion-dollar scale, in compressed time, in front of everyone. Jevons noticed in 1865 that making coal-burning more efficient did not reduce coal consumption. It increased it, because efficiency unlocked uses that were previously uneconomic. Steam engines became more practical at smaller scales. Whole industries that could not afford coal at the old price suddenly could. Britain's coal consumption rose sharply, not despite the efficiency gains, but because of them. The same thing is happening to AI compute right now and it is happening faster than any analogous historical cycle. Falling token prices did not contract demand. They unlocked agents, deep research, code-writing systems, multi-step reasoning, persistent memory, the entire next layer of AI products. Every product in that next layer consumes orders of magnitude more compute than the chat interfaces it is replacing. The math at the aggregate level is brutal: 100x cheaper tokens times 10 000 more tokens equals a 100x larger total bill. The implications stack quickly. If you are running a hyperscaler, your 2026 capex guide is not a peak. It is a step on a curve. Inference is structurally always-on, twenty-four hours a day, in a way that training never was. Training is bursty. You spin up a cluster, run for weeks or months, and stop. Inference runs continuously, scales with usage, and the usage curve is exponential. Your power bill, your cooling bill, your transceiver count, your storage footprint, all of these were sized for a workload mix that no longer exists. If you are running an AI software company built on top of someone else's closed API, you have a problem that did not exist a year ago. Your gross margins get worse as your customers get more value out of your product, because the more they use it, the more compute you pay for. The companies that win this are the ones that figured out vertical integration before the math caught them. If you are watching this from a distance and trying to understand where the next bottlenecks form, the answer is everywhere downstream of "more inference compute, always-on, with massive memory state per session." The KV cache, the running memory state of a long conversation or an agent loop, is the silent monster of the inference era. It does not scale linearly with parameters. It scales linearly with context length and number of agent steps. A long agent session can hold tens of gigabytes of state per user, per session. Multiply that by every concurrent user of every product, and you understand why $MU, $SNDK, $TOWCF, and the entire memory and packaging layer have re-rated the way they have. The CPU-to-GPU ratio is evolving. Training is 1:8. Basic chat inference is 1:4. Agentic inference is 1:1, sometimes CPU-heavy. Google has split its TPU line in two, with a dedicated inference chip carrying tripled SRAM for KV cache. $INTC and $AMD just spent two earnings calls explaining that this shift is structural, not cyclical. The hardware map is redrawing in real time and the financial press is mostly still writing about training clusters. The right framing of where we are right now is not that AI is hitting a wall. The framing a year ago that scaling was hitting a wall was the most expensive bad take of the cycle. The right framing is that AI got dramatically cheaper, dramatically more capable, and dramatically more useful, and the cost of running it at the new equilibrium of demand is much higher than the cost at the old equilibrium of demand, because the new equilibrium is enormous. A meaningful share of what we actually do at Token Factory, day to day, is help customers stop their bills from running away from them. KV-cache management. Speculative decoding. Quantization. Routing. The kind of vertical integration that, eighteen months ago, every product team was happy to leave abstracted away behind a closed API. The reason this stack matters now is the same reason this whole essay matters: at the new equilibrium of inference demand, the cost of treating compute as a commodity is no longer survivable. The companies that figure out the layer beneath the API are the ones who keep their margins. Cheaper tokens. More tokens. Same coal as 1865.

@LuisArencibiaUS @Osint613 Trump supporter?!? You were retweeting anti ICE posts a year ago. Kick rocks.

BREAKING: 🔴 U.S. President Donald Trump: "I have instructed Secretary of War, Pete Hegseth, The Chairman of The Joint Chiefs of Staff, General Daniel Caine, and The United States Military, that we will NOT be doing the scheduled attack of Iran tomorrow, but have further instructed them to be prepared to go forward with a full, large scale assault of Iran, on a moment’s notice, in the event that an acceptable Deal is not reached. Thank you for your attention to this matter! President DONALD J. TRUMP."

RT @Geiger_Capital: Americans are overwhelmingly against AI, specifically the buildout of data centers… Everyone you talk to outside of te…

Sanders et AOC veulent geler la construction de tous les data centers IA aux États-Unis. Il faut comprendre ce qui se passe vraiment. Ce n'est pas une bataille politique parmi d'autres. C'est la dernière convulsion d'une vision du monde qui a compris, inconsciemment, qu'elle est condamnée. Le socialisme n'est pas une théorie économique. C'est une structure morale qui a besoin de trois choses pour exister : 1. De la rareté à redistribuer 2. Des victimes à défendre 3. Une classe d'intermédiaires pour orchestrer le tout Retirez un seul de ces trois piliers et l'édifice s'effondre. L'IA est en train de retirer les trois en même temps. La rareté d'abord. Pendant 200 ans, l'économie politique a tourné autour d'une question : comment répartir une production limitée ? Marx, Keynes, Piketty — tous bâtissent sur ce postulat. Mais l'IA inverse l'équation. Le coût marginal de l'intelligence tend vers zéro. La production de logiciel, de design, d'analyse, de code, bientôt de matière manufacturée par robotique avancée — tout cela devient quasi-gratuit. Dans un monde d'abondance, la question "qui mérite quoi" perd son sens. Il n'y a plus rien à arbitrer. Les victimes ensuite. L'IA est le plus grand égalisateur d'accès au savoir et aux compétences de l'histoire humaine. Un gamin au fin fond du Bangladesh a aujourd'hui accès au même tuteur que l'héritier d'une famille new-yorkaise. Un développeur solo produit ce qu'une équipe de 20 produisait il y a trois ans. Les barrières s'effondrent. Or sans victimes structurelles, plus de cause à défendre, plus de mandat moral à exercer. Les intermédiaires enfin. C'est le point le plus douloureux pour eux. Le socialisme a toujours eu besoin d'une caste : journalistes-militants, fonctionnaires-experts, ONG-prescriptrices, politiques-redistributeurs. Cette caste vit du fait qu'elle prétend traduire la réalité aux masses. L'IA rend cette traduction obsolète. Tout le monde peut interroger directement la source, vérifier un chiffre, comparer des modèles, simuler une politique publique. Le monopole de l'interprétation est mort. Voilà pourquoi je dis que l'IA est un catalyseur de vérité. Elle ne crée pas la vérité — elle la rend ininterprétable. Les systèmes qui produisent de la valeur deviennent visibles. Ceux qui en captent sans en produire deviennent visibles aussi. Le voile tombe. Et c'est ça qui est insupportable. Pas la perte de pouvoir — la perte de sens. Réaliser que ta vision du monde, ton militantisme, ta carrière entière reposaient sur un édifice qui ne tenait que par la rareté et l'opacité. C'est une blessure narcissique d'une profondeur abyssale. La réaction est mécanique : il faut bloquer le catalyseur. Pas pour des raisons rationnelles (l'argument "énergie" est risible quand on voit leurs positions sur le nucléaire). Pour des raisons existentielles. Il faut empêcher l'avenir d'advenir, parce que l'avenir les efface. 300 lois locales. Un moratoire fédéral. Des moratoires européens (AI Act). Tout le pattern est le même partout : freiner, ralentir, encadrer, taxer. Pas réguler intelligemment — paralyser. Mais ils ont déjà perdu. Et au fond d'eux, ils le savent. La Chine ne s'arrêtera pas. Les Émirats ne s'arrêteront pas. L'Inde, Singapour, l'Argentine de Milei, certains États américains — personne ne s'arrêtera. Bloquer la construction de data centers à San Francisco ne fait que déplacer le centre de gravité. Le seul effet net est d'appauvrir ceux qu'ils prétendent défendre. C'est le rebond du chat mort. Un dernier sursaut avant l'immobilité définitive. PS : tout n'est pas perdu pour eux. La porte est ouverte. Il suffit de comprendre que créer de la valeur est plus gratifiant que la redistribuer, que construire est plus puissant que dénoncer, et que l'entrepreneuriat est la seule forme contemporaine d'action politique qui change réellement le monde. La reconversion est possible. Elle commence par accepter une chose simple : personne n'a besoin de toi pour être sauvé. Mais beaucoup de gens ont besoin de toi pour construire.

@WrenHamilt61477 @jag2060 @elonmusk Opinion irrelevant

@jag2060 @elonmusk Don't be daft we can't eat AI food. It serves no purpose for ordinary folk in fact it's a threat to us in the hands of evil operators and we are subsidizing it's energy, which they are already stealing for 'industry'. Only it will be an even heavier burden bc it wants nuclear tec

Hmm

Are you easily discouraged?

He'd immediately start sailing.

@zephyr_z9 The goalposts will continue to move until we reach AGI

Grok did a good job, but it's missing a very important point that will determine the duration of these bottlenecks

@about2face @BitcoinSapiens Eat a dick

@BitcoinSapiens Illegals imply they came in illegally. Undocumented more aptly describes those we take issue with this administration pursuing without having committed any criminal offenses. Scott also hates not being the only one who can talk over people. He's a greedy, disingenuous dick.

Cameron Kasky: "You don’t get to say the word illegals anymore." Scott Jennings: "Who are you to tell me what I can and can't say? I've never met you, brother. I can say whatever I want. They're illegal aliens, and that's what the law calls them."

What it’s like to be a white male medical student in 2026 compactmag.com/article/medici…

@XPOLogistics One of your semis just about killed my family on 494, near the Wayzata exit, in MN. Your vehicle swerved into the passing lane, forcing them to almost hit the concrete barrier. Driver was talking in his phone.

This Military Appreciation Month, we honor the veterans, active-duty service members and military families who are part of XPO. Your commitment, leadership and teamwork strengthen our company and inspire those around you. Thank you for your service.

Make this viral She should Rot

the best 3 minutes of video I've watched this year

EXCLUSIVE: NIH has removed virologist Ralph Baric from all his grants; UNC placed Baric on leave. Senior HHS officials says UNC was complicit in starting the COVID pandemic. “Baric designed the gun,” he said. “But the Chinese built it, and then they pulled the trigger.” realclearinvestigations.com/articles/2026/…

"RealClearInvestigations learned that the federal government has quietly removed [EcoHealth Alliance and Wuhan Institute of Virology collaborator Ralph] Baric from all his NIH grants." "RCI has also learned that UNC placed Baric on leave." realclearinvestigations.com/articles/2026/…

Writing your own story, @GovTimWalz? @nickshirleyy already exposed the parts you were hoping nobody would read.

Hey @ABC You fired Roseanne Barr for a simple tweet which cost an entire production crew to lose their jobs. WHY THE FCK are you still employing the CANCER of late night TV: Jimmy "the talentless Hack" Kimmel?