https://aijourney.space/google/news/ Changes at Google, logged from its own announcements, documentation and price pages, never from another publisher, each with what it means for you. en https://aijourney.space/updates/2026-11-03-we-re-providing-funding-for-organizations-building-ai-for-teachers/ 2026-11-03-we-re-providing-funding-for-organizations-building-ai-for-teachers Tue, 03 Nov 2026 00:00:00 GMT What happened. On November 3, 2026, Google.org said it is providing over $9.5 million in funding across five organizations building AI tools with and for educators, spanning lesson planning, digital literacy and learning standards work. Sources. Google newsroom post on Google.org funding for AI education tools, published November 3, 2026: https://blog.google/products-and-platforms/products/education/google-org-funding-ai-education (read 3 Nov 2026) Why it matters. A grant list is not a product launch, so a district weighing whether to adopt any resulting tool should ask who owns the software once the money runs out and whether Google keeps a stake in what gets built. https://aijourney.space/updates/2026-11-03-cross-lingual-persona-effects-in-llm-negotiation/ 2026-11-03-cross-lingual-persona-effects-in-llm-negotiation Tue, 03 Nov 2026 00:00:00 GMT What happened. On October 30, 2026, researchers Saehee Eom and Tejas Khandwekar posted a paper describing 432 attempted negotiations run with identical buyer and seller instances of Google's Gemini 3.6 Flash, testing the same 12 CraigslistBargain scenarios in English and Korean under a pilot-scale design that crossed interaction language, seller urgency, and buyer persona. Sources. arXiv paper, Cross-Lingual Persona Effects in LLM Negotiation, posted October 30, 2026: https://arxiv.org/abs/2611.00300 (read 3 Nov 2026) Why it matters. For anyone building a Gemini powered negotiation, support, or sales agent meant to serve customers in more than one language, this work means a tone instruction tuned and tested in English cannot be assumed to carry the same weight once the conversation shifts into Korean, so a team shipping that workflow abroad owes itself a fresh round of evaluation rather than a straight translation of its prompts. https://aijourney.space/updates/2026-11-03-hacking-or-just-failing-a-more-capable-llm-monitor-better-separates-rewa/ 2026-11-03-hacking-or-just-failing-a-more-capable-llm-monitor-better-separates-rewa Tue, 03 Nov 2026 00:00:00 GMT What happened. On 30 October 2026, a paper posted to arXiv described a sandboxed reward hacking test in which Google's Gemini 3.1 Flash-Lite, used as a monitor, reached an AUC of 0.80 against a pre-registered primary monitor that reached AUC 0.64, while completing 9/10 clean tasks against 2/10 for the smaller agent it was set to judge. Sources. arXiv paper, Hacking or Just Failing? A More Capable LLM Monitor Better Separates Reward Hacking from Honest Failure, submitted 30 October 2026: https://arxiv.org/abs/2611.00407 (read 3 Nov 2026) Why it matters. For teams building oversight into AI agents, the gap between a monitor that reaches AUC 0.80 and one stuck near 0.64 is the difference between catching cheating and missing it, so the choice of judge model becomes a safety decision rather than just a cost line, especially since even the stronger monitor still missed a manipulation it could describe correctly but never flagged across dozens of episodes. https://aijourney.space/updates/2026-11-01-gemini-notebook-makes-hundreds-of-textbooks-available-to-help-students-l/ 2026-11-01-gemini-notebook-makes-hundreds-of-textbooks-available-to-help-students-l Sun, 01 Nov 2026 00:00:00 GMT What happened. On October 29, 2026, Google said Gemini Notebook now offers students free access to hundreds of textbooks from OpenStax, the Open Textbook Library and S Chand Publishing, part of the Expert Intelligence initiative it introduced the previous month to connect Google AI products to trusted source material. Sources. Google newsroom post on Gemini Notebook textbook access, published October 29, 2026: https://blog.google/innovation-and-ai/products/gemini-notebook/new-access-textbooks (read 1 Nov 2026) Why it matters. A student who once paid for a textbook or hunted a free PDF can now add the same course text to a notebook and get answers tied to its pages, which shifts the cost of studying away from the book itself and toward whichever notebook tool a school or student settles on. https://aijourney.space/updates/2026-11-01-how-to-use-gemini-to-update-your-resume/ 2026-11-01-how-to-use-gemini-to-update-your-resume Sun, 01 Nov 2026 00:00:00 GMT What happened. On October 29, 2026, Google published a newsroom post by contributor Kimberly Fusaro explaining a four step method for using the Gemini app to write resume bullets, in which a person describes their past roles in plain, unedited language, asks Gemini to act as a professional resume writer and produce four action verb bullets for a named role, chats back and forth to adjust tone and detail, then copies the result into their resume. Sources. Google newsroom post, how to use Gemini to update your resume, published October 29, 2026: https://blog.google/innovation-and-ai/products/gemini-app/update-resume-with-gemini (read 1 Nov 2026) Why it matters. A job seeker who treats a chat assistant as a drafting partner rather than a search box gets a document shaped for applicant tracking software without paying for a resume writer, but the method still asks the person to supply accurate details and to read every bullet back, since a model asked to sound executive and ATS-friendly will happily invent a polished phrase that overstates what actually happened on the job. https://aijourney.space/updates/2026-11-01-dive-into-new-topics-with-immersive-explorations-in-the-gemini-app/ 2026-11-01-dive-into-new-topics-with-immersive-explorations-in-the-gemini-app Sun, 01 Nov 2026 00:00:00 GMT What happened. On October 28, 2026, Google said the Gemini app now carries a library of immersive explorations, reachable from the student hub by selecting Try immersive explorations, with direct access from a chat conversation coming later. Sources. Google newsroom post on immersive explorations in the Gemini app, published October 28, 2026: https://blog.google/innovation-and-ai/products/gemini-app/immersive-explorations-student-hub (read 1 Nov 2026) Why it matters. A feature tucked inside a student hub rather than the main chat box is easy to miss, so a parent or teacher checking what a child's account can do has one more place to look before judging whether the tool suits a classroom task. https://aijourney.space/updates/2026-10-27-multi-agent-retrieval-augmented-causality-assessment-marca-of-individual/ 2026-10-27-multi-agent-retrieval-augmented-causality-assessment-marca-of-individual Tue, 27 Oct 2026 00:00:00 GMT What happened. On October 23, 2026, researchers posted a preprint describing MARCA, a four-agent causality assessment framework for pharmacovigilance that combines Gemini 2.5 Pro and DeepSeek-V3.2 to review individual case safety reports, testing it against a consensus expert reference standard of 215 ICSRs and 1,562 drug-event combinations drawn from the FDA's FAERS database covering the first two quarters of 2025. Sources. arXiv preprint describing the MARCA framework, submitted October 23, 2026: https://arxiv.org/abs/2610.33279 (read 27 Oct 2026) our Google API price table, which lists Gemini 2.5 Pro at $1.25 per million input tokens: https://aijourney.space/google/gemini/#api-prices (read 27 Oct 2026) Why it matters. For a site that tracks the Gemini API price table, the result matters because it shows Gemini 2.5 Pro doing document-heavy literature retrieval and clinical reasoning work, not just chat replies, at a listed rate of $1.25 per million input tokens for prompts under 200,000 tokens. A pharmacovigilance team weighing whether to run such a four-agent pipeline across thousands of case reports now has a published agreement benchmark to set against that per-token bill, rather than relying on marketing claims about model capability. https://aijourney.space/updates/2026-10-21-get-hands-free-personalized-help-on-the-road-with-gemini/ 2026-10-21-get-hands-free-personalized-help-on-the-road-with-gemini Wed, 21 Oct 2026 00:00:00 GMT What happened. On October 20, 2026, Google said Gemini's Personal Intelligence feature is rolling out globally to cars, starting with Android Auto and arriving on cars with Google built-in in the coming months, letting the assistant read a driver's connected Gmail, Google Calendar, Google Photos, Google Maps, YouTube and Search data to answer spoken questions without the driver touching a phone. Sources. Google newsroom post on Gemini Personal Intelligence in cars, published October 20, 2026: https://blog.google/products-and-platforms/platforms/android/android-auto-cars-with-google-built-in-gemini-personal-intelligence (read 21 Oct 2026) Why it matters. For a driver, this turns a navigation assistant into a data-linked one, so declining Personal Intelligence keeps the car's voice controls working but the assistant stops drawing on personal messages, calendar entries or saved photos, a choice now made once in the Gemini settings rather than feature by feature as Google adds more connected apps over time. https://aijourney.space/updates/2026-10-21-new-gemini-tools-for-small-business-owners/ 2026-10-21-new-gemini-tools-for-small-business-owners Wed, 21 Oct 2026 00:00:00 GMT What happened. On October 20, 2026, Google announced a new business landing page inside the Gemini app, giving small business owners a single hub for branding, financial summaries and customer insight tools, alongside direct connections to Google Business Profile, Google Ads, Google Workspace and third party apps including Adobe, Canva, Dropbox, Shopify, Wix, Zoho and Xero. Sources. Google newsroom post announcing Gemini tools for small business owners, published October 20, 2026: https://blog.google/innovation-and-ai/products/gemini-app/new-tools-small-business-owners (read 21 Oct 2026) Why it matters. For an owner running a shop or a farm with no marketing staff, this moves work that used to mean separate logins and separate subscriptions into one chat window, which lowers the cost of trying AI tools for the first time but also ties a business's branding and bookkeeping more tightly to a single vendor's app. A business that uploads its brand kit and connects its finance records to Gemini has fewer reasons to leave later, and that lock-in is worth weighing against the hours saved. https://aijourney.space/updates/2026-10-21-pronouncing-hard-words-phoneme-adaptation-of-a-pretrained-conversational/ 2026-10-21-pronouncing-hard-words-phoneme-adaptation-of-a-pretrained-conversational Wed, 21 Oct 2026 00:00:00 GMT What happened. On October 16, 2026, a team of researchers, including authors affiliated with Google, posted a paper to arXiv describing how to retrofit explicit phoneme control onto an already trained conversational text to speech model so it can pronounce rare drug names correctly without full retraining, using an ARPAbet vocabulary extension, continued phoneme augmented fine tuning, and an inference time acoustic biasing step, then evaluated the result on a 7,500 clip clinical drug pronunciation set judged by Gemini. Sources. arXiv paper 2610.24164, Pronouncing Hard Words, posted October 16, 2026: https://arxiv.org/abs/2610.24164 (read 21 Oct 2026) Why it matters. For anyone building a voice agent that reads back a prescription or a dosage over a phone line, the gap between a model that sounds natural and one that says the right word is the entire safety case, so a method that closes that gap without retraining the whole pipeline changes what a clinic or a pharmacy can ship on a reasonable budget. It also shows Gemini being used as a judge of spoken correctness rather than only as a chat assistant, which widens what the service gets asked to grade inside somebody else's research pipeline. https://aijourney.space/updates/2026-10-17-we-re-sharing-3-tips-to-study-smarter-with-gemini-notebook/ 2026-10-17-we-re-sharing-3-tips-to-study-smarter-with-gemini-notebook Sat, 17 Oct 2026 00:00:00 GMT What happened. On October 16, 2026, Google said it has launched three new features in Gemini Notebook: 60-second Short Video Overviews that unpack a tricky concept, Interactive Learning Overviews that walk a student through notes, lectures and readings while weaving in summaries, slides, quizzes and flashcards, and expanded quiz formats covering short-answer, multi-select and fill-in-the-blank questions, all grounded in the material a student uploads. Sources. Google newsroom post on three new Gemini Notebook study features, published October 16, 2026: https://blog.google/innovation-and-ai/products/gemini-notebook/three-tips-study-gemini-notebook (read 17 Oct 2026) Why it matters. A student revising for exams now gets a single notebook that turns uploaded lecture notes into a short video, a stepped walkthrough and a graded quiz, which shifts study prep away from juggling separate apps for flashcards, video and notes toward one workflow built on the files already in hand. Because the tools stay grounded in material the student supplies rather than open web search, the tradeoff is coverage: a notebook with thin source material produces a thin overview, so the feature rewards students who already upload a complete set of readings rather than those hoping the tool fills gaps on its own. https://aijourney.space/updates/2026-10-16-meddex-a-retrieval-augmented-llama-3-system-with-gemini-vision-for-evide/ 2026-10-16-meddex-a-retrieval-augmented-llama-3-system-with-gemini-vision-for-evide Fri, 16 Oct 2026 00:00:00 GMT What happened. On September 30, 2026, researchers posted a paper on arXiv describing MeddeX, a medical report analyzer that combines a QLoRA fine-tuned LLaMA-3 8B model, retrieval-augmented generation, and a multimodal Gemini Vision layer to produce evidence-backed summaries of clinical documents, including scanned PDFs and direct radiological images. Sources. arXiv paper 2610.20632, MeddeX: A Retrieval-Augmented LLaMA-3 System with Gemini Vision for Evidence-Backed Medical Report Analysis, submitted September 30, 2026: https://arxiv.org/abs/2610.20632 (read 16 Oct 2026) Why it matters. For a clinic weighing whether to add an AI reading layer to its records pipeline, the choice here is telling: the authors did not train a vision model from scratch, they bolted Gemini onto an open fine-tuned language model, which is the cheaper path for a team with a tight budget and no in-house vision team. That design choice shifts the ongoing cost and uptime risk onto Google's API rather than onto a model the clinic would have to retrain and host itself, a tradeoff worth weighing before a hospital IT department signs off on a pilot. https://aijourney.space/updates/2026-10-15-google-published-pricing-changed/ 2026-10-15-google-published-pricing-changed Thu, 15 Oct 2026 00:00:00 GMT What happened. On October 15, 2026, Google's Gemini subscriptions page began showing a banner pointing users toward Gemini 4 Argon, stating that selecting AI Pro or AI Ultra gives access to the new model starting today, while the Free, Google AI Plus, Google AI Pro and Google AI Ultra tiers kept their published monthly prices of $0, $4.99, $19.99 and a starting $99.99 respectively. Sources. Gemini subscriptions page, read October 15, 2026: https://gemini.google/us/subscriptions?hl=en (read 15 Oct 2026) our Gemini plans and limits table, which records the same per-tier prices and usage multipliers quoted here: https://aijourney.space/google/gemini/#plans (read 15 Oct 2026) Why it matters. A subscriber deciding whether to pay for Pro or Ultra now has to weigh a model name that is new to them against a ladder of usage multipliers that has not moved, so the actual question for a reader's bill is still whether four times or twenty times the free usage ceiling covers their week, not which model letter sits behind it. https://aijourney.space/updates/2026-10-15-dualview-leveraging-broad-and-output-specific-evidence-for-llm-based-mul/ 2026-10-15-dualview-leveraging-broad-and-output-specific-evidence-for-llm-based-mul Thu, 15 Oct 2026 00:00:00 GMT What happened. On October 13, 2026, researchers Yiheng Su, Junyi Jessy Li and Matthew Lease posted a paper on arXiv describing DualView, a parameter-efficient method for multi-output text regression that extracts broad and output-specific evidence from a frozen large language model, and reported that DualView using a smaller open-weight model outperformed unsupervised prediction with Google's Gemini 3.1 Pro across several heterogeneous regression tasks. Sources. arXiv abstract page for DualView, posted 13 October 2026: https://arxiv.org/abs/2610.19206 (read 15 Oct 2026) Why it matters. For teams that score text on several dimensions at once, such as grading essays or rating survey responses, the result means a frozen Gemini call is no longer the strongest baseline on offer. A smaller open-weight model trained with this dual-branch method can match or beat Gemini's unsupervised output while fitting far fewer parameters, which changes the calculus for anyone weighing a per-token API bill against hosting a compact model themselves. https://aijourney.space/updates/2026-10-15-evaluating-large-language-models-for-multilingual-abstractive-summarizat/ 2026-10-15-evaluating-large-language-models-for-multilingual-abstractive-summarizat Thu, 15 Oct 2026 00:00:00 GMT What happened. On October 13, 2026, researchers Khan Mahi Al Atahar, Nazifa Tasneem and Ajwad Abrar posted a paper to arXiv benchmarking eight closed-source and open-weight language models on zero-shot abstractive summarization in English and Bengali, using size-matched XL-Sum test sets and a mix of lexical, semantic and factuality checks, with Google's Gemini 2.5 Flash recorded as the model that reached the highest observed factuality score on the Bengali portion of the test. Sources. arXiv paper on multilingual summarization evaluation, posted October 13, 2026: https://arxiv.org/abs/2610.19003 (read 15 Oct 2026) our Google API price table, which lists Gemini 2.5 Flash's per-token rate: https://aijourney.space/google/gemini/#api-prices (read 15 Oct 2026) Why it matters. A reader choosing a model for a Bengali-language summarization tool now has independent evidence that factual reliability and surface-level fluency do not move together, since the paper found GPT-5.6 Luna's strength on English lexical metrics did not carry over to Bengali, where LLaMA 3.3 70B took the lexical lead while Gemini 2.5 Flash held the factuality edge. That split matters for a team billed per call, because Gemini 2.5 Flash sits at the cheap end of Google's own paid tier, priced at $0.30 per million input tokens and $2.50 per million output tokens on the API price table this site tracks, so the factuality result lands on a model many low-resource-language projects can already afford rather than on a premium option. https://aijourney.space/updates/2026-10-15-harness-2-recursive-agent-harnessing-for-an-open-world/ 2026-10-15-harness-2-recursive-agent-harnessing-for-an-open-world Thu, 15 Oct 2026 00:00:00 GMT What happened. On October 9, 2026, researchers including authors with Google affiliations posted a paper to arXiv describing Harness^2, a recursive agent harnessing framework meant for open-world tasks. The paper tests the method on four model-harness configurations, including Gemini 3.5 Flash and GLM-5, across three professional benchmarks, reporting that a single recursive editing step raises scores over each model's base harness without needing a ground-truth verifier to check the edits. Sources. Harness^2 paper abstract page, arXiv:2610.14224, submitted October 9, 2026: https://arxiv.org/abs/2610.14224 (read 15 Oct 2026) Why it matters. For a team deciding whether to build a custom agent scaffold or buy one off the shelf, a harness that edits itself without labeled answers changes the calculus: the expensive part of agent deployment has usually been hand-tuning prompts and tool-call sequences for each new task, and this work argues that step can be automated even when nobody has scored the task in advance. If the approach generalizes past these three benchmarks, it lowers the engineering bill for adapting an agent to a new workflow rather than lowering the price of the underlying model itself, which is a different kind of saving than the headline figures published elsewhere on this site usually track. https://aijourney.space/updates/2026-10-14-gemini-4-argon-is-now-available-for-developers/ 2026-10-14-gemini-4-argon-is-now-available-for-developers Wed, 14 Oct 2026 00:00:00 GMT What happened. On October 13, 2026, Google opened Gemini 4 Argon to developers, saying the model handles long-horizon software engineering, enterprise knowledge work and cybersecurity defense tasks. Access runs through the paid tier of the Gemini API in Google AI Studio and Android Studio, while Google AI Ultra subscribers can try it inside AI Studio and the Antigravity agent-first development platform. Sources. Google newsroom post announcing Gemini 4 Argon for developers, published October 13, 2026: https://blog.google/innovation-and-ai/technology/developers-tools/build-with-gemini-4-argon (read 14 Oct 2026) Why it matters. For developers already paying for Gemini API access, Argon arrives as a paid-tier model with no separate price sheet published alongside the newsroom post, so teams weighing a move from an older release have to check Google's pricing page rather than this announcement before budgeting for longer, multi-step coding tasks. The jump from a rumored 3.5 generation straight to a fourth full version also means teams planning around version numbers should treat Google's naming as a marketing choice rather than a technical roadmap. https://aijourney.space/updates/2026-10-09-study-in-the-lancet-suggests-ai-could-improve-patient-physician-relation/ 2026-10-09-study-in-the-lancet-suggests-ai-could-improve-patient-physician-relation Fri, 09 Oct 2026 00:00:00 GMT What happened. On October 8, 2026, Google and physicians at Beth Israel Deaconess Medical Center published a study in The Lancet describing a real world clinical trial in which 98 patients consulted Google's AMIE research diagnostic chatbot ahead of urgent primary care visits, with supervising physicians monitoring every conversation in real time and no conversation interrupted on safety grounds. Sources. Google newsroom post on the AMIE clinical study published in The Lancet, October 8, 2026: https://blog.google/innovation-and-ai/technology/health/amie-clinical-study-lancet (read 9 Oct 2026) Why it matters. For a clinic weighing whether to add a chatbot to patient intake, the trial gives a concrete figure to argue from: the suggested diagnosis lined up with the attending physician's call nine times out of ten, which turns the workflow question from whether to trust the tool into how much preparation time it actually saves before a visit starts. A reader judging any vendor's clinical AI pitch now has a public benchmark to ask that vendor to beat, rather than taking a marketing claim on faith. https://aijourney.space/updates/2026-10-08-google-cloud-introduces-the-gemini-agent/ 2026-10-08-google-cloud-introduces-the-gemini-agent Thu, 08 Oct 2026 00:00:00 GMT What happened. On October 8, 2026, at the Gemini at Work event, Google Cloud announced the Gemini agent, described as a universal agent that starts work from a single prompt window and draws on an organization's business context to handle knowledge work, content creation and coding. Sources. Google Cloud newsroom post announcing the Gemini agent, published October 8, 2026: https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/gemini-at-work (read 8 Oct 2026) Why it matters. A single prompt box that plans the task, picks a model and reaches into existing inboxes and code editors shifts the decision a buyer faces from which chatbot to open toward which systems get agent access, and that access question is where procurement and security teams will spend their review time. https://aijourney.space/updates/2026-10-08-when-the-governor-becomes-the-disturbance-control-generated-disturbance-/ 2026-10-08-when-the-governor-becomes-the-disturbance-control-generated-disturbance- Thu, 08 Oct 2026 00:00:00 GMT What happened. On 6 October 2026, researcher Veronique Ziegler posted a paper to arXiv describing experiments in which a supervisory governor, built to watch a tool-using agent and intervene when it judges the agent risky, instead causes the agent to get stuck. The paper tests a cost-blind governor against a cost-aware backoff rule in a controlled file-recovery environment, then repeats the comparison with Gemini 2.5 Flash running 576 episodes across 6 tasks. Sources. arXiv paper 2610.09037, posted 6 October 2026: https://arxiv.org/abs/2610.09037 (read 8 Oct 2026) our Google API price table, which lists Gemini 2.5 Flash at $0.30 input and $2.50 output per million tokens: https://aijourney.space/google/gemini/#api-prices (read 8 Oct 2026) Why it matters. A governor that tightens oversight and then will not loosen it again turns a safety mechanism into the reason a job never finishes, which matters to anyone building on Gemini behind an agent framework that adds its own guardrails on top of the model. If a deployed pipeline layers a cost-blind monitor over Gemini calls, the paper's result suggests that monitor, not the model priced in the Google API price table, becomes the thing that decides whether a task completes, so teams tuning agent cost against that table should budget for retries a governor itself may cause. https://aijourney.space/updates/2026-10-07-we-re-making-it-easier-to-identify-ai-generated-content-globally/ 2026-10-07-we-re-making-it-easier-to-identify-ai-generated-content-globally Wed, 07 Oct 2026 00:00:00 GMT What happened. On October 7, 2026, Google said it opened its SynthID Detector tool to everyone online in English, ending the limit that had reserved the tool for media professionals since it introduced an early version last year. Sources. Google DeepMind newsroom post announcing global SynthID Detector access, published October 7, 2026: https://blog.google/innovation-and-ai/models-and-research/google-deepmind/synth-id-ai-content (read 7 Oct 2026) Why it matters. For someone trying to judge whether a photo or clip circulating online is genuine, a single checking page that reads watermarks from several companies at once changes the question from which company made this to whether any detector flags it, which matters as more image and video generators enter daily use and a reader has no way to ask the maker of each one directly. https://aijourney.space/updates/2026-10-07-who-wrote-it-is-not-enough-detecting-who-contributed-the-insight/ 2026-10-07-who-wrote-it-is-not-enough-detecting-who-contributed-the-insight Wed, 07 Oct 2026 00:00:00 GMT What happened. On October 5, 2026, researchers posted a paper on arXiv introducing Insight Provenance, a task for telling whether a peer review's underlying insight came from a human, an LLM, or a hybrid of both, built on a new dataset called InsightProv-v0 drawn from scientific papers and human reviews, with LLM involvement simulated using GPT-4o, Gemini, and DeepSeek. Sources. arXiv paper, Who Wrote It Is Not Enough: Detecting Who Contributed the Insight, submitted October 5, 2026: https://arxiv.org/abs/2610.07365 (read 7 Oct 2026) Why it matters. For a journal editor or a conference chair, this reframes what a plagiarism or an AI use check is supposed to catch: not the sentence structure a writer used, but whose judgment actually produced the finding underneath it. Tools built only to flag machine phrasing can be rewritten around by anyone who runs a draft through a paraphraser, which is the shortcut the paper shows current detectors already exploit. A detector aimed at the idea's origin instead of its wording changes what a conference asks authors and reviewers to disclose, and it changes what counts as evidence when a dispute over authorship reaches an editor's desk. https://aijourney.space/updates/2026-10-02-vamos-part-deux-harder-more-realistic-voice-agent-simulation/ 2026-10-02-vamos-part-deux-harder-more-realistic-voice-agent-simulation Fri, 02 Oct 2026 00:00:00 GMT What happened. On September 29, 2026, researchers posted VAmoS Energy, a voice-agent benchmark built from 100 calls about utility billing and payment help, where each caller makes two to four requests and the agent must verify identity before touching an account. Fourteen voice stacks were tested, including Google's Gemini 3.8 Live, Grok Voice and GPT-Live, each run three times per task, with an LLM verifier checking both what the agent said and what it actually changed in a stateful billing system built on Stripe and the Apache Fineract loan engine. Sources. VAmoS Part Deux paper on arXiv, submitted 29 September 2026: https://arxiv.org/abs/2609.38512 (read 2 Oct 2026) Why it matters. For anyone deciding whether to put a voice agent in front of paying customers, the spread between stacks matters more than any single headline number, because a call center choosing a vendor on a demo alone has no way to see how that vendor degrades once a television is on in the background. The paper's verifier checks what an agent did to an account, not just what it said aloud, which is the failure mode a billing dispute actually turns on: a caller who hears a correct-sounding answer has no way to check whether the balance behind it was ever touched. https://aijourney.space/updates/2026-10-02-scientific-agents-evaluating-profession-specific-system-prompts-on-scien/ 2026-10-02-scientific-agents-evaluating-profession-specific-system-prompts-on-scien Fri, 02 Oct 2026 00:00:00 GMT What happened. On September 7, 2026, researcher Timothy Kassis posted a paper testing Gemini 3.8 Flash, run through OpenRouter in the Pi agent harness, against 503 profession-specific AGENTS.md prompts compared with four simpler control prompts across nine science benchmarks. Sources. Scientific Agents paper, arXiv:2610.00084, submitted September 7, 2026: https://arxiv.org/abs/2610.00084 (read 2 Oct 2026) our Google API price table, showing Gemini 3.8 Flash output priced far above input: https://aijourney.space/google/gemini/#api-prices (read 2 Oct 2026) Why it matters. Anyone building a Gemini-based agent and paying per output token should read this before writing a long professional persona into the system prompt, because the paper found the longer prompt mainly adds billable tokens rather than reliably better answers, a bill-shaping result that sits right beside what this site's own API price table already shows about the cost gap between Gemini's input and output rates. https://aijourney.space/updates/2026-10-01-gemini-4-argon-our-next-era-of-frontier-intelligence/ 2026-10-01-gemini-4-argon-our-next-era-of-frontier-intelligence Thu, 01 Oct 2026 00:00:00 GMT What happened. On September 30, 2026, Google announced Gemini 4 Argon, a new frontier model aimed at coding, enterprise knowledge work such as legal and finance tasks, and cybersecurity defense, rolling out first to trusted cyber defenders through its Fairwind Program ahead of wider access for paid API customers and Google AI Ultra subscribers. Sources. Google newsroom post introducing Gemini 4 Argon, published September 30, 2026: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon (read 1 Oct 2026) Why it matters. The announcement matters for buyers weighing frontier models against each other, because Google set an introductory per-token rate well below the price that applies once the discount period ends, so budgets built around the early figure face a steep jump later. The staged release also means the model's heaviest workflow claims, on coding and vulnerability patching, cannot yet be checked by anyone outside Google's chosen testers. https://aijourney.space/updates/2026-10-01-google-s-ai-ranks-1-for-predicting-flu-hospitalizations/ 2026-10-01-google-s-ai-ranks-1-for-predicting-flu-hospitalizations Thu, 01 Oct 2026 00:00:00 GMT What happened. On September 30, 2026, Google said the Centers for Disease Control and Prevention's end of season FluSight analysis found that a flu forecasting model it built, using an AI tool called Empirical Research Assistance, best matched observed hospital admissions among 39 eligible models submitted for the 2025-26 season. Sources. Google Research newsroom post on the CDC FluSight evaluation, published September 30, 2026: https://blog.google/innovation-and-ai/models-and-research/google-research/google-science-ai-flu-forecasts (read 1 Oct 2026) Why it matters. For hospital planners and local health departments, the result means a model trained with a commercial AI company's optimization tool, not a university lab or a government agency alone, now sets the published benchmark used to anticipate emergency room demand, which raises the question of how much weight officials should give a single vendor's forecast against the combined pool before changing staffing or supply decisions next flu season. https://aijourney.space/updates/2026-09-29-see-what-4-builders-are-making-with-gemini-3-8-flash/ 2026-09-29-see-what-4-builders-are-making-with-gemini-3-8-flash Tue, 29 Sep 2026 00:00:00 GMT What happened. On September 28, 2026, Google's blog published a roundup naming four developers who built with Gemini 3.8 Flash, the model the company calls its most intelligent workhorse, covering a satellite tracker, an animated ink painting, a dinosaur skeleton model and a transmission simulation. Sources. Google newsroom post on developer builds with Gemini 3.8 Flash, published September 28, 2026: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-8-flash-developers (read 29 Sep 2026) our Google API price table, which lists Gemini 3.8 Flash at $0.75 per million input tokens through the end of 2026: https://aijourney.space/google/gemini/#api-prices (read 29 Sep 2026) Why it matters. For a team choosing a model for a multistep agentic task, the post is a signal that Flash tier pricing at $0.75 per million input tokens can now carry work that used to need a pricier frontier model, which changes the calculus on whether to pay Gemini 3.1 Pro's higher per-token rate for reasoning-heavy pipelines. https://aijourney.space/updates/2026-09-26-criss-a-retrieval-augmented-ai-chatbot-for-assisting-cancer-registrars/ 2026-09-26-criss-a-retrieval-augmented-ai-chatbot-for-assisting-cancer-registrars Sat, 26 Sep 2026 00:00:00 GMT What happened. On September 24, 2026, researchers posted a paper on arXiv describing CRISS, the Cancer Registry Intelligent Support System, a retrieval-augmented generation chatbot built to help cancer registrars and Oncology Data Specialists interpret coding and staging standards, tested across open-weight, proprietary, and non-RAG baseline models drawn from the Gemini and GPT families. Sources. CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars, arXiv:2609.29075, submitted 24 September 2026: https://arxiv.org/abs/2609.29075 (read 26 Sep 2026) Why it matters. For anyone weighing a retrieval-augmented deployment over a bare chat model, the reported gap between grounded and ungrounded scores is a concrete argument for building a citation layer before trusting an assistant with a coding decision, and the finding that local models led on the hardest questions while hosted models stayed more cautious suggests the cheapest API tier is not automatically the safest choice for a regulated workflow. https://aijourney.space/updates/2026-09-25-introducing-gemini-3-8-live-with-live-avatar/ 2026-09-25-introducing-gemini-3-8-live-with-live-avatar Fri, 25 Sep 2026 00:00:00 GMT What happened. On September 24, 2026, Google introduced Gemini 3.8 Live with Live Avatar, a feature that pairs its native live dialogue model with near real-time video generation so the assistant listens, sees and speaks through an animated visual persona, available now in Gemini Enterprise. Sources. Google newsroom post introducing Gemini 3.8 Live with Live Avatar, published September 24, 2026: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-8-live-with-live-avatar (read 25 Sep 2026) Why it matters. A support desk that swaps a text or voice bot for an animated face changes what a buyer has to budget for: video generation adds compute cost and latency risk that a plain audio agent never carried, so an enterprise choosing this over the week-old Gemini 3.8 Live has to weigh a richer interaction against a slower or pricier one, especially at the scale a customer service queue demands. https://aijourney.space/updates/2026-09-24-a-new-wave-of-connected-apps-is-rolling-out-to-gemini/ 2026-09-24-a-new-wave-of-connected-apps-is-rolling-out-to-gemini Thu, 24 Sep 2026 00:00:00 GMT What happened. On September 23, 2026, Google said it began rolling out a new wave of Connected Apps inside the Gemini app, adding tools across three categories: productivity software including Airtable, Linear, monday.com, PandaDoc, Wispr AI and Zoho; creative tools including Adobe, Picsart, Squarespace and Webflow; and lifestyle services including apartments.com, Experian, Peloton and SeatGeek. Google's Gemini App group product manager Mai Lowe framed the update as a way to manage projects, design assets and plan workouts inside one interface rather than switching between browser tabs. Users can turn on the connections in Gemini settings, or invoke a connected app directly in a chat by typing an @ mention or asking for it by name. Sources. Google newsroom post announcing new Connected Apps in Gemini, published September 23, 2026: https://blog.google/innovation-and-ai/products/gemini-app/new-connected-apps-gemini (read 24 Sep 2026) Why it matters. Every connection added here is another account a user has to trust Gemini with read and write access to, so the practical question for a reader is not whether the list is long but which of these accounts, once linked, quietly stay linked after the task that prompted the connection is finished. A workflow built around Peloton for workouts or Experian for credit checks inside a single chat window also means a single login breach or a single Google outage now touches tools that previously failed independently, which raises the cost of the assistant becoming a single point of failure for a household's software stack. For anyone weighing whether to link a productivity tool like Linear or a financial one like Experian, the choice is really about how much cross-app default access is worth trading for not switching tabs. https://aijourney.space/updates/2026-09-17-5-things-to-know-about-teens-views-on-ai-today/ 2026-09-17-5-things-to-know-about-teens-views-on-ai-today Thu, 17 Sep 2026 00:00:00 GMT What happened. On September 16, 2026, Google published the U.S. Future Report, research conducted with its partner RXN that surveyed American teenagers between the ages of thirteen and seventeen about how they use generative AI for schoolwork, creativity and fact-checking, drawing on quantitative surveys across several states and follow-up focus groups. Sources. Google newsroom post on teen AI research findings, published September 16, 2026: https://blog.google/innovation-and-ai/technology/families/teens-ai-research-findings (read 17 Sep 2026) Why it matters. The findings matter because they complicate the assumption that schools and parents are the main source of guidance on AI, since teens told researchers they lean on family for safety questions but rely on their own judgment to check facts, which puts pressure on schools to add digital literacy coursework rather than assume home guidance covers it. https://aijourney.space/updates/2026-09-16-introducing-gemini-3-8-live-and-3-8-live-extended-thinking/ 2026-09-16-introducing-gemini-3-8-live-and-3-8-live-extended-thinking Wed, 16 Sep 2026 00:00:00 GMT What happened. On September 15, 2026, Google introduced Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, its newest voice dialogue models, through a newsroom post on blog.google. Gemini 3.8 Live is built for fast, low-cost conversation with visual grounding, while Gemini 3.8 Live Extended Thinking reasons and speaks at the same time for multi-step tasks, narrating progress as it works. Both models detect and switch between many supported languages mid-conversation and can run tool calls or API requests in the background without pausing the exchange. Google says the models are rolling out immediately to developers through the Gemini API and Google AI Studio, to enterprises in private preview through Gemini Enterprise, and to consumers through Search Live, the Gemini app, and Google Workspace apps including Docs, Gmail, and Keep. Sources. Google newsroom, Introducing Gemini 3.8 Live and 3.8 Live Extended Thinking, 15 September 2026: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-8-live-gemini-3-8-live-extended-thinking (read 16 Sep 2026) Why it matters. For a developer choosing a voice model, the split into a fast, cheaper Live tier and a slower Extended Thinking tier is a workflow decision, not just a spec bump. A support bot that only needs to acknowledge a request and hand off to a background tool call fits the plain Live model, while a bot walking a customer through a multi-step booking or a coding task benefits from Extended Thinking's live narration, at whatever added latency and cost that reasoning carries. Google has not published per-token pricing for either model on its public pricing page yet, so buyers evaluating the swap from an older Gemini Live model still have to test cost against their own call volumes before committing. https://aijourney.space/updates/2026-09-16-sharpen-your-study-routine-with-new-gemini-notebook-tools/ 2026-09-16-sharpen-your-study-routine-with-new-gemini-notebook-tools Wed, 16 Sep 2026 00:00:00 GMT What happened. On September 15, 2026, Google introduced new study features in Gemini Notebook, including real-time voice conversations grounded in a user's own sources, a mobile audio recorder for lectures, interactive learning overviews with quizzes and flashcards, and short video summaries covering complex topics. Sources. Google newsroom post on new Gemini Notebook study tools, September 15, 2026: https://blog.google/innovation-and-ai/products/gemini-notebook/new-study-tools-september-2026 (read 16 Sep 2026) Why it matters. The update turns Gemini Notebook from a research aid into a study companion that competes with dedicated flashcard and quiz apps, and ties higher usage limits to a time-limited student offer that ends in 2026, pushing students to decide before the trial window closes. https://aijourney.space/updates/2026-09-16-the-university-of-melbourne-wmt-2026-creolemt-submission-a-domain-balanc/ 2026-09-16-the-university-of-melbourne-wmt-2026-creolemt-submission-a-domain-balanc Wed, 16 Sep 2026 00:00:00 GMT What happened. On 2026-09-15, researchers at the University of Melbourne posted a WMT 2026 Creole Language Translation Shared Task submission describing machine translation models for Tok Pisin, Bislama and Solomon Pijin. The paper states they used distillation from Gemini to generate text for domains that were not present in their existing training data, alongside LLM-assisted respelling, alignment and back-translation. Sources. arXiv:2609.13615, WMT 2026 CreoleMT submission paper: https://arxiv.org/abs/2609.13615 (read 16 Sep 2026) Why it matters. The choice shows a research team treating a general-purpose chat model as a data generator rather than a translator, a workaround for languages where collecting real text in a needed domain is often impossible. It also means the reported gains partly rest on synthetic text whose quality depends on Gemini's own output in these low-resource languages, a dependency the paper does not quantify. https://aijourney.space/updates/2026-09-11-google-published-pricing-changed/ 2026-09-11-google-published-pricing-changed Fri, 11 Sep 2026 00:00:00 GMT What happened. Our poll of Google's United States Gemini subscription page on 11 September 2026 found the stored snapshot out of date. Read again, the personal ladder still runs Free at $0, Google AI Plus at $4.99 a month, Google AI Pro at $19.99, and Google AI Ultra from $99.99 with a $199.99 line for its highest usage. The page states that Gemini app limits are compute-based, refresh every 5 hours until a weekly limit, and can be extended by buying AI credits. Sources. Google, Gemini subscriptions, United States page, read 11 September 2026: https://gemini.google/us/subscriptions?hl=en (read 11 Sep 2026) our Gemini plans and limits table, which stamps the same monthly prices on 5 September 2026: https://aijourney.space/google/gemini/#plans (read 11 Sep 2026) Why it matters. The refresh wording is the part that decides your bill. A limit that resets every 5 hours and again every week means a heavy Tuesday cannot be repaid by a quiet Sunday, so the step from $4.99 to $19.99 is a choice about how long you wait when you hit the wall, not only about which model answers you. Buying AI credits turns that hard stop into a variable cost, which is the point at which a fixed monthly subscription stops being predictable. https://aijourney.space/updates/2026-09-06-gemini-3-8-flash-promotional-price/ 2026-09-06-gemini-3-8-flash-promotional-price Sun, 06 Sep 2026 00:00:00 GMT What happened. Google's Gemini API pricing page prices Gemini 3.8 Flash at $0.75 per million input tokens and $3.75 per million output tokens, and prints the end of that price on the same line: "$0.75 through December 31, 2026. $1.50 starting January 1, 2027", with output moving to $7.50 and caching from $0.075 to $0.15. We read the page on 5 September 2026 and recorded the promotional figures as the current price with the scheduled ones in the row's note. Sources. Gemini API pricing, read 5 September 2026: https://ai.google.dev/gemini-api/docs/pricing (read 6 Sep 2026) our own Google price table, where the row and its note live: https://aijourney.space/google/gemini/#api-prices (read 6 Sep 2026) Why it matters. This is a price with an expiry date on it, and the expiry is a doubling. If you are choosing a model now for work that runs into next year, the figure to plan against is $1.50 rather than $0.75, and the comparison you make today against other vendors is a comparison against a number that has four months to live. We record both figures with the date the page was read, so when January arrives the change will be a logged event rather than a surprise on an invoice. Google also publishes no second official page confirming this price, which is noted on the row. https://aijourney.space/updates/2026-09-06-google-lyria-3-5-in-gemini/ 2026-09-06-google-lyria-3-5-in-gemini Sun, 06 Sep 2026 00:00:00 GMT What happened. On 4 September 2026 Google released Lyria 3.5, its music generation model, inside the Gemini app and the Gemini API. A user selects or describes a genre, picks vocal or instrumental styles, chooses a short or a longer track, and can start from a new template for anything from background music to a custom birthday track. Google says it is available to all users globally on the web and in the mobile app, and names the other surfaces by audience: Flow Music for artists and AI creatives, AI Studio and Google Vids for developers and technologists. The announcement states no tier, no price and no usage limit. Sources. Google announcement, 4 September 2026: https://blog.google/innovation-and-ai/products/gemini-app/better-tracks-lyria-gemini/ (read 6 Sep 2026) Why it matters. Google put no price in the announcement, so the free reading is ours and not theirs: it says all users on the web and in the app, and our Gemini plans table records a free rung at $0, so a free account should reach this without paying for a music subscription. What Google published nowhere is an allowance. Our plans table is therefore unchanged and stays that way until a limit appears somewhere we can verify it, and that gap is the story here: a feature with no stated cap is a feature whose cap you will meet without warning. https://aijourney.space/updates/2026-09-06-github-copilot-retires-four-models/ 2026-09-06-github-copilot-retires-four-models Sun, 06 Sep 2026 00:00:00 GMT What happened. On 3 September 2026 GitHub said it will retire Gemini 3.5 Flash, Gemini 3.6 Flash, Kimi K2.7 Code and Claude Opus 4.7 from every GitHub Copilot surface on 2 October 2026, naming Gemini 3.8 Flash, Kimi K3 and Claude Opus 5 as the replacements. The changelog lists Copilot Chat, inline edits, ask and agent modes and code completions as the surfaces affected, and tells Business and Enterprise administrators they may have to enable the replacement models in their own model policies first. Read on 6 September 2026, GitHub's supported-models reference still listed all four as generally available and its retirement-history table still had no row for any of them. Sources. GitHub Copilot changelog, 3 September 2026, where the retiring models and the retirement date are named: https://github.blog/changelog/2026-09-03-upcoming-deprecation-of-selected-github-copilot-models (read 6 Sep 2026) GitHub's supported-models reference, including its model retirement history table: https://docs.github.com/copilot/reference/ai-models/supported-models (read 6 Sep 2026) Why it matters. A suggested replacement is not a like-for-like swap, and our own price table says so in both directions. Gemini 3.5 Flash sits at $1.50 per million input tokens and Gemini 3.8 Flash at $0.75, both checked on 5 September 2026, so that substitution halves the rate for the rest of 2026 only: the $0.75 is promotional and Google's pricing page returns it to $1.50 on 1 January 2027, which leaves the Gemini swap costing what the model it replaced costs today. Kimi K2.7 Code sits at $0.95 and Kimi K3 at $3.00 on the same check, so that one triples it. If Copilot is the only place you meet these models the swap costs you nothing, because the seat price does not itemise them. If you also call them directly, the retirement is a nudge toward a rate card you did not choose, and it is worth pricing your own traffic before 2 October rather than after. https://aijourney.space/updates/2026-09-06-google-weathernext-3/ 2026-09-06-google-weathernext-3 Sun, 06 Sep 2026 00:00:00 GMT What happened. On 3 September 2026 Google DeepMind and Google Research released WeatherNext 3, which produces a new global forecast every hour from live geostationary satellite data rather than from the output of physics simulations that reach it about six hours late. Temperature and moisture come out on a 5-kilometre grid, other surface variables at 10 kilometres and upper-air variables such as wind speed at 25 kilometres, against the 25-kilometre grid in 6-hour steps that WeatherNext 2 produced. Google puts precipitation forecasts up to 50% more accurate when you are planning a day or more ahead, and says the model begins powering weather in Search, the Gemini app, Google Maps, the Maps Platform Weather API and Earth Engine from the same day. Sources. Google announcement, 3 September 2026: https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/ (read 6 Sep 2026) our Gemini plans table, read from Google's subscription page on 5 September 2026: https://aijourney.space/google/gemini/#plans (read 6 Sep 2026) Why it matters. There is nothing here to buy, which is the point worth noticing. This is a research model shipped straight into products a reader already uses, and our Gemini plans table records what those cost: nothing on the free tier, $4.99 a month for Google AI Plus and $19.99 for Google AI Pro, read from Google's own subscription page on 5 September 2026. The announcement names no price and no new tier, so the change reaches a free account the same way it reaches a paid one. If you work with wind or solar output, the addition of turbine-height wind speed and cloud cover is the part to test, because an hourly refresh is worth more to a dispatch decision than to a weekend plan. https://aijourney.space/updates/2026-09-06-declarative-attention-kv-cache/ 2026-09-06-declarative-attention-kv-cache Sun, 06 Sep 2026 00:00:00 GMT What happened. On 2 September 2026 researchers at KAIST AI and Google DeepMind posted Declarative Attention to arXiv as 2609.02737. The method has the model say, inside its own chain of thought, whether the next stretch of writing needs the whole context, one named region of it, or only what it has written so far; the serving engine reads those declarations the way it reads a tool call and skips most of the stored context instead of scanning all of it. Run zero-shot across 15 long-context tasks on open models taken off the shelf, it cut attended tokens during decoding by 52.0% on Gemma-4-31B and 31.1% on Qwen-3.6-27B, for accuracy drops of 1.27 and 2.75 percentage points, and the paper projects decode wall-clock cost at 0.71 and 0.77 times the usual on a well-optimised serving stack. Sources. arXiv listing and abstract, submitted 2 September 2026: https://arxiv.org/abs/2609.02737 (read 6 Sep 2026) the paper's own HTML version, where the affiliations and the wall-clock projection are printed: https://arxiv.org/html/2609.02737 (read 6 Sep 2026) Why it matters. Nothing on your invoice changes because of this, and the reason is worth understanding, because it is the same reason long conversations feel expensive. Vendors bill you for context they have seen before as a discounted input token: our table records Claude Fable 5.1 at $10 per million input tokens with cache reads at $0.25, and GPT-6 Astra at $10 with cached input at $1, both read on 5 September 2026. That price covers sending the context again. What this paper cuts is how much of that stored context the model actually reads while it writes each token, which nobody bills separately and which shows up instead as somebody else's serving cost and as your latency. If it holds up, the first thing you will notice is a long chat that answers faster, and the price sheet will be the last thing to move. https://aijourney.space/updates/2026-09-06-gemini-agentic-video-understanding/ 2026-09-06-gemini-agentic-video-understanding Sun, 06 Sep 2026 00:00:00 GMT What happened. On 1 September 2026 Google turned on agentic video understanding for Gemini 3.7 Flash, 3.6 Flash and 3.5 Flash-Lite. Instead of taking a video in at a fixed frame rate, the model searches, scans and loads only the segments, audio and transcript it decides it needs. Google's own benchmark figures put token consumption down by up to 88%, analysis cost down by up to 66% and accuracy up by up to 7% against the default of sampling one frame a second. It is live through the Gemini API in Google AI Studio and the Gemini Enterprise Agent Platform, for uploaded files and YouTube links, on standard Gemini API token prices with no feature fee. Sources. Google announcement, 1 September 2026: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-agentic-video-in-gemini/ (read 6 Sep 2026) our Google API price table, read from Google's pricing page on 5 September 2026: https://aijourney.space/google/gemini/#api-prices (read 6 Sep 2026) Why it matters. A video bill is a token bill, so the only way to make video cheaper is to stop sending frames nobody asked about, and that is exactly what this does. Our Google table prices Gemini 3.5 Flash-Lite, the cheapest of the models named here, at $0.30 per million input tokens and $2.50 per million output, read from Google's own pricing page on 5 September 2026. The rest of the list is worth checking before you budget: our table, read that same day, carries no row for 3.7 Flash or 3.6 Flash, so the model Google puts at the front of this feature is one we cannot yet price. Cutting the tokens on a per-query basis is worth more to a long-video pipeline than any list price cut this year has been.