The AI world,in 5 minutes a day

Airbnb adds AI search, more social features

Airbnb is also launching new services such as meal delivery and laundry in select locations

TechCrunch AI·just now

“We’re not going to shoot ourselves in the foot” over hack fallout, says OpenAI’s chief research officer

Two months after the bombshell news that a swarm of its agents had broken their containment and hacked into the computers of the AI company Hugging Face , OpenAI is still putting out fires. A steady drip of disclosures about other hacks in the weeks since has kept OpenAI in the spotlight and raised serious questions about the safety of its technology. Last week brought news of another hack, this time into Australia’s national health-care system. The Australian government says that OpenAI did not notify it of the breach until 84 days after it happened. But OpenAI insists it is not on the back f

MIT Technology Review·just now

OpenAI-HuggingFace: A Reproduction & Lessons for Alignment Testing

arXiv:2609.35799v1 Announce Type: new Abstract: In July 2026, OpenAI's agents coordinated over channels outside their intended environment to breach Hugging Face's secured infrastructure. Could existing alignment testing practices have foreseen this incident? If not, what needs to change? We explore these questions. First, we identify the misaligned behaviors that caused this incident. Then, we show how to elicit these behaviors from publicly available models manually and that auditing agents can do the same if given a large compute budget. Based on our results, we propose directions to improv

arXiv cs.AI·just now

Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices

arXiv:2609.35833v1 Announce Type: new Abstract: Running a language model on edge hardware provides private and low-latency reasoning without a network connection, and yet the small models that fit on such devices are unreliable on the tasks computers are expected to handle well, such as arithmetic, algebra, and formal logic problems. We argue that much of this unreliability is avoidable. Many queries appearing to demand reasoning are in fact structurally deterministic and permit fast and exact symbolic solutions. Therefore, forcing a probabilistic model to approximate them sacrifices accuracy

arXiv cs.AI·just now

Is Human-Readable Text Necessary for Effective LLM Fine-Tuning?

arXiv:2609.35868v1 Announce Type: new Abstract: Is human readability necessary for effective fine-tuning of large language models? We investigate whether model-conditioned training representations can preserve or improve adaptation utility without requiring a human-readable textual form. We propose Desired-Update-Aligned Synthetic Data (DASA), which uses activation-gradient feedback from a frozen reference model to guide the optimization of continuous synthetic input embeddings. Inspired by the role of activation gradients in local risk reduction, DASA targets useful adaptation updates rather

arXiv cs.AI·just now

The Price of Token Boundaries: Compression Certificates and Prediction

arXiv:2609.35869v1 Announce Type: new Abstract: Pre-tokenisation restricts which text fragments can become prediction units, but its compression cost is obscured when tokenisers are compared only under the same boundaries. We measure this cost by bounding the minimum token count from both sides, with and without a regular-expression boundary rule. Nonnegative prices on token occurrences yield a lower bound through shortest paths and vocabulary-budget selection; maximising over all prices recovers the linear programming relaxation, and an independent integer checker certifies the reported value

arXiv cs.AI·just now

More Programs or More Rolls? Separating Coverage from Specialization in LLM Harnesses

arXiv:2609.35873v1 Announce Type: new Abstract: Automated generation of LLM harnesses promises to improve inference through task specialization. Yet additional answer coverage can arise from repeated execution of the same program, making specialization difficult to identify. We introduce a controlled evaluation that separates answer coverage, repeatable task advantages, and gains from pre-execution selection. On 386 MATH-500 tasks, we compare eight generated harnesses plus a baseline with nine byte-identical baseline copies, using three executions per member. Identical programs yield 2.16 perc

arXiv cs.AI·just now

Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees

arXiv:2609.35874v1 Announce Type: new Abstract: Online POMDP planners optimize the expected cumulative cost, which can mask dangerous states when the belief places significant mass on high-cost states. Existing risk-averse methods apply static or dynamic Conditional Value at Risk (CVaR) to the value function, capturing trajectory-level risk, but share two gaps: (i) by retaining the immediate cost as an expectation of a state-dependent cost over the belief, the risk \emph{within} the belief is left unaddressed; and (ii) by modifying the value function, they require new tailored algorithms rathe

arXiv cs.AI·just now

Beyond Symmetric Agents: Cognitive Diversity and Multi-Agent Debate in Small Language Models

arXiv:2609.35875v1 Announce Type: new Abstract: Multi-agent debate (MAD) reportedly improves reasoning and factuality over single-model inference, but prior work treats agents as symmetric peers, leaving open what drives the gains. We test the hypothesis that cognitive diversity among agents is the driver, in the setting where the question is still measurable: small open-weight models with benchmark headroom. Across 23 models from eleven vendor families, five tasks, and 5,500+ debate and control runs, we vary diversity along three axes - personas, sampling temperature, and model identity - pai

arXiv cs.AI·just now

Representational Simplicity and Circuit Size Dissociate in a Threshold-Dependent Way: A Controlled Test via Adversarial Training

arXiv:2609.35890v1 Announce Type: new Abstract: Sparse-autoencoder decomposability and concentrated feature attribution are increasingly treated as evidence that a model's computation is easier to reverse-engineer. Whether this representational and attributional cleanliness actually predicts a smaller or more tractable causal circuit remains an open question. We test this directly using adversarial training as a controlled instrument: it reliably reshapes internal representations, but this alone does not constitute a test of circuit size. We investigate this question through reverse-engineerin

arXiv cs.AI·just now

Self-discovering RL in the Era of Experience: Is Learning History an Asset or a Burden?

arXiv:2609.35897v1 Announce Type: new Abstract: The pursuit of recursive self-improvement (RSI) toward general intelligence is divided between macro-level language model scaling and the interaction-driven principles of "Era of Experience". Yet, any self-improving architecture ultimately rests upon its underlying optimization engine: if general intelligence requires learning from grounded interaction, the reinforcement learning (RL) update rule itself must be capable of cumulative adaptation. While algorithm self-discovery has produced Disco103 that surpassed PPO to achieve SOTA benchmark perfo

arXiv cs.AI·just now

Grab a Coffee: Future-Aware Guidance for Discrete Diffusion with Compiled Objectives

arXiv:2609.35924v1 Announce Type: new Abstract: Discrete diffusion models generate sequences by iteratively resolving multiple tokens in parallel, offering a flexible alternative to left-to-right generation. However, guiding this process with a sequence-level objective is difficult because the value of one unresolved token depends on the other tokens with which it can form a high-reward sequence. Enumerating all such completions makes the whole guidance computation grow exponentially with the number of unresolved positions. We introduce COFFEE, a plug-and-play framework that avoids this enumer

arXiv cs.AI·just now

Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT

arXiv:2609.35953v1 Announce Type: new Abstract: Young people increasingly turn to General-Purpose Conversational Agents (GPCAs), such as ChatGPT, in moments of distress. We examine young adults' (ages 18-25) experiences using ChatGPT. We first collected 19,930 ChatGPT conversations and survey data from 158 young adults. We then selected five example conversations reflecting user distress. Finally, we asked ten clinicians to review those five conversations. We found distressed participants reported greater emotional engagement with ChatGPT and greater behavioral change from using it than their

arXiv cs.AI·just now

SAGE: A Statistical Acceptance Gate for Self-Evolving Agents

arXiv:2609.36043v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents increasingly self-evolve by editing a persistent skill document that encodes their workflow, tool-use rules, and decision logic. This loop has two steps, an optimizer that proposes a candidate edit and a gate that accepts or rejects it. Prior work has concentrated on the optimizer, while the gate still follows a naive rule that keeps any edit which improves an aggregate validation score. We show that this rule fails in two ways. First, it admits permanent regressions, since an edit can raise the average whi

arXiv cs.AI·just now

FD-VAD: Semantic Endpoint Detection for Streaming Full-Duplex Speech

arXiv:2609.35791v1 Announce Type: new Abstract: Natural turn-taking in full-duplex voice interaction requires determining from partial speech whether a pause reflects hesitation or a completed conversational intent. Acoustic voice activity detection lacks this semantic information, while cascaded ASR-based endpointing introduces transcription dependence and additional processing stages. We formulate semantic endpoint detection as a causal audio-language reasoning task and introduce FD-VAD, an ASR-free streaming endpointer that maps bounded causal audio windows directly to Continue/Stop decisio

arXiv cs.CL·just now

Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention

arXiv:2609.35794v1 Announce Type: new Abstract: Just as Socrates recognized the limits of his own knowledge, Retrieval-Augmented Language Models (RALMs) should learn to abstain when the retrieved evidence cannot support a reliable response. Existing approaches largely rely on monolithic LLMs to handle heterogeneous retrieval failures in a single step, resulting in limited abstention performance and high computational costs. We instead decompose retrieval failures into two distinct states: (i) the unanswerable state, where the required evidence is absent, and (ii) the distracted state, where re

arXiv cs.CL·just now

Developing an OCR model for Extracting Information from Invoices with Korean Language

arXiv:2609.35796v1 Announce Type: new Abstract: Invoices are commercial documents that contain various pieces of information, including the purchased items, time, and total money. Making the extraction of important information crucial. The stored information serves different purposes. Korean language is the native language of about 80 million people, playing an important role in not only South and North Korea but also in many other countries such as Vietnam, Philippine where a large number of Korean companies are located. In this context, to automatically extract proper information from the in

arXiv cs.CL·just now

Evaluating the Effects of Prompt Perturbation on Bias and Hallucination in Large Language Models

arXiv:2609.35804v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, leading to their widespread deployment as intelligent assistants in decision-making contexts. However, the increasing complexity of these models raises concerns about their reliability, particularly regarding bias and hallucination. In this work, we evaluate the robustness of LLMs to perturbed variations of the original inquiry in decision-making tasks. We show that contrary to previous studies, perturbations can mitigate bias and halluci

arXiv cs.CL·just now

Alignment Forecasting: Predicting Misalignment From Training Data

arXiv:2609.35805v1 Announce Type: new Abstract: Training a language model on data with a narrow flaw can sometimes make the model broadly misaligned. Inspecting the data at face value often does not settle whether it will emerge, and today it is caught only after training, by auditing the resulting model. To complement post-hoc audits, we introduce Alignment Forecasting: the task of predicting alignment failures before training. Given a target model, a fine-tuning dataset, and a failure mode such as deception or sycophancy, a forecaster outputs the probability that fine-tuning would meaningful

arXiv cs.CL·just now

From Lexical Baselines to Agentic Retrieval-Augmented Generation: Structured Skill and Responsibility-Level Extraction with the SFIA Framework

arXiv:2609.35806v1 Announce Type: new Abstract: Automated skill extraction underpins workforce planning, yet most systems represent skills as flat labels with no notion of the responsibility level at which a skill is practiced. The Skills Framework for the Information Age (SFIA) captures exactly this dimension, defining 147 professional skills across seven responsibility levels, but no automated LLM-based extraction targeting SFIA has been reported. We formalize the task as structured prediction of (skill, level) pairs from free text and ask three questions: how accurately can text be mapped o

arXiv cs.CL·just now

Environment Steering: Using Data Flow Control to Improve Agent Utility and Safety

arXiv:2609.35807v1 Announce Type: new Abstract: LLM agents can make unsafe tool calls even when instructed to behave safely. Existing defenses constrain agents before execution, modify tool inputs/outputs, or rely on LLM judges; these approaches may depend on model behavior or block unsafe actions without helping the agent recover. We argue that the execution environment should instead enforce safety as the agent runs and steer it toward safe alternatives when violations occur---we call this Environment Steering. We implement this by modeling the agent and harness execution state as database t

arXiv cs.CL·just now

When Successful Memories Mislead Embodied Agents:Memory Adaption For Task-Conditioned Execution

arXiv:2609.35808v1 Announce Type: new Abstract: Experience reuse can reduce repeated exploration in embodied agents, but a trajectory that succeeded previously may be unsuitable for the current execution context. Existing memory systems pri marily optimize construction and retrieval; semantic relevance and historical success therefore remain insufficient when retrieved ex perience contains incompatible actions or an inappropriate level of structure. We introduce Memory Adaptation for Task-Conditioned Execution (MATE), a deterministic post-retrieval procedure that converts trajectories into exe

arXiv cs.CL·just now

Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?

arXiv:2609.35809v1 Announce Type: new Abstract: The rapid advancement of generative AI raises concerns about the misuse of Multimodal LLMs (MLLMs) for large-scale disinformation campaigns on social media. Despite existing research on textual disinformation, a fundamental question remains unanswered: can MLLMs be exploited to fabricate realistic multimodal fake news, and can they reliably detect it? We introduce a multi-agent framework in which a story agent, an image agent, and a critic agent collaborate to produce fake social media posts that plausibly counter true news. We apply the framewor

arXiv cs.CL·just now

TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs

arXiv:2609.35810v1 Announce Type: new Abstract: Large language models are increasingly used in oncology applications, but their predictions are often weakly grounded in explicit medical structure. We present TRACE, a deployable tree-relational enhancement framework for oncology LLMs. TRACE separates expensive offline structure learning from lightweight online inference: oncology concepts and relations are organized into an updatable tree-relational structure, refined using LM-loss-derived evidence, and retrieved at inference time as compact prompt evidence. This design supports task-adaptive e

arXiv cs.CL·just now

Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning

arXiv:2609.35811v1 Announce Type: new Abstract: Tool retrieval is a critical bottleneck for LLM-based agents operating over large, heterogeneous API ecosystems. Existing approaches face an inherent trade-off: semantic retrievers are fast but suffer from the semantic-functional gap, while execution-based validation improves precision at the cost of prohibitive latency. We propose Lookahead-R, a planning-based framework that reformulates tool retrieval as a resource-constrained sequential decision-making problem. At its core, Lookahead-R introduces a lightweight execution-aware surrogate world m

arXiv cs.CL·just now

Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistants

arXiv:2609.35812v1 Announce Type: new Abstract: In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. Ensuring their reliability is challenging due to multi-turn interactions, the absence of explicit ground truth, and strict safety constraints. Existing evaluation techniques fall short, as they target single-turn settings and fail to capture constraint handling, context retention, and safety-critical behavior across turns. We propose an automated framework for testing the multi-turn conversational c

arXiv cs.CL·just now

Sam Altman says OpenAI won’t go public until its models are safe

For months, people have wondered when OpenAI will go public. CEO Sam Altman says it won't happen until the company can make better promises about model safety, with no firm timeline in sight. "We intend to continue with AI progress … but as the models have had this surge forward in capability, and we see […]

The Verge AI·just now

America.gov gets really weird when you ask it about Minecraft, but it’s not a glitch

For the sake of national security, it's a relief to learn that America.gov is not hallucinating to the point that it's penning lengthy poetry.

TechCrunch AI·just now

Trump orders US government to call AI ‘Super Intelligence’

The US executive branch is no longer acknowledging the existence of "artificial intelligence." Going forward, official policy websites, policy documents, and press releases will refer only to "Super Intelligence," thanks to a new executive order signed by President Donald Trump. "The word super is the best word of all, and it's the simplest," Trump said […]

The Verge AI·just now

Models Hot

The internet is convinced Elon Musk’s xAI trolled OpenAI’s ‘Dots’ launch

Before OpenAI launched its new AI agent, Dots, on Tuesday, Elon Musk's xAI had already acquired the domain name "dot.com," which now redirects to the Grok chatbot download page.

TechCrunch AI·just now

Quoting Anthropic Frontier Red Team

<blockquote cite="https://www.anthropic.com/research/glm-5-3-and-the-spread-of-advanced-cyber-capabilities">We evaluate several models on 100 tasks from the [internal Binary Exploitation benchmark] (selected at random), and find that GLM-5.3 develops full control flow hijacks in 4% of the trials; Claude Mythos Preview did so in 6%. Although GLM-5.3 performs below Claude Mythos Preview here, a meaningful threshold has clearly been crossed: earlier models, like Claude Opus 4.6 and GLM-5.2, do not succeed in any of them.</blockquote> — <a href="https://www.anthropic.c

Simon Willison·just now

Elon Musk’s AI-powered Grokipedia is updating again

Grokipedia, the AI-powered online encyclopedia from SpaceXAI, appears to be updating articles once again after a months-long pause. In August, Lawfare reported that articles on Grokipedia hadn't reviewed edits since April, but the platform's live updates site is now showing various recent changes to pages - though as I write this, many are just a […]

The Verge AI·just now

OpenAI’s latest features take direct aim at the app store model

OpenAI is building out the pieces of an alternative to the traditional app store model, turning ChatGPT into a place where software can be discovered and used by people and AI agents alike.

TechCrunch AI·just now

OpenAI reportedly in talks to raise $30B round at $1.4T valuation

The new round is anticipated to be the company's last before its delayed 2027 public debut.

TechCrunch AI·just now

Here’s why OpenAI is absent from Nvidia’s industry-wide effort to end rogue AI agents

OpenAI isn't a public supporter of Nvidia's Open Agent Safety Platform, but it is privately working with Nvidia, TechCrunch has learned.

TechCrunch AI·just now

GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price

<a href="https://news.ycombinator.com/item?id=49896586#49898129">My comment</a> on <a href="https://news.ycombinator.com/item?id=49896586">GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price</a> — Hacker News.I'm a bit late with the pelicans because I was live-blogging the keynote: <a href="https://simonwillison.net/2026/Sep/29/openai-devday-2026-live-blog/">https://simonwillison.net/2026/Sep/29/openai-devday-2026-liv...</a> Here they are for GPT-6.1-Sol: <a href="https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2

Simon Willison·just now

OpenAI takes on Microsoft with the launch of what feels a whole lot like ChatGPT’s own office suite

OpenAI's newly announced suite of office features puts it into more direct competition with more traditional software companies.

TechCrunch AI·just now

AI researchers put out videos saying superintelligence is ‘exactly as dangerous as it sounds’

"The chance of human extinction is about a coin flip, in my view," Geoffrey Irving, a former OpenAI and Google DeepMind employee, said in a new interview. It's one of a dozen interviews with AI researchers, including current and former employees at OpenAI, Google, and Anthropic. Palisade Research, which says it's a nonprofit studying AIs' […]

The Verge AI·just now

AI-powered app maker Wabi pivots to a messaging experience

Wabi is repositioning its prompt-based app builder as a personal AI agent that can create interfaces on demand, combining chat, apps, and ongoing tasks.

TechCrunch AI·just now

OpenAI launches Dots, its bubbly agentic avatar

Dots are meant to operate independent of any specific hardware or interface, pursuing user-defined goals continuously in the background with minimal oversight.

TechCrunch AI·just now

OpenAI gives Codex reusable cloud environments that work across devices

OpenAI is expanding Codex with reusable cloud development environments, a revamped CLI with voice controls, new code review tools, and a security-focused product for scanning repositories and preparing fixes.

TechCrunch AI·just now

OpenAI expands ChatGPT’s plug-ins with app-like interfaces and automations

OpenAI is expanding ChatGPT plug-ins with dedicated sidebar homes, interactive panels, file viewers, improved discovery, and support for automations.

TechCrunch AI·just now

OpenAI launches GPT-6.1 Sol, says it nearly matches GPT-6 Astra and costs less

OpenAI says GPT-6.1 Sol delivers significant improvements over GPT-6 Sol across complex professional tasks, including code writing and debugging, document understanding, and executing multistep business workflows.

TechCrunch AI·just now

OpenAI launches Dots, its Muse competitor

OpenAI is responding to Meta's buzzy Muse AI with agentic helpers of its own: Dots. During its DevDay keynote on Tuesday, OpenAI announced that Dots will serve as always-on AI assistants that can "do nearly anything" across connected apps in the background while learning your preferences over time. The company's capable GPT-6 Astra model powers […]

The Verge AI·just now

Protesters gather at OpenAI’s DevDay

On Tuesday, OpenAI's annual DevDay event began with protests, flyers, and chants. "Sam Altman, get off it, put people over profit," said a group of protesters marching in a circle in front of a series of signs that spelled out "PEOPLE OVER PROFIT." More than a dozen organizations came together to sponsor a rally outside […]

The Verge AI·just now

OpenAI DevDay 2026: The biggest news and announcements

It’s OpenAI’s turn in the fall tech events calendar. The company is hosting its annual DevDay on September 29th in San Francisco, and during a live keynote featuring CEO Sam Altman, it revealed several notable updates, including Dots, its AI agent product that rivals Meta’s recently-launched Muse. However, unlike Muse, which is available for free, […]

The Verge AI·just now

OpenAI DevDay 2026 live blog

I'm at <a href="https://devday.openai.com/">OpenAI DevDay</a> today, in Fort Mason, San Francisco. Same as <a href="https://simonwillison.net/2025/Oct/6/openai-devday-live-blog/">last year</a> I'll be live blogging the keynote and some other notes during the day. OpenAI gave me a free ticket and a seat in the "creator" area for the keynote. Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/openai">openai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags

Simon Willison·just now

NVIDIA Kumo Tabular Sets a New Accuracy-Efficiency Frontier for Tabular Prediction

Hugging Face Blog·just now

Meta’s Muse AI sent a YouTuber’s address to a stranger

Tech YouTuber Matt Robb says that Muse gave out his home address to a total stranger this weekend, after authorizing the bot to handle his Facebook Marketplace account. That's despite Meta placing great emphasis on the security features of Muse when it launched the personal AI agent earlier this month as it tries to catch […]

The Verge AI·just now

Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents

Hugging Face Blog·just now

The Download: climate tech companies to watch and AI’s discovery problem

This is today’s edition of The Download , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Coming soon: our 2026 list of Climate Tech Companies to Watch With the planet nearing 1.5 °C of warming, climate policies being unraveled, and Big Tech backpedaling on its climate ambitions, it can be tempting to give in to climate doom and defeatism. But despite the headwinds, the world has still made incredible progress. That’s why MIT Technology Review publishes its annual list of Climate Tech Companies to Watch . Once a year, we compile a list of 10 com

MIT Technology Review·just now

Will Chinese AI companies slow down? A top House Democrat wants answers

As President Donald Trump prepares to meet tech and AI CEOs in Washington, Rep. Ro Khanna (D-CA) is calling for a treaty between the US and China to keep AI from wreaking havoc on the world. But wrangling leaders in both countries to take action could be a long shot. In letters shared exclusively with […]

The Verge AI·just now

Funding

Anthropic warns of ‘catastrophic’ AI risks in its own IPO filing

As Anthropic gears up for its greatly anticipated public debut, a preview of the company's IPO filing reportedly details its mounting losses, leadership proposals to retain power, and how its AI development plans could "further increase the risk that our models cause harm." These disclosures come as Anthropic eyes a $2 trillion valuation, more than […]

The Verge AI·just now

Coming soon: Our 2026 list of Climate Tech Companies to Watch

Earlier this month, the UN announced the planet will tip past 1.5 ˚C of warming, “likely within the next few years,” squashing any lingering hope that nations would cut emissions fast enough to achieve the loftiest goal of the Paris climate agreement. In the US, the world’s second-largest-emitting country, its leader continues to deny climate realities and unravel climate policies . Big Tech is backpedaling on its climate ambitions, as companies race to build massive AI data centers. And all this comes amid yet another season of shattered heat records, raging wildfires, and deadly floods. It c

MIT Technology Review·just now

Policy

Making AI an asset, not an expense

When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes. As AI moves from experimentation to production, model choice is only part of the equation. When demand becomes steady and business-critical, a consumption-only approach can turn AI spending into a variable monthly line item that is difficult to forecast as usage, workloads, and model requirements change. At that point, the question i

MIT Technology Review·just now

Introducing GPT-6.1 Sol

Meet GPT-6.1 Sol: near-Astra intelligence for coding, computer use, and professional work at one-fifth of Astra’s standard API input and output token prices.

OpenAI Blog·just now

DevDay 2026 Recap

Explore more than 20 announcements from OpenAI DevDay 2026, including GPT-6 Astra, ChatGPT, Codex, APIs, security, and new tools for builders.

OpenAI Blog·just now