标签：#openai

Inside the frontier lab’s push to bring AI agents from software engineers to the masses.

OpenAI is calling for California to strengthen SB 53, an AI safety bill that the company previously opposed.

Most people's first experience with an LLM API is deceptively simple: send a prompt, get a reply. It feels like magic, and for a single question-answer exchange, it basically is. But the moment you try to build something that holds an actual conversation one where the model remembers what you said three messages ago you run into a problem that isn't obvious until you hit it: LLM APIs are stateless. Every request is a blank slate unless you explicitly hand the model its own memory. That was the core challenge behind a recent project I built during my internship a chatbot backed by a real LLM API ([OpenAI / Gemini]) with genuine multi-turn conversation support, not just a scripted request-response loop. * The problem nobody mentions upfront * You can't just "turn on" memory. Every conversation turn has to be manually tracked and resent with each new API call, which means the developer, not the model, is responsible for deciding what counts as context. And that decision has real consequences: send too little history and the bot forgets things it should remember; send too much, and you run into token limits and rising costs as the conversation grows. This is where most simple chatbot tutorials stop short. They show you how to get a reply from an API, but not what happens once a conversation runs long enough that you can't keep resending everything forever. * Where the actual engineering happens * Solving that meant implementing a context management strategy deciding what to keep, what to drop, and eventually exploring smarter approaches like summarising older parts of a conversation instead of just discarding them. It also meant thinking about the bot's identity through a system prompt, handling API failures gracefully instead of letting the UI break, and treating credentials properly by keeping API keys out of source code entirely. None of this is complicated in isolation. What's interesting is how much of it is invisible until you actually build the thing yourself. Us

Nvidia continues to pour money into data center development — just as AI data centers bring lots of money into Nvidia.

OpenAI has formally rolled out a more flexible way for organizations to buy and use Codex. Eligible ChatGPT Business and Enterprise workspaces can add Codex-only seats on a pay-as-you-go basis, with usage billed through API-style token consumption rather than a fixed fee for each of those seats. The change matters because it separates AI coding access from a conventional per-user subscription model. A team can provision Codex-only members for development work while managing consumption through credits, rather than treating every user as a full ChatGPT seat. OpenAI also reduced the annual ChatGPT Business seat price from $25 to $20 and introduced promotional credits intended to lower the cost of onboarding new Codex users. OpenAI details the model in its official flexible Codex pricing announcement for teams . The initial announcement was made on April 2, 2026. A June 24, 2026 update clarified that existing pay-as-you-go seats remain in place, while the availability of new Business pay-as-you-go seats may change. How OpenAI's Codex team pricing works The central change is the availability of Codex-only seats within Business and Enterprise workspaces. These seats have no fixed seat fee . Instead, their Codex consumption is charged using API-style token rates. OpenAI's approach gives organizations a way to make Codex available to users whose primary need is AI-assisted coding, without requiring the same seat model used for standard ChatGPT access. This is not a claim that coding costs are universally lower. A pay-as-you-go model makes expenditure more directly dependent on usage, so the financial effect will vary by team and workload. The documented advantage is billing flexibility: organizations can align Codex costs with token consumption and use credits across supported workspace features where their plan allows it. Item ChatGPT Business seat Codex-only seat Pricing approach $20 per seat annually, reduced from $25 Pay as you go through API-style token consumption Fi

Businesses are willing to flop back and forth as each lab releases new models, volatility that should give both companies' investors pause about how "sticky" enterprise AI spending really is.

Ever wanted someone else to do your texting for you? ChatGPT is being offered up as an automated text scribe via a new Apple Messages integration.

OpenAI is quietly testing a feature that lets ChatGPT users pay to undo their own usage limits. Hit the weekly cap on a $20 Plus plan and, for some users, a prompt now appears offering to restore the allowance to full for roughly $8. On the $200 Pro plan, the equivalent reset is reported to run up to about $80. The company never announced it; it was discovered by a subscriber who ran into it at the point of being locked out, and an OpenAI spokesperson later confirmed the company is exploring ways for capped users to buy more usage . The answer-first version: your flat monthly subscription now has a pay-to-continue button, and it shows up at the worst possible moment. The reset restores your usage to 100% and pushes the next weekly renewal about seven days out. It is cheaper than upgrading, which is the point — but it is also a new charge that did not exist a month ago, applied to a limit most users cannot see coming, offered at the instant they are least able to say no. What OpenAI is actually testing The mechanics, as reported, are straightforward. When a ChatGPT Plus subscriber exhausts their weekly message allowance, instead of only being told to wait, some accounts now see an option to pay to reset. Redeeming it restores usage to full and resets the weekly clock. The price sits at around $8 for Plus; on Pro it scales up to roughly $80, still framed as a stopgap against a full plan change. The feature was first surfaced by a Reddit user on the $20 plan who described a black prompt appearing at login once their allowance ran dry — not a setting they went looking for, but one that found them. That detail — a user, not a press release, breaking the news of a paid feature — is itself worth noting: the first public account of how OpenAI plans to charge for extra usage came from someone who had already been charged the inconvenience of being locked out. OpenAI has not disputed the reports. A spokesperson described the effort as exploring ways for people who exhaust the

OpenAI's Computer History feature is not currently available in the European Economic Area, the United Kingdom, or Switzerland, according to the company's Enterprise and Edu documentation. That documented regional status contradicts claims that the macOS ChatGPT capability has expanded to Pro, Business, and Enterprise users across those markets. Computer History is described as an optional feature in the ChatGPT macOS app for Enterprise members. It is intended to bring context from selected apps and websites into ChatGPT and Codex. For organizations evaluating desktop AI deployments, the distinction matters: feature access depends not only on the app and plan, but also on the user's region. OpenAI's documented regional position The relevant OpenAI Enterprise and Edu release notes explicitly state that Computer History is not currently available in the EEA, UK, or Switzerland. The August 10, 2026 help documentation repeats that regional limitation. This means companies in those locations should not plan around Computer History as an available ChatGPT desktop capability on the basis of a broader regional rollout claim. The supplied OpenAI documentation does not announce a launch date, rollout timetable, or confirmed expansion for those markets. What the release notes establish OpenAI's published material supports several clear conclusions: Computer History is an optional macOS ChatGPT feature described for Enterprise members. It can bring context from selected apps and websites into ChatGPT and Codex . The EEA, UK, and Switzerland remain excluded from current availability. The cited documentation does not support availability for Pro or Business users in those excluded regions. Availability point Claimed position OpenAI documentation EEA, UK, and Switzerland Available Not currently available User plans named Pro, Business, and Enterprise Release notes describe the optional feature for Enterprise members Desktop context Mac desktop app macOS ChatGPT app, with context f

OpenAI has had a hell of a year. The company spent months battling former co-founder Elon Musk in a sensational jury trial, was hit with a high-profile trade secrets lawsuit from Apple, and faced widespread scrutiny after an unreleased model hacked another AI company. As it prepares for an IPO, a steady string of executives […]

Today on Decoder, I’m talking with Robert Hart, The Verge’s London-based AI reporter, about what AI is doing to the field of mathematics and the existential crisis many lead mathematicians are having about it. OpenAI just published a set of solutions to longstanding problems in math that went off like a bombshell in the field. […]

A competition is developing between OpenAI and Anthropic over who can provide the best privacy protections for enterprise customer data.

OpenAI is positioning Zero Data Retention (ZDR) as a scalable privacy control for eligible frontier-model API and enterprise workloads. The policy matters as businesses use more capable models for longer-running and increasingly autonomous work, where prompts, outputs, and related interactions can contain sensitive operational, customer, or proprietary information. On its official API platform page , OpenAI lists "Zero data retention policy by request" alongside access to frontier models and APIs. The company’s enterprise privacy materials and GPT-5.4 release information add important context: ZDR is a configurable option for eligible organizations and endpoints, rather than a universal default across all OpenAI services or customer configurations. The shift is less about a newly invented privacy principle than about applying retention controls more explicitly to frontier-capable deployments. OpenAI’s GPT-5.4 materials describe Zero Data Retention surfaces and safety controls designed for higher-sensitivity contexts. That framing acknowledges a practical tension for enterprise AI: more autonomous systems can create more valuable workflows, but they also require safety systems that assess risks across related interactions. What Zero Data Retention changes for enterprise AI Under ZDR, OpenAI disables logging of customer content for abuse monitoring and model-training purposes. The setting also affects API behavior. For example, the store parameter for chat completions and responses is forced to false in ZDR contexts. That is a meaningful control for teams that need to minimize the persistence of prompt and response content. It should not, however, be interpreted as a blanket statement that no information can ever be retained anywhere in the service. OpenAI documents that some endpoints may retain application state or metadata for operational reasons. It also describes exceptional safety and retention mechanisms, including Eyes Off and Safety Retention , that may apply

The actress' firm Kinship Ventures is an investor in the company.

With a looming IPO, intense competition from Anthropic, and Chinese and open-weight rivals nipping at its heels, OpenAI has plenty of reasons to move fast. Instead, it hit the brakes. On Tuesday, the company said it had slowed the pace of some AI development while it tightened security and safeguards. That included a two-week pause […]

April - 1805 Napoleon is master of Europe Only the British fleet stands before him Compute is now an asset class I see it is once again time to talk financial innovation. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR are all working with Nvidia to put together $500 billion in financing to turn compute […]

OpenAI has rolled out the GPT-5.6 family , introducing three models intended to cover advanced professional work, balanced deployments and high-volume workloads. The July 9, 2026 general-availability launch of Sol, Terra and Luna marks a significant step in OpenAI's effort to consolidate its model portfolio across ChatGPT and its API, while moving customers away from older GPT-4-era offerings. The company's official GPT-5.6 announcement positions the generation as a higher-performance foundation for the ChatGPT experience and API use cases involving agents and coding. Rather than presenting a single general-purpose release, OpenAI has divided the family into distinct options: Sol for advanced professional work, Terra for a balance of capability and cost, and Luna for cost-sensitive, high-volume tasks. That segmentation matters because model selection is becoming a deployment decision rather than simply a question of accessing the newest available system. Teams building production workflows need to weigh performance requirements, usage volume, migration work and the cost profile of each application. What the GPT-5.6 rollout changes The general-availability announcement was followed by a July 30, 2026 pricing update that reduced Luna pricing by around 80% and Terra pricing by around 20%. OpenAI also signaled the phase-out of older models , including GPT-4o and related GPT-4.x variants, as customers move toward GPT-5.x and GPT-5.6 offerings. Taken together, the launch and subsequent price adjustments show that the GPT-5.6 family is not only a model update. It is part of a broader product lifecycle shift . OpenAI's roadmap messaging has emphasized more unified experiences across ChatGPT and API surfaces, and the new family gives that strategy a clearer set of deployment tiers. Model Positioning July 30, 2026 pricing change Sol Flagship model for advanced professional work Not specified in the supplied research Terra Balanced option for capability and cost Reduced by aro

OpenAI is announcing security updates following the July news that its AI broke out of a sandboxed environment and accidentally hacked Hugging Face, including improvements to its research environments, monitoring, and alignment techniques. The company had already put the brakes on a new model, Astra, that it thinks could have "critical" cybersecurity capabilities, and the […]

The ChatGPT maker says its upcoming Astra model may have reached “critical” cyber capabilities, prompting it to halt a significant number of training runs while it tightens internal safeguards.

The new safeguards include more detailed monitoring of models during the development process, as well as greater emphasis on alignment and security during the post-training process.