AI news: OpenAI bets on autonomous Dots and cheaper models at DevDay 2026

Nic Reeve14:00 01.10.20265 min read

OpenAI used its September 29, 2026, developer conference to introduce more than 20 products and features, led by Dots, always-on autonomous agents, GPT-6.1 Sol, a lower-cost model, and ChatGPT Space for collaborative work. The announcements are the biggest AI news from DevDay 2026 because they move OpenAI’s products beyond question-and-answer chat toward software that can act across digital tasks.

What did OpenAI announce at DevDay 2026?

OpenAI presented a broad set of launches in San Francisco on September 29, 2026. CNBC reported that the company rolled out Dots across paid plans, while Axios described more than 20 new products and features. The event also covered model updates, developer APIs, Codex, ChatGPT workspaces and faster paid service tiers.

September 29, 2026: OpenAI held its annual developer conference in San Francisco, according to CNBC and Axios.
More than 20 launches: Axios reported that the company announced over 20 products and features.
Core focus: OpenAI highlighted autonomous agents, coding, computer use, collaboration tools and model pricing.

The scale of the event mattered to developers because the announcements were not limited to a new model. OpenAI tied consumer-facing ChatGPT features to tools that businesses and programmers can build into their own applications.

What are Dots, OpenAI’s new autonomous agents?

Dots are always-on agents designed to work on a user’s behalf rather than waiting for every individual prompt. CNBC reported that OpenAI made Dots available across paid tiers. Axios said the agents can operate beyond the traditional chatbot format, while other event coverage described Dots as having access to a computer of their own.

Availability: CNBC reported that Dots launched across OpenAI’s paid plans.
Operating model: Axios described Dots as agents that can work independently on users’ behalf.
Underlying technology: OpenAI’s DevDay materials and event summaries connected Dots with GPT-6 Astra.

The practical difference is autonomy. A conventional chatbot responds after a request. An agent can potentially monitor work, use software and complete several steps without a new instruction at every stage. That shift also raises questions about permissions, reliability and the limits of unattended computer access.

What is GPT-6.1 Sol, and how much does it cost?

GPT-6.1 Sol is a new model positioned as a cheaper alternative to GPT-6 Astra. OpenAI’s published event information said Sol approaches Astra’s intelligence at one-fifth of Astra’s token price. WTOP separately reported that OpenAI announced GPT-6.1 Sol as an upgraded model alongside other product launches.

Price comparison: OpenAI said GPT-6.1 Sol costs one-fifth as much per token as Astra.
Capabilities: OpenAI described Sol as improving coding and computer use while approaching Astra’s performance.
Release timing: OpenAI’s developer community announcement said Astra was available and Sol was coming soon.

Token pricing directly affects developers that process large volumes of text, code or tool calls. A lower-cost model can make agent-based applications cheaper to operate, especially when a system needs many intermediate steps before completing a task.

What is ChatGPT Space?

ChatGPT Space is a shared workspace intended for teams, ChatGPT and an agent to work on the same material. Event summaries described the product as a collaborative environment connected to OpenAI’s broader push into workplace software. The feature places the assistant inside a group workflow instead of keeping each conversation tied to one person.

Users: ChatGPT Space is designed for teammates working together.
Participants: The workspace can include people, ChatGPT and a user’s Dot.
Purpose: The product is intended to support shared work on documents, projects and tasks.

The launch gives OpenAI a more direct role in team collaboration. It also creates a need for clear controls over ownership, access, activity logs and the actions an agent can take inside a shared project.

How did OpenAI expand Codex and its developer tools?

OpenAI announced cloud-based Codex capabilities and computer use in its Agents API. The changes allow developers to build applications that can write code, interact with software and complete tasks in remote environments. CNBC and Engadget coverage identified Codex and developer tooling as central parts of the conference program.

Codex in the cloud: OpenAI introduced cloud-based access for coding workflows.
Agents API: OpenAI added computer-use capabilities for developers building autonomous systems.
Target users: The tools are aimed at programmers and companies creating software with agent behavior.

Cloud execution can reduce the need for developers to run every task on a local machine. Computer-use tools can also connect models to existing applications that lack a dedicated artificial-intelligence interface.

What are Astra Ultrafast and the new Pro plans?

OpenAI introduced Astra Ultrafast as a premium speed tier and announced a Pro 500 subscription. The company said Astra Ultrafast can deliver faster generation in the API, Codex and ChatGPT Work. Independent event coverage reported that the new Pro plan costs $500 per month and carries the highest usage limits.

API speed: OpenAI reported that Astra Ultrafast can be up to six times faster in the API.
Codex and ChatGPT Work speed: OpenAI reported speeds up to eight times faster in those products.
Pro 500 price: OpenAI’s plan information listed the subscription at $500 per month.
Pro 200: OpenAI reopened the lower-priced plan to new subscribers with a lower usage cap, according to event reporting.

The pricing structure separates access, capacity and speed. Developers running time-sensitive services can pay for faster output, while other users may favor the less expensive model option over premium throughput.

What happens next for developers and users?

OpenAI’s next test will be adoption. Developers must determine whether Dots and computer-use agents can complete real work reliably, while companies must decide how much authority to give systems that act without constant supervision. The near-term rollout also depends on access, pricing and the arrival of GPT-6.1 Sol.

Immediate access: CNBC reported that Dots were introduced across paid tiers.
Coming model: OpenAI’s developer community announcement said GPT-6.1 Sol was coming soon.
Product pressure: The launches place OpenAI’s agent tools, coding products and workplace features at the center of competition among major AI companies.

Sam Altman said during the event, “No matter what governments do we will work to keep AI safe,” according to Simon Willison’s live account of the keynote. The statement came as OpenAI expanded tools that can make decisions and take actions across software, giving safety controls a practical role alongside model performance and cost.

Sources

AI & Tech

ECRI Targets Hidden AI Failures with New Patient Safety Reporting Channel

Global patient safety nonprofit ECRI is widening its national problem-reporting network to explicitly capture errors, malfunctions, and near misses linked to artificial intelligence (AI) tools and AI-enabled devices used in patient care. The move aims to close a critical data gap as hospitals and health systems rapidly deploy AI for diagnosis, triage, documentation, and patient communication. A New Channel for Tracking AI-Related Harm For decades, ECRI has collected confidential reports on medical device issues and health IT problems from frontline clinicians and healthcare organizations, investigating them and sharing lessons learned back to the reporting sites and the wider industry. With the latest expansion, its Problem Reporting Network now includes a dedicated pathway for incidents where an AI-enabled tool may have contributed to an error, produced an incorrect output, or introduced new risks into care delivery. ECRI is urging healthcare providers, health systems, and clinicians across the United States to submit reports whenever they suspect an AI application played a role in a safety event—whether the error reached a patient or was caught beforehand as a near miss. Reports can cover a broad range of technologies, including diagnostic algorithms, clinical decision-support tools, radiology image analysis systems, risk prediction models, and AI-driven chatbots used in patient engagement. Underreported AI Problems in Clinical Practice The expansion reflects growing concern that AI-related safety issues are significantly underreported compared with more traditional device failures. In a recent ECRI survey of 124 quality, safety, risk, and compliance leaders, nearly one‑third said they had encountered an AI output they believed was incorrect or misleading within the past year. About 9% said an AI error had reached a patient or affected a care decision. ECRI argues that without formal reporting mechanisms, many of these incidents remain invisible to oversight bodies and technology developers. ECRI has previously warned that adverse events involving AI-enabled medical devices and applications are often missed because staff may not realize when AI is running in the background, or they may attribute problems solely to human error or workflow issues. The organization’s guidance emphasizes the need to recognize reportable AI events , identify which products incorporate AI, and conduct risk assessments specific to AI-enabled devices. Examples of AI Errors ECRI Wants Reported The expanded reporting network is designed to capture a spectrum of AI-related safety concerns, including: Incorrect or misleading outputs that influence diagnostic or treatment decisions, such as misclassification of imaging findings or inaccurate risk scores. False positives and false negatives in AI-driven diagnostic tools, even when performance metrics appear acceptable, if they contribute to missed or unnecessary care. Algorithmic bias leading to poorer performance for specific patient groups, including women and racial or ethnic minorities. Unexpected behavior or unsafe recommendations from AI chatbots or virtual assistants used in clinical or patient-facing workflows. System malfunctions or integration failures where AI components interact incorrectly with electronic health records or medical devices, causing delays, data loss, or wrong information displays. All reports submitted to ECRI are kept confidential, and the service is free to participating organizations and clinicians. ECRI triages and investigates reports, may notify manufacturers when appropriate, and incorporates findings into its safety alerts, guidance documents, and member resources. AI Risks Already Top ECRI’s Safety Agendas The expanded reporting network aligns with ECRI’s broader assessment that AI-related risks are now among the most pressing patient safety challenges. In its annual list of Top 10 Health Technology Hazards for 2026 , ECRI ranked the misuse of AI chatbots in healthcare as the number‑one hazard, warning that poorly governed or inadequately supervised chatbots can provide inaccurate or unsafe guidance to patients and clinicians. ECRI’s 2026 Top Patient Safety Concerns report similarly identified “Navigating the AI Diagnostic Dilemma” as the leading safety concern, citing a growing risk of missed, delayed, or incorrect diagnoses when AI tools are deployed without robust validation, governance, and clinical oversight. The organization recommends structured logging of when AI informs diagnostic decisions, maintenance of detailed audit trails, and clear processes for clinicians to override AI outputs when they conflict with clinical judgment. How the Expanded Network Fits into Broader Oversight Regulatory agencies such as the U.S. Food and Drug Administration maintain databases of adverse events and cleared AI-enabled medical devices, but ECRI’s reporting network offers a complementary channel focused specifically on patient safety and practical implementation issues. ECRI encourages organizations to report AI-related events not only to regulatory databases but also to its own system, where incidents can be analyzed in the context of broader patterns of health technology hazards. Reports submitted through the expanded AI pathway will feed into ECRI’s internal databases and may prompt targeted safety alerts, practice recommendations, or deeper investigations of specific products or use cases. Over time, ECRI expects that richer reporting will help quantify how often clinical AI tools produce incorrect or misleading outputs, what types of workflows are most vulnerable, and which controls are effective at preventing harm. Call to Action for Clinicians and Health Systems With AI adoption accelerating across radiology, pathology, emergency triage, scheduling, and patient messaging, ECRI’s message to healthcare organizations is direct: treat AI incidents as reportable patient safety events and submit them through established channels, including ECRI’s expanded network. The organization urges hospitals to educate frontline staff on how to spot potential AI errors, document them consistently, and escalate concerns for review. By systematically capturing AI-related problems—from subtle misclassifications to major diagnostic failures—ECRI aims to give clinicians and technology developers clearer visibility into real‑world risks, ultimately shaping safer AI deployment across the healthcare system.

Nic Reeve·16:00 29.08.2026

AI & Tech

AInews: TAO Price Forecasts Spike As Fresh AI Agent Platforms Go Live

AInews: TAO Price Forecasts Spike As Fresh AI Agent Platforms Go Live On 13–20 September 2026, analysts pushed Bittensor’s TAO token price forecasts sharply higher while new autonomous AI agent tools arrived on the market, turning AInews into a window onto both speculative crypto bets and practical software shifting how developers build automated digital workers. What is happening to TAO’s price right now? TAO is trading in the mid‑$250 range and has swung 8–9% in recent days as traders respond to protocol upgrades and the broader AI token rally. Forecasts for September 2026 cluster around a bullish band between roughly $220 and $541, depending on the model and risk assumptions. Recent market data shows: According to CoinMarketCap’s TAO coverage on 18–20 September 2026, TAO has traded roughly between $233 and $260 during several short rallies and pullbacks, with daily moves of 3–9% common in the last week. CoinLore’s short‑term forecast dated 20 September 2026 puts TAO’s current price near $262.32 and projects $250.14 for 21 September 2026, with a 24‑hour trading range expectation between $245.27 and $277.89. CoinMarketCap’s analysis of an 8–9% surge in the 25 hours to 18 September 2026 links the move to protocol tokenomics changes, AI‑linked capital rotation and a technical breakout pattern in the chart. Those swings sit on top of a still‑volatile backdrop. TAO dropped about 4% in a single overnight window in June 2026 when emissions changes and a soft market hit at the same time, according to CoinMarketCap’s June price series review. That context keeps short‑term traders cautious even as price forecasts rise. How high are current TAO price predictions for September 2026? Retail‑focused forecasting sites now show TAO’s possible September 2026 range spanning from the low‑$170s in bearish scenarios up to more than $1,100 in aggressive bullish models. Most cluster in a narrower band, around $220–$370 for traders and $364–$541 in analytical long‑range models. Different services publish notably different numbers: Cryptopolitan’s September 13, 2026 report on Bittensor expects TAO’s September average price around $210, with a bullish breakout target at $275 and a worst‑case low near $176 based on technical resistance and support. OpenPR’s September 13, 2026 note on “TAO Targets $300 After AI Upgrades” argues that $300 is a key recovery level, with TAO trading around $234 at the time and facing overhead resistance near $260. Changelly’s long‑range Bittensor forecast updated September 20, 2026 places September 2026 price scenarios between a minimum of $363.90 and a maximum of $541.27, with an average of $452.59, based on historical volatility and expected AI‑sector demand. CoinLore’s near‑term model, refreshed September 20, 2026, sees the week of 21–28 September sliding from about $250.14 down toward the low $240s before a small bounce, which sketches a more cautious near‑term path. CoinCodex’s predictive model, updated in late August 2026, projected a short dip to around $176.10 by early September, showing how older data can under‑state the later rally driven by AI token narratives. These predictions use different inputs. Some rely mainly on chart patterns and momentum, while others embed assumptions about expanding decentralized AI demand and sustained attention to Bittensor’s role as a specialist network for machine learning workloads. Why are TAO forecasts climbing despite recent volatility? Forecasts are climbing because TAO sits at the intersection of two stories: an evolving tokenomics design that reduces emissions from idle subnets and a global wave of interest in decentralized AI infrastructure. Upgrades, narrative shifts and external AI milestones all feed into traders’ expectations. Analysts point to several concrete factors: CoinMarketCap’s September 18, 2026 coverage of an 8–9% TAO surge highlights “protocol tokenomics upgrades” that alter staking rewards and emissions, making the asset look more deflationary and thus more attractive to investors betting on scarcity. Another CoinMarketCap analysis from mid‑September notes that the Bittensor core team turned off emissions to 57 “dead” subnets — those with no active miner mechanism or usable code — with plans for weekly clean‑ups, sharpening focus on productive parts of the network. CryptoRank’s April 11, 2026 report on TAO’s 25% crash during Covenant AI’s exit from the network, updated in September, reminds traders of governance risks yet also shows the market’s ability to absorb shocks, with TAO bouncing back from lows near $250 toward the $260s. Cryptonews and other outlets emphasise the 21 million TAO maximum supply cap maintained by the Bittensor protocol, framing it as a parallel to bitcoin’s fixed issuance model but linked to AI model training and inference instead of pure store‑of‑value use. When combined with higher‑profile AI milestones such as OpenAI’s GPT‑6 “Astra” preview and Anthropic’s pre‑IPO positioning, reported by CoinMarketCap on September 18, 2026, TAO’s positioning as a “decentralized AI pure play” encourages some traders to treat the token as a leveraged bet on AI demand rather than just another altcoin. How do new AI agent tools intersect with this crypto rally? A burst of new AI agent frameworks and services appearing in early September 2026 connects directly to the AI‑token story, because both trends express demand for autonomous software that can schedule tasks, execute workflows and operate across multiple apps without step‑by‑step human input. Recent reporting on the software side lists several developments: DutchStartup.ai’s September 7, 2026 feature describes “a wave of new AI agent tools” landing around September 4. Teams shipped fresh frameworks that let developers define long‑running goals, with agents planning, executing and revising tasks based on feedback. The same report notes that existing platforms updated their orchestration layers so agents can call APIs, trigger cloud functions and coordinate with other agents, making them capable of handling complex, multi‑step processes such as grant applications or sales outreach. Several open‑source projects are highlighted as “agent platforms” that run on commodity cloud but could, in principle, connect to networks such as Bittensor to outsource heavy inference workloads, reinforcing the potential link between TAO‑priced compute and agent deployments. These tools arrive as AI companies explore how far they can push automation. Traders watching TAO price action view expanding agent ecosystems as indirect confirmation that demand for decentralized, market‑priced compute services may grow, which feeds back into bullish token forecasts. Who is most exposed to TAO’s swings and the new agent ecosystem? TAO’s volatility and the emerging agent tools touch different groups. Short‑term crypto traders face sharp swings. Long‑term Bittensor stakers and subnet developers watch governance and emissions changes. Meanwhile, AI builders and startups evaluate whether new agents can lower operating costs or open fresh revenue streams. On the TAO side, exposure breaks down into: Day traders and derivatives users speculating on 3–9% daily moves documented by CoinMarketCap’s mid‑September 2026 charts, often using leverage that magnifies both gains and losses. Stakers who lock TAO to support subnets and earn emissions. The decision by Bittensor’s root governance to cut rewards to inactive subnets changes their yield expectations and influences how they allocate capital across the network. Developers who build AI models that rely on TAO‑denominated incentives. Their income streams depend on protocol rules and market prices staying supportive enough to cover infrastructure and research costs. For AI agent tools, exposure looks different: Startups adopting agent frameworks reported by DutchStartup.ai in early September 2026 hope to automate tasks such as booking meetings, handling first‑line customer support or managing data pipelines with minimal supervision. Enterprise IT teams test new orchestration features that link agents to internal systems, raising questions about security, audit trails and compliance as more processes execute without direct human commands. Individual professionals experiment with off‑the‑shelf agents that promise to manage email, research or scheduling, which shifts some white‑collar workloads toward software but still requires careful oversight. What might happen next for TAO and AI agent platforms? The next months will likely hinge on whether Bittensor’s tokenomics changes and governance debates calm investors, and whether AI agent tools move from pilot projects into production deployments. Forecasts remain wide, signalling uncertainty but also strong expectations of continued AI‑linked activity. On TAO’s path forward, available analyses highlight: OpenPR’s mid‑September 2026 prediction of a $300 recovery target assumes smooth integration of recent protocol upgrades and sustained AI‑sector interest. If governance disputes resurface, that path could stall. Cryptopolitan’s broader 2026–2032 outlook places a possible high near $371 for 2026, with an average around $210, underscoring that even optimistic scenarios see extended periods of consolidation and correction. Changelly’s longer‑term estimate of a near‑$1,000 average for TAO in 2026, framed as a theoretical end‑of‑year level, depends on aggressive adoption of decentralized AI and continued scarcity thanks to the 21 million token cap reported by crypto data services. For AI agent tools, DutchStartup.ai’s early‑September 2026 survey suggests that the coming quarters will bring more competition: new frameworks, tighter integrations into productivity suites and cloud providers, and experiments in combining financial incentives with agent performance metrics. As these systems scale, they may drive more demand for flexible AI compute networks, including those priced via tokens like TAO.

Nic Reeve·14:00 21.09.2026

AI & Tech

AI’s Advance Leaves China’s Workers Uneasy as Judges and Officials Push to Protect Jobs

Across China, workers from factory floors to tech startups are increasingly anxious that artificial intelligence could cost them their livelihoods, even as they scramble to adapt to new tools reshaping their jobs. The rapid spread of generative AI and automation is transforming work processes faster than many employees can upskill, intensifying concerns about job security in a slowing economy. Surveys and official data suggest that anxiety is broad-based and acute. A 2025 survey of around 11,800 professionals conducted by the Cheung Kong Graduate School of Business in Beijing found that 85.5% of respondents believed they could face unemployment within three years because of AI replacing human work. Separate research cited by international media indicates that while many Chinese workers see productivity benefits from AI, more than a quarter of those who expect positive impacts also fear that technology might ultimately replace them. Automation Quietly Reshapes the Labor Market China’s companies are adopting AI at speed, often without public announcements of job cuts. Analysts describe a pattern of “quiet layoffs”, where headcount is reduced as AI tools take over routine tasks in sectors such as customer service, editing, visual design and data processing, allowing firms to maintain output with fewer staff. Recruitment platform data reported by Chinese media shows that hiring demand for several white-collar roles fell sharply in early 2026: editing jobs dropped 29%, customer service positions 23% and visual designers 21% year-on-year, while demand for AI-related skills jumped 73% over the same period. A recent Citibank estimate suggested that about 9.6% of all jobs in China—roughly 70 million positions—are at high risk of AI-driven displacement , with the risk rising to 13.6% among workers in their twenties. Individual stories illustrate the shifting landscape. One data analyst in the AI industry told Chinese business media that she helped automate many of her own repetitive tasks, only to later be laid off when her employer concluded the work could be done with fewer people. She subsequently took a significant pay cut to move into a more traditional sector, remarking that “no industry can hide from AI”, a sentiment echoed across online discussion forums and social platforms. From Factory Workers to Coders: Anxiety Spreads AI’s impact is not limited to white-collar roles. Government-linked commentary notes that workers from assembly-line operators to designers and translators are already feeling pressure from automation and algorithmic management tools. Gig workers and service employees, including delivery drivers and content creators, expressed fear in recent interviews that recommendation algorithms, robotics and generative content systems could dramatically reduce demand for human labor or erode pay. At the same time, highly educated workers in tech and media say they are being asked to use AI to increase output without corresponding increases in pay or job security. According to detailed reporting by Caixin, AI often compresses multiple tasks into a single role rather than eliminating jobs outright, leading remaining staff to shoulder heavier workloads while colleagues are quietly let go. This dynamic, combined with already elevated youth unemployment, is intensifying frustration among recent graduates. International wire reports describe programmers, copywriters and office clerks who now rely on AI chatbots and coding assistants, yet fear those same systems could soon make their roles redundant. Some workers interviewed said they have started learning new skills in machine learning or data labeling, while others are exploring career shifts into fields seen as less easily automated, such as hands-on manufacturing or certain service jobs. Courts Push Back on AI-Driven Layoffs China’s legal system has begun to respond to mounting tension over AI-related redundancies. In late 2025 and early 2026, several courts and arbitration panels ruled that replacing an employee with AI software is not, by itself, a valid reason for termination under existing labor law. Employers were told they must first explore contract renegotiation, retraining or internal reassignment before resorting to layoffs on the grounds of automation. In one widely reported case, a Chinese court found that a technology company had illegally dismissed a worker after substituting his role with AI, ordering compensation and signaling that similar dismissals could face legal challenges. Media coverage in August 2026 highlighted a growing number of rulings in favor of employees displaced by AI, describing them as a significant victory for worker rights—but noting that anxiety about the future of work remains widespread. Labor officials in Beijing have also indicated that AI replacement alone should not justify firing an employee in arbitration proceedings, further underlining the government’s concern about social stability in the face of rapid technological change. Official Response: Shield Jobs, Guide Innovation China’s leadership continues to promote aggressive AI development as a pillar of economic modernization, but has increasingly paired this agenda with explicit commitments to protect employment. Policy documents issued since 2025 under the “AI+” banner include directives to strengthen employment risk assessments for AI applications, guide innovation toward sectors with greater job-creation potential and reduce negative impacts on jobs. At national political meetings, officials and legislators have floated an “AI + Employment” framework featuring tax incentives, reskilling schemes, wage subsidies and possible limits on automation in certain sensitive occupations. One representative of the National People’s Congress proposed a dedicated “AI unemployment insurance” fund to support workers most vulnerable to automation, a suggestion echoed in subsequent commentary on state-linked platforms. The Ministry of Human Resources and Social Security has pledged targeted employment support for key industries and expanded vocational training to help workers adapt to an AI-centric labor market. In early 2026, labor authorities announced plans for a dedicated policy package addressing AI’s impact on employment, promising measures for job stabilization, expansion and quality improvement. China’s official trade union newspaper, Workers’ Daily, has called for regulators to improve labor standards and strengthen oversight of AI algorithms, including mechanisms to ensure workers and unions have a greater say in how AI is deployed in workplaces. Economists interviewed by government outlets argue that AI is more likely to restructure the labor market than eradicate jobs outright, drawing parallels with earlier technological revolutions that ultimately created new professions even as they destroyed old ones. Adapting to an Uncertain Future Despite emerging legal protections and policy initiatives, many Chinese workers remain unsure how to prepare for an AI-driven future. Researchers at the Chinese Academy of Social Sciences warn that rapid advances in large language models and automation technologies could hit both younger and older workers particularly hard, and have urged authorities to pursue a “U-shaped” human-capital strategy focused on early childhood education and lifelong retraining. For now, employees across sectors are experimenting with strategies ranging from embracing AI tools to maximize their own productivity, to seeking roles that involve uniquely human skills, to exploring entrepreneurial ventures built on AI applications rather than competing against them. As one white-collar worker told a reporter, using AI at work has become unavoidable—but the question of who ultimately benefits from that productivity remains unresolved.

Nic Reeve·14:30 24.08.2026

AI news: OpenAI bets on autonomous Dots and cheaper models at DevDay 2026

What did OpenAI announce at DevDay 2026?

What are Dots, OpenAI’s new autonomous agents?

What is GPT-6.1 Sol, and how much does it cost?

What is ChatGPT Space?

How did OpenAI expand Codex and its developer tools?

What are Astra Ultrafast and the new Pro plans?

What happens next for developers and users?

Sources

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AI’s Advance Leaves China’s Workers Uneasy as Judges and Officials Push to Protect Jobs