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AI & Tech

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

Nic Reeve5 min read
AI news: OpenAI bets on autonomous Dots and cheaper models at DevDay 2026

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

  1. 1.cnbc.com
  2. 2.axios.com
  3. 3.ground.news
  4. 4.wtop.com
  5. 5.engadget.com
  6. 6.ground.news
  7. 7.simonwillison.net
  8. 8.aiagentslibrary.com
  9. 9.pasqualepillitteri.it
  10. 10.note.com
  11. 11.echai.ventures
  12. 12.community.openai.com
  13. 13.stocktwits.com
  14. 14.9to5mac.com
  15. 15.analyticsinsight.net

Read more →

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Layer in domain‑specific AI, such as commercial credit monitoring and customer‑data platforms, only where oversight frameworks are mature. Portfolio by BISA reports that Edison and lineage platforms help the bank trace data sources for AI outputs, supporting internal audits and regulator reviews. Windows Forum’s analysis of Western New York banks highlights M&T’s published AI policies, inventory of models and continuous monitoring with alerts for drift and anomalies as part of its risk controls. Which employees and business units are most affected by M&T Bank’s AI expansion? M&T Bank’s AI deployment affects office staff, technologists and risk professionals far more than frontline customers. Copilot access is wide across corporate functions, while specialized platforms target commercial credit teams and analytics groups using the bank’s cloud data environment. Based on reports from Yahoo Finance, Forbes and AIM Media House, the main groups using new AI tools include: Customer service and call‑center agents, who use AI to summarize calls and identify next‑best actions. Operations staff, who rely on copilots to draft emails, reports and meeting notes. Software engineers and technology teams, where copilots assist with coding, documentation and troubleshooting. Risk managers and credit associates, who use RDC.AI’s commercial credit monitoring platform. Marketing and analytics teams, who work with Amperity’s customer data cloud and Rich Data Co’s decisioning tools to refine targeting and product offers. AIM Media House quotes M&T’s data leadership stressing that the bank is “not going to the customer‑facing side yet” for broad generative AI use, underscoring a decision to focus on staff productivity and risk insights before AI touches customer decisions directly. What strategy guides M&T Bank’s AI investments and future plans? M&T Bank’s AI strategy combines three main pathways: expanding enterprise fluency, embedding AI into existing applications and building proprietary models using the bank’s own data, all within a regulated and inspection‑ready environment. Forbes reports that the three pathways are: Enterprise fluency, where around 16,000 employees experiment with generative tools to improve productivity and learn their capabilities. Embedded capabilities inside roughly 1,800 applications, many sourced from third‑party vendors, where targeted AI features support tasks like document review and risk scoring. Proprietary development of large language model and agentic solutions built around M&T’s data and processes. ArtificialIntelligence‑News reports that Chief Data Officer Andrew Foster and his team articulated a similar framework: general employee use of generative AI, AI embedded in existing systems and custom solutions aligned with M&T’s distinctive workflows. 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M&T Bank now stands out among regional banks in Western New York for its focus on centralized data strategy and broad internal AI adoption, while peers such as Five Star Bank are documented as concentrating on AI in specific product lines like indirect auto lending. Windows Forum summaries of local coverage describe two tracks in the region: M&T Bank invests in cloud data architecture, governed customer insights and large‑scale employee access to generative tools. Five Star Bank applies AI more narrowly to product‑level automation and credit decisioning, with smaller deployments. These comparisons show that M&T Bank is using its size and long technology overhaul to treat AI as a company‑wide capability rather than an isolated experiment, emphasizing data discipline and internal fluency before aggressive customer‑facing innovation.

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