OpenAI’s Revenue Measure Falls $20 Billion Short of Earlier Estimates in Fresh AI News

Nic Reeve14:00 09.10.20265 min read

OpenAI told investors its annualized revenue was nearing $50 billion at the end of September 2026, about $20 billion below a figure widely reported weeks earlier, according to the Financial Times and reporting that followed. The discrepancy has shaken technology stocks and given fresh scrutiny to how artificial-intelligence companies report sales. The development is the latest major piece of AI news affecting OpenAI’s investors, cloud partners and chip suppliers.

What changed in OpenAI’s revenue picture?

OpenAI’s latest investor disclosures put its annualized revenue near $50 billion, while earlier media reports had placed the figure at roughly $68 billion to $70 billion. The gap does not necessarily represent a sudden $20 billion collapse in sales. Reporting indicates that the two figures used different accounting comparisons, with the higher estimate built partly from investor calculations rather than a direct company statement.

According to the Financial Times, as reported on October 8, 2026, OpenAI’s annualized revenue approached $50 billion at the end of September.
According to the Financial Times and CNBC reporting published on October 8 and 9, 2026, earlier estimates ranged from about $68 billion to $70 billion.
According to TechCrunch on October 8, 2026, the difference between the two figures was approximately $20 billion.

Why were the earlier estimates higher?

The higher number appears to have resulted from an attempt to compare OpenAI with Anthropic using similar revenue measures. A person familiar with the matter told the Financial Times that investors had tried to calculate an equivalent figure after receiving information about OpenAI’s growth. The calculation may have included revenue generated through business partners, while the newer figure reflects OpenAI’s own reported basis.

According to the Financial Times, cited by CNN on October 8, 2026, investors initially worked from an estimate of about $40 billion in annualized sales.
According to Yahoo Finance’s October 8, 2026 report, investors later applied a reported 70% growth rate to that estimate and reached approximately $70 billion.
According to reporting carried by SiliconANGLE on October 8, 2026, the larger calculation included gross revenue from partners, while the newer figure was based on net revenue.

How does OpenAI compare with Anthropic?

The comparison with Anthropic is central to the dispute. Anthropic’s reported annualized sales include business conducted through cloud providers such as Amazon Web Services and Google Cloud, according to reporting on the figures. OpenAI’s investor presentation reportedly excludes those partner sales from its own revenue measure, creating a mismatch when the two companies are placed side by side.

According to Yahoo Finance on October 8, 2026, Anthropic’s annualized sales were reported at about $65 billion.
According to TechCrunch on October 8, 2026, investors sought a direct comparison between OpenAI’s figures and Anthropic’s reported run rate.
According to CNN reporting published October 8, 2026, Anthropic’s calculation includes gross revenue from cloud providers.

That accounting difference matters. A revenue run rate annualizes current sales to estimate a full-year pace. It is not the same as audited annual revenue, and changes in customer demand, contract timing or reporting methods can materially alter the result.

What happened in financial markets?

Technology shares fell after the Financial Times report appeared on October 8, 2026. Investors treated the revised figure as a warning about the scale of demand supporting the artificial-intelligence infrastructure boom, even though the reports also described a measurement dispute rather than a confirmed collapse in OpenAI’s business.

According to Morningstar on October 8, 2026, shares of Oracle, AMD and other technology companies declined after the report.
According to CNBC on October 9, 2026, investors sold shares connected to OpenAI’s ecosystem, including Nvidia, Oracle and CoreWeave.
According to CNN reporting published October 8, 2026, the disclosure contributed to a broader drop in technology stocks.

The reaction reflects OpenAI’s links to a wider network of companies. Cloud providers supply computing capacity, chip companies provide processors and infrastructure firms build data-center capacity. Any change in expectations for OpenAI’s sales can therefore affect assumptions about future spending across that network.

Is OpenAI still targeting $70 billion?

OpenAI may still be aiming for a $70 billion annualized run rate by the end of 2026. Bloomberg reported on October 9, 2026, that people familiar with the company’s plans said OpenAI expected to reach or exceed that level, driven largely by enterprise growth. The report places the company near $50 billion at the end of September.

According to Bloomberg on October 9, 2026, OpenAI expects annualized revenue to reach or exceed $70 billion by the end of 2026.
According to Bloomberg, the forecast depends largely on expansion in OpenAI’s enterprise business.
According to the Financial Times, OpenAI’s annualized revenue was approaching $50 billion at the end of September.

Those figures describe two different points in time. The $50 billion figure reflects the reported run rate at the end of September. The $70 billion figure represents a later target or expectation for the end of the year, not revenue already recorded.

What will investors watch next?

Investors will focus on whether OpenAI explains the accounting basis behind its revenue figures and whether enterprise sales can close the gap between the September run rate and the year-end target. They will also watch how cloud and infrastructure partners describe their OpenAI-related demand in upcoming disclosures.

OpenAI’s next investor update could clarify whether reported revenue is measured on a net or gross basis.
Enterprise customer growth will determine whether the company can move from nearly $50 billion toward a $70 billion annualized pace.
Market attention will remain on suppliers such as Nvidia, Oracle and CoreWeave because their valuations are tied partly to expectations for artificial-intelligence spending.

The immediate issue is not simply whether OpenAI has lost $20 billion in sales. The central question is which revenue definition investors used, which definition OpenAI uses, and whether the company’s projected growth can support the enormous infrastructure commitments built around its products.

Sources

AI & Tech

Google’s Gemini and OpenAI’s ChatGPT Get Major Upgrades in August 2026

Artificial intelligence platforms from Google and OpenAI are undergoing rapid change in August 2026, with new model releases, pricing shifts and feature upgrades that signal how the next generation of AI assistants will be delivered to consumers and businesses. Google Accelerates Gemini Rollout With New Flash Model Google is expanding its Gemini family of models, focusing on efficient systems tailored for coding and automated workflows rather than only headline-grabbing flagship models. On August 13, 2026, Google introduced Gemini 3.7 Flash , describing it as its latest AI model for software engineering support and agent-style business automation. The release comes just three weeks after Gemini 3.6 Flash, underscoring the rapid cadence at which Google is iterating its mid-tier “Flash” models. Gemini 3.7 Flash is being positioned as a workhorse for developers and operations teams. According to Google’s developer documentation, the model offers substantial improvements in web development, software engineering and agentic workflows, and is classified as generally available for use through the Gemini API. To encourage adoption, Google is discounting usage: through the end of 2026, Gemini 3.7 Flash is offered at an introductory rate of $0.75 per million input tokens and $3.75 per million output tokens, roughly half the cost of its 3.6 predecessor. The model is rolling out immediately to Gemini Spark , Google’s subscription-based AI agent service aimed at Pro and Ultra customers in more than 160 countries. These changes build on earlier July announcements detailing Gemini 3.6 Flash, 3.5 Flash‑Lite and 3.5 Flash Cyber models, which were designed to balance efficiency and quality for scalable “agentic” workflows across Google’s products and cloud services. Gemini Crosses a Billion Users as Pro Model Timeline Remains Unclear Alongside the new model, Google is highlighting Gemini’s reach. In early August, CEO Sundar Pichai said that Gemini had surpassed 1 billion monthly active users , calling it the fastest‑growing product in the company’s history on that metric. Usage is being driven both by consumer-facing Gemini interfaces and enterprise integrations. Google Cloud, for example, now uses Gemini powered tools to assist with code conversion in its Database Migration Service, translating stored procedures, triggers and custom functions from databases such as Oracle and SQL Server into PostgreSQL’s PL/pgSQL language. Despite that growth, the status of Google’s flagship Gemini Pro remains uncertain. Industry reporting indicates that an anticipated Gemini 3.5 Pro release has been shelved internally, even as Flash-tier models become widely available across consumer products. Google has not publicly detailed timelines for higher-end Pro updates in the same way it has for Flash models. OpenAI Revamps ChatGPT With GPT‑5.6 Models OpenAI is simultaneously pushing a major upgrade to ChatGPT’s underlying models and user experience, focusing on more capable reasoning and broader access for free-tier users. On August 6, 2026, OpenAI announced that GPT‑5.6 Luna will become the default model for Free and Go plans, replacing earlier versions used in the mass-market chatbot. Luna is designed as a general-purpose assistant for everyday conversations, and will soon be paired with a new Think button that lets users trigger more intensive reasoning on harder questions, subject to safety guardrails. At the same time, Plus and Pro subscribers are receiving an updated GPT‑5.6 Sol model. This version introduces a slider that allows users to choose how much effort—and effectively how much computational “thinking”—ChatGPT applies to a response, trading speed against depth when necessary. OpenAI’s deployment safety documentation categorizes both Luna and Sol as high capability in cybersecurity and biological and chemical domains, reflecting ongoing scrutiny of advanced models in sensitive areas. These upgrades are replacing GPT‑5.5 Instant in ChatGPT’s lineup and redefining what each subscription tier offers. Independent analysis of ChatGPT plans notes that the August change significantly increases the value of the free tier: Luna becomes the only model available to free accounts, but is paired with notable usability improvements. Unlimited Text Chats and Expanded Automation for ChatGPT Users A notable shift in OpenAI’s strategy is a decision to remove core rate limits on text conversations for non-paying users. Starting the week of August 10, free and Go accounts are scheduled to receive unlimited text chats with GPT‑5.6 Luna, although separate limits continue to apply to images, file uploads and other resource-intensive features. OpenAI’s August release notes for ChatGPT add further refinements: the system now has a more accurate sense of a user’s local time, long conversations load more efficiently on the web, and interactive content can appear while it is still being generated, improving responsiveness. On the productivity side, OpenAI is expanding its automation tools under the ChatGPT Work offering. Recent updates include webhook-triggered scheduled tasks, shared task management, and more flexible limits for free users. ChatGPT’s browser capabilities have also been extended to work on signed-in websites, with support for password managers and confirmations before consequential actions, allowing AI agents to safely complete workflows across services like Gmail, Slack and GitHub. OpenAI is simultaneously retiring some legacy models and features from the consumer ChatGPT product. Support articles and release notes indicate that the o3 model will be removed from ChatGPT as of August 26, 2026, and GPT‑4.5 will be retired following earlier sunset dates. The official DALL·E GPT, used for image generation within ChatGPT, is scheduled for retirement on August 30, 2026, although these changes do not affect the separate API offerings. Where LaMDA Fits in Google’s Current Strategy Google’s earlier conversational AI model, LaMDA , is now largely overshadowed by the Gemini family in public announcements and developer materials. Recent update logs and product blogs focus almost exclusively on Gemini-branded models and agent services. While LaMDA played a central role in Google’s first wave of large language models, the company has effectively repositioned its AI story around Gemini, particularly in tools exposed to third-party developers. Industry observers note that this represents not just a rebranding but a consolidation of research and product roadmaps under a single architecture, mirroring how OpenAI has centered its offerings on the GPT‑5.x series. In practice, users interact with Gemini-powered systems in Google products, while LaMDA persists mainly as a reference point in the history of conversational AI. Competitive Outlook: Faster Iteration, Broader Access Taken together, Google and OpenAI’s August moves highlight two intertwined trends in the AI industry: ever-faster iteration on core models and a push to make advanced capabilities available to wider audiences. Google is betting that frequent updates to specialized models like Gemini 3.7 Flash, paired with discounted pricing and agent-focused services such as Gemini Spark, will attract developers and enterprises seeking reliable automation at scale. OpenAI, in turn, is using GPT‑5.6 Luna and Sol to raise the baseline quality of ChatGPT, while removing text-chat limits for free users and strengthening its automation tools through ChatGPT Work. As both companies refine their AI assistants, the competition increasingly centers not only on raw model capability but also on safety frameworks, pricing, and the way these systems integrate into everyday tools—from cloud databases to email and code repositories. For users of ChatGPT and Gemini, the immediate impact this month is better models, more generous usage terms and an expanding set of task-oriented features woven into the platforms they already use.

Nic Reeve·15:00 27.08.2026

AI & Tech

OpenAI’s GPT-5.6 And ChatGPT Work Signal A New Era For The AI Super App

OpenAI has rolled out GPT-5.6 alongside a redesigned, unified desktop experience called ChatGPT Work , marking one of the biggest shifts yet toward an “AI super app” that can plan, create, and execute complex digital work across tools and devices. At the same time, the launch is reverberating across the broader tech and productivity ecosystem, with developers, enterprises, and everyday users reassessing how they will structure work around increasingly autonomous AI agents. GPT-5.6: A New Frontier Model Family GPT-5.6 is OpenAI’s latest frontier model family and is now available across ChatGPT, Codex, the new ChatGPT Work app, and the OpenAI API. The release moves the model out of limited preview and into general availability, opening access to developers and businesses worldwide. OpenAI and early reviewers describe GPT-5.6 as significantly more capable at multi-step reasoning, complex coding tasks, and long-horizon projects compared with its predecessors. The model family is organized into three main variants often described as: Sol – the flagship, highest-capability model for the most demanding reasoning and creation tasks. Terra – a balanced, cost-efficient option aimed at everyday workloads. Luna – a more affordable tier optimized for scale, enabling high-volume use cases. Early technical reviews note that Sol, previewed in late June and fully released in July, shows notable gains in building end-to-end applications, including web apps and internal business tools, with fewer iterations and better adherence to specifications. Testers report that the model can now manage entire project structures, from planning data schemas and APIs to generating front-end components, while maintaining consistency across files. ChatGPT Work: The New Super App For Productivity Alongside GPT-5.6, OpenAI has introduced ChatGPT Work , a unified desktop “super app” intended to be a single entry point for white‑collar productivity. Instead of being just a chat interface, ChatGPT Work acts as an AI agent that can operate continuously on tasks, orchestrating workflows across local files, cloud storage, communication tools, and browsers. The app merges previously separate experiences—standard ChatGPT, the Codex coding assistant, and the Atlas browser experiment—into one redesigned desktop, web, and mobile interface. Within this environment, ChatGPT Work can: Connect to work tools such as email, calendars, messaging platforms, cloud drives, and CRMs via plugins or native integrations. Gather context across these sources to understand ongoing projects, deadlines, and available materials. Generate deliverables ranging from documents and spreadsheets to slide decks, websites, and internal tools. Run long-lived workflows in the background, breaking big goals into multi-step plans and updating the user as milestones are completed. OpenAI positions ChatGPT Work as both a local and cloud agent. On the desktop, the app can read and organize local files, edit documents, and interact with other installed software. In the cloud, it can coordinate actions across connected services, making it possible to design workflows that span multiple teams and systems. Sunsetting Atlas And Consolidating The Ecosystem The push toward a super app has also led OpenAI to simplify its product lineup. The company has confirmed that ChatGPT Atlas , its standalone desktop AI browser launched in 2025, is being retired. The browser’s agentic web navigation features and lessons from user data are being absorbed into the unified ChatGPT desktop application, including a new in-app browsing experience and integrations like a Chrome sidebar. By consolidating Chat, Work, and Codex into a single desktop experience, OpenAI aims to reduce fragmentation and present users with one coherent hub for conversational AI, coding assistance, and agentic task execution. Industry observers see this as part of a broader trend, with AI companies seeking to become the primary interface for digital work rather than just one tool among many. Access And Rollout Strategy The rollout of GPT-5.6 and ChatGPT Work is staged across OpenAI’s subscription tiers. The unified Mac and Windows desktop app is available immediately, including for free users, who typically access a more cost-optimized everyday model variant. Higher tiers such as Pro, Enterprise, and Edu receive priority access to the full capabilities of GPT-5.6—particularly the Sol and Terra variants—along with advanced features in ChatGPT Work. Plus and Business customers are being onboarded over several days, with features like multi-agent collaboration, programmatic tool calling via the Responses API, and hosted “Sites” for quickly publishing web content being highlighted as differentiators for professional use. The company frames this as an evolution from isolated chat sessions to persistent, project-level AI collaboration. Competitive And Market Implications The timing and scope of the launch carry clear competitive implications. ChatGPT Work, powered by GPT-5.6, is explicitly positioned against workspace-focused offerings from rival AI firms, including agentic tools that help teams manage documents, knowledge bases, and project tasks. Analysts note that by bundling coding, browsing, and productivity features into one app, OpenAI is attempting to anchor user workflows around its ecosystem in advance of a potential public offering. Some commentators have likened the move to previous platform shifts, such as the rise of integrated office suites and smartphone operating systems that combined communication, productivity, and media into a single environment. If successful, the super app strategy could make AI agents feel less like separate services and more like the default operating layer for knowledge work. Early Reception From Developers And Power Users Initial feedback from developers has focused on GPT-5.6 Sol’s performance in real-world coding scenarios. Early adopters report building multiple functioning applications—such as internal dashboards, customer portals, and automation scripts—within days, with the model handling more of the glue work between services and frameworks than previous generations. Power users in productivity and operations roles highlight the promise of ChatGPT Work’s “control tower” style interface, which allows them to set goals and monitor progress as the agent works through tasks, rather than constantly issuing one-off prompts. Still, questions remain around governance, auditability, and integration depth in complex enterprise environments, where compliance and data residency issues can slow adoption. From AI News Feeds To Everyday Workflows The rapid evolution from GPT-4-class models to GPT-5.6 and a fully merged super app underscores how quickly AI is shifting from novelty to infrastructure. In weekly AI news roundups, commentators emphasize that the story is no longer just about raw model benchmarks, but about how these systems are packaged into tools that reshape daily routines—from software development and financial analysis to marketing, research, and administrative coordination. OpenAI’s latest launch suggests that the next phase of competition will center on who can provide the most powerful, trustworthy, and seamless agentic workspace. GPT-5.6 and ChatGPT Work are designed as a combined answer to that challenge: a new model family paired with an operating environment where AI can not only respond to questions, but also carry out projects across the digital landscape in which modern work happens.

Nic Reeve·15:00 28.08.2026

AI & Tech

Late-August AI Shifts: Cheaper Models, New Agents, and Rising Risks

From rapidly falling model prices to tighter regulatory scrutiny and surging AI cyberattacks, the past two weeks have brought major shifts in how artificial intelligence is built, priced, and governed worldwide. For marketers and business leaders, these changes are reshaping both the economics of AI and the risk landscape in which they deploy it. Costs Drop as Frontier Models Get Cheaper One of the most significant developments has been a fresh round of price cuts for advanced AI models. Reuters reports that OpenAI reduced developer pricing for its frontier GPT-5.6 Sol model by more than 20%, signaling intensifying competition on cost and making high‑end capabilities more accessible to enterprise users and startups alike. These cuts follow a broader August trend in which several providers have lowered prices on their latest models to drive volume usage and cement market share. Industry trackers note that this downward pressure on pricing is accompanied by improvements in performance and scalability. Anthropic’s Claude Opus 5 has been highlighted for offering a 1 million‑token context window aimed at complex document analysis and research workflows, while Google’s Gemini 3.6 Flash focuses on reduced output costs and more efficient long‑running agents. For marketing teams, these shifts mean more affordable large‑scale content generation, campaign testing, and customer insight analysis, with less concern about token budgets and more focus on creative and strategic deployment. Agents Move Into Everyday Workflows Alongside cheaper models, August has seen continued momentum toward "agentic" AI—systems that can act continuously on behalf of users. Coverage of recent releases underscores how major platforms are embedding agents into common productivity and consumer tools. Google and Anthropic have both pushed always‑on and desktop agents meant to automate routine tasks inside normal workflows, such as managing email, scheduling, search, and document editing. On the consumer side, AI is increasingly being integrated into daily services. The Verge’s August archive highlights OpenAI’s expansion of ChatGPT’s capabilities, including the ability to make dinner reservations and book tables via partners like OpenTable and Resy, further blurring the line between conversational assistance and full‑service transaction agents. TechCrunch’s coverage of new plugins for Apple Messages shows ChatGPT gaining the ability to send text messages directly for users, extending AI’s reach into mobile communication. For marketers, these developments are particularly relevant. As agents enter messaging and reservation flows, brands gain new touchpoints for personalized, AI‑mediated interactions—from automated outreach and reminders to real‑time customer service embedded in chat. The challenge will be maintaining brand voice and trust when interactions are increasingly handled by semi‑autonomous systems. Physical AI and Robotics Attract Investor Capital A parallel trend is the surge of investment into "physical AI"—robots and drones that pair machine learning with hardware. An August digest notes that Unitree’s IPO was oversubscribed more than 5,000 times, while defense‑oriented drone firm Neros raised $250 million. Orders for industrial robots hit $622 million in the second quarter as automation demand expanded beyond the automotive sector into logistics, manufacturing, and warehousing. These developments suggest that AI’s impact is rapidly extending from software to physical infrastructure. For businesses in retail, logistics, and manufacturing, this means more accessible automation options and, potentially, new forms of data‑driven operations—such as real‑time inventory tracking and AI‑controlled fulfillment. For marketing professionals, robotics‑enabled experiences—from automated in‑store demos to AI‑powered events—may become part of an emerging experiential toolkit. Regulation and Risk: From Chatbot Harm to AI‑Driven Cybercrime As AI diffuses into more parts of daily life, regulators and law enforcement are sharpening their focus on risks and misuse. A recent Al Jazeera analysis draws attention to the relatively light regulation of AI compared with everyday products, noting public concern after cases in which people engaged with AI chatbots prior to suicides and violent incidents. The report underscores growing pressure on the long‑standing Silicon Valley position that innovation should proceed with minimal oversight. New data on cybercrime underscores that risks are not only psychological or social. A study highlighted by CNBC shows that between March 2025 and February 2026, one in four data breaches was AI‑enabled, a 56% increase from the previous year. INTERPOL’s African Cyberthreat Assessment similarly reports that AI is involved in 55% of reported cybercrimes across Africa, illustrating how automation and generative tools are being used to scale phishing, fraud, and network attacks. These trends intersect directly with marketing and customer engagement. As AI tools become standard in campaign, CRM, and analytics stacks, organizations must strengthen security practices around data access, model outputs, and automated communication, ensuring that AI does not inadvertently assist attackers or expose sensitive customer information. Macroeconomic and Policy Implications Policymakers are beginning to factor AI into economic forecasts and industrial strategy. Reuters coverage notes that officials at the Swiss National Bank have warned that artificial intelligence could contribute to higher inflation, as productivity gains and new demand patterns ripple through labor markets and pricing. At the same time, governments are pursuing national AI and chip initiatives. South Korea plans a "chip windfall" fund aimed at supporting youth employment and AI investment, positioning the country as a long‑term hub for semiconductor‑driven AI growth. Brazil is pushing forward with an AI supercomputer program that splits projects between Chinese and U.S. firms, reflecting the broader geopolitical competition around AI infrastructure and standards. Meanwhile, China and Indonesia have agreed to deepen cooperation on minerals, energy, and technology, including AI, further entrenching the technology as a strategic priority in regional partnerships. In the corporate sector, Reuters reports a surge in "AI debt"—large, multi‑year investments in AI infrastructure and capabilities—as U.S. companies race to keep up with technological change. Analysts warn that investor fatigue may be emerging as firms struggle to demonstrate near‑term returns on ambitious AI programs. For marketing and sales teams, this intensifies pressure to show measurable business impact from AI deployments, particularly in customer acquisition, personalization, and revenue growth. Platform Competition and User Adoption On the platform front, Google continues to expand its Gemini ecosystem. The Verge notes that an upgraded Flash model now powers Gemini Spark, improving responsiveness and cost efficiency for search‑integrated AI experiences. Additional reporting from independent trackers suggests the Gemini app has surpassed roughly 1 billion monthly users, cementing AI assistants as mainstream consumer products. TechCrunch coverage points to intensifying competition between OpenAI and Anthropic in the business market, with new data indicating that OpenAI is gaining ground with enterprise users. Combined with ChatGPT’s rapid user growth highlighted in industry blogs and August roundups, this suggests that many organizations are standardizing on a small number of leading foundation models, even as they experiment with niche or open‑source alternatives. For marketers, rising user familiarity with AI assistants changes audience expectations. Consumers increasingly anticipate conversational support, personalized recommendations, and seamless handoffs between human and AI channels. Brands that lag in integrating AI into their customer journey risk appearing outdated, while those that move quickly must balance innovation with transparency and responsible data use. What It Means for Marketing and Business Leaders Taken together, the developments of the past two weeks point to a new phase in AI’s evolution: costs are dropping, capabilities are expanding into agents and robotics, and regulatory and security concerns are intensifying. For marketing teams, this environment offers powerful tools for content, segmentation, and engagement—but also demands disciplined governance, clear disclosure, and careful vendor selection. As industry outlets such as MarketingProfs track these changes in regular AI updates, the central message is consistent: AI is no longer an experimental add‑on. It has become a core layer of modern marketing and business operations, requiring strategic oversight comparable to that applied to data, brand, and customer trust.

Nic Reeve·14:30 22.08.2026

OpenAI’s Revenue Measure Falls $20 Billion Short of Earlier Estimates in Fresh AI News

What changed in OpenAI’s revenue picture?

Why were the earlier estimates higher?

How does OpenAI compare with Anthropic?

What happened in financial markets?

Is OpenAI still targeting $70 billion?

What will investors watch next?

Sources

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Late-August AI Shifts: Cheaper Models, New Agents, and Rising Risks