OpenAI has launched Codex Labs, a new initiative aimed at accelerating enterprise adoption of its Codex platform, as usage continues to grow rapidly. The company said weekly active developers using Codex have surpassed 4 million, up from 3 million just two weeks earlier.

The expansion reflects strong demand from both individual developers and large organizations, with enterprises increasingly integrating Codex into real-world workflows. Companies including Virgin Atlantic, Ramp, Notion, Cisco, and Rakuten are using the platform across tasks such as testing, code review, feature development, and incident response.

OpenAI said Codex is increasingly being used beyond engineering teams, helping organizations analyze information, generate documents, and automate workflows across departments.

Bringing AI Into Enterprise Workflows

Codex Labs is designed to help companies move from early experimentation to production-scale deployment. Through workshops and hands-on sessions, OpenAI experts will work directly with organizations to identify high-value use cases, integrate Codex into existing systems, and establish repeatable workflows.

The initiative addresses a key challenge in enterprise AI adoption: translating early success into consistent, organization-wide impact. OpenAI said demand for Codex support is exceeding its internal capacity, prompting the creation of a structured program to guide deployment.

The company is also emphasizing Codex’s ability to support the full software development lifecycle, from writing and reviewing code to testing and maintaining systems.

Partnering to Scale Adoption

To expand its reach, OpenAI is working with major global systems integrators, including Accenture, Capgemini, Cognizant, Infosys, PwC, and Tata Consultancy Services.

These partners will help enterprises identify opportunities, implement solutions, and scale Codex deployments across complex organizations. Many are also using Codex internally to refine best practices and accelerate delivery for clients.

The move reflects a broader shift in enterprise software, where AI tools are becoming central to how companies build and operate systems. By combining direct support with partner-led implementation, OpenAI is aiming to accelerate adoption globally.

As Codex usage expands beyond coding into broader business workflows, the company is positioning it as a general-purpose productivity layer, capable of transforming how teams work across the enterprise.

LinkedIn is entering the AI training market with a new platform that allows users to earn up to $150 per hour by helping improve AI systems. The initiative, first reported by Business Insider, is currently in testing and reflects rising demand for human input in developing AI models.

The platform focuses on “AI trainers,” workers who evaluate chatbot responses, identify weaknesses, and help refine outputs across domains such as coding, finance, and medicine. LinkedIn said roles related to AI training are among the fastest-growing job categories in the United States, driven by the rapid expansion of generative AI technologies.

The move positions LinkedIn to connect professionals with emerging opportunities in AI development, expanding beyond its traditional role as a hiring and networking platform.

High Demand for AI Trainers

AI training roles have become increasingly important as companies seek to improve the accuracy and reliability of their models. Human feedback is used to evaluate outputs, test system limitations, and guide improvements in performance.

LinkedIn listings show a range of pay levels depending on expertise. Developers working as AI trainers can earn up to $150 per hour, while specialists in finance or Excel-related tasks can earn up to $100 per hour. Linguists with expertise in German or Scandinavian languages are also in demand, with similar pay ranges, while AI system testers are offered between $40 and $50 per hour.

The company has already posted more than a dozen openings tied to these roles and introduced a notification feature to alert users about new AI training opportunities.

Part of a Broader AI Talent Boom

The initiative reflects a broader trend in the AI industry, where demand for human-in-the-loop training is fueling the growth of specialized startups. Companies like Mercor and Surge AI have rapidly scaled by providing data labeling and evaluation services to major AI developers, including Anthropic.

These firms have reached multibillion-dollar valuations as the need for high-quality training data and feedback continues to grow. AI systems still rely heavily on human expertise to refine outputs, particularly in complex or high-stakes domains.

LinkedIn’s entry into this space signals how mainstream platforms are adapting to the AI economy. By facilitating access to training roles, the company is positioning itself at the intersection of workforce development and AI innovation, as demand for skilled contributors continues to expand.

Google is expanding its AI-powered browser assistant, Gemini, within Google Chrome to seven additional countries. The rollout includes Australia, Indonesia, Japan, the Philippines, Singapore, South Korea, and Vietnam, with availability across desktop and iOS devices, except in Japan where mobile support is not yet included.

The expansion builds on Google’s broader effort to embed AI directly into everyday tools, turning Chrome into a more interactive and task-oriented platform. Gemini in Chrome was first launched in the United States earlier this year, with subsequent rollouts to India, Canada, and New Zealand.

AI Assistant Integrated Into Browsing

Gemini in Chrome operates through a floating window and sidebar interface, allowing users to interact with AI while browsing. The assistant can answer questions across multiple tabs, summarize content, and provide contextual insights without leaving the browser.

A key feature is its integration with Google services. Users can connect Gemini to tools such as Gmail, Google Calendar, and Google Photos, enabling tasks like drafting emails, scheduling meetings, and retrieving personal data. The system also supports location-based queries through Maps, making it a central interface for managing both information and actions.

Google has also added creative capabilities, including the ability to transform images directly within the browser using its image tools.

Moving Toward Agentic Browsing

The rollout reflects a broader shift toward “agentic” AI systems that can perform tasks on behalf of users. Google is currently testing more advanced capabilities that allow Gemini to control the browser itself, completing actions such as navigating websites or executing workflows.

However, these agentic features remain limited in availability. They are currently in testing and restricted to users on paid plans in the United States, indicating a cautious rollout as Google refines the technology.

Expanding Global Reach

By extending Gemini in Chrome to additional markets, Google is accelerating its global AI strategy and increasing competition with other platforms integrating AI into productivity tools and browsers.

The move signals a shift in how users interact with the web, with AI assistants increasingly acting as intermediaries between users and online content. As these systems evolve, browsers like Chrome are becoming not just access points to information, but active participants in completing tasks and managing workflows.

Apple is entering a new leadership era as John Ternus prepares to take over as CEO on September 1, 2026, succeeding Tim Cook. While Cook leaves behind a company valued at roughly $4 trillion, the transition comes at a critical moment as investors increasingly focus on Apple’s position in artificial intelligence.

Despite its dominance in consumer devices, Apple has largely stayed on the sidelines of the generative AI boom. Competitors such as Microsoft, Google, Amazon, and Meta have committed hundreds of billions of dollars to AI infrastructure, data centers, and custom chips. Apple, by contrast, has taken a more measured approach, avoiding large capital expenditures and relying partly on external partners for AI capabilities.

The company’s current strategy includes integrating third-party models such as Gemini and ChatGPT into its ecosystem, alongside its own “Apple Intelligence” features. However, consumer response has been mixed, and Apple has yet to introduce a flagship AI product comparable to offerings from its peers.

Hardware-Centric Approach to AI

Ternus, a longtime hardware leader, is expected to lean into Apple’s core strength: tightly integrated devices. The company has been building AI capabilities into its chips since 2017 and is betting that future workloads will increasingly run on-device rather than in the cloud.

This strategy could differentiate Apple in areas such as privacy, performance, and energy efficiency. However, it also raises questions about whether the company can keep pace with competitors developing large-scale AI models and cloud-based services.

Apple continues to see strong demand for its core products. iPhone revenue recently jumped 23% year over year to $85.3 billion, driven by the latest device lineup. Yet analysts warn that hardware growth alone may not satisfy investors if AI becomes the primary driver of value in the tech sector.

Expanding Into AI-Driven Devices and Services

The company is reportedly exploring new AI-powered hardware categories, including smart glasses, enhanced AirPods, and other wearable devices. A foldable iPhone is also expected, which some analysts view as a potential catalyst for the next phase of hardware innovation.

At the same time, Apple’s services business presents another opportunity for AI integration. Subscription offerings such as iCloud, Apple TV+, and payment services could benefit from more personalized, AI-driven experiences. However, this may require balancing Apple’s long-standing emphasis on privacy with the data demands of advanced AI systems.

A Defining Moment for Apple’s Next Chapter

Ternus will inherit a company that remains highly profitable and influential but faces growing pressure to articulate its role in the AI era. Analysts suggest that Apple may need to return to a faster pace of innovation, particularly as consumer behavior shifts toward AI-driven interfaces and services.

While Apple has avoided the costly infrastructure race dominating the industry, the coming years may force a clearer strategic choice: whether to double down on device-centric AI or expand more aggressively into cloud-based intelligence.

As the leadership transition approaches, investors will be watching closely for signals of how Apple plans to compete in what is rapidly becoming the most important battleground in technology.