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.

AI & Machine Learning, News

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.

AI & Machine Learning, Consumer Tech, News

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.

AI & Machine Learning, News

Apple has announced a major leadership transition, naming John Ternus as its next chief executive officer, effective September 1, 2026. Current CEO Tim Cook will move into the role of executive chairman, continuing to support the company’s strategy and global policy engagement.

The decision, approved unanimously by Apple’s board, follows a long-term succession planning process. Cook will remain CEO through the summer to ensure a smooth transition before formally stepping into his new position. Ternus, currently senior vice president of Hardware Engineering, will also join Apple’s board of directors.

The transition marks the end of a 15-year tenure in which Cook transformed Apple into one of the most valuable companies in the world, overseeing major product expansions and a shift toward services and custom silicon.

A Successor from Within

Ternus is a longtime Apple executive, having joined the company in 2001. He has played a central role in hardware engineering across key product lines, including iPhone, Mac, iPad, and Apple Watch. He became vice president of Hardware Engineering in 2013 and joined Apple’s executive team in 2021.

Under his leadership, Apple introduced multiple product innovations and improvements in design, durability, and materials. He has also been involved in advancing Apple’s transition to in-house silicon and expanding its hardware portfolio.

His appointment reflects Apple’s preference for internal leadership continuity, maintaining a consistent approach to product development and long-term strategy.

Cook’s Legacy and Continued Role

Since becoming CEO in 2011, Cook has overseen significant growth at Apple. During his tenure, the company’s market capitalization increased from roughly $350 billion to $4 trillion, while annual revenue nearly quadrupled to more than $400 billion.

Cook also led the expansion of Apple’s services business into a major revenue driver and oversaw the launch of new product categories, including wearables and spatial computing devices. His leadership emphasized privacy, sustainability, and accessibility as core company values.

As executive chairman, Cook will remain closely involved in Apple’s direction, particularly in external relations and policy discussions, ensuring continuity during the leadership transition.

Board Changes and Future Direction

As part of the reshuffle, Arthur Levinson will transition from non-executive chairman to lead independent director. The changes take effect alongside Ternus’s appointment.

The leadership transition comes at a time when Apple faces evolving challenges in areas such as artificial intelligence, hardware innovation, and global competition. With Ternus at the helm, the company is expected to maintain its focus on integrated hardware and software ecosystems while continuing to expand into new product categories.

The move signals a new chapter for Apple, balancing continuity with a generational shift in leadership as it navigates the next phase of growth.

AI & Machine Learning, News

Amazon has agreed to invest up to $25 billion more in Anthropic, significantly expanding their partnership around artificial intelligence infrastructure. The deal builds on Amazon’s previous $8 billion investment and includes an initial $5 billion injection, with up to $20 billion tied to future milestones.

As part of the agreement, Anthropic will commit to spending more than $100 billion over the next decade on Amazon Web Services (AWS) technologies. This includes using Amazon’s custom AI chips, particularly the Trainium family, to train and deploy its Claude models. Anthropic has also secured up to 5 gigawatts of compute capacity, reflecting the scale of infrastructure required to support growing demand.

The announcement comes as both companies seek to strengthen their positions in the increasingly competitive AI market, where access to large-scale compute resources is becoming a key differentiator.

Scaling AI Infrastructure at Massive Levels

Anthropic said it plans to bring nearly 1 gigawatt of Trainium2 and Trainium3 capacity online by the end of the year. The company’s reliance on AWS as its primary cloud and training partner signals a deepening alignment between the two firms.

The investment also highlights Amazon’s broader push into AI infrastructure. The company has indicated it could spend around $200 billion on capital expenditures this year, largely focused on expanding data center capacity and supporting generative AI workloads.

At the same time, Anthropic is facing rapidly growing demand. The company said increased enterprise adoption and rising consumer usage of its Claude models have begun to strain its infrastructure, affecting performance and reliability. The expanded AWS partnership is intended to address these constraints.

Intensifying Competition Among AI Leaders

The deal comes amid intensifying competition between leading AI companies and cloud providers. Anthropic’s main rival, OpenAI, has also secured large-scale infrastructure commitments, including a recent agreement with Amazon reportedly worth up to $50 billion.

Anthropic, founded in 2021 by former OpenAI researchers, has positioned itself as a major enterprise AI provider, with annualized revenue exceeding $30 billion. The company has also formed partnerships with other cloud providers, including Microsoft and Google, reflecting a multi-cloud strategy despite AWS being its primary partner.

The scale of these investments underscores a broader shift in the AI industry, where access to compute infrastructure is becoming as critical as model innovation. Companies are racing to secure capacity and build custom hardware to support increasingly complex models.

For Amazon, the expanded deal strengthens AWS’s position as a key provider of AI infrastructure. For Anthropic, it ensures access to the compute resources needed to scale its models and meet growing demand, as both companies prepare for what could be a new phase of competition in enterprise and consumer AI markets.

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