Apple is reportedly developing its own AI-driven search engine, a move that could significantly shift the dynamics of its long-standing relationship with Google. The revelation, reported by Bloomberg, suggests Apple may be looking to reduce or even sever its dependency on Google’s search technology—a partnership that has been a cornerstone of both companies’ mobile strategies for over a decade.

A New Chapter in Search

The news comes amid growing interest in next-generation, AI-powered search engines, such as those developed by Perplexity, OpenAI, and Anthropic. These tools use large language models to provide more conversational, context-aware answers—posing a direct challenge to traditional search engines like Google.

Apple appears to be eyeing a similar path. The company is exploring the integration of artificial intelligence into its search experience, possibly laying the groundwork for a native AI search solution across its ecosystem.

Confirmed in Court

Apple’s intent was disclosed during Google’s ongoing antitrust trial, where Eddy Cue, Apple’s Senior Vice President of Services, testified to the shifting search landscape. Cue noted a recent decline in user searches—a trend he attributed to growing adoption of AI-powered tools that offer faster, more relevant results.

Cue also revealed that Apple is “actively looking at” integrating AI search providers—such as OpenAI and Anthropic—directly into its Safari browser, further signaling a break from its reliance on Google.

This shift could have major financial implications. Google currently pays Apple billions annually to remain the default search engine on iOS devices. If Apple ultimately replaces or deprioritizes Google in favor of its own AI solution, it could disrupt a significant revenue stream tied to Google’s advertising business.

Market Reaction

Investors responded swiftly to the potential shake-up. Shares of Alphabet Inc., Google’s parent company, fell more than 7% following the news, erasing over $75 billion in market value. Apple, by contrast, saw a relatively modest decline of around 2%.

The stark contrast reflects growing investor concern that Google could lose its grip on one of the most valuable digital entry points: the default search position on Apple devices.

The Bigger Picture

While Apple has yet to make any official announcements via a press release or public blog post, its ambitions in AI search align with broader trends reshaping the tech industry. As consumer expectations evolve, driven by advances in generative AI and large language models, traditional web search is increasingly being reimagined.

Should Apple move forward with its own AI-powered search product, the move would not only intensify competition in search—it could also redefine how users interact with information on mobile devices.

Image: Apple Newsroom

IBM has introduced a suite of new technologies aimed at scaling enterprise AI across hybrid environments, as announced today at its annual THINK conference. This move reinforces IBM’s commitment to breaking down barriers to AI deployment with integrated solutions that unify data, orchestrate AI agents, and simplify enterprise operations.

With over one billion applications projected to emerge by 2028, businesses are under increasing pressure to streamline operations across fragmented systems. IBM is responding with hybrid technologies, enhanced agent capabilities, and the consulting expertise of IBM Consulting, aiming to help clients accelerate AI adoption and realize measurable business value.

Arvind Krishna, Chairman and CEO of IBM

“The era of AI experimentation is over,”

said Arvind Krishna, Chairman and CEO of IBM.

“Today’s competitive advantage comes from purpose-built AI integration that drives measurable business outcomes.”

Enterprise AI Agents Powered by watsonx Orchestrate

Central to IBM’s announcement is the expanded functionality of watsonx Orchestrate, designed to help businesses create, deploy, and manage AI agents across a wide array of enterprise tools.

Key features include:

• Agent Builder: Enables users to create custom AI agents in under five minutes using no-code to pro-code tools.
• Pre-built Domain Agents: Ready-to-use agents tailored for HR, sales, procurement, and more.
• Wide Integration: Supports over 80 leading enterprise applications, including solutions from Adobe, AWS, Microsoft, Oracle, Salesforce Agentforce, SAP, ServiceNow, and Workday.
• Agent Orchestration: Coordinates multi-agent workflows and tools across vendors.
• Agent Observability: Provides monitoring, guardrails, and lifecycle governance.

IBM also unveiled the Agent Catalog, offering access to over 150 agents and tools co-developed with partners like Box, Mastercard, Symplistic.ai, 11x, and others. Sample integrations include a Salesforce-native prospecting agent and a conversational HR agent for Slack.

Tackling Integration Challenges with webMethods Hybrid Integration

A common obstacle to enterprise AI adoption is integration complexity. IBM is addressing this with the launch of webMethods Hybrid Integration, a solution for automating workflows across applications, APIs, partners, and clouds.

According to a Forrester Total Economic Impact (TEI) study, organizations using webMethods realized:

• 176% ROI over three years
• 40% reduction in downtime
• Up to 67% time savings on project execution
• Improved ease of use, security, and visibility

This integration technology complements IBM’s existing automation offerings and is enhanced by collaborations with HashiCorp, including integrations with Terraform and Vault, to support secure, scalable hybrid cloud operations.

Unlocking the Value of Unstructured Data

Unstructured data—such as contracts, spreadsheets, and presentations—represents a largely untapped asset in enterprise AI. IBM is advancing watsonx.data to help organizations activate this data with greater accuracy.

Highlights include:

• Open Data Lakehouse: Now with data fabric features like lineage tracking and governance.
• watsonx.data integration: A unified tool for orchestrating data pipelines across formats.
• watsonx.data intelligence: AI-driven tools for deep insights from unstructured data.

Early testing indicates watsonx.data enables up to 40% more accurate AI compared to traditional retrieval-augmented generation (RAG) methods.

In support of this strategy, IBM recently announced plans to acquire DataStax, a leader in unstructured data handling for generative AI. Additionally, watsonx is now integrated with Meta’s Llama Stack, further enhancing its generative AI capabilities.

IBM’s Content-Aware Storage (CAS) is also now available on IBM Fusion, with support for IBM Storage Scale arriving in Q3. CAS enables contextual processing of unstructured data to speed up AI inferencing.

Infrastructure for AI at Scale: Introducing IBM LinuxONE 5

To support high-performance AI workloads, IBM introduced IBM LinuxONE 5, its most secure and powerful Linux platform to date. It can process up to 450 billion AI inference operations per day.

Innovations include:

• Telum II AI Processor & IBM Spyre Accelerator: High-speed processing for generative AI, with Spyre available in late 2025.
• Confidential Containers & Quantum-Safe Encryption: Advanced security for sensitive workloads.
• Cost and Energy Efficiency: Compared to x86 systems, LinuxONE 5 can lower total cost of ownership by up to 44% over five years.

IBM also expanded partnerships with AMD, Intel, CoreWeave, and NVIDIA to deliver new compute and storage solutions for AI-driven applications.

The Road Ahead

IBM’s latest announcements represent a strategic push toward operationalizing AI at enterprise scale through modular, secure, and hybrid-ready technologies. As businesses move from AI experimentation to enterprise-wide adoption, IBM aims to be the infrastructure backbone enabling that transformation.

Image: IBM video

Zencoder, a provider of AI agents integrated directly into developers’ environments, announced today that it has acquired Machinet, a developer of context-aware AI coding assistants with over 100,000 downloads in JetBrains IDEs. The strategic acquisition further cements Zencoder’s position in the rapidly expanding AI coding assistant market, while broadening its multi-integration ecosystem across popular development platforms.

Expanding the Developer Experience

Following the acquisition, Machinet users will gain access to a significantly enhanced developer experience, including:

• Enhanced JetBrains Integration

By combining Machinet’s specialized expertise in JetBrains IDEs with Zencoder’s existing support, developers can look forward to even more powerful tools tailored for these widely-used environments.

• Augmented Unit Testing

Machinet’s context-aware unit test generation technology will be integrated with Zencoder’s advanced testing agents, offering developers a more comprehensive and automated testing experience.

• Industry-Leading Customization

Machinet’s developer community will now benefit from Zencoder’s deep capabilities in understanding large codebases, adapting to team-specific coding styles, and aligning with organizational architecture patterns.

“This acquisition aligns perfectly with our mission to turn everyone into a 10x engineer by providing AI solutions that handle routine coding tasks and let developers focus on innovation,”

said Andrew Filev, CEO and Founder of Zencoder.

“By bringing our advanced coding agent to Machinet’s thriving JetBrains community, we’re fulfilling our mission to deliver the best AI coding experience regardless of development environment.”

Streamlined Transition for Customers

As part of the acquisition, Machinet’s domain and marketplace presence will be transferred to Zencoder. Current Machinet customers will receive detailed guidance on transitioning to Zencoder’s platform, which leverages its proprietary Repo Grokking technology and AI agents.

Existing Machinet users will now gain access to Zencoder’s full feature set, including:

• Advanced multi-file editing and refactoring capabilities
• Deep codebase understanding across repositories using Repo Grokking™
• Sophisticated self-repair mechanisms that automatically test and refine outputs
• Expanded integration with over 20 developer tools, including Jira, GitHub, and GitLab
• Access to Zencoder’s specialized coding and unit testing AI agents

Industry-Leading Performance

Earlier this year, Zencoder’s AI platform demonstrated benchmark-breaking performance:

• A 2x improvement over previous best results on SWE-Bench-Multimodal
• State-of-the-art results on the challenging “IC SWE (Diamond)” section of SWE-Lancer, outperforming top published results by 23%

With the integration of Machinet’s technologies, Zencoder aims to further enhance its capabilities, reinforcing its leadership position in AI-assisted software development.

Availability and Next Steps

Zencoder’s full suite of AI coding and testing tools, including the newly enhanced JetBrains integration, is now available through zencoder.ai, with subscription options ranging from free basic plans to comprehensive enterprise solutions.

Current Machinet users will be provided with detailed transition instructions in the coming weeks.

About Zencoder

Based in Silicon Valley, Zencoder offers powerful AI coding and testing agents designed to empower professional developers. Founded by serial entrepreneur Andrew Filev, Zencoder’s globally distributed team of over 50 engineers helps organizations accelerate innovation and ship impactful software faster. The company holds ISO 27001 certification, is SOC 2 Type II compliant, and is in the process of finalizing its ISO 42001 certification.

Snyk has introduced a groundbreaking solution to tackle the next wave of cybersecurity challenges. The company announced the launch of Snyk API & Web, a next-generation Dynamic Application Security Testing (DAST) tool engineered to secure modern, AI-powered applications.

Bridging the Security Gap in AI-Driven Development

Traditional DAST solutions have long struggled to keep up with the rapidly evolving architectures and increasing complexity of APIs brought by AI-centric development. Recognizing this critical need, Snyk acquired Probely in 2024, integrating its advanced DAST capabilities into Snyk’s broader security ecosystem—complementing existing offerings like SAST, SCA, container, and Infrastructure as Code security.

The result is a unified platform that delivers a holistic, end-to-end view of application security throughout the entire Software Development Life Cycle (SDLC).

AI-Powered Innovations for Modern Applications

Snyk API & Web introduces several forward-thinking features designed specifically for the AI era:

• AI-Driven API Testing: Leveraging in-house fine-tuned Large Language Models (LLMs), the platform automates API discovery and vulnerability scanning—detecting complex issues like Broken Object Level Authorization (BOLA) more efficiently.
• Code-Informed Dynamic Testing: By correlating DAST results with SAST findings, Snyk provides deeper context for vulnerabilities, supporting more accurate prioritization and enabling AI-powered auto-remediation via DeepCode AI Fix.
• CI/CD-Ready: Designed with DevOps in mind, the tool offers seamless CI/CD integration, allowing developers to perform self-service scans within pipelines, guided by organizational AppSec policies.

Strategic Vision from the Top

“Our vision is to empower developers and AppSec teams to secure their entire application surface without slowing down innovation,”

said Geva Solomonovich, Snyk CIO.

“Snyk API & Web is the culmination of that vision—bringing together the speed of AI with the depth of full-lifecycle security.”

Momentum and Market Response

Since integrating Probely, Snyk has reported a 245% quarter-over-quarter growth in Annual Recurring Revenue (ARR) for DAST services—a clear signal of strong market demand for modern, AI-augmented security tools.

Looking forward, the company plans to deepen its AI capabilities, broaden API coverage, and provide even richer context for faster, more accurate vulnerability remediation.

Lace AI, a startup using artificial intelligence to supercharge customer service for home service companies, has raised $19 million in total funding, according to company’s website referring a TechCrunch article. The Mountain View-based company disclosed a previously unannounced $5 million pre-seed round led by Canvas Ventures, and a new $14 million seed round led by Bek Ventures. Other investors include Horizon VC, Launchub, Snowflake co-founder Marcin Zukowski, and Vivino’s Heini Zachariassen.

Founded in early 2022, Lace is the brainchild of Boris Valkov, a former AI engineer at Meta who helped develop PyTorch. Drawing from his early experiences in his family’s grocery store and later engineering roles at VMware and Meta, Valkov teamed up with Stan Stoyanov to launch a platform that blends AI with customer service.

Lace’s software analyzes 100% of incoming customer calls for home services companies—like HVAC, plumbing, and roofing—to detect missed sales opportunities and improve booking rates. The company says some clients have reported double-digit revenue growth and claims a 1,000% increase in annual recurring revenue (ARR) in 2024, after beginning sales at the end of 2023.

Currently working with over 100 businesses, including A1 Garage Door Service and Sage Home, Lace operates on a SaaS model, charging per agent. With 20 employees today, the company plans to triple its headcount using the new capital.

Bek Ventures’ managing partner Mehmet Atici told TechCrunch the firm was drawn to Lace’s strong team and its focus on applying AI to traditionally underserved industries—an approach he believes represents a major opportunity.

Photo: Lace AI

The Washington Post has announced a strategic partnership with OpenAI to enhance access to reliable journalism within ChatGPT, the popular conversational AI platform. Through this collaboration, users will now see summaries, key quotes, and direct links to The Post’s original reporting when querying relevant topics.

A Shared Vision for Accessible, High-Quality Information

The integration underscores a joint mission to make factual, well-sourced reporting easier to access—particularly on fast-moving or nuanced issues. ChatGPT will now surface journalism from The Post on subjects including politics, global affairs, business, and technology, ensuring clear attribution and direct links to full articles for users seeking deeper insights.

“We’re all in on meeting our audiences where they are,” said Peter Elkins-Williams, Head of Global Partnerships at The Washington Post. “Ensuring ChatGPT users have our impactful reporting at their fingertips builds on our commitment to provide access where, how and when our audiences want it.”

Varun Shetty, Head of Media Partnerships at OpenAI, emphasized the importance of journalistic integrity in AI responses: “More than 500 million people use ChatGPT each week to get answers to all kinds of questions. By investing in high-quality journalism by partners like The Washington Post, we’re helping ensure our users get timely, trustworthy information when they need it.”

Expanding Media Partnerships in the AI Era

This collaboration builds on OpenAI’s broader efforts to integrate trusted news content into its tools. To date, the company has established partnerships with more than 20 publishers, expanding access to over 160 news outlets and hundreds of content brands across more than 20 languages.

AI at the Heart of The Post’s Digital Evolution

The Washington Post has increasingly embraced artificial intelligence as a tool to improve news accessibility and audience engagement. Recent innovations include Ask The Post AI, Climate Answers, and newsroom tools like Haystacker, all developed to leverage generative AI for journalistic purposes. The organization has also introduced AI-powered article summaries and audio formats to expand how readers consume news.

“Images in ChatGPT are wayyyy more popular than we expected (and we had pretty high expectations),” Altman wrote.

GPT-4o’s Image Tools Initially Reserved for Paid Users

The delay comes just one day after OpenAI introduced its new GPT-4o model, which includes native support for image generation and editing. The update allows users to upload, modify, and interact with images directly within ChatGPT.

In a blog post published Tuesday, OpenAI said these new features would roll out to all ChatGPT users “soon.” However, as of Wednesday, the capabilities remain limited to paying subscribers—specifically those on ChatGPT Plus, Pro, and Teams plans.

Ongoing Capacity Challenges at OpenAI

This isn’t the first time OpenAI has scaled back or delayed product rollouts due to infrastructure limitations. The company has repeatedly cited insufficient compute capacity as a bottleneck for launching new features.

Earlier this year, OpenAI faced a similar issue following the debut of its text-to-video model Sora. Signups for the product were temporarily paused just days after launch due to demand.

Looking ahead, OpenAI is betting big on infrastructure to address these recurring issues. The company is reportedly planning a $500 billion initiative, dubbed Stargate, to build one of the largest data centers in the world in partnership with Microsoft. The project is aimed at dramatically increasing the compute power available for AI workloads.

Image: OpenAI

Google has introduced Gemini 2.5, its most advanced AI model to date, marking a major leap in artificial intelligence with enhanced reasoning and performance across a wide array of complex tasks. Released today as an experimental version of Gemini 2.5 Pro, the model is already topping industry benchmarks and is now available in Google AI Studio and the Gemini app for Advanced users.

Gemini 2.5 Pro Experimental tops the LMArena leaderboard. Image: Google

Described as a “thinking model,” Gemini 2.5 is engineered to reason through problems before generating a response — a capability that significantly improves accuracy, contextual understanding, and decision-making. Unlike earlier models focused mainly on pattern recognition and prediction, Gemini 2.5 emphasizes logical analysis, contextual nuance, and informed decision-making.

“We’re building these thinking capabilities directly into all of our models going forward,”

Google stated in the launch announcement.

“This enables our AI to solve more complex problems and support more context-aware, capable agents.”

The new model debuts at #1 on the LMArena leaderboard, a benchmark driven by human preferences, highlighting its superior reasoning, coding, and stylistic coherence. It also performs exceptionally well on advanced math and science benchmarks, including GPQA and AIME 2025, without relying on expensive test-time methods like majority voting.

Notably, Gemini 2.5 Pro achieved a state-of-the-art 18.8% score on Humanity’s Last Exam, a rigorous dataset built by hundreds of subject matter experts to test human-level reasoning across disciplines.

The model also scores a state-of-the-art 18.8% across models without tool use on Humanity’s Last Exam

In coding, Gemini 2.5 Pro represents a significant jump from its predecessor, Gemini 2.0. It excels in generating functional, visually appealing web apps, agentic code transformations, and code editing. It also leads on SWE-Bench Verified, the industry standard for evaluating agentic coding abilities, with a score of 63.8% using a custom agent configuration.

As a showcase of its power, Google shared an example where Gemini 2.5 Pro generates a fully executable video game from a single-line prompt — demonstrating its potential for developers, educators, and creators alike.

Pricing for Gemini 2.5 Pro will be announced in the coming weeks, with options for higher rate limits and scaled production use. The model will soon be available on Vertex AI, further expanding its reach across Google’s ecosystem. Gemini 2.5 Pro is available now in Google AI Studio and in the Gemini app for Gemini Advanced users, and will be coming to Vertex AI soon.

In a pair of groundbreaking studies conducted by OpenAI in partnership with the MIT Media Lab, researchers have uncovered a growing trend: people are turning to AI chatbots not just for information, but for emotional support. These studies delve deep into the psychological and behavioral impacts of chatbot usage, and while they highlight some benefits, they also raise red flags about the potential downsides of forming emotional bonds with AI.

Human-Like Sensitivity in Machines

At the core of this phenomenon is the increasing perception among users that AI—particularly voice-enabled chatbots—can display “human-like sensitivity.” This perception is drawing users to open up to bots during challenging emotional moments. Whether people are dealing with loneliness, stress, or the desire for companionship, they’re finding comfort in AI’s always-available, non-judgmental presence.

The First Study: How Chatbots Influence Loneliness and Dependence

The first study, “How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Randomized Controlled Study”, involved a four-week experiment with 981 participants and over 300,000 messages exchanged. Researchers examined how different modes of interaction—text, neutral voice, and engaging voice—and different conversation types (personal, non-personal, open-ended) influenced users’ emotional states.

Key findings include:

• Voice chatbots initially helped reduce loneliness more effectively than text-based ones. However, this benefit faded with high usage, particularly with neutral-voiced bots.
• Conversation topics mattered: Talking about personal issues slightly increased loneliness but decreased emotional dependence. Meanwhile, non-personal chats led to higher dependence among heavy users.
• High daily usage was a risk factor, consistently associated with increased loneliness, emotional reliance on the chatbot, and reduced social interaction with real people.
• Users with a stronger emotional attachment style or higher trust in the AI were more likely to experience negative psychosocial effects, including greater dependence and loneliness.

These results suggest that while AI chatbots may offer short-term emotional support, overreliance can be counterproductive, possibly replacing human interaction rather than supplementing it.

The Second Study: Affective Use and Emotional Well-Being with ChatGPT

The second study, “Investigating Affective Use and Emotional Well-being on ChatGPT”, expanded the lens by analyzing over 4 million ChatGPT conversations and surveying more than 4,000 users. In addition, a separate 28-day randomized controlled trial with nearly 1,000 participants looked at how different interaction modes affected emotional well-being.

This study found:

• Very high usage was again linked to emotional dependence, echoing the results of the first study.
• Voice mode’s impact varied depending on the user’s initial emotional state and duration of use—suggesting that voice interactions are more emotionally potent, but also potentially more risky.
• A small number of users accounted for the majority of emotionally charged interactions, hinting that those most vulnerable may be engaging more intensely with AI.

What This Means for the Future

Together, these studies shed light on the complex relationship between AI chatbot design and human emotional behavior. On one hand, the emotional responsiveness of AI—especially with voice-enabled features—can offer comfort, empathy, and a sense of connection. On the other, excessive use or reliance can increase feelings of loneliness and dependence, undermining genuine social connections.

As AI becomes more deeply integrated into daily life, these findings urge caution. Developers and designers may need to rethink how chatbot experiences are structured, potentially incorporating features that promote healthy usage and encourage real-world socialization.

Moreover, the research calls for ongoing studies to determine how AI can be emotionally supportive without replacing vital human relationships. The goal isn’t to eliminate emotional engagement with AI, but to better understand its boundaries—and to design responsibly within them.

In a significant leap forward for artificial intelligence-driven voice technology, OpenAI has unveiled its latest speech-to-text and text-to-speech audio models. This release marks a major milestone in developing more intuitive, customizable, and accurate AI voice agents.

Revolutionizing AI-Driven Voice Agents

Over the past few months, the company has been dedicated to advancing the intelligence, capabilities, and practical applications of text-based AI agents. Their previous innovations, including Operator, Deep Research, Computer-Using Agents, and the Responses API, have laid the groundwork for more sophisticated AI interactions. However, true usability demands deeper and more natural engagements beyond simple text-based conversations.

With the launch of these cutting-edge audio models, developers now have access to powerful tools that enhance AI’s ability to understand and generate human speech with remarkable accuracy and expression. These models provide a new benchmark in speech technology, significantly improving performance in challenging scenarios such as diverse accents, noisy environments, and varying speech speeds.

Breakthroughs in Speech-to-Text Accuracy

The newly introduced gpt-4o-transcribe and gpt-4o-mini-transcribe models exhibit remarkable advancements in word error rate reduction and language recognition. Outperforming previous Whisper models, these models utilize reinforcement learning and extensive training on high-quality audio datasets, leading to:

• Enhanced transcription reliability
• Improved recognition of nuanced speech patterns
• Reduction in misinterpretations across different speech conditions

These advancements make them particularly well-suited for applications such as customer service call centers, meeting transcription services, and accessibility tools for users with hearing impairments.

Next-Level Customization with Text-to-Speech

For the first time, developers can instruct AI voice models not just on what to say, but also on how to say it. The gpt-4o-mini-tts model introduces a new level of steerability, allowing users to dictate tone and style—for example, requesting speech in the manner of a “sympathetic customer service agent.” This unlocks potential applications for:

• Dynamic customer support interactions
• Expressive narration for audiobooks and storytelling
• More human-like AI companions for various digital interfaces

While the current models are limited to artificial, preset voices, ongoing monitoring ensures they align with synthetic voice standards.

Innovations Driving the New Audio Models

The company’s latest AI speech models are built upon extensive research and cutting-edge methodologies, including:

• Pretraining with authentic audio datasets: Using specialized datasets tailored to speech applications, the models capture and interpret speech nuances with exceptional precision.
• Advanced distillation techniques: The use of self-play methodologies ensures realistic conversational dynamics, enhancing user-agent interactions.
• Reinforcement learning enhancements: By leveraging RL-heavy paradigms, the speech-to-text models achieve unprecedented levels of accuracy, reducing hallucinations and misrecognitions.

API Availability

These new audio models are now available via OpenAI’s API, empowering developers to build more responsive and interactive AI-driven voice applications. Additionally, an Agents SDK integration simplifies the development process for those looking to incorporate AI voice interactions seamlessly.

Future Developments

Looking ahead, OpenAI plans to expand its investments in multimodal AI experiences, including video, to further enhance agentic interactions. Future efforts will also explore custom voice development while ensuring adherence to ethical and safety standards. As AI continues to evolve, these advancements pave the way for more sophisticated and natural human-machine communication.