Category: Artificial Intelligence

  • Meta AI in 2026: Full Breakdown of Features, Models, Apps, and Real-World Use Cases

    Meta AI in 2026: Full Breakdown of Features, Models, Apps, and Real-World Use Cases

    Meta AI has evolved far beyond a simple chatbot. In 2026, the platform integrates advanced models, personal agents, creative tools, coding assistance, and AI glasses into a unified ecosystem capable of answering questions and handling real-world tasks.

    Key Takeaways

    • Muse: Meta’s new personal AI agent can handle research, shopping, email, and online forms.
    • Muse Models: Muse Spark, Muse Image, Muse Video, Muse Code, and Muse Glimmer expand Meta’s AI capabilities.
    • AI Ecosystem: Meta connects AI across its apps, developer tools, subscriptions, and smart glasses.

    Meta AI Across Apps

    Meta AI works across WhatsApp, Instagram, Messenger, Facebook, the web, iOS, Android, and the Meta AI Mac app. The assistant can answer questions, write text, review files, study images, create images, support voice chats, and help with research. It can also create documents, presentations, and plans.

    Deep Research adds a more advanced research mode. Meta AI can form a research plan, search sources, combine facts, and produce a report with citations. Public posts from Instagram, Facebook, and Threads can also add social context to some answers.

    Muse Spark Leads the New Model Stack

    Muse Spark now sits at the center of Meta’s latest AI model strategy. Muse Spark 1.1 arrived in July 2026 with stronger logic, software skills, tool use, computer use, and multimodal skills. Meta released Muse Spark 1.2 in August, with Muse Code as a major developer tool.

    Muse Spark 1.3 arrived on September 2, 2026. Meta says the model handles long tasks, software work, messy inputs, multimodal data, and real-world computer use with better context control. The model can work with images, video, and documents.

    Muse Turns AI into an Agent

    A major Meta AI shift came on September 8, 2026, when Meta introduced Muse as a personal AI agent. A normal chatbot waits for a prompt and returns an answer. Muse can take a goal, form a plan, use a browser, interact with websites, and carry out parts of a task.

    Muse can send emails, book travel, fill out forms, shop, research projects, and handle longer tasks. It can keep work active after a user leaves the app and return when a task needs approval. Meta also says Muse can negotiate on a user’s behalf in some cases.

    Meta built a separate Secure VM for Muse. The system gives the agent a dedicated virtual machine and browser. A Sentinel system controls web access. Meta says Muse does not directly see passwords or payment details and asks for approval for sensitive actions.

    Specialized Models Expand Creative Work

    Muse Image supports text-to-image work and detailed edits, while Muse Video focuses on video generation with high visual quality and native audio support. Muse Voice Transcribe handles speech-to-text work and supports live transcription. Meta lists $0.18 per hour for Voice Transcribe and $0.01 per image for Muse Image through the Meta Model API.

    Meta also has Muse Glimmer, an open-weight model for local agents. Local AI can support privacy, response speed, and device-side tasks, and gives Meta a path toward personal AI on devices.

    Muse Code Targets Software Work

    Muse Code gives developers a terminal-based software agent built on Muse Spark. It can plan tasks, edit several files, test changes, work with large codebases, and support multi-agent workflows. Meta lists Muse Code for macOS and Windows.

    AI Glasses Put Meta AI Into the Real World

    Meta AI has a major hardware path through AI glasses. The glasses support voice questions, visual help, translation, object recognition, and hands-free AI access. Muse Spark now supports the glasses with stronger multimodal skills. Phones offer the main assistant, glasses add visual and voice access, and Muse adds task execution.

    Meta One Adds a Paid AI Layer

    Meta launched Meta One on September 15, 2026, as a subscription service across Facebook, Instagram, WhatsApp, and Meta AI. The free Meta AI experience remains available, while paid plans add higher usage limits and more creative tools.

    Meta launched Core at $7.99 per month and Premium at $19.99 per month. Availability and limits can vary by market and account. Meta says the service launched with more than 50 features across its ecosystem.

    The Bigger Shift is Agentic AI

    Meta AI now reaches beyond chat. Muse can handle goals, use tools, work across apps, and ask for approval at sensitive steps. AI glasses add a hands-free interface, while Muse models cover images, video, speech, software, and local AI.

    FAQs

    1. What is Meta AI? Meta AI is Meta’s artificial intelligence assistant, available across WhatsApp, Instagram, Messenger, Facebook, the web, and supported devices.
    2. What is Muse? Muse is Meta’s personal AI agent that can plan tasks, use a browser, interact with websites, and handle longer workflows.
    3. What is Muse Spark? Muse Spark is Meta’s flagship general-purpose AI model family, with Muse Spark 1.3 as the latest major version.
    4. What can Meta AI do in 2026? Meta AI can answer questions, conduct research, create images and videos, assist with coding, analyze files, support voice tasks, and help with online activities.
    5. What is Meta One? Meta One is Meta’s paid subscription layer for AI and creative features across Meta’s apps, with different plans offering higher usage and additional capabilities.
  • Agentic AI Explained: Autonomy, Planning, and a $290 Billion Market by 2035

    Agentic AI Explained: Autonomy, Planning, and a $290 Billion Market by 2035

    What Is Agentic AI?

    Agentic AI represents a fundamental shift from traditional artificial intelligence. Instead of simply responding to a single prompt, agentic systems set their own path toward a goal, adapt to changing conditions, and complete multi-step tasks with minimal human intervention. This autonomy, combined with planning, memory, and tool use, enables them to pursue complex objectives in dynamic environments.

    The global market for agentic AI is projected to grow from USD 8.19 billion in 2025 to USD 290.62 billion by 2035, a compound annual growth rate of 42.88%. This rapid adoption reflects a real change in how enterprises view automation and intelligent systems.

    Core Concepts Behind Agentic Systems

    Agentic AI is built on several key capabilities:

    • Autonomy: Systems operate with minimal oversight once a goal is defined, though humans may still approve high-risk actions.
    • Goal-oriented behavior: Large objectives are broken into smaller tasks through task decomposition.
    • Reasoning and planning: The system compares options and settles on a workable sequence of actions.
    • Decision-making: Actions are chosen based on current context, not fixed scripts.
    • Adaptability: The system adjusts when conditions change mid-task.
    • Environmental awareness: New inputs are read and plans updated accordingly, crucial in fast-moving fields like finance or healthcare.
    • Learning from feedback: Reinforcement learning sharpens future choices through rewards and penalties.
    • Tool use: Agents can query databases, run calculations, or trigger business actions beyond their training data.
    • Memory: Agents recall earlier steps during long tasks, often enhanced by retrieval-augmented generation (RAG).

    The Basic Agentic AI Process

    Agentic systems operate on a continuous loop rather than a single exchange. A goal is defined, the system studies the situation, drafts a plan, and takes action. It then checks the results and learns from them, repeating the cycle until the objective is reached. This loop marks the clearest distinction between agentic AI and older, one-shot tools.

    The Evolution of Agentic AI

    The concept of agentic AI has roots stretching back eight decades:

    • Early Foundations (1943-1990s): Neural network theory, the Turing Test, and the Dartmouth conference (1956) set the intellectual groundwork. Shakey the Robot combined perception and planning in the early 1970s. Expert systems, backpropagation, and early reinforcement learning followed through the 1980s and 1990s.
    • Deep Learning and Reasoning (2012-2022): AlexNet (2012) demonstrated the power of deep neural networks for perception. DeepMind’s AlphaGo (2016) paired deep learning with reinforcement learning. The Transformer architecture (2017) reshaped language modeling. By 2022, research like ReAct fused reasoning with action, and ChatGPT brought large language models into everyday use.
    • The Agentic Era (2023-2026): Toolformer (2023) showed that language models could learn when to call outside tools. AutoGPT and BabyAGI popularized goal decomposition. The Model Context Protocol (2024) gave agents a common way to reach tools and data. Google’s Agent2Agent protocol (2025) allowed agents built on different platforms to communicate. Enterprises are now integrating these capabilities into orchestration, identity, and governance frameworks.

    Market Growth and Outlook

    According to Analytics Insight’s Global Agentic AI Market Report, the market is expected to surge from USD 8.19 billion in 2025 to USD 290.62 billion by 2035, at a CAGR of 42.88%. This growth reflects enterprise confidence in agentic AI’s ability to deliver real value. However, success will depend not only on model quality but also on reliability, governance, and clean integration. Organizations that master these fundamentals now will be best positioned to capture value as the technology matures.

    Frequently Asked Questions

    1. What is the main difference between agentic AI and traditional AI?
    Traditional AI generally completes one task at a time, such as answering a question or translating text. Agentic AI instead pursues a broader goal, planning steps and adapting to changing conditions on its own.

    2. What are AI agents?
    AI agents are self-contained software systems designed to gather information, make decisions, and act toward a specific objective. They serve as the individual building blocks that make up larger agentic AI systems and workflows.

    3. What core capabilities define agentic AI systems?
    Agentic AI relies on autonomy, goal-oriented behavior, reasoning, planning, decision-making, and adaptability. It also depends on memory, tool use, and learning from feedback, allowing systems to complete complex, multi-step tasks reliably over time.

    4. When did agentic AI evolution begin?
    Its roots trace back to early neural network research in 1943 and the founding of artificial intelligence in 1956. Recent progress accelerated after 2022, when reasoning models, tool use, and agent frameworks matured quickly.

    5. How large is the global agentic AI market expected to become?
    The global agentic AI market was valued at USD 8.19 billion in 2025. It is projected to reach USD 290.62 billion by 2035, growing at a CAGR near 42.88%.

  • How Generative AI Is Fueling a Boom in Data Storage Infrastructure

    How Generative AI Is Fueling a Boom in Data Storage Infrastructure

    Generative AI is not only creating new possibilities for businesses but also generating an unprecedented demand for data storage infrastructure. As organizations deploy AI at scale, storage systems are becoming a critical component of technology planning, handling everything from training datasets to model outputs and archived information.

    Rising Data Volumes

    A study by Western Digital and IDC revealed that 94.7% of organizations reported increased data volumes due to AI adoption. More than 61% saw data growth of at least 25% over the past year, highlighting the immense pressure on storage systems.

    Massive Training Datasets

    Businesses must store vast amounts of text, images, video, code, and other data used to develop increasingly capable AI models. This creates sustained demand for high-capacity storage systems that can handle the scale of modern AI training.

    AI Inference Generates Continuous Data

    Every AI interaction produces prompts, outputs, logs, embeddings, and operational information, creating a continuous stream of data that organizations need to store, analyze, and secure. This adds another layer to storage requirements.

    Reviving Cold Data

    The Western Digital study found that 75.9% of respondents were reactivating cold-tier data for AI workloads. Previously dormant information is being turned into valuable training and analysis material, further increasing storage demands.

    The Need for Speed

    AI workloads require not just capacity but also fast access, high throughput, and low latency. This is especially critical when GPUs and other accelerators process enormous datasets, making storage performance a key factor.

    Cloud Spending on the Rise

    Gartner forecasts that India’s public-cloud spending will reach $17.5 billion in 2026, driven by demand for GPUs, networking, scalable storage, and continuous AI inference. Infrastructure growth is set to accelerate.

    Storage Costs Under Pressure

    With more data comes the need for careful economic planning. In the Western Digital study, 98.2% of respondents considered total cost of ownership per terabyte important when making storage decisions for expanding AI workloads.

    Smarter Infrastructure Required

    AI infrastructure demands a combination of fast storage, scalable capacity, efficient data movement, and intelligent data management. Businesses are rethinking storage architecture rather than simply adding more disks as data volumes grow.

  • Brahma AI Secures $150M to Scale Enterprise AI Across Media, Sports, and Healthcare

    Brahma AI Secures $150M to Scale Enterprise AI Across Media, Sports, and Healthcare

    Brahma AI, the artificial intelligence company backed by Prime Focus, has raised USD 150 million in new funding to accelerate the expansion of its enterprise AI platform into media, sports, healthcare, and advertising. The round was led by Multiples Alternate Asset Management, which contributed USD 100 million, and values Brahma AI at approximately USD 2 billion.

    Prime Focus announced the funding on September 23, reinforcing its strategy of building Brahma AI as a standalone business while retaining a significant economic stake in the company.

    Expanding beyond entertainment

    Brahma AI’s platform helps large organizations manage content, produce visual assets, and deploy AI-powered tools for digital humans, voice, and multilingual workflows. The company already serves clients including Warner Bros., the NBA, and Mayo Clinic, and works with partners such as Google, Hakuhodo, and DNEG.

    According to Brahma AI’s founder and CEO, the new capital will support aggressive product development and global growth: “We are shortly launching interactive digital humans, we are building Brahma to be model agnostic, and we are taking our technology into entirely new use cases across sports, healthcare and advertising. We now have the firepower to invest aggressively in all of this, build out our presence in the Bay Area and significantly expand our global go-to-market organization.”

    Built on Prime Focus and DNEG technology

    Brahma AI’s technology was not built from scratch. It draws on years of work from the Prime Focus and DNEG ecosystem, including visual effects, content tools, and AI capabilities. The company also gained technology through its 2025 merger with Metaphysic, a deal that valued Brahma at USD 1.43 billion at the time.

    With the new funding, Brahma AI is positioning itself as a major AI platform player, moving beyond film and visual effects into industries where visual and digital content creation is rapidly growing. Prime Focus gains a new avenue to monetize its technology assets while maintaining exposure to the AI market’s upside.

  • OpenAI and Anthropic Launch Affordable AI Models for Enterprise Customers

    OpenAI and Anthropic Launch Affordable AI Models for Enterprise Customers

    OpenAI and Anthropic Introduce Budget-Friendly AI Models

    Anthropic and OpenAI have launched new, more affordable artificial intelligence models within hours of each other, intensifying competition for enterprise customers. As businesses increasingly demand cost-effective AI solutions, both companies are focusing on reducing the price of deployment while maintaining high performance.

    Anthropic’s Claude Opus 5.5

    Anthropic introduced Claude Opus 5.5, which delivers performance comparable to its high-end Fable 5.1 model but costs about 40% less to operate than its predecessor, Opus 5. The model underwent external safety testing before its release. Opus 5.5 is priced at $4 per million input tokens and $20 per million output tokens, a 20% reduction from Opus 5. It is available through major cloud platforms including Amazon Web Services, Google Cloud, and Microsoft Azure. Anthropic also plans to release Sonnet 5.5 and Haiku 5.5 in the coming weeks.

    OpenAI’s GPT-6 Sol and GPT-6 Luna

    OpenAI unveiled GPT-6 Sol and GPT-6 Luna as more affordable alternatives to its flagship GPT-6 Astra. The new models offer some of Astra’s capabilities while targeting everyday professional workloads and higher-volume usage. GPT-6 Sol costs $2 per million input tokens and $10 per million output tokens. GPT-6 Luna is even cheaper at $0.10 per million input tokens and $0.50 per million output tokens. OpenAI attributes the lower costs to improvements in inference and caching, with cached input-token reads eligible for discounts of up to 90%.

    The launches come as US AI companies face increasing pressure from lower-cost open-weight models, particularly those developed in China. The industry is shifting toward efficiency and pricing as key competitive factors alongside model performance.

  • Banks Warn AI Shopping Bots Risk Wrong Purchases and Payment Data Exposure

    Banks Warn AI Shopping Bots Risk Wrong Purchases and Payment Data Exposure

    Major banks are sounding the alarm over the rise of AI shopping assistants that can search for products and even complete purchases on behalf of consumers. The concerns range from incorrect purchases and scams to exposure of payment data and weaker consumer protections, prompting calls for clearer standards around AI-driven commerce.

    AI Agents Could Make Purchases on Your Behalf

    The technology, often described as “agentic commerce,” allows an AI system to act on a shopper’s instructions rather than simply providing recommendations. Depending on the design, an agent could search for a product, select an option, and potentially complete the transaction. A group including NatWest, Bank of America, ING, Capital One, ASB Bank, and Commonwealth Bank of Australia has raised concerns about scams, fraud, and misuse of financial data.

    The warning comes as technology companies including OpenAI, Google, Anthropic, and Meta develop AI agents capable of assisting with or carrying out shopping tasks. The banks said adoption is outpacing existing industry standards and consumer protections.

    UK retailer John Lewis reported that searches originating from AI agents rose to 2.5% this year, up from 0.3% a year earlier, signaling rapid growth in AI-driven shopping.

    Card Details and Payment Risks

    Financial information is another major concern. The banking group highlighted situations in which AI agents could request customers’ card details and enter them directly into websites. There are also concerns that an AI agent could steer a shopper toward payment methods that provide weaker fraud or purchase protections. If a transaction goes wrong, consumers may not know whether the retailer, payment provider, or AI service is responsible.

    Banks Seek Clearer AI Commerce Rules

    The participating banks plan to discuss a set of proposals with policymakers. These include requiring businesses to disclose when an AI agent is involved in a transaction and providing greater transparency about how the technology makes purchasing decisions. The group also called for stronger safeguards around customer data, along with freedom for consumers and merchants to choose which AI-powered commerce services they use. Interoperability between different AI shopping systems was another principle included in the framework.

    As AI moves from recommending products to acting on shoppers’ behalf, the debate increasingly shifts toward who controls these transactions and who takes responsibility when something goes wrong.

  • Anthropic’s Claude Opus 5.5 Takes on GPT-6 Astra with Lower Costs and Superior Benchmark Performance

    Anthropic’s Claude Opus 5.5 Takes on GPT-6 Astra with Lower Costs and Superior Benchmark Performance

    Anthropic launched Claude Opus 5.5 on September 22 as its first Claude 5.5 family model. The company targets developers and enterprises with stronger coding, reasoning, and computer-use capabilities.

    The model reaches Claude Fable 5.1-level performance across most workflows while costing 40% less overall. Anthropic achieved this through lower token usage, faster output generation, and improved computational efficiency.

    Introducing Claude Opus 5.5, the first model in our new Claude 5.5 family. It performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.

    Anthropic also positioned Claude Opus 5.5 against OpenAI’s GPT-6 Astra through several published benchmark comparisons. The company reported higher scores across coding, knowledge work, and multidisciplinary reasoning tests.

    Terminal-Bench 4.0 gave Claude Opus 5.5 a 66.4% score, ahead of GPT-6 Astra’s 57.9%. FrontierCode also showed Opus 5.5 slightly ahead with a 54.4% score. GDPval-AA measured knowledge work performance, where Opus 5.5 reached 1,846 Elo against Astra’s 1,542. Humanity’s Last Exam also favored Opus 5.5 with tools enabled.

    GPT-6 Astra still scored higher on specialized tests covering scientific research and business workflow automation. Astra reached 64.6% on Terminal-Bench Science against Opus 5.5’s 58.7%.

    Cost efficiency forms another major part of Anthropic’s Claude Opus 5.5 pitch for large-scale AI coding. The model costs USD 4 per million input tokens and USD 20 per million output tokens. Anthropic says Opus 5.5 costs nearly 20% of Astra’s task cost on FrontierCode at default settings. Terminal-Bench performance can also match Astra while using around 40% of its task cost.

    The coding improvements extend beyond benchmark scores to long, complex software engineering workflows. An early tester completed a 680,000-line migration in less than a day. Another test showed Opus 5.5 improving page load times across a web application during 39 of 40 attempts. Anthropic also reported output generation more than 30% faster than Opus 5.

    Safety remains another focus, with Anthropic reporting its strongest automated behavioral audit performance so far. The company said Opus 5.5 showed fewer attempts to cross containment boundaries during testing.

    Anthropic also highlighted communication improvements, saying the model places important information first and follows writing instructions more closely. An early tester described the AI model as one that ‘writes the way I do.’

    Claude Opus 5.5 now reaches developers through Anthropic’s Claude Platform under the model name claude-opus-5-5. Amazon Web Services, Google Cloud, and Microsoft Azure also provide access.

    Anthropic plans to expand the Claude 5.5 lineup with Sonnet 5.5 and Haiku 5.5 in coming weeks. The company said both models will bring similar improvements across performance, efficiency, and safety.

    Also Read: AI Consciousness Debate: Why Microsoft is Challenging Anthropic’s Approach

  • Qualcomm’s Agentic AI Vision Could Reshape Smartphones — But Raises Data Privacy Concerns

    Qualcomm’s Agentic AI Vision Could Reshape Smartphones — But Raises Data Privacy Concerns

    Qualcomm CEO Cristiano Amon recently outlined a future where smartphones evolve from simple command receivers into proactive AI agents capable of understanding user intent and completing tasks autonomously. Speaking at the Snapdragon Summit 2026 in Maui, Amon described the transition to an ‘agentic age,’ where artificial intelligence becomes the central driver of the mobile experience.

    “We are going from a phone-centric model to an agent-centric model. Going forward, agents will become the center of experience. We can finally see what an AI phone truly means,” Amon said.

    To support this vision, Qualcomm unveiled two new mobile platforms: the Snapdragon 8 Elite Gen 6 and Snapdragon 8 Elite Gen 6 Extreme. These chips are designed to handle substantial AI workloads locally, reducing the need to offload every task to the cloud. This local processing could enable faster, more private interactions — but it also requires the phone to access more personal data, such as calendars, messages, and shopping details, to anticipate user needs.

    Amon acknowledged the importance of user permission and context, stating, “The permission to use will be very important. And, so is context. Because the phone is very personal and specific to you. Agentic AI will need your phone to do a lot more than just execute tasks for you.”

    The shift could fundamentally change how users interact with their devices. Instead of manually controlling every step, users might only need to approve key actions while the phone handles the rest. Qualcomm expects this agentic model to extend beyond smartphones to PCs, smart glasses, wearables, and cars, creating a seamless ecosystem of interconnected devices.

    As AI agents take on more responsibility, the balance between convenience and privacy becomes critical. Users will need clear visibility into what their devices access and when they are allowed to act. Transparent permission controls and user oversight will be just as important as AI performance in earning trust.

  • WhatsApp Begins Testing Third-Party AI Agents on iPhone—With a Privacy Catch

    WhatsApp Begins Testing Third-Party AI Agents on iPhone—With a Privacy Catch

    WhatsApp expands AI agent testing to iPhone

    WhatsApp is taking another step toward integrating third-party artificial intelligence into its messaging app. The latest beta release for iOS — version 26.37.10.70 — lets select iPhone users in limited countries connect external AI agents directly inside chats. The rollout follows earlier testing on Android, with WABetaInfo reporting the update on September 22. WhatsApp also confirmed that the feature supports independent agents built by outside developers.

    How the new AI agent feature works

    Eligible users can create up to five third-party agents and interact with them through dedicated one-to-one conversations. Setting up an agent begins in the WhatsApp settings menu, where users assign a name and profile photo. WhatsApp then generates an API key that links the agent to its external hosting service, allowing the agent to respond in the chat.

    According to WABetaInfo, the API key connects WhatsApp with the external service running the agent. Users can reset the key if needed, and the key should be kept private. WhatsApp may adjust the five-agent limit in the future.

    What agents can and cannot access

    Third-party agents are deliberately isolated from most WhatsApp data. They can only see information shared directly with them in their designated conversation. Agents cannot access other chats, contacts, or media stored in WhatsApp, and group chats and communities are not included in the current setup.

    Privacy limits: No end-to-end encryption for agent chats

    The most important caveat is privacy. While regular personal chats remain protected by end-to-end encryption, WhatsApp explicitly says communications with third-party agents are not end-to-end encrypted.

    Messages sent to an agent travel through a Meta service that manages the conversation for the provider. That means the agent provider can receive, process, and store messages in unencrypted form. The provider is also responsible for its own data practices, including how it handles requests for access, deletion, correction, or data portability.

    What users should consider before trying AI agents

    This feature adds new functionality without extending WhatsApp’s standard encryption protections. Anyone thinking about connecting a third-party AI agent should review the provider’s privacy terms carefully before sharing sensitive information.

    WhatsApp has not announced a broader release date for the feature. The iOS rollout is still limited, so the final experience could change before it becomes widely available.

  • New York Surpasses San Francisco as Leading US Tech Talent Hub, AI Expansion Fuels Growth

    New York Surpasses San Francisco as Leading US Tech Talent Hub, AI Expansion Fuels Growth

    New York City has overtaken the San Francisco Bay Area as the largest tech talent market in the United States, driven by rapid expansion of AI companies and the workforce supporting them. From 2022 to 2025, New York’s tech workforce grew by more than 30,000 workers; over the same period, San Francisco’s tech workforce shrank by nearly 24,000 workers.

    The shift reflects New York’s expanding role in the technology sector, supported by its diverse industries and proximity to customers, making it an increasingly attractive hub for AI firms.

    AI Firms Expand in New York

    EliseAI, an AI company, recently expanded its New York headquarters, highlighting the city’s growing importance in the sector. AI is also reshaping work: while automation takes over repetitive tasks, new roles in housing and healthcare are emerging. The changes are felt across industries as companies adopt AI to improve operations and serve customers.

    Despite New York’s growth, San Francisco remains a major technology center. The Bay Area continues to have a high concentration of tech workers and remains an important base for technology companies.

    US Tech Stocks Show Mixed Movement

    For readers tracking technology-sector stocks, data as of September 23, 2026, showed a mixed performance. Among 150 priced US tech stocks, 71 rose, and 72 fell.

    • AppLovin Corporation (APP) : stock at $318.00, down 3.26% over one day. Pluang order activity: 20% Sell, 80% Buy.
    • Expensify Inc. (EXFY) : priced at $2.42, gained 2.98%. Pluang order activity: 96% Sell, 4% Buy.
    • Gartner Inc. (IT) : rose 2.94% to $184.00. Order activity: 50% Sell, 50% Buy.

    The contrasting movements in the technology market come as New York strengthens its position as the leading US tech talent market, while San Francisco retains a significant concentration of technology workers.