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AI Chips Market to Grow by USD 389.25 Billion (2024-2028), Driven by Rising Adoption in Data Centers, Market Evolution Powered by AI – Technavio

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NEW YORK, Nov. 21, 2024 /PRNewswire/ — Report on how AI is driving market transformation – The global artificial intelligence (AI) chips market size is estimated to grow by USD 389.25 billion from 2024-2028, according to Technavio. The market is estimated to grow at a CAGR of over 68.13% during the forecast period. Increasing adoption of ai chips in data centers is driving market growth, with a trend towards convergence of AI and IoT. However, dearth of technically skilled workers for ai chips development poses a challenge.Key market players include Advanced Micro Devices Inc., Alphabet Inc., Baidu Inc., Broadcom Inc., Cerebras, Fujitsu Ltd., Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc..

Key insights into market evolution with AI-powered analysis. Explore trends, segmentation, and growth drivers- View Free Sample PDF

Artificial Intelligence (Ai) Chips Market Scope

Report Coverage

Details

Base year

2023

Historic period

2018 – 2022

Forecast period

2024-2028

Growth momentum & CAGR

Accelerate at a CAGR of 68.13%

Market growth 2024-2028

USD 389251.3 million

Market structure

Fragmented

YoY growth 2022-2023 (%)

53.8

Regional analysis

North America, Europe, APAC, South America, and Middle East and Africa

Performing market contribution

North America at 51%

Key countries

US, China, UK, Germany, and Taiwan

Key companies profiled

Advanced Micro Devices Inc., Alphabet Inc., Baidu Inc., Broadcom Inc., Cerebras, Fujitsu Ltd., Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc.

Market Driver

Artificial Intelligence (AI) is revolutionizing industries from healthcare to retail, finance, and automotive. Deep learning and machine learning algorithms require powerful hardware components like AI chips. Advanced Micro and Nvidia lead the market with their Trainium2 chip and A100 chip, respectively. Quantum computing and highbandwidth memory are the next frontiers. Major cloud providers like Microsoft Azure, Amazon Web Services, and Google Cloud offer AI technologies. Edge computing reduces latency for real-time applications. AI chip lines, including CPU, GPU, FPGA, and ASICs, power data processing in centralized cloud servers and edge devices. Emerging trends include generative AI, cognitive computing, and image recognition. Ethical concerns are rising as AI is integrated into everyday life, from wearable devices to smart homes and connected cars. Energy efficiency is crucial as AI data centers grow. Patent filings for AI technologies are surging. ML and DL are key to computer vision, pose detection, and behavioral pattern analysis. AI applications in healthcare, elder care, and IoT devices are transforming industries. Industry 4.0 and smart manufacturing machines benefit from AI and parallel computing. Despite advancements, system failure and malfunctioning remain concerns. Mobile applications, health monitoring, and personalized health treatments are driving demand. ML and DL are essential for big data processing and AI applications. AI chips, GPUs, FPGAs, CPUs, DSPs, and microcontrollers power various applications, from graphic applications to frame buffer and display devices. Theoretical and algorithmic basis are crucial for visual understanding and human-like AI.

The Internet of Things (IoT) market is experiencing significant growth due to the numerous advantages it offers in various industries such as aerospace and defense, automotive, consumer electronics, healthcare, and others. IoT devices, which include cameras, drones, smart speakers, smartphones, smart TVs, and more, are making decisions based on data they receive without human intervention. To enable power-efficient data processing and machine learning computation in these devices, IoT manufacturers are integrating Artificial Intelligence (AI) chips. This integration allows IoT devices to perform complex tasks and learn from data, enhancing their functionality and value to users. The demand for AI chips in IoT devices is expected to continue growing as the market expands.

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Market Challenges

Artificial Intelligence (AI) is revolutionizing industries from healthcare to retail, finance, and automotive. However, the growth of AI technologies relies heavily on the development of efficient AI chips. These hardware components, including CPUs, GPUs, FPGAs, and ASICs, power deep learning and machine learning algorithms. Companies like Advanced Micro Devices and Nvidia are leading the AI chip market with their Trainium2 chip and A100 chip, respectively. However, challenges persist. Energy efficiency is a major concern as AI applications require high computing power, leading to increased energy consumption. Quantum computing and generative AI are pushing the boundaries of AI technologies, requiring even more powerful chips. Ethical concerns also arise as AI is integrated into various industries, from healthcare to manufacturing. Major cloud providers like Microsoft Azure, Amazon Web Services, and Google Cloud are investing in AI data centers, while edge computing gains popularity for real-time applications. Edge devices, such as IoT devices and autonomous vehicles, require specific integrated chips for data processing. As AI applications expand, so do the challenges. System failure and malfunctioning are concerns for mobile applications, while big data requires advanced parallel computing capabilities. Patent filing and theoretical/algorithmic basis are crucial for the development of AI technologies. In conclusion, the AI chip market is evolving rapidly, with companies investing in high-performance chips to meet the demands of various industries. However, challenges such as energy efficiency, ethical concerns, and system failure must be addressed to ensure the continued growth of AI technologies.The AI chips market is witnessing significant expansion due to the potential financial gains that businesses can reap from artificial intelligence. However, the absence of a sufficient workforce with technical expertise in AI is posing a significant challenge to market growth. Companies are keen on integrating AI into their operations but face high research and development costs and the need for information from experienced AI professionals. The scarcity of talent with the necessary knowledge of AI technology is currently impeding the growth of enterprise AI applications.

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Segment Overview

This artificial intelligence (ai) chips market report extensively covers market segmentation by

Product1.1 ASICs1.2 GPUs1.3 CPUs1.4 FPGAsEnd-user2.1 Media and advertising2.2 BFSI2.3 IT and telecommunication2.4 OthersGeography3.1 North America3.2 Europe3.3 APAC3.4 South America3.5 Middle East and Africa

1.1 ASICs- Artificial Intelligence (AI) chips, specifically Application-Specific Integrated Circuits (ASICs), are becoming increasingly popular in data center applications due to their superior performance and speed. ASICs are customized, non-configurable chips that offer an instruction set and libraries for local data processing, acting as an accelerator for parallel algorithms. Google’s Tensor Processing Unit (TPU) is a prime example, designed for deep neural networks and already in use for Google Search and Google Street View. ASICs provide faster performance than GPUs, FPGAs, and CPUs, making them a preferred choice for data centers. TPUs have an instruction set that allows TensorFlow programs to be modified and new algorithms to be developed, making them a valuable asset for managing data effectively. The use of ASIC-based AI chips is expected to witness significant growth in the forecast period.

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Research Analysis

Artificial Intelligence (AI) Chips Market: The global AI Chips Market is experiencing significant growth due to the increasing adoption of AI technologies in various industries. Deep learning and machine learning algorithms are driving the demand for AI chips, which are specialized hardware components designed to accelerate AI computations. These chips are essential for robotics, quantum computing, and advanced AI applications. The market includes CPU, FPGA, GPU, System on Chip (SoC), and Multichip Module (MCM) solutions. AI chips are finding applications in sectors like healthcare, retail, finance, automotive, autonomous vehicles, IoT devices, and more. High-performance AI chips are crucial for training generative AI models and powering supercomputers. Ethical concerns surrounding AI are also fueling the development of specific integrated circuits. Key technologies include highbandwidth memory and Trainium2 chip. The market is evolving with the shift from cloud to edge computing.

Market Research Overview

Artificial Intelligence (AI) Chips Market: Overview The AI Chips Market is witnessing significant growth due to the increasing demand for advanced AI technologies such as deep learning and machine learning in various industries. AI chips are specialized hardware components designed to accelerate AI algorithms and technologies, including neural networks, quantum computing, and cognitive computing. These chips are essential for powering AI applications in robotics, computer vision, natural language processing, and other fields. The market for AI chips includes various types of hardware components, such as CPUs, GPUs, FPGAs, ASICs, DSPs, and microcontrollers. Companies are investing heavily in the development of AI chip lines, including Nvidia’s A100 chip, Ascend 910B chipset, and H200 chipset, to meet the growing demand for energy-efficient and high-performance AI solutions. AI applications are widespread across industries, including healthcare, retail, finance, automotive, and manufacturing. The use of AI in healthcare for health monitoring, health information access, personalized health, and treatment devices is gaining popularity, especially for the elderly population. In retail, AI is used for customer behavior analysis, inventory management, and personalized marketing. In finance, AI is used for fraud detection, risk assessment, and algorithmic trading. The automotive industry is also adopting AI technologies for autonomous vehicles, advanced driver assistance systems, and connected cars. The use of AI in manufacturing machines, smart homes, and IoT devices is increasing, leading to the growth of AI data centers and edge computing. However, ethical concerns surrounding AI and the potential for system failure or malfunctioning are major challenges for the market. The development of specific integrated chips and multichip modules is a potential solution to address these challenges. The AI Chips Market is expected to continue growing due to the increasing demand for real-time applications, low latency, and big data processing. The market is also being driven by the development of generative AI, large language models, and other advanced AI technologies. The use of AI in mobile applications, gaming consoles, and personal computers is also expected to drive market growth. In conclusion, the AI Chips Market is a rapidly growing market, driven by the increasing demand for advanced AI technologies and applications across various industries. The market is expected to continue growing due to the development of energy-efficient and high-performance AI solutions, the increasing use of AI in various industries, and the growing demand for real-time applications and low latency. However, ethical concerns and the potential for system failure or malfunctioning are major challenges that need to be addressed.

Table of Contents:

1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation

ProductASICsGPUsCPUsFPGAsEnd-userMedia And AdvertisingBFSIIT And TelecommunicationOthersGeographyNorth AmericaEuropeAPACSouth AmericaMiddle East And Africa

7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Company Landscape
11 Company Analysis
12 Appendix

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavio’s report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio’s comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contacts

Technavio Research
Jesse Maida
Media & Marketing Executive
US: +1 844 364 1100
UK: +44 203 893 3200
Email: media@technavio.com
Website: www.technavio.com/

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Safetyfirst Systems, LLC Provides Notice of Data Security Event

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PARSIPPANY, N.J., July 23, 2026 /PRNewswire/ — Safetyfirst Systems, LLC (“SFS”) is providing notice of a data security event that may involve information relating to certain individuals. While SFS is not aware of any misuse of information associated with this event, it is providing notice to potentially affected individuals out of an abundance of caution.

On January 19, 2026, SFS identified suspicious activity involving a limited portion of its server environment. Upon discovering the activity, SFS quickly took steps to secure its systems, notified federal law enforcement, engaged leading third-party forensic specialists, and performed a detailed investigation into the nature, scope, and impact of the activity. The investigation determined that an unauthorized actor accessed and/or acquired certain files from limited SFS systems between January 16, 2026, and January 19, 2026. SFS then conducted a comprehensive review of the affected files to determine what information may have been involved and identify the individuals to whom the information relates. The review has recently concluded, and SFS is providing this notification to potentially impacted individuals out of an abundance of caution. Although the types of information vary by individual, the affected information may include names, Social Security numbers, and driver’s license numbers.

Protecting the privacy and security of the information entrusted to SFS is a responsibility the company takes very seriously. In response to this event, SFS promptly strengthened security measures, continues to enhance its technical safeguards and monitoring capabilities, and is reviewing existing policies and procedures to further protect against similar incidents in the future. SFS is also providing notice to potentially affected individuals and, where required, appropriate regulatory authorities.

Although SFS is unaware of any misuse of personal information impacted by this event, individuals are encouraged to remain vigilant against events of identity theft by reviewing account statements, explanation of benefits, and monitoring free credit reports for suspicious activity and to detect errors. Any suspicious activity should be reported to the appropriate insurance company, health care provider, or financial institution.

Individuals seeking additional information regarding this event can contact SFS’s dedicated assistance line at 1-833-289-5523 between the hours of 7:00 a.m. to 7:00 p.m. Eastern time, Monday through Friday, excluding holidays. Individuals may also write to SFS at PO Box 101, 3299 US Highway 46, Parsippany, NJ 07054-9998.

 

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SOURCE Safetyfirst Systems, LLC

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Sunrate and Mastercard Release White Paper on Agentic AI and the Future of B2B Global Payments

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SHANGHAI, July 24, 2026 /PRNewswire/ — Sunrate, the global payment and treasury management platform, and Mastercard, a global technology company in the payments industry, unveiled a joint white paper, Beyond Automation: Defining Agentic Global Payments, at the 2026 World Artificial Intelligence Conference (WAIC).

Among the first reports in the payments industry to examine the impact of Agentic AI on B2B cross-border payments, the white paper provides a comprehensive framework for understanding how AI agents are reshaping enterprise payment operations. It proposes that cross-border payments are evolving beyond digitisation and automation into a new stage: Autonomy—where AI agents with reasoning, planning, and execution capabilities can independently orchestrate and optimise end-to-end payment and treasury workflows within defined governance frameworks.

As businesses expand across borders, B2B cross-border payments continue to be constrained by fragmented workflows, disconnected systems, foreign exchange inefficiencies, rising compliance requirements, and complex reconciliation processes. While traditional automation improves individual tasks, the white paper demonstrates that Agentic AI represents a fundamental shift by enabling intelligent agents to coordinate entire payment journeys across systems, counterparties, and approval workflows.

Drawing on Sunrate’s global payment infrastructure and AI-native product capabilities, together with Mastercard’s expertise in secure payment networks and data intelligence, the white paper defines Agentic Global Payments — a new category of AI-native global payment infrastructure built to automate and manage complex enterprise workflows.

The report identifies 16 major pain points across the B2B payment lifecycle and outlines 13 high-value AI use cases spanning supplier onboarding, accounts payable and receivable, virtual commercial cards, payment routing, foreign exchange management, compliance screening, fraud detection, reconciliation, and conversational operational support. It also demonstrates how AI agents can automate complex workflows—from extracting information across multiple document formats and conducting compliance checks to initiating payments, optimising FX execution, and completing reconciliation—while operating within enterprise governance and control frameworks.

The white paper further highlights that trusted adoption of agentic payments depends on more than technological capability. It identifies governance, transparency, security, and ecosystem collaboration as essential foundations for enterprise deployment, supported by frameworks such as Know Your Agent (KYA), payment tokenisation, auditability, and cross-industry interoperability.

Sunrate.AI portfolio currently includes the Payment Agent, FX Agent, Compliance Agent, Onboarding Agent, and Chat Agent, designed to help enterprises automate and optimise critical payment and treasury processes while maintaining compliance and operational control.

Mastercard has also been actively building the foundations for trusted agentic commerce – combining AI capabilities with verifiable authorisation, clear accountability and proven payments security. Its work in this area, including Agent Pay (alongside Agent Pay for Machines) and Verifiable Intent, are proof points in how Mastercard is enabling AI to participate in commerce safely and transparently. 

“Our mission is to make global payments seamless, compliant, and intelligent,” said Paul Meng, Co-founder and CEO of Sunrate. “As businesses continue expanding internationally, AI agents will fundamentally reshape how enterprises manage global payments—enabling smoother capital flows, reducing operational friction, and embedding real-time intelligence into every payment decision. This white paper represents an important step in helping the industry understand how Agentic AI can be deployed responsibly at enterprise scale.”

“Agentic commerce is changing how businesses make and execute payment decisions, but speed without accountability creates new categories of risk,” said Anouska Ladds, Executive Vice President, Commercial & New Payment Flows, Asia Pacific, Mastercard. “As AI starts to act on behalf of businesses, autonomous payment decisions need a clear, auditable chain of identity, intent and action. That’s what allows organisations to delegate with genuine confidence — and what will determine whether agentic commerce scales past pilots.”

Released under WAIC 2026’s theme, “Intelligent Partners, Co-creating the Future,” the white paper provides business leaders with practical guidance on adopting AI-driven payment capabilities, covering implementation approaches, governance considerations, and real-world enterprise applications.

By combining Sunrate’s expertise in global payments and treasury management with Mastercard’s trusted payment infrastructure and network capabilities, the collaboration reflects a shared commitment to accelerating the next generation of intelligent, secure, and autonomous B2B global payments.

Click here to check the white paper.

About Sunrate

Sunrate is a leading global payment and treasury management platform for businesses worldwide. Founded in 2016, Sunrate has enabled companies to operate and scale both locally and globally in 190+ countries and regions with its cutting-edge infrastructure, global network, and unified solutions.

Sunrate operates through offices across key markets, including Singapore, Kuala Lumpur, Jakarta, Hong Kong, Shanghai, and London. The company partners with the top global financial institutions, such as Citibank, Standard Chartered, Barclays, J.P. Morgan. Sunrate is also the principal member of Mastercard and Visa. To learn more about Sunrate, visit https://www.sunrate.com/.

About Mastercard

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. 

www.mastercard.com

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JAMS Launches AI for Enterprise Job Scheduling: JAX and JAMS MCP

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A new AI agent and an open-standard connector let IT teams query, diagnose, and manage automation in plain language, on the model they choose, with operational data able to stay onshore inside their own network

SYDNEY, July 24, 2026 /PRNewswire/ — JAMS Software, an orchestration solution for scheduled and event-driven automation, today announced the general availability of two AI capabilities for enterprise job scheduling: JAX, an AI agent built into the JAMS Web Client, and JAMS MCP, a connector built on the open Model Context Protocol standard that brings JAMS into external AI coding tools. Both capabilities ship at no additional cost as part of JAMS Web.

Automation environments grow faster than the teams that run them. Jobs multiply across SQL Server, Azure Data Factory, Airflow, SAP, JDE, and Banner, and when one fails, finding the root cause often means searching several consoles at once, frequently outside business hours. At the same time, IT leaders carry pressure to adopt AI while staying accountable for where operational data goes. JAX and JAMS MCP close both gaps together.

Full details on how JAX and JAMS MCP work, including the control model behind every action, are available at jamsscheduler.com/product/ai.

JAX is an AI agent that runs inside the JAMS Web Client. It finds jobs, troubleshoots failures, and answers how-to questions in plain language, with each response grounded in the JAMS user guide and checked against a built-in glossary. JAX acts only when a user asks it to. Reads flow freely, and every write action pauses for the user’s explicit approval before it runs. JAX does not learn between sessions, and conversations are not retained on the server.

JAMS MCP is a connector, built on the open Model Context Protocol standard, that brings JAMS into the AI tools engineering teams already use, including Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex. Users query jobs, investigate failures, and manage runs in plain language without leaving their tool.

Both capabilities run inside the customer’s own network and act as the signed-in user, with that user’s exact JAMS permissions. There is no elevated AI account: whatever a user cannot do in the JAMS interface, JAX and JAMS MCP cannot do on that user’s behalf. Every JAX and MCP operation is recorded in its own dedicated log, and changes made through the JAMS API land in the JAMS audit trail like any other change. Customers choose their own AI model, whether a commercial provider such as OpenAI or Anthropic or a model running entirely on their own hardware, and JAMS never trains on customer data. In the current release, neither feature edits or deletes a job, folder, schedule, or agent definition. For teams that need operational data to stay onshore, JAX runs on a local model entirely inside the customer’s own network, so nothing leaves at all.

“Adopting AI usually means giving something up, most often visibility into where your data goes,” said Pete Hegland, Chief Executive Officer of JAMS Software. “We built JAX and JAMS MCP so that trade does not have to happen. Every action runs as the signed-in user, every change waits for approval, and the model can run on the customer’s own hardware, keeping operational data onshore.”

“For teams across Australia, New Zealand, and Singapore, two things matter: keeping data onshore, and getting answers when a job fails after hours,” said Shayne Cooper, Account Executive for APAC at JAMS Software. “JAX and JAMS MCP address both. The model can run on the customer’s own hardware, and the answer arrives in plain language at the moment it is needed.”

JAX and JAMS MCP are available now to all JAMS Web customers across Australia, New Zealand, and Singapore, with no separate licence, SKU, or additional cost. AI-assisted creation of new jobs and workflows from a plain-language description is on the roadmap for a future release, gated by the same approvals and permissions as every other action.

Learn how JAX and JAMS MCP work at https://jamsscheduler.com/product/ai.

Fast facts

JAX is an AI agent built into the JAMS Web Client for job scheduling and workflow automation.JAMS MCP is a connector built on the open Model Context Protocol standard, for Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex.Both act as the signed-in user, with that user’s exact JAMS permissions, and there is no elevated AI account.Customers choose the AI model, including a local model that runs entirely inside their own network.JAMS never trains on customer data.Both are available now at no additional cost as part of JAMS Web.

About JAMS Software
Founded in 1987, JAMS Software is an orchestration solution that helps IT teams centralize, automate, and manage scheduled and event-driven jobs across complex, hybrid environments. Over 850 customers rely on JAMS to run their automated workloads. JAMS Software, LLC is headquartered at 108 Patriot Drive, Suite A, Middletown, DE 19709.

Media Contact
Bobby Schmidt, Vice President of Marketing
press@jamssoftware.com
800.261.4267

 

 

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