NeuroAI Market Size, Share, Opportunities, and Trends By Component (Hardware, Software, Services), By Technology (Deep Neural Networks, Spiking Neural Networks, Brain-Computer Interface, Neuromorphic Computing), By Application (Autonomous Vehicles, Robotics, Finance and Trading, Industrial Automation, Others), By End-User Industry (Healthcare, Automotive, IT & Telecom, Manufacturing, Others), And By Geography – Forecasts From 2025 To 2030

NeuroAI Market Size:

The neuroAI market is expected to show steady growth in the forecasted timeframe.

The NeuroAI sector is developing rapidly, propelled by humanity's advances in adaptive neurotechnology and AI-based therapeutics. In 2025, Fasikl's Felix NeuroAI wristband became the first personalised, non-invasive treatment of essential tremor. Felix demonstrated clinically important effects over sham devices in a pivotal trial. In addition to therapeutic wearables, firms such as Sensori.Ai are adding neuroscience to GenAI decision making, making algorithms that analyse, understand, and design for nonconscious consumer signs to produce products, packaging, and messages. These trends in both compounding incidences reflect NeuroAI moving from early-stage invention to being referenced in health and consumer environments.

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NeuroAI Market Overview & Scope:

The neuroAI market is segmented by:

• By Component: The category is split into three segments: hardware, software, and services. We will focus on hardware, so first we define AI hardware as GPUs, TPUs, NPUs, AI accelerators, and neuromorphic chips, which are required for firing up NeuroAI workloads. Nvidia will likely continue to dominate this business segment with about a 60% share, and its forecasted AI chip revenue is expected to grow from $100?billion in 2024 to about $262?billion by 2030. On the other hand, OpenAI has not yet moved away from its heavy reliance on Nvidia and AMD GPUs and early tests on its own TPU, while also testing its in-house chip designs. These specialised chips will allow for the high compute, fast inference, and energy-efficient performance needed for deployable NeuroAI systems in data centres and edge environments.
• By Technology: The segment is divided into deep neural networks, spiking neural networks, brain–computer interfaces, and neuromorphic computing. SNNs tend to offer energy-optimal, low-latency computation features, especially in the use of neuromorphic hardware settings. Their level of research is increasing, with increased hardware-software co-design research with a focus on implementing SNNs on FPGAs and neuromorphic chips in order to have better energy efficiency compared to using standard GPUs.
• By Application: The market is segmented into autonomous vehicles, robotics, finance & trading, industrial automation, and others. NeuroAI has been used for early detection of neurological disorders, EEG interpretation, detection of stroke and epilepsy, and enhanced safety monitoring in hospitals. For instance, Mayo Clinic is currently testing a NeuroAI tool to detect brain diseases hitting various modalities of patient data, while the NHS plans to use AI to assist with the early warning of patient-safety issues in hospitals. These systems can look at complex signals from brains and real time data from hospitals, and facilitate better outcomes with early detection and early intervention.
• By End-User Industry: End-user industries include healthcare, automotive, IT & telecom, manufacturing, BFSI, and others. The end-user industries include healthcare, automotive, IT & telecom, manufacturing, and other industries. To focus on automotive, NeuroAI is helping with sensor fusion, perception, and real-time decision making to support robotic vehicles and advanced driver-assistance (ADAS) systems. Cognizant and Qualcomm have partnered to launch generative AI at the automotive edge to create context-aware driving experiences. NeuroAI use cases are also being applied to improve out-of-distribution detection in self-driving helping vehicles understand rare situations in a way that more closely resembles human ability to respond to new environments.
• Region: Geographically, the market is expanding at varying rates depending on the location. Across North America, Asia-Pacific, and Europe, NeuroAI has gained strong traction globally. In North America, the U.S. is dominating, guided by superior R&D work and chips, including work from Intel and IBM to produce neuromorphic chips, and funded defence and healthcare applications. Asia-Pacific is receiving the highest growth, primarily driven by large investments into AI, chip production, and smart systems in China, Japan, and South Korea. Europe is also emerging to be an important hub of innovation, supported by public–private programs like Horizon Europe and the neuromorphic computing hub in the Netherlands, which focuses on energy-efficient solutions for industrial and Internet of Things (IoT) applications.

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Top Trends Shaping the NeuroAI Market:

1. Personalized Neurotherapeutics

• Wearable devices like Fasikl's Felix NeuroAI wristband use real-time biosignals and paired with AI, can change neuromodulation based on real-time biosignals throughout the day, demonstrating clinically significant benefits for essential tremor sufferers in a published pivotal trial.

2. Consumer-Driven NeuroAI Solutions

• Companies like Sensori.Ai leverage nonconscious data based on the neuroscience of desire to drive product development, from fragrances to packaging, allowing brands to better align with the psychology of consumers.

NeuroAI Market Growth Drivers vs. Challenges:

Drivers:

• Clinical Validation of NeuroAI Devices: Significant clinical developments being carried out, like Fasikl's TRANQUIL trial, for the Felix wristband act as a leader in investment and regulatory momentum. Establishing superiority over sham controls in clinical settings demonstrates clinical evidence of therapeutic value in NeuroAI wearables, promoting both clinician and investor confidence in temedly partially-commercially viable products. Regulatory agencies, like the FDA, notice and may explore reimbursement pathways to adaptive neuromodulation treatment. Also, these validations establish momentum in funding and collaborations/commercialisation, supporting the market towards scalable/infinitely applicable evidence based NeuroAI products that push trials to clinical care.
• Cross-Sector Expansion into Consumer Goods: NeuroAI is expanding well beyond healthcare, as reflected in Sensori.AI's uses of neuroscience-powered GenAI to innovate consumer products for consumers. Sensori.Ai's NeuroAI algorithms uncover unconscious consumer desires and effectively provide input into the fragrance, flavour, pack, and messaging of over 20 consumer products and over 100 campaigns, supporting large domestic brands (likely with some barefoot rounds in the pipeline). The all-important cross-over supports the type of external development that accelerates revenue potential and ultimately widespread adoption of a new category of product (and NeuroAI) market position. As many brands pursue developing neuroscience-powered product development, NeuroAI is a clear market strategy differentiator to help create significant growth, deeper data monetisation, and create new commercial use cases on top of their healthcare use case. 

 Challenges: 

• Regulatory Hurdles & Clinical Validation: NeuroAI devices must adhere to a wide spectrum of validations, like Fasikl's randomised trials, that need clinical evidence to indicate that these devices are valid cases of safety and efficacy. Right now, and in assessment processes, getting approved and onboarding clinician acceptance is still convoluted between funding and science-based long-term studies like proprietary clinical trial development.
• Ethical Concerns Around Neurodata Use: There are ethical (and liability) importantly privacy concerns that are presented in capturing nonconscious descriptors from consumers in accepting brain data (whose data), consent, and trust connotations. As NeuroAI further rightfully bridges neuroscience and marketing with original intent into a field (erase the marketing), ethical frameworks that provide explicit and transparent constructs add host-best trust value between what are mutually expected relationships.

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NeuroAI Market Regional Analysis:

• North America: In North America is at the forefront of growth in the NeuroAI sector due to factors such as strong healthcare infrastructure, regulatory backing, and early-stage startups. To illustrate, U.S.-based Fasikl's Felix NeuroAI wristband had groundbreaking success in the important TRANQUIL trial demonstration of tremor reduction that was significantly better than sham devices, indicating strong clinical and commercial momentum. For example, Paradromics from Austin, TX, had significant advancements in its Connexus brain-computer interface technology and received two FDA Breakthrough Device designations in 2023-2024, indicating a further accelerated regulatory pathway. These developments, in combination with large R&D dollar amounts and federal funding drives, establish North America as the overall leader for NeuroAI innovation and investment.

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NeuroAI Market Competitive Landscape:

The neuroAI market is competitive, with a mix of established players and specialised innovators driving its growth.

• Launch of NeuroAI’s BRIDGES Platform for Autism Care: In April 2025, Canadian startup NeuroAI launched BRIDGES, a comprehensive AI-driven digital platform focused on neurodevelopmental care. Funded with government and private funds (~ CAD 450K), BRIDGES provide diagnostic assessments, personalised intervention plans, and pilot programs in Canada and the U.S.

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NeuroAI Market Segmentation: 

By Component

• Hardware
• Software
• Services

By Technology

• Deep Neural Networks
• Spiking Neural Networks
• Brain-Computer Interface
• Neuromorphic Computing

By Application

• Autonomous Vehicles
• Robotics
• Finance and Trading
• Industrial Automation
• Others

By End-User Industry

• Healthcare
• Automotive
• IT & Telecom
• Manufacturing
• Others

By Geography

• North America
• Europe
• Asia Pacific
• South America
• Middle East & Africa

1. EXECUTIVE SUMMARY 

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE 

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities 

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations 

3.7. Strategic Recommendations 

4. TECHNOLOGICAL OUTLOOK

5. NEUROAI MARKET BY COMPONENT

5.1. Introduction 

5.2. Hardware

5.3. Software

5.4. Services

6. NEUROAI MARKET BY TECHNOLOGY

6.1. Introduction 

6.2. Deep Neural Networks

6.3. Spiking Neural Networks

6.4. Brain-Computer Interface

6.5. Neuromorphic Computing

7. NEUROAI MARKET BY APPLICATION

7.1. Introduction 

7.2. Autonomous Vehicles

7.3. Robotics

7.4. Finance and Trading

7.5. Industrial Automation

7.6. Others

8. NEUROAI MARKET BY END?USER INDUSTRY

8.1. Introduction 

8.2. Healthcare

8.3. IT & Telecom

8.4. Manufacturing

8.5. Others

9. NEUROAI MARKET BY GEOGRAPHY

9.1. Introduction

9.2. North America

9.2.1. By Component

9.2.2. By Technology

9.2.3. By Application

9.2.4. By End-User Industry

9.2.5. By Country

9.2.5.1. USA

9.2.5.2. Canada

9.2.5.3. Mexico

9.3. South America

9.3.1. By Component

9.3.2. By Technology

9.3.3. By Application

9.3.4. By End-User Industry

9.3.5. By Country

9.3.5.1. Brazil

9.3.5.2. Argentina

9.3.5.3. Others

9.4. Europe

9.4.1. By Component

9.4.2. By Technology

9.4.3. By Application

9.4.4. By End-User Industry

9.4.5. By Country

9.4.5.1. United Kingdom

9.4.5.2. Germany

9.4.5.3. France

9.4.5.4. Spain

9.4.5.5. Others

9.5. Middle East and Africa

9.5.1. By Component

9.5.2. By Technology

9.5.3. By Application

9.5.4. By End-User Industry

9.5.5. By Country

9.5.5.1. Saudi Arabia

9.5.5.2. UAE

9.5.5.3. Others

9.6. Asia Pacific

9.6.1. By Component

9.6.2. By Technology

9.6.3. By Application

9.6.4. By End-User Industry

9.6.5. By Country

9.6.5.1. China

9.6.5.2. Japan

9.6.5.3. India

9.6.5.4. South Korea

9.6.5.5. Taiwan

9.6.5.6. Others

10. COMPETITIVE ENVIRONMENT AND ANALYSIS

10.1. Major Players and Strategy Analysis

10.2. Market Share Analysis

10.3. Mergers, Acquisitions, Agreements, and Collaborations

10.4. Competitive Dashboard

11. COMPANY PROFILES

11.1. Intel

11.2. IBM

11.3. Qualcomm

11.4. BrainChip Holdings

11.5. SynSense

11.6. Innatera

11.7. NeuraMatrix

11.8. Samsung

11.9. Google DeepMind

11.10. Applied Brain Research

12. APPENDIX

12.1. Currency 

12.2. Assumptions

12.3. Base and Forecast Years Timeline

12.4. Key benefits for the stakeholders

12.5. Research Methodology 

12.6. Abbreviations 

Intel

IBM

Qualcomm

BrainChip Holdings

SynSense

Innatera

NeuraMatrix

Samsung

Google DeepMind

 

Applied Brain Research