Neuromorphic and Photonic Computing Market Outlook 2026–2038: Market Size, Growth Drivers, Key Players, Strategic Developments & Adoption Forecast for Brain-Inspired Chips, Optical Compute and Co-Packaged Optics in AI Infrastructure — A Meticulous Next™ Foresight Brief

This Meticulous Next™ brief examines how neuromorphic and photonic computing could reshape AI infrastructure over the next 5–15 years. These technologies are being developed to address some of the fundamental limitations facing conventional computing architectures as AI workloads continue to expand.

AI infrastructure is increasingly constrained by two challenges. The first is power consumption. As model sizes and computing requirements grow, data centres face rising energy demands and increasing difficulty securing sufficient power capacity. The second is data movement. Existing electrical interconnects between chips, servers, and racks are becoming bottlenecks as AI systems require larger volumes of data to move faster and more efficiently across computing infrastructure.

Photonic computing addresses these challenges by using light rather than electrical signals to transmit and, in some cases, process information. Its earliest impact is expected in optical interconnects and high bandwidth communications within AI infrastructure, with broader computing applications emerging over time. Neuromorphic computing takes a different approach by designing hardware architectures inspired by the structure and operation of biological neural systems, with a focus on highly energy efficient processing, particularly for edge and autonomous systems.

The brief examines both technology domains, including their indicative market size and growth outlook, major adoption drivers, significant developments over the past 24 months, leading companies active in the space, and the expected adoption pathway through 2038.

This focused 108 page decision brief is intended for semiconductor companies, systems providers, hyperscalers, data centre operators, AI developers, foundries, advanced packaging providers, defense organizations, edge device manufacturers, and investors evaluating next generation computing technologies. It presents an indicative market trajectory rather than a segmented market model. The objective is to identify which applications each technology is likely to capture first, how AI infrastructure evolves beyond traditional GPU centric architectures, and where value is likely to be created and captured across the emerging compute ecosystem.

Neuromorphic and photonic computing represent two distinct approaches to improving the performance and efficiency of AI infrastructure beyond conventional digital architectures.

Photonic computing

 uses light to transmit and, increasingly, process information. Its first major application is in data movement rather than computation. Silicon photonic interconnects can replace electrical connections between chips, packages, circuit boards, and racks, reducing energy consumption per bit while significantly increasing bandwidth. A key development is co packaged optics, where optical engines are integrated alongside networking and accelerator silicon to improve communication within large AI clusters.

Over the longer term, photonic processors may also perform selected computational tasks, particularly matrix operations and other linear algebra workloads that are fundamental to AI. These architectures offer the potential for lower energy consumption, especially for inference and specialized training workloads. However, adoption remains constrained by challenges related to precision, data conversion, programmability, and software integration. As a result, photonic computing is expected to reach commercial deployment in targeted applications before expanding into broader computing environments.

Neuromorphic computing

 takes inspiration from biological neural systems. Rather than relying on conventional processor architectures, neuromorphic chips use large numbers of event driven processing elements that activate only when required. Memory and computation are located close together, reducing data movement and improving energy efficiency.

These architectures are particularly well suited for:

• Computer vision and image processing.

• Audio and speech analysis.

• Vibration and sensor monitoring.

• Radar and signal processing.

• Robotics and autonomous systems.

• Defense, aerospace, and satellite applications.

• Always on edge intelligence workloads.

Neuromorphic processors are already appearing in selected edge deployments, while larger scale systems remain primarily in development and research environments. The ecosystem also includes event based sensors that generate data only when meaningful changes occur, further reducing energy consumption. Related approaches, including analog computing and in memory computing, pursue similar efficiency gains by reducing the distance between data storage and computation.

Two structural challenges are accelerating interest in these technologies:

• Rising AI power consumption

  , which is increasing pressure on electricity supply, cooling infrastructure, and data centre economics.

• Interconnect bottlenecks

  , where data movement between accelerators has become a major constraint on AI training and inference performance.

As a result, technologies such as co packaged optics have moved from research programs into commercial roadmaps across leading semiconductor, networking, and foundry companies. At the same time, neuromorphic architectures are attracting attention for applications where energy efficiency, low latency, and continuous operation are critical. Together, these technologies represent complementary approaches to extending AI computing beyond the limits of conventional digital systems.

The neuromorphic and photonic computing market, including co packaged optics, optical interconnect technologies for AI infrastructure, photonic computing systems, neuromorphic processors, related intellectual property, and design services, is estimated at USD 1.5–2.5 billion in 2026. Current market activity is led by early deployments of co packaged optics within advanced computing environments and the commercialization of neuromorphic processors for edge applications.

Meticulous Next™ expects the market to reach USD 45–70 billion by 2038, representing an indicative CAGR of 32–36%. Growth is expected to be driven primarily by the increasing need for more efficient AI infrastructure and the limitations of conventional electronic architectures in addressing future compute and data movement requirements.

Over the forecast period, market development is expected to occur in three phases:

• Optical interconnect adoption

  , where co packaged optics and silicon photonic technologies become increasingly integrated into AI clusters and high performance computing environments. This segment is expected to represent the largest share of the market throughout the forecast period.

• Photonic computing expansion

  , with specialized photonic processors gaining adoption for selected inference and linear algebra workloads as technical and software challenges are addressed.

• Neuromorphic computing growth

  , beginning with edge sensing, defense, aerospace, robotics, and autonomous systems before expanding into broader low power and always on computing applications.

Growth is supported by:

• Rising power consumption across AI infrastructure.

• Increasing bandwidth requirements within large scale AI clusters.

• Demand for more energy efficient computing architectures.

• Advances in semiconductor packaging, photonic integration, and specialized processor design.

Regionally:

• North America

   is expected to lead in system design, AI infrastructure investment, and hyperscale demand.

• Taiwan and East Asia

   are expected to play a central role in manufacturing, advanced packaging, and semiconductor production.

• Europe

   is expected to maintain strengths in neuromorphic research, photonic technologies, and integrated photonics manufacturing capabilities.

While neuromorphic and photonic computing address different challenges, both are positioned to play important roles in the evolution of AI infrastructure as the industry seeks alternatives and complements to conventional GPU centered architectures.

The base case assumes co-packaged optics scales through 2030 and photonic and neuromorphic compute reach production for defined workloads in the early 2030s. An accelerated case adds severe power and grid constraints or a breakthrough in photonic precision, pulling inflections forward by one to two years and the 2038 value to the top of the range. A delayed case assumes reliability and manufacturing yield issues in photonic integration, or conventional electronics and packaging extend efficiency further than expected, pushing compute inflections to the mid-2030s and confining growth to interconnect.

Growth drivers

• Power and energy limits: AI data centres cannot obtain enough electricity, and digital electronics are approaching energy-per-operation floors.
• Interconnect bottleneck: bandwidth between accelerators, packages and racks limits training and serving of large models.
• Edge and always-on AI: robots, vehicles, satellites, wearables and industrial sensors need inference at milliwatts, which neuromorphic architectures deliver.
• Hyperscaler and defense demand for efficiency and sovereignty in compute supply.

Enablers

• Foundry and packaging platforms for silicon-photonic integration and co-packaged optics.
• Hyperscaler design wins and standards for optical I/O.
• Neuromorphic software frameworks and event-based sensor ecosystems.
• Public research programmes and defense funding for brain-inspired and photonic computing.

Restraints and barriers

• Manufacturing yield, reliability and cost of photonic integration and co-packaging.
• Precision, conversion overhead and programmability limits of photonic compute.
• Software: neuromorphic and photonic systems require new programming models and toolchains.
• Incumbent momentum: conventional accelerators, packaging and interconnect keep improving and absorb investment.

Five converging forces will determine the pace and scale at which neuromorphic and photonic computing expand AI infrastructure beyond conventional GPU based architectures:

• Power, energy, and grid constraints on AI infrastructure

  , increasing demand for more efficient approaches to computation and data movement.

• The growing interconnect bottleneck within AI clusters

  , where moving data between accelerators, servers, and racks is becoming a major performance and cost challenge.

• Advances in foundry capabilities and packaging technologies

  , particularly for photonic integration, heterogeneous architectures, and next generation semiconductor manufacturing.

• The maturity of software ecosystems and programming models

  , enabling developers to effectively utilize photonic processors and event driven computing architectures.

• The pace of efficiency improvements in conventional electronic computing, which will influence how quickly alternative architectures become economically and technically attractive.

The brief assesses each of these forces in terms of direction, pace of adoption, and confidence level, highlighting the factors most likely to influence the commercial adoption of neuromorphic and photonic computing over the coming decade.

The key players operating in neuromorphic and photonic computing include NVIDIA Corporation, Broadcom Inc., Marvell Technology Inc., Intel Corporation, International Business Machines Corporation, Taiwan Semiconductor Manufacturing Company Ltd., GlobalFoundries Inc., Tower Semiconductor Ltd., Cisco Systems Inc., Lightmatter Inc., Ayar Labs Inc., Celestial AI, Lightelligence Inc., Q.ANT GmbH, Salience Labs, Xscape Photonics, iPronics, Nubis Communications, Enlightra, BrainChip Holdings Ltd., SynSense, Innatera Nanosystems B.V., Prophesee S.A., SpiNNcloud Systems GmbH, Rain AI, Mythic Inc., Aspinity, d-Matrix Inc., EnCharge AI, Cerebras Systems Inc., Groq Inc., SambaNova Systems Inc., Tenstorrent Inc., Etched, and Samsung Electronics Co. Ltd. The brief profiles representative players in each archetype and assesses which are positioned to own the beyond-GPU stack.

The competitive landscape is forming around six archetypes. Switch, accelerator and networking incumbents adopt co-packaged optics and optical fabrics. Foundries and packaging providers supply photonic and heterogeneous integration. Optical-interconnect and photonic-compute specialists build the optical silicon and systems. Neuromorphic and event-based specialists supply brain-inspired processors and sensors. Alternative AI-accelerator vendors — wafer-scale, dataflow, in-memory and inference-specific — compete for the same beyond-GPU workloads. Research institutions, defense agencies and hyperscalers fund, specify and deploy. Competitive intensity is high in 2026 and is expected to consolidate around foundry platforms and hyperscaler design wins by 2030.

In 2026 value sits in silicon-photonic transceivers, early co-packaged optics and neuromorphic edge processors. By 2030 it moves to co-packaged optics across AI fabrics, photonic-integration foundry and packaging capacity, and neuromorphic systems in automotive, defense and industrial control. By 2038 it settles in foundries and packaging providers that manufacture heterogeneous photonic-electronic systems, in optical and neuromorphic silicon with hyperscaler and defense design wins, and in system vendors that integrate the beyond-GPU stack. Interconnect specialists without foundry alignment or design wins are absorbed; neuromorphic vendors without software ecosystems remain confined to niches.

Investment activity is increasingly concentrated in optical interconnect technologies, photonic computing platforms, alternative AI accelerator companies, and neuromorphic computing developers. Companies developing next generation interconnect and compute architectures are attracting significant interest from hyperscalers, semiconductor manufacturers, and infrastructure providers seeking solutions to AI power and bandwidth constraints. At the same time, semiconductor and systems companies are expanding their capabilities through acquisitions, partnerships, and strategic investments, while government and defense organizations continue to support advanced computing research and development programs.

Foundries and advanced packaging providers are also increasing investment in photonic integration capabilities to support the growing demand for optical interconnects and heterogeneous computing architectures.

Innovation activity is focused on several key areas:

• Co packaged optics and optical interconnect technologies.

• Optical switching and high bandwidth networking.

• Photonic processors for AI and linear algebra workloads.

• Spiking neural network hardware and neuromorphic processors.

• Event based sensing technologies.

• In memory and near memory computing architectures.

The brief tracks four key indicators:

• The share of AI networking and fabric infrastructure using co packaged optics.

• Deployment of photonic computing accelerators in production inference environments.

• Neuromorphic processor shipments by application and end market.

• Energy efficiency improvements across emerging and conventional computing architectures.

Regionally:

• North America

   leads in architecture development, hyperscale demand, AI infrastructure investment, and advanced computing research, supported by major semiconductor companies, accelerator developers, photonic computing startups, and defense related programs.

• Taiwan and East Asia

   play a critical role in semiconductor manufacturing, advanced packaging, and photonic integration capacity.

• Europe

   maintains leadership in neuromorphic computing research, integrated photonics development, event based sensing technologies, and spiking processor innovation, supported by strong research institutions and public technology initiatives.

The brief assesses how investment activity, manufacturing capabilities, technology development, and regional strengths are shaping the long term evolution of neuromorphic and photonic computing within the broader AI infrastructure landscape.