A primer on investing in neurotechnology

Neurotechnologies are a category of solutions that are widely discussed yet still significantly underappreciated. These technologies are designed to monitor, interpret, and even communicate with the nervous system, while reducing friction in human-machine interaction by enabling direct access to the body’s most natural and efficient information transmission network. In my view, advances in neurotechnology may become one of the most important technological developments of our time, perhaps comparable in impact to Artificial Intelligence (AI).

But what exactly are neurotechnologies? Are they science fiction? Can mind control, like the technology portrayed in the Black Mirror episode “Plaything,” directly interpret emotions, memories, and perceptions from the brain? In reality, neurotechnologies encompass a broad range of technologies developed to connect with, interpret, stimulate, or interact with the human nervous system. Among these are human-machine interfaces and Brain-Computer Interfaces (BCIs), which can translate neural signals into computational language and enable communication with and interpretation of brain activity. These technologies are opening new possibilities for diagnosis, rehabilitation, and everyday assistance. Another important field is neuromodulation, which uses electrical, magnetic, or ultrasound-based stimulation to modulate nervous system activity, supporting pain management, motor rehabilitation, and the treatment of various neurological conditions.

When viewed through this lens, success in neurotechnology has the potential to profoundly transform the human condition. These technologies could help treat movement disorders, paralysis, and other neurological conditions, while also expanding capabilities related to memory, creativity, and complex thinking. Beyond healthcare, they may drive an unprecedented transformation in both the economy and the future of computing. In this article, I present an overview of the neurotechnology landscape, with a particular focus on Brain-Computer Interfaces (BCIs) and neuromodulation systems, as well as the key market trends and criteria for evaluating the growth potential and long-term evolution of this emerging sector.

Inside the market

The human nervous system operates through electrical signals that continuously travel throughout the brain, muscles, and nerves. Today, these signals can already be captured through technologies such as EEG and EMG, allowing computers to interpret aspects of human neural activity. Within this context, BCIs have emerged as neurotechnology systems that enable direct communication between the nervous system and external devices. In practice, these systems can translate neural signals into machine commands and, in some applications, stimulate the nervous system itself.

Human-machine neural interfaces can be broadly divided into three categories: technologies capable of reading, writing, and closing the loop with the nervous system. BCIs read brain activity by capturing and interpreting neural signals associated with motor intentions, cognitive states, attention, and other aspects of brain function. Key technologies used for this purpose include electroencephalography (EEG), electrocorticography (ECoG), intracortical recordings, functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). In these systems, information flows from the brain to the machine.

In contrast, neuromodulation technologies can “write” to the nervous system by targeting neural activity with stimulation. Examples include electrical neural stimulation, transcranial magnetic stimulation (TMS), focused ultrasound, and other techniques designed to modulate neural circuits. In these systems, information flows from the machine to the brain or nervous system. The convergence of these two capabilities gives rise to closed-loop systems that combine neural signal acquisition and neuromodulation in a continuous feedback cycle. In a closed-loop architecture, neural activity is monitored in real time, processed by intelligent algorithms, and used to automatically adjust stimulation parameters. Rather than simply reading or writing to the nervous system, these systems create a bidirectional dialogue between humans and technology, enabling interventions that continuously adapt to the user’s condition and response.

Today, much of the neurotechnology industry remains focused on isolated devices and highly specific applications, such as moving a cursor, contracting a muscle, or controlling a robotic arm. In other words, the market is still largely composed of individual devices rather than integrated systems capable of broadly interacting with the human body. The next major transformation in this market will likely come from generalized platforms that integrate hardware, software, and AI into a more comprehensive neural infrastructure. This is one of the directions pursued by Orby.co: developing integrated systems that can interpret neural signals, modulate the nervous system, and coordinate more complex motor functions. After all, there is a significant difference between a technology that simply contracts a muscle and a system capable of helping a person walk, recover functional movements, or even naturally grasp a cup.

A significant part of this evolution depends on AI, which enables the interpretation of neural patterns, learning from brain data, and adapting responses in real time. According to Grand View Research, the global BCI market was estimated at USD 168.27 billion in 2025, driven primarily by the potential of these technologies to restore motor function and communication in patients with ALS, spinal cord injuries, and stroke.

BCIs can be divided into three main categories: non-invasive, which use external sensors positioned on the scalp; endovascular, which access the nervous system through blood vessels without requiring skull penetration; and invasive, which implant electrodes near brain tissue through surgical procedures. These systems can be applied across multiple domains, including therapeutic applications such as the treatment of neurodegenerative conditions, depression, and anxiety; communication, by transforming brain signals into commands for writing, speaking, or controlling devices; neural monitoring to support diagnosis and treatment; and movement restoration through prosthetics, robotic devices, and neuromodulation systems. One of the central challenges facing BCIs is invasiveness. The human skull evolved to protect neural tissue and prevent pathogen entry, but it also acts as a highly resistant electrical barrier. As a result, capturing neural signals through non-invasive systems remains a significant technical challenge. For this reason, the closer a system is positioned to brain tissue, the higher the spatial resolution of neural signals and, in many cases, the greater the effectiveness of neuromodulation.

On the other hand, invasive BCIs require complex surgical procedures and involve high costs, which can exceed USD 100,000 when postoperative care is included. Furthermore, there is still limited long-term clinical data regarding the safety and durability of implants in the human brain. Implanted materials may also trigger inflammatory responses, scar tissue formation, and progressive degradation of signal quality over time. Another important challenge is that invasive BCIs often provide limited brain coverage, necessitating more sensors to access broader brain regions. This challenge is compounded by social and regulatory barriers: implanting a brain chip remains significantly more complex than using removable external devices, both from a clinical perspective and in terms of public perception.

Currently, companies such as Neuralink and Synchron are among the few organizations authorized to perform permanent neural implants in humans. However, these authorizations are subject to highly restrictive regulatory frameworks that focus primarily on clinical studies and safety validation rather than on broad commercial deployment. In the United States, invasive BCIs are generally classified by the FDA as high-risk medical devices and require extensive clinical trials, biocompatibility assessments, long-term monitoring, and rigorous demonstrations of safety and efficacy before receiving approval for large-scale commercial use. Consequently, many current applications remain confined to controlled research environments with strict eligibility criteria and continuous patient monitoring.

Given these challenges, interest in non-invasive systems continues to grow. Such approaches seek to compensate for physical limitations through advanced signal processing and intelligent manipulation of neural data. Achieving this requires deep expertise in signal processing, computational neuroscience, biomedical hardware, and Artificial Intelligence. Ultimately, the greatest challenge for BCIs is not simply capturing brain signals, but interpreting complex neural patterns, reducing noise, adapting responses in real time, and transforming neural activity into useful, scalable, and impactful.

Source: Developed by the author (2026).

Reading, Writing, and Closing the Loop: The Evolution of Human-Machine Neural Interfaces

Human-machine interfaces in neurotechnology can be broadly divided into three categories: technologies capable of reading, writing, and adapting interactions with the nervous system. Brain-Computer Interfaces (BCIs) are responsible for reading brain activity, capturing and interpreting neural signals to understand motor intentions, cognitive states, and other aspects of brain function. Key technologies used for this purpose include electroencephalography (EEG), electrocorticography (ECoG), intracortical recordings, functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). In contrast, neuromodulation technologies are capable of “writing” to the nervous system by influencing neural activity through targeted stimulation. Examples include electrical neural stimulation, transcranial magnetic stimulation (TMS), focused ultrasound, and other approaches designed to modulate neural circuits. The convergence of these two capabilities gives rise to closed-loop systems, which combine neural signal acquisition and neuromodulation within a continuous feedback cycle. In these systems, neural activity is analyzed in real time and used to automatically adjust stimulation parameters, creating a bidirectional communication pathway between the brain and technology. This integration of neural reading and writing represents one of the most important frontiers in modern neurotechnology, enabling more precise, personalized, and adaptive interventions. In this context, Orby.co is advancing toward closed-loop neurotechnology systems by integrating artificial intelligence and neuromodulation to develop solutions that dynamically understand and interact with the nervous system.

From Science to Scale: What Will Define the Next Generation of Neurotechnology Leaders

Another useful lens for analyzing neurotechnology companies is understanding which type of user they are targeting, something that is often reflected in their go-to-market (GTM) strategy.

Historically, much of the market’s attention has been focused on brain-computer interfaces (BCIs) aimed at complex neurological conditions such as Alzheimer’s disease, Parkinson’s disease, and other neurodegenerative disorders. However, the neurotechnology landscape is far broader than BCIs alone. Beyond reading and interpreting neural signals, there is a rapidly growing market focused on motor system control and recovery, including neurorehabilitation, neuromodulation, functional electrical stimulation (FES), electromyographic monitoring, and assistive technologies designed to restore movement and function. Within this landscape, a clear trend has emerged toward non-invasive technologies. In addition to reducing patient risk, these solutions generally face fewer barriers than implantable devices, enabling faster development cycles, clinical validation, and commercial adoption. While scientific rigor and regulatory compliance remain essential, non-invasive approaches can significantly reduce the complexity associated with large-scale implementation.

As a result, one of the most important factors when evaluating companies in this sector is not simply the technology itself, but rather the combination of scientific excellence, execution capability, and market understanding. The most promising organizations tend to bring together highly qualified teams, strong scientific foundations, regulatory expertise, and products designed to facilitate adoption by healthcare professionals and institutions. Beyond demonstrating safety and efficacy, neurotechnology companies must also master complex industrial processes, including quality management systems, good manufacturing practices (GMP), traceability, supply chain management, and scalable production. Consequently, an often-overlooked competitive advantage lies in the ability to industrialize products rapidly without compromising quality, safety, or regulatory compliance.
In the long term, the ability to scale manufacturing may become just as important as technological innovation itself. Companies that successfully transform scientific research into reproducible, accessible, and globally distributed products are likely to build stronger barriers to entry than those relying solely on laboratory breakthroughs. At the same time, intellectual property protection and the continuous generation of clinical evidence remain critical. Areas such as functional electrical stimulation, non-invasive neuromodulation, neural signal processing, advanced electromyography, and intelligent clinical decision-support systems offer substantial opportunities for sustainable value creation.

Perhaps most importantly, the future leaders of neurotechnology will not be those that build isolated devices, but those that create complete ecosystems. These ecosystems integrate hardware, software, artificial intelligence, data infrastructure, and intuitive user experiences into cohesive platforms. In such models, the true competitive advantage does not come merely from collecting biological signals. Rather, it stems from the ability to transform data into actionable insights, personalize interventions, improve usability, reduce operational friction, and accelerate adoption across clinical and commercial environments. As the industry matures, intelligent and integrated systems will likely create significantly more value than standalone devices. Ultimately, the companies that combine strong science, scalable manufacturing, robust intellectual property, regulatory excellence, and data-driven ecosystems will be best positioned to define the next generation of neurotechnology.

The Functional Applications and Competitive Dynamics Shaping the Future of Neurotechnology

Neurotechnologies can also be categorized according to the problems they solve. While the underlying technologies frequently overlap, the market can broadly be divided into six major application categories. The framework below is adapted from the concepts discussed in the text you shared.

Therapeutic applications
Technologies capable of modulating nervous system activity to treat neurological, psychiatric, and chronic conditions. This includes neuromodulation platforms for pain management, neurorehabilitation, depression, movement disorders, neurodegenerative diseases, and autonomic dysfunction.

Communication applications

Systems that translate signals from the nervous system into commands, enabling users to communicate, speak, type, or interact with digital environments through neural intent. These technologies are particularly valuable for individuals with severe motor impairments and represent one of the most promising areas of human-machine interaction.

Monitoring and diagnostics

Platforms capable of monitoring, visualizing, and analyzing neural and physiological activity to assess cognitive performance, detect neurological decline, predict treatment responses, estimate biological age, and support clinical decision-making.

Movement restoration, control, and optimization

Technologies designed to restore, recover, control, enhance, or optimize motor function through approaches such as neuromodulation, neuroprosthetics, brain-computer interfaces (BCIs), human-machine interfaces, and intelligent motor recovery systems. These solutions can support individuals with neurological or musculoskeletal impairments while also improving motor performance in healthy populations by directly interacting with the nervous system.

Human-Machine Interfaces (HMI)

Systems that enable bidirectional communication between humans and machines using signals derived from the nervous system. This category extends beyond traditional brain-computer interfaces (BCIs) to include controlling prosthetics, assistive devices, robots, wearables, computers, connected environments, and other digital or physical systems via neural, muscular, or physiological signals.

Performance, wellness, and longevity

Technologies focused on optimizing human performance, physical and cognitive recovery, sleep quality, stress management, focus, learning, mental well-being, and healthy aging. This category includes neuromodulation solutions, physiological monitoring platforms, human-machine interfaces, and intelligent systems that personalize interventions based on data from the nervous system. Due to lower regulatory barriers and faster adoption cycles, this segment has attracted substantial investment and may serve as a gateway to more advanced clinical applications.

While therapeutic applications have historically received most of the market’s attention, performance, wellness, and longevity may become some of the most significant growth drivers in neurotechnology over the coming decades. As individuals increasingly seek tools to improve focus, recovery, physical and cognitive performance, emotional resilience, and quality of life, opportunities emerge for platforms that combine physiological monitoring, neuromodulation, artificial intelligence, and personalized experiences. Similar to the rise of wearables and continuous monitoring technologies, large-scale adoption may generate massive datasets, accelerate innovation cycles, and create durable competitive advantages.

Furthermore, the boundaries between healthcare, rehabilitation, performance, and longevity are becoming increasingly blurred. Many technologies initially developed for clinical applications eventually find use among healthy populations, while performance-focused solutions often contribute to disease prevention and improved quality of life. In this context, neurotechnologies have the potential not only to restore lost function but also to enhance human capabilities throughout the lifespan.

The Neurotechnology Technology Stack

From a technological perspective, the neurotechnology sector consists of multiple layers ripe for innovation:

Physics and Energy Delivery

Developing technologies capable of generating and delivering electrical, magnetic, ultrasonic, optical, or other forms of energy to interact safely and effectively with the nervous system.

Hardware and Materials Science

Building sensors, stimulators, wearables, implantable devices, electrodes, and biomaterials capable of interfacing with the nervous system. This includes technologies such as EEG, EMG, fNIRS, ECoG, vagus nerve stimulation, spinal cord stimulation, transcranial magnetic stimulation, focused ultrasound, and many other forms of neuromodulation.

Signal Acquisition and Processing

Capturing raw physiological data and transforming it into usable information through filtering, artifact removal, feature extraction, and signal interpretation.

Artificial Intelligence and Neural Decoding

Converting neural, muscular, and physiological signals into actionable outputs. These systems can identify biomarkers, predict treatment responses, generate control commands, personalize interventions, and continuously improve performance.

Applications and digital ecosystems

Software, clinical workflows, user experiences, data platforms, and intelligent systems that ultimately drive adoption and long-term value creation.

In many ways, the neurotechnology stack resembles the AI ecosystem, combining infrastructure, hardware, software, and applications. However, it introduces an additional layer of complexity: direct interaction with the human body. As a result, some of the most defensible competitive advantages emerge from the combination of scientific excellence, intellectual property, proprietary hardware, clinical evidence, regulatory expertise, quality systems, manufacturing capability, and commercialization. While much of the industry’s attention remains focused on BCIs, some of the largest opportunities may emerge from broader platforms that integrate neuromodulation, physiological monitoring, artificial intelligence, and human-machine interfaces into complete ecosystems.

The Global Neurotechnology Landscape

Geographically, the United States continues to lead the sector, driven by world-class universities, neuroscience and AI talent, capital availability, and a strong environment for high-risk innovation. China and the United Kingdom have also established themselves as important players through substantial investments in research, advanced manufacturing, and technology development. At the same time, new innovation hubs are emerging. Brazil is gaining relevance in areas such as non-invasive neuromodulation, neurorehabilitation, AI-powered healthcare, human performance optimization, and human-machine interfaces. The country benefits from strong clinical demand, qualified research centers, highly skilled professionals, and a healthcare ecosystem that offers significant opportunities for validation and adoption. In this context, Orby.co represents a new generation of neurotechnology companies that expand the traditional concept of BCIs. By combining non-invasive neuromodulation, artificial intelligence, motor recovery, physiological signal analysis, clinical decision support, and human performance optimization, these platforms point toward a future in which the nervous system becomes a new interface layer between humans and technology.

The Companies Most Likely to Win

In the long term, the leaders of the industry will likely not be those that build the best standalone devices, but those capable of creating complete ecosystems that read, interpret, modulate, and leverage nervous system data to improve health, human function, performance, and human-machine interaction. The most valuable neurotechnology companies may not be those with the most advanced hardware alone, but those that combine scientific rigor, scalable manufacturing, regulatory excellence, intellectual property, artificial intelligence, and seamless user experiences into platforms that continuously learn and improve. As the industry matures, intelligent ecosystems, not isolated devices, are likely to define the next generation of neurotechnology leaders.

Why invest and Why Now?

Have you ever considered what happens when artificial intelligence can directly interact with the human nervous system?

For decades, computing has evolved through successive interfaces: personal computers, smartphones, cloud computing, and most recently, AI-powered language models. Yet every generation has relied on intermediary layers, keyboards, screens, touch, voice, and gestures to translate human intent into machine-readable commands.

Neurotechnology has the potential to fundamentally change that equation.

If AI can interpret signals from the nervous system while neuromodulation technologies can influence and optimize those same neural pathways, we may be witnessing the emergence of an entirely new computing paradigm. One where technology is no longer limited to observing human behavior but can understand, predict, and interact with it in real time. The opportunity extends far beyond monitoring. Imagine systems capable of detecting motor decline before symptoms appear, predicting therapeutic outcomes, accelerating rehabilitation, enhancing cognitive and physical performance, and continuously personalizing interventions based on physiological data. In this future, the nervous system becomes a new computational layer.

This vision helps explain why billions of dollars have flowed into neurotechnology. Neuralink has raised over $1 billion throughout its lifetime, including a recent $650 million Series E round, while Synchron has secured hundreds of millions of dollars and formed partnerships with leading technology companies to advance neural interfaces.

Yet the real opportunity may be far larger than that offered by traditional brain-computer interfaces alone.

What to Look for in Neurotechnology Companies

Source: Hamran, 2025; Rao, 2025.

The breakthrough occurs when the loop is closed: data is collected from the nervous system, interpreted by AI, used to guide neuromodulation, and continuously fed back into increasingly intelligent models. This creates a self-improving cycle in which better data leads to better predictions, better interventions, and ultimately better outcomes. While much of the industry’s focus has historically centered on invasive technologies, some of the largest opportunities may emerge from non-invasive approaches that reduce risk, cost, regulatory complexity, and barriers to adoption while maintaining meaningful clinical and functional impact.

This is where companies such as Orby.co are positioning themselves. Rather than viewing neurotechnology solely as a medical device category, Orby approaches the nervous system as a new computational frontier. By combining non-invasive neuromodulation, artificial intelligence, physiological signal analysis, motor recovery, human performance optimization, and intelligent decision-support systems, the company is building an infrastructure that connects biological data, human behavior, and machine intelligence into a unified platform.

If the internet connects computers and artificial intelligence connected data, neurotechnology may become the technology that directly connects humans and machines. Should that vision materialize, we will not simply witness the emergence of a new class of devices.

We may be witnessing the birth of the next computing paradigm.

What to Look for in Neurotechnology Companies

Neurotechnology is one of the most complex industries in the world, combining neuroscience, engineering, artificial intelligence, medical devices, regulatory strategy, and user experience. As a result, investors must look beyond the science itself and evaluate a company’s ability to transform innovation into scalable products.

1. Hybrid Founders
One of the most important factors is the quality of the founding team. Many companies have exceptional expertise in medicine and neuroscience but lack strength in engineering, product development, and technology execution. Others possess world-class engineering capabilities but have limited understanding of biology, clinical practice, and patient needs.

The strongest founders are those who can operate across neuroscience, engineering, AI, product development, and business strategy. Neurotechnology requires a multidisciplinary vision because successful products depend on the integration of multiple domains of expertise.

2. Differentiated Technology
The company should possess a technological foundation that is difficult to replicate, with meaningful advances in hardware, software, artificial intelligence, neuromodulation, signal processing, or brain-computer interfaces.

3. Defensible Intellectual Property (IP)
Patents, proprietary algorithms, know-how, and exclusive datasets can create significant barriers to entry and substantially increase enterprise value.

4. Product, Not Just Research
Great science alone does not guarantee adoption. Investors should evaluate whether the company can transform scientific knowledge into products that are usable, scalable, and valued by the market.

5. Learning Velocity
In neurotechnology, development cycles are long. More important than short-term metrics is a team’s ability to generate technical breakthroughs, validate hypotheses, build intellectual property, and continuously accelerate learning.

6. Long-Term Vision
The best companies in the sector are built by founders who are deeply committed to the problem they are solving and capable of navigating years of technical, clinical, and regulatory challenges without losing focus.

What Do Neurotechnology Exits Look Like?

Neurotechnology tends to have longer time horizons than traditional software due to the scientific, clinical, and regulatory complexity of the sector. However, these same barriers create highly defensible businesses and the potential for outsized returns.

The most common exit pathways include:
Pharmaceutical Acquisitions – Pharma companies acquiring neurotechnology firms to access neural biomarkers, proprietary datasets, digital therapeutics, and new approaches to drug development and clinical trials.
Medical Device Acquisitions – The most common exit today. Large medtech companies acquire neurotechnology businesses to expand their portfolios in neuromodulation, rehabilitation, chronic pain, movement disorders, and neurological care.
Big Tech Acquisitions – Technology companies increasingly view neurotechnology as a strategic layer for the future of human-computer interaction. Neural interfaces could become as important as smartphones, touchscreens, and AI are today.
Industry Consolidation – As the ecosystem matures, larger neurotechnology companies may acquire smaller players to gain access to intellectual property, talent, clinical assets, datasets, or specialized technologies.
Initial Public Offerings (IPOs) – Companies with strong clinical evidence, scalable technology platforms, reimbursement pathways, and commercial traction may eventually access public markets.
Creation of New Technology Giants – The most exciting outcome. Just as companies such as Microsoft, Apple, Google, Meta, and NVIDIA emerged from previous computing waves, neurotechnology may create an entirely new generation of platform companies.

The reason is simple: every major technology wave has been driven by reducing friction.
The keyboard reduced the friction of writing.
The mouse reduced the friction of interacting with computers.
Smartphones reduced the friction of accessing information.
Artificial Intelligence reduces the friction of creating and processing information.
Neurotechnology may reduce the friction between human intention and action.

Today, most companies are focused on reading the nervous system to decode movement, speech, cognition, emotion, and intent. However, the largest opportunities may belong to companies that can also write to the nervous system, delivering targeted interventions that restore function, treat disease, enhance recovery, and optimize performance. The ultimate winners may be companies that master both capabilities through closed-loop systems, continuously reading neural activity, interpreting it with AI, and delivering personalized stimulation in real time.

These companies would not simply build medical devices. They could become the foundational platforms that connect the human nervous system to the digital world, potentially creating some of the largest technology companies over the next several decades.

Risks in Neurotechnology and What Will Define the Winners

Despite its enormous potential, neurotechnology remains one of the most challenging sectors in technology. The companies that create the most value will not necessarily be those with the most advanced science, but those that successfully remove the industry’s biggest barriers to adoption and scale.

Long Development Timelines

Neurotechnology companies often require years of scientific validation, product development, clinical studies, and regulatory approvals before reaching meaningful commercialization. Companies that can generate evidence efficiently, shorten validation cycles, and accelerate market access are likely to have a significant advantage.

Regulatory Complexity

Many neurotechnology products face lengthy and expensive regulatory pathways. Solutions with stronger safety profiles, lower risk, and reduced regulatory complexity may reach patients and customers faster while requiring less capital to achieve adoption.

Dependence on Advanced Hardware

Historically, progress in neurotechnology has relied on increasingly sophisticated sensors, implants, miniaturized electronics, and advanced materials. Companies that can deliver meaningful outcomes through scalable, cost-effective, and practical architectures may be better positioned to achieve widespread adoption.

Scientific Uncertainty

The brain remains one of the least understood systems in biology. Many approaches still face challenges related to signal quality, long-term stability, reproducibility, and mechanistic understanding. Platforms that combine neuroscience, artificial intelligence, and continuous data collection may create compounding advantages as they learn and improve over time.

Market Adoption

Perhaps the greatest challenge is not technological but human.
Even if a technology works, success ultimately depends on whether patients, clinicians, healthcare systems, and consumers are willing to use it. Throughout the history of technology, the largest companies have emerged by reducing friction for users.

In neurotechnology, this means building solutions that are:
– Non-invasive or minimally invasive;
– Safe and comfortable for repeated use;
– Easy to deploy and operate;
– Integrated into existing clinical workflows;
– Capable of delivering measurable outcomes;
– Economically accessible for healthcare systems and consumers;

Privacy and Trust

As neurotechnology becomes capable of interpreting and influencing neural activity, concerns around privacy, security, ownership of neural data, and user trust become increasingly important. Companies that prioritize transparency, safety, and user confidence are likely to achieve broader adoption.

What May Define the Industry Leaders

The next generation of neurotechnology leaders will likely be the companies that solve three fundamental challenges simultaneously:
1. Reducing regulatory complexity and improving accessibility.
2. Reducing adoption friction for patients, clinicians, and consumers.
3. Building systems capable of both reading from and writing to the nervous system, transforming neural data into personalized and measurable outcomes.

Just as smartphones became dominant by making computing accessible to billions of people, the most successful neurotechnology companies may be those that democratize interaction with the nervous system without requiring complex procedures, high costs, or significant barriers to use. Ultimately, the winners may not be the companies with the most invasive, complex, or futuristic technologies. They may be the companies that make neurotechnology simple, scalable, accessible, and seamlessly integrated into the lives of millions of people.

Conclusion

From the perspective of someone who closely follows technology, engineering, healthcare, innovation, and market development, I believe neurotechnology is approaching an inflection point similar to the one artificial intelligence experienced over the past decade.

This comparison is not driven by hype. It is driven by fundamentals.

AI became transformative because it unlocked a new layer of data and converted it into actionable intelligence. Neurotechnology has the potential to do something similar by transforming the nervous system into a new interface layer between humans and machines.

For the first time, we are developing technologies capable not only of observing human behavior but also of understanding, predicting, and influencing biological functions in real time. The convergence of physiological sensing, neuromodulation, artificial intelligence, and human-machine interfaces creates a technology platform with applications spanning healthcare, rehabilitation, human performance, communication, wellness, longevity, defense, and entirely new categories that may not even exist today.

From a market perspective, the signals are already visible. Capital is flowing into the sector. Regulatory pathways are becoming clearer. Hardware costs continue to decline. Artificial intelligence is dramatically improving the interpretation of biological signals. Scientific evidence continues to expand year after year. And an increasing number of researchers are leaving academic institutions to build companies focused on real-world applications.

From an engineering perspective, many of the fundamental building blocks are finally converging. Advances in sensors, low-power electronics, embedded computing, artificial intelligence, biomaterials, wireless communication, and advanced manufacturing have reduced barriers that seemed insurmountable only a few years ago.

But perhaps the most compelling aspect is economic.

Neurotechnology is not a single market. It is an enabling infrastructure capable of creating multiple markets simultaneously. Just as the internet enabled ecommerce, social media, cloud computing, and the broader digital economy, and just as artificial intelligence is creating an entirely new generation of software and services, neurotechnology may give rise to a new economic layer built around the interaction between intelligence, biology, and machines.

Naturally, significant challenges remain. Questions surrounding ethics, privacy, safety, accessibility, and regulation will require thoughtful leadership from companies, researchers, investors, and policymakers alike. After all, these technologies interact directly with the human nervous system. Yet history shows that when a new interface emerges that fundamentally changes how humans interact with information, entirely new industries are created, markets are redefined, and global leaders emerge.

The internet connected computers.

AI connected data.

Neurotechnology may become the platform that connects intelligence, biology, and machines.

If this thesis proves correct, we are not simply looking at another promising healthcare segment or a new category of medical devices. We may be witnessing the birth of a new global technological and economic wave, one that could ultimately become as consequential as artificial intelligence itself.

Acknowledgements

This article was heavily inspired by Armin Hamrah’s excellent essay, A Primer on Investing in Brain Computer Interfaces, which I highly recommend to anyone interested in the future of neurotechnology, brain computer interfaces, and investing in frontier technologies. While many of the ideas presented here reflect my own perspectives on neurotechnology, investing, neuromodulation, and the future of human-machine interaction, Armin’s work helped frame several key questions about founder quality, defensibility, exits, and long-term risks in the sector.

If you found this article valuable, I strongly encourage you to read the original piece:

A Primer on Investing in Brain Computer Interfaces by Armin Hamrah

It is one of the most thoughtful analyses available today on the investment landscape, opportunities, and challenges surrounding brain-computer interfaces and the broader neurotechnology ecosystem.

References

Hamrah, A. (2024). A primer on investing in brain-computer interfaces. Substack. https://hamrah.substack.com/p/a-primer-on-investing-in-brain-computer.
Grand View Research. Brain Computer Interface Market Size | Industry Report, 2033 [Internet]. San Francisco: Grand View Research; 2025 [cited 2026 Jun 3]. Available from: https://www.grandviewresearch.com/industry-analysis/brain-computer-interfaces-market/
Rao, N. (2025, March 10). 2024 neurotech funding snapshot. Neurotech Futures. https://neurotechnology.substack.com/p/2024-funding-snapshot