Category: Neurotech

    AI Makes Machines Smarter. Neurotech Makes Humans Smarter.

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    A Cost Case for Neurotechnology in Operations

    Abstract. AI offers scale and powerful assistance in operations, but its operating costs, energy use, and effects on labour deserve scrutiny. The human brain runs on about twenty watts, making human intelligence remarkably energy efficient. Neurotechnology could help make better use of that capacity. Neuroscience informed interfaces may help operators find signals, sustain attention, and make decisions with less effort, sometimes reducing the need for AI. Neurotechnology could also help people use AI more selectively and collaborate with it more effectively. A credible cost case would measure operator time, decision quality, AI usage, incident outcomes, and total operating costs, alongside environmental and labour effects.

    As a Solutions Architect in operational infrastructure, I analyze how AI can improve systems and tasks. Its ability to scale brings clear benefits, but operating costs vary with the model and the volume of work. At published standard API rates, one million requests containing 2,000 input tokens and 500 output tokens each would cost about US$225 with OpenAI’s GPT-6 Luna, US$4,500 with GPT-6 Sol, or US$22,500 with GPT-6 Astra. Those are model charges; integration and human review add costs of their own. AI’s growth also raises broader questions about energy demand and changes to labour (OpenAI, 2026; International Energy Agency, 2026; International Labour Organization, 2025).

    As a neurotechnology specialist, I see another opportunity: make better use of human intelligence. The human brain uses roughly 10–20 watts, comparable to a small light bulb (Yu et al., 2018). That figure does not establish that human work is cheaper than AI. It does invite us to examine whether neurotechnology can help people make better decisions with the expertise and cognitive capacity they already have.

    Neurotechnology

    Neurotechnology refers to devices, software, and procedures that directly measure or interact with the nervous system to understand or influence its activity (UNESCO, 2025). I also use the term in a broader, applied sense: findings from neuroscience can inform the design of interfaces, training, and collaborative systems that require no neural sensor during everyday use. This broader use extends UNESCO’s definition. Table 1 summarizes the opportunities, potential benefits, and maturity of several approaches.

    OpportunityContributionPotential operational benefitMaturity
    Neuroscience-informed interfacesUse neural measures during testing to examine attention, workload, and error recognitionBetter decisions with less unnecessary cognitive effortResearch methods demonstrated; operational benefit needs testing (Frey et al., 2016)
    Neuroadaptive interfacesAdjust information or assistance in response to estimated workloadSupport operators during demanding tasksDemonstrated in simulations; live deployment needs validation (Aricò et al., 2016)
    Brain–computer interfacesUse neural signals as an additional channel of input or feedbackHands-free interaction for suitable tasksDemonstrated in a simulator; advantage over conventional input needs testing (Dehais et al., 2022)
    Neural biometricsUse individual neural responses as an identity signalA possible additional factor for sensitive accessExperimental; long-term stability and usability need evaluation (Zhang et al., 2025)
    Human–AI error detectionInvestigate neural responses when a person notices an automated errorPotentially earlier correction of mistakesResearch stage (Dimova-Edeleva et al., 2023)
    Training and simulationMeasure cognitive demands during practiceDirect training toward persistent difficultiesStudied in flight simulation; transfer needs testing (Mark et al., 2022)
    Digital collaborationStudy neural activity during shared tasksBetter understanding of coordination and handoffsExploratory; findings about team performance are mixed (Reinero et al., 2021; Réveillé et al., 2025)

    These are opportunities to investigate, not demonstrated savings in infrastructure operations. They differ substantially in cost. A one-time interface study may yield a design that everyone can use without sensors. A live adaptive system requires equipment, reliable measurement, support, and rules governing neural data.

    Making human computation more effective

    Neuroscience-informed interface design offers a concrete starting point for the cost analysis. Neural measures can be used during design research without requiring operators to wear sensors in everyday work. The question is whether that research leads to an interface whose benefits justify the study and redesign.

    An operator diagnosing an incident must notice changes, identify relevant signals, hold competing explanations in mind, and decide what to do while new information arrives. A dashboard can display every necessary metric and still make those mental tasks unnecessarily difficult.

    Traditional usability testing shows whether someone completed a task, how long it took, and where errors occurred. Neurotechnology can add evidence about what happened during the interaction. In controlled research, Frey and colleagues (2016) used EEG measures related to workload, attention, and error recognition to compare interaction methods. Such measures could help explain why one operational interface places greater demands on an operator than another. They are estimates that require interpretation alongside behaviour and task outcomes, not direct readings of thought.

    Consider an incident in which an operator consults an AI assistant to connect evidence scattered across a dashboard, logs, and a runbook. A neuroscience-informed redesign might put related evidence together so that the operator can make the same diagnosis without the model call. The potential saving is the number of calls avoided multiplied by their actual cost, provided operators reach an equally accurate diagnosis without taking longer. This is a testable claim, not an assumed result.

    Operators could diagnose matched incidents using the current interface and the redesigned one. Researchers would compare accuracy, time, missed signals, workload, and AI use, including cases that become more complex or require sustained attention. Neural measures would be valuable if they reveal a design problem that ordinary observation, interviews, and performance measures miss—or help predict where performance will fail as demands rise. Lower measured workload alone would not prove that an interface is better: some cognitive effort is necessary for sound judgment.

    Neuroadaptive interfaces extend the idea from research to live operation. In realistic air-traffic-control tasks, Aricò and colleagues (2016) used an EEG-based workload estimate to trigger assistance and observed reduced workload. This provides a reason to investigate adaptive support in infrastructure operations, but it does not establish that continuous sensing would outperform assistance triggered by incident severity, operator requests, or other information already available to the system.

    Extending the cost analysis

    Interface design is one starting point. The other opportunities require similarly specific comparisons. Neural biometrics would have to justify themselves against existing authentication methods on security, speed, usability, and ongoing maintenance. A brain–computer interface would have to improve a particular task enough to outweigh its setup and operating burden. Training and collaboration tools would need to produce lasting gains in performance or coordination, not merely an interesting neural measurement.

    Different applications may yield different kinds of value. One might save money; another might improve reliability or the quality of work at an added cost. A third might offer no advantage over a simpler method. Comparing each with the best available non-neural alternative is how neurotechnology becomes a defensible operational choice.

    Comparing the cost cases

    Operational decisions draw on several forms of intelligence. They work together, and each has costs beyond the price of its immediate output. Table 2 compares the measures that would inform a cost case for each.

    Form of intelligencePotential role in operationsWhat a cost case would measure
    Human judgmentInterpret unusual situations and decide what action to takeOperator time, decision quality, workload, errors, and incident duration
    Rules and conventional modelsFilter known conditions and detect recurring patternsCompute and maintenance costs, false alerts, and missed events
    Large language modelsInterpret unstructured evidence and assist with unfamiliar incidentsUsage charges, accuracy, review effort, and work avoided
    NeurotechnologyImprove interfaces or adapt assistance using neural evidenceResearch, sensor, governance, and deployment costs; changes in performance, AI use, and total cost

    A credible comparison would begin with a baseline: incident volume and duration, operator time, errors, escalations, and AI usage. It would then measure those same outcomes after a change. For the dashboard example, the key question is whether fewer model calls and less operator effort produce at least the same quality of diagnosis—and whether the benefit pays for the study and redesign.

    The analysis should also report broader effects that a financial calculation may miss. Compute and sensing equipment require energy and materials. Automation can change skill requirements and the quality of work; an interface can reduce task time while increasing strain. If neural sensing continues into everyday operations, the cost case must include the burden of wearing equipment and the governance of sensitive data, including consent, access, and retention (UNESCO, 2025). Those requirements are different for a time-limited, voluntary design study.

    Conclusion

    The brain’s modest energy use does not prove that human work is cheaper than AI. It points to an opportunity to use human intelligence more effectively. Neuroscience-informed interfaces offer one way to test that idea; adaptive assistance, neural biometrics, training, and collaboration require their own comparisons. The cost case for each rests on measurable improvements in decisions, total cost, or the experience of the people doing the work.

    References

    Aricò, P., Borghini, G., Di Flumeri, G., Colosimo, A., Bonelli, S., Golfetti, A., Pozzi, S., Imbert, J.-P., Granger, G., Benhacene, R., & Babiloni, F. (2016). Adaptive automation triggered by EEG-based mental workload index: A passive brain-computer interface application in realistic air traffic control environment. Frontiers in Human Neuroscience, 10, 539. https://doi.org/10.3389/fnhum.2016.00539

    Dehais, F., Ladouce, S., Darmet, L., Nong, T.-V., Ferraro, G., Torre Tresols, J., Velut, S., & Labedan, P. (2022). Dual passive reactive brain-computer interface: A novel approach to human-machine symbiosis. Frontiers in Neuroergonomics, 3, 824780. https://doi.org/10.3389/fnrgo.2022.824780

    Dimova-Edeleva, V., Soto Rivera, O., Laha, R., Figueredo, L., Zavaglia, M., & Haddadin, S. (2023). Error-related potentials in a virtual pick-and-place experiment: Toward real-world shared-control. Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/EMBC40787.2023.10340244

    Frey, J., Daniel, M., Castet, J., Hachet, M., & Lotte, F. (2016). Framework for electroencephalography-based evaluation of user experience. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/2858036.2858525

    International Energy Agency. (2026). Key questions on energy and AI. https://www.iea.org/reports/key-questions-on-energy-and-ai

    International Labour Organization. (2025). Generative AI and jobs: A refined global index of occupational exposure. https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure

    Mark, J. A., Kraft, A. E., Ziegler, M. D., & Ayaz, H. (2022). Neuroadaptive training via fNIRS in flight simulators. Frontiers in Neuroergonomics, 3, 820523. https://doi.org/10.3389/fnrgo.2022.820523

    OpenAI. (2026). API pricing. https://developers.openai.com/api/docs/pricing

    Reinero, D. A., Dikker, S., & Van Bavel, J. J. (2021). Inter-brain synchrony in teams predicts collective performance. Social Cognitive and Affective Neuroscience, 16(1–2), 43–57. https://doi.org/10.1093/scan/nsaa135

    Réveillé, C., Vergotte, G., Dray, G., Jean, P.-A., Perrey, S., & Bosselut, G. (2025). Trajectories of interbrain synchrony during teamwork: Links with team composition and performance. Social Cognitive and Affective Neuroscience, 20(1), nsaf081. https://doi.org/10.1093/scan/nsaf081

    UNESCO. (2025). Recommendation on the ethics of neurotechnology. https://www.unesco.org/en/legal-affairs/recommendation-ethics-neurotechnology

    Yu, Y., Herman, P., Rothman, D. L., Agarwal, D., & Hyder, F. (2018). Evaluating the gray and white matter energy budgets of human brain function. Journal of Cerebral Blood Flow & Metabolism, 38(8), 1339–1353. https://doi.org/10.1177/0271678X17708691

    Zhang, Y., Zhang, H., Li, Y., Wang, Y., Gao, X., & Yang, C. (2025). A longitudinal EEG dataset of event-related potential. Scientific Data, 12, 1069. https://doi.org/10.1038/s41597-025-05378-x

    1971 Seeing Inside the Living Brain

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    Until the early 1970s, physicians could rarely see inside the living brain without surgery. Computed tomography (CT) changed that by combining X-rays with computer reconstruction to create detailed cross-sectional images of the brain. For the first time, strokes, tumours, and bleeding could be identified quickly and non-invasively. Modern medical imaging began with CT and transformed neurological diagnosis.

    1968 Listening to Individual Neurons

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    A Neurotech History Series

    Until the 1960s, scientists could study the brain’s electrical activity, but connecting a single neuron to a deliberate movement remained difficult. Edward Evarts changed this using implanted microelectrodes.

    Evarts recorded individual neurons in the motor cortex of awake monkeys as they pulled a lever. He discovered that some neurons changed their firing with the timing and force of movement.

    For the first time, researchers could listen to a single brain cell while an animal acted, linking neural activity directly to behaviour. The experiments involved invasive surgery, restraint, and trained primates—methods that also raise enduring ethical questions about the use of animals in neuroscience.

    1957 The Brain Learns by Changing Connections

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    A Neurotech History Series

    Why do we remember some experiences for a lifetime while others disappear?

    Canadian psychologist Donald Hebb proposed that learning occurs when neurons repeatedly become active together, strengthening their connections. His famous idea is often summarized as, “Cells that fire together, wire together.”

    Although later refined, Hebb’s theory became one of the foundations of modern neuroscience.

    Today’s understanding of learning, neuroplasticity, and many artificial neural networks traces its intellectual roots to Hebb’s insight.

    1952 Explaining the Nerve Impulse

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    A Neurotech History Series

    How does a nerve cell produce an electrical signal? By the mid-twentieth century, scientists knew neurons were electrical, but no one understood the mechanism.

    Working with the giant axon of the squid, Alan Hodgkin and Andrew Huxley measured the flow of ions across the neuronal membrane. Their mathematical model showed how sodium and potassium ions generate the action potential—the electrical impulse that carries information through the nervous system.

    Their work remains one of the greatest achievements in neuroscience.

    Modern neurotechnology, from neural simulations to brain-computer interfaces, is built on the principles they uncovered.

    1937 Mapping the Human Cortex

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    A Neurotech History Series

    Imagine remaining awake while a surgeon gently stimulates different parts of your brain with a tiny electrode.

    During epilepsy surgery, Canadian neurosurgeon Wilder Penfield asked awake patients to describe what they experienced as he stimulated the cerebral cortex. Different locations produced different sensations, movements, and occasionally vivid memories, allowing him to map the functional organization of the human brain.

    Penfield’s work transformed neurosurgery and deepened our understanding of the cortex. Functional brain mapping remains fundamental to neurosurgery, neurostimulation, and modern neurotechnology.

    1924 Recording the Brain’s Electrical Activity

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    A Neurotech History Series

    Until the 1920s, no one had successfully recorded the brain’s electrical activity from outside the skull.

    German psychiatrist Hans Berger developed the electroencephalogram (EEG), demonstrating that tiny electrical signals from the brain could be measured non-invasively from the scalp.

    EEG opened a completely new window into brain function and quickly became an essential clinical and research tool.

    Modern hospital EEG systems and consumer brain-sensing devices trace their origins to Berger’s pioneering work.

    1897 The Synapse Is Born

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    A Neurotech History Series

    Scientists knew neurons communicated, but how one cell influenced another remained unclear.

    Charles Sherrington introduced the term ‘synapse’ to describe the tiny junction where one neuron communicates with the next. His work explained how billions of individual cells form coordinated networks.

    The concept of the synapse became central to understanding learning, memory, and neurological disease.

    Today’s neuroscience and many neurotechnologies are built on understanding and influencing synaptic communication.

    1888 The Neuron Comes Into Focus

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    A Neurotech History Series

    By the late nineteenth century, many scientists believed the brain formed one continuous network.

    Using Golgi’s staining technique, Santiago Ramón y Cajal carefully examined nervous tissue and concluded that the brain is built from individual neurons separated by tiny gaps. He proposed the neuron doctrine, one of the foundational principles of neuroscience.

    His work transformed our understanding of how information flows through the nervous system.

    Every modern model of neural circuits and brain function builds on Cajal’s insight.

    1873 Seeing Neurons for the First Time

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    A Neurotech History Series

    Looking through a microscope, the brain once appeared as an indistinct mass of tangled tissue. Scientists could see cells, but not how individual neurons were organized or connected.

    In 1873, Italian physician and scientist Camillo Golgi transformed neuroscience by developing the Black Reaction (la reazione nera), a silver chromate staining technique that randomly stained only a small number of neurons. Because just a few cells were coloured while their neighbours remained transparent, each stained neuron could be seen in its entirety—its cell body, branching dendrites, and long axon. For the first time, researchers could appreciate the extraordinary complexity and diversity of individual neurons.

    One laboratory technique opened an entirely new window into the brain’s architecture, laying the foundation for modern neuroscience.

    Golgi’s method also enabled Santiago Ramón y Cajal to demonstrate that the brain is composed of individual neurons rather than a continuous network, establishing the neuron doctrine that underpins modern neuroscience.