UPDATE: There is no meditation session this Monday, October 12, due to Thanksgiving. See you again for meditation on Monday, October 19. The final session will now be on Monday, November 16.
QUESTION: Have you had any trouble joining the online meeting? Please let me know by email on my About page (or comment on the Substack newsletter). I have been using the free Swiss platform Proton, but if it is not stable, I will switch to something else.
Instead of amplifying sound, what if a device could speak directly to the nervous system? Modern cochlear implants convert sound into electrical signals that stimulate the auditory nerve, allowing many profoundly deaf people to perceive speech. They demonstrated that technology could successfully replace part of a damaged sensory system. Cochlear implants remain one of neurotechnology’s greatest clinical successes.
What if doctors could see the brain without using ionizing radiation? Magnetic resonance imaging (MRI) answered that question by using powerful magnets and radio waves to produce exceptionally detailed images of soft tissue. MRI rapidly became one of medicine’s most important diagnostic tools. Today’s structural and functional brain imaging depends on the technologies pioneered through MRI.
“I take the position there’ll be no cure, that’s sure. But there may be respite. The torque may leave off my hand and my foot, and put them both in the manner of stillness. And if it does, what shall I do with that stillness? What do I think the tremours are keeping me from now, the arms of which I see myself running into? Is there a clarity waiting, of the kind that is elsewhere now but will be bountiful then?” — Stephen Jenkinson, Trembling Still, p. 133
I will share the passage above at the beginning of our gathering. It is worthy of contemplation, whether or not you have Parkinson’s.
Our online meditation invites you to create a sacred inner space in your own home. We establish a meditation posture, then guide you in developing focus through the breath and other meditation anchors before continuing into a period of silence. We close with a compassion meditation that supports your return to daily life.
To join us, find the meeting link on the Meditation Community webpage:
Reminder Meditation Community gathers online this evening 7 p.m. EDT. The session last about 30 minutes.
In my preamble, I read a short passage from Stephen Jenkinson’s book, “Trembling, Still.” It is a deeply personal and contemplative account of living with Parkinson’s disease and neurodegeneration. Written in a poetic, sometimes difficult style, the book turns his diagnosis into a meditation on five recurring themes: impermanence, embodiment, uncertainty, limitation, and attention in adversity. His particular experience becomes a way of contemplating the universal experience of being embodied, vulnerable and alive.
The meditation invites you to create a safe, sacred space in your home. We establish a meditation posture, then guide you in developing focus through the breath and other meditation anchors, continuing in a period of silence. We close with a compassion meditation that supports your return to daily life.
To join us, find the meeting link on the Meditation Community webpage
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 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.
| Opportunity | Contribution | Potential operational benefit | Maturity |
| Neuroscience-informed interfaces | Use neural measures during testing to examine attention, workload, and error recognition | Better decisions with less unnecessary cognitive effort | Research methods demonstrated; operational benefit needs testing (Frey et al., 2016) |
| Neuroadaptive interfaces | Adjust information or assistance in response to estimated workload | Support operators during demanding tasks | Demonstrated in simulations; live deployment needs validation (Aricò et al., 2016) |
| Brain–computer interfaces | Use neural signals as an additional channel of input or feedback | Hands-free interaction for suitable tasks | Demonstrated in a simulator; advantage over conventional input needs testing (Dehais et al., 2022) |
| Neural biometrics | Use individual neural responses as an identity signal | A possible additional factor for sensitive access | Experimental; long-term stability and usability need evaluation (Zhang et al., 2025) |
| Human–AI error detection | Investigate neural responses when a person notices an automated error | Potentially earlier correction of mistakes | Research stage (Dimova-Edeleva et al., 2023) |
| Training and simulation | Measure cognitive demands during practice | Direct training toward persistent difficulties | Studied in flight simulation; transfer needs testing (Mark et al., 2022) |
| Digital collaboration | Study neural activity during shared tasks | Better understanding of coordination and handoffs | Exploratory; 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.
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.
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.
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 intelligence | Potential role in operations | What a cost case would measure |
| Human judgment | Interpret unusual situations and decide what action to take | Operator time, decision quality, workload, errors, and incident duration |
| Rules and conventional models | Filter known conditions and detect recurring patterns | Compute and maintenance costs, false alerts, and missed events |
| Large language models | Interpret unstructured evidence and assist with unfamiliar incidents | Usage charges, accuracy, review effort, and work avoided |
| Neurotechnology | Improve interfaces or adapt assistance using neural evidence | Research, 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.
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.
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
The fall series of the Meditation Community begins tonight at 7 p.m. EDT.
This first gathering will be an information session plus a short meditation.
I will explain the explain how meditation works.
I will discuss the new Meditation for Parkinson’s initiative.
I will introduce a method of noticing and reducing distractions by counting them. This is optional.
We will also have a short meditation.
Future sessions will include 30 minutes of meditation, along with approximately ten minutes for settling in and questions.
To join us, find the meeting link on the Meditation Community webpage:
There is an interesting possible link between meditation, Parkinson’s and brain-derived neurotrophic factor (BDNF).
BDNF is a protein that helps the brain maintain and strengthen neurons and supports neuroplasticity—the brain’s ability to reorganize its neural connections. BDNF activity may be reduced in Parkinson’s, while exercise is known to increase it temporarily.
Could meditation have a similar effect? A meta-analysis of eight controlled trials found that mindfulness-based interventions were associated with increased BDNF measured in the blood. The studies were not specifically about Parkinson’s, and some interventions included movement, so we should be cautious about drawing conclusions.
Still, it raises an intriguing possibility: meditation may support some of the same biological processes involved in the brain’s resilience and adaptation. It’s a hypothesis worth exploring, not evidence that meditation slows Parkinson’s.
Read the study
https://pmc.ncbi.nlm.nih.gov/articles/PMC7522212
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.
Over 75 meditation sittings, I counted genuine distractions and recorded sitting length, time of day, and internal and external conditions. Standardized to 30 minutes, distractions declined by about 28 percent. Progress was uneven, conditions mattered, and no ideal time of day emerged. The experiment shows how a simple meditation log can reveal gradual changes in attention that are difficult to perceive within any single sitting.
A basic instruction in breath meditation is to notice when attention has wandered and gently return it to the breath. With practice, we expect attention to become steadier. Yet this change can be difficult to perceive. A meditation may feel settled one day and restless the next, while gradual improvement remains hidden within the variability of individual sittings.
To make this change more visible, I began counting genuine distractions during meditation with a small mechanical tally counter. I did not count every passing thought or sensation. I counted only those moments when I had clearly lost contact with the breath and then recognized that my attention had wandered. After each sitting, I recorded the number of distractions, the length and time of the meditation, and simple ratings of my internal and external conditions.
This report examines 75 sittings recorded between December 2025 and September 2026. It asks whether distractions became less frequent over time, whether time of day made a difference, and how strongly attention was affected by conditions within me and around me. The result is not a controlled scientific study, but a structured record of one meditation practice over time.
I used a small mechanical tally counter during each meditation. After allowing approximately one minute to settle, I began counting genuine distractions. I did not count every thought, sound or bodily sensation. I pressed the counter only when I realized that I had clearly lost contact with the breath and had been carried away by something else. Each count marked both the recognition of distraction and the return of attention.
After each sitting, I recorded the date, time, length of meditation and total number of distractions. I also rated the internal and external conditions on a simple scale from 1 to 3. An internal rating described such factors as mental agitation, emotion, fatigue or physical restlessness. An external rating described conditions such as noise, movement, animals or other activity in the environment. A rating of 1 indicated relatively favourable conditions, while 3 indicated substantial difficulty. Brief notes sometimes identified the particular circumstances.
The sittings ranged from 20 to 45 minutes. Raw counts therefore could not be compared directly because a longer meditation provides more opportunities for distraction. Each count was converted to its equivalent rate per 30 minutes. A sitting with six distractions in 20 minutes, for example, became a standardized rate of nine distractions per 30 minutes.
The log recorded how many distractions occurred, but not when they occurred. Any statement that translates the rate into an average interval, such as one distraction every three or four minutes, is therefore a statistical expression rather than an observation of their actual spacing. Distractions may have been evenly distributed, clustered together or concentrated in one part of a sitting.
The distraction rate varied considerably from one sitting to the next. Highly distracted sessions appeared beside relatively settled ones, and the overall decline was interrupted by plateaus and temporary reversals. The practice did not improve in a smooth or orderly way.
The greatest variability occurred around the middle of the record. Several sittings during this period lasted only 20 minutes. When their counts were standardized to 30 minutes, each additional distraction added 1.5 to the reported rate. Some of these sittings also occurred under more difficult internal or external conditions. Shorter duration, changing circumstances and ordinary variation between meditations all contributed to the sharp movement in the chart.
Beneath these fluctuations, the underlying rate gradually moved downward. The later sittings still varied, but they generally varied around a lower level than the earlier ones.
Figure 1. Distractions across 75 meditation sittings, standardized to 30 minutes. The thin green line represents individual sittings, and the thicker blue line represents the ten-sitting moving average. The first 25 sittings averaged 10.6 distractions per 30 minutes; the final 25 averaged 7.6.
The first 25 sittings averaged 10.6 distractions per 30 minutes. The final 25 averaged 7.6, a decrease of about 28 percent. This was the expected direction of change: repeated meditation practice should help attention remain with the breath more consistently.
Expressed as a statistical average, the rate declined from roughly one distraction every three minutes to one every four minutes. This does not mean that distractions actually arrived at regular intervals. Their timing was not recorded. The comparison simply gives a more intuitive sense of the change in frequency.
The chart also shows why many sittings were needed. No single count would have established the direction of the practice. A high count might reflect a difficult day, while a low count might be followed immediately by a restless sitting. The pattern only became visible across the longer record.
I expected that meditation might be easier at certain times of day. Alertness, fatigue, meals and the rhythm of work could all plausibly influence attention. The log offered little support for a consistently better time.
The raw averages initially appeared to favour midday and evening sittings, while afternoon sittings had a higher distraction rate. However, many afternoon sittings occurred early in the record, when distraction rates were generally higher. Once the stage of practice, sitting length and internal and external conditions were considered, the differences largely disappeared.
This personal record cannot establish that time of day never matters. The sittings were not distributed evenly across the day, and relatively few took place in the morning or late afternoon. Within this practice, however, accumulated experience appeared to matter more than finding an ideal hour.
Conditions had a clearer relationship with distraction. When internal conditions were rated 1, the average was 8.7 distractions per 30 minutes. The rate rose to 9.5 at rating 2 and 12.8 at rating 3. External conditions followed a similar pattern: 8.7 at rating 1, 10.1 at rating 2 and 12.6 at rating 3.
Neither type of condition clearly mattered more than the other. Internal agitation, emotion and physical restlessness could affect attention much as noise, animals and activity in the room did. The source of the disturbance differed, but its relationship with the distraction count was broadly similar.
The notes make these categories less abstract. They mention dogs walking or barking, an ant on the mat, a fly in the room, illness, post-exam restlessness, grief, meditation planning and difficult memories. Ordinary life remained present in the practice and visible in the data.
The main result was expected: distractions became less frequent over the course of 75 sittings. What the log adds is a clearer picture of how that improvement appeared. It did not arrive as a steady sequence of increasingly settled meditations. Individual sittings remained unpredictable, while the underlying level of distraction gradually declined.
The effect of conditions is equally important. A higher count did not necessarily mean that the practice was deteriorating. It could reflect what was happening within me or around me on that particular day. This is one reason a meditation log is more useful than an isolated count. The log places each number within a longer practice and, when needed, within the circumstances of the sitting.
Time of day was less important than I expected. No hour emerged as reliably superior once the progression of the practice and other conditions were considered. This finding may be useful for meditators who spend too much effort looking for perfect circumstances. A regular time may help establish a habit, but this record gives little reason to believe that attention depended on identifying an ideal part of the day.
Sitting length also changed during the experiment. The first 25 sittings averaged 27 minutes, while the final 25 averaged a little more than 31 minutes. I chose to sit longer; the data do not show that counting increased my capacity to do so. The change matters mainly because it makes raw counts misleading. Standardizing the results to 30 minutes allowed shorter and longer sittings to be compared on the same basis.
Several limitations remain. This was one person’s observational record rather than a controlled experiment. The definitions of distraction and difficult conditions depended on personal judgement. Counting may itself have changed how quickly I noticed distraction, and the method cannot distinguish the effects of meditation practice from growing familiarity with the counter. The sittings were also unevenly distributed across dates, times and conditions.
These limitations do not make the log uninformative. They define the scale of the conclusion. The results do not prove a general law about meditation. They show that within one sustained practice, a simple and consistent method revealed a meaningful decline that would have been difficult to detect from memory alone.
Across 75 sittings, the standardized distraction rate declined by about 28 percent. Expressed as an average rate, this was the equivalent of moving from roughly one distraction every three minutes to one every four minutes. The change was uneven, and difficult internal and external conditions continued to affect individual sittings. Time of day, by contrast, did not appear to have a consistent effect.
The deepest lesson of the log may be that progress in meditation is easier to see across many sittings than within any single one. A restless meditation does not erase the larger direction of practice, and a settled meditation does not establish it. Counting provided a modest form of feedback: not a score to pursue, but a way of seeing gradual changes in attention that otherwise might have remained hidden.