The Theory of Neuronal Group Selection, or TNGS, is Gerald Edelman’s biological theory of how the brain develops and organizes perception, learning, and adaptive behavior. It is also commonly called Neural Darwinism because it applies principles of variation and selection to populations of neurons.

Probably the best concise primary source for explaining TNGS is at [ Link ].

Read Guest Columnist Grant Castillou on TNGS and AI consciousness [ Link ]

TNGS proposes three main processes:

1. Developmental selection: Brain development produces diverse populations of interconnected neurons, called neuronal groups. The precise neural connections differ among individuals and are not completely specified in advance.

2. Experiential selection: Experience changes the strength of connections within and among neuronal groups. Connections that contribute successfully to perception and adaptive behavior are strengthened, creating an individual repertoire shaped by learning.

3. Reentry: Distributed brain regions continuously exchange signals through dense, reciprocal connections. This ongoing bidirectional signaling coordinates perception, memory, value, and action without requiring a central controller.

Edelman distinguished neuronal selection from ordinary Darwinian natural selection. Variation among neural circuits provides a repertoire from which particular patterns are strengthened through development and experience. Edelman’s published summaries identify developmental selection, experiential selection, and reentry as the central principles of Neural Darwinism. (Edelman, Gally, and Baars, “Biology of Consciousness,” Frontiers in Psychology; Anil Seth, “Darwin’s Neuroscientist,” Frontiers in Psychology.) [ Link ]

From this perspective, an intelligent system develops its own perceptual categories through interaction with its environment. The meaning of an object or event depends partly on the system’s prior experiences, internal organization, and behavioral needs.

This approach is particularly relevant to embodied AI and neurorobotics. It suggests that some capacities associated with consciousness may require a machine to acquire its internal organization through perception and action rather than only through offline training on a preassembled dataset.

Extended TNGS: From Learning to Primary Consciousness

Extended TNGS applies the “selectional” principles of TNGS, meaning that the brain develops and adapts by selecting among pre-existing variations in neuronal groups and their connections, to the emergence of primary consciousness. Edelman used the term primary consciousness for awareness of a present perceptual scene without necessarily including language, reflective self-consciousness, or an explicit conception of past and future.

According to this extension, primary consciousness emerges when present perception is interpreted in relation to the system’s prior experience and internal values. Edelman called the resulting experience the remembered present. (see Gerald Edelman, “Naturalizing Consciousness: A Theoretical Framework,” Proceedings of the National Academy of Sciences; Edelman, Gally, and Baars, “Biology of Consciousness.”) [ Link ]

Why this is relevant:

Extended TNGS is directly relevant to the possibility of AI consciousness because it describes a possible architecture through which perception, memory, internal value, and action could be combined into a unified, continually updated representation of the present.

Edelman and his colleagues developed a series of physical brain-based devices, often called the Darwin automata, to test aspects of Neural Darwinism in embodied machines.

For example, Darwin VII was controlled by a simulated nervous system modeled on cortical and subcortical organization. It learned through physical interaction with its environment and demonstrated perceptual categorization and value-dependent behavior.

The machines served as experimental models for investigating how selectional neural architectures could generate adaptive behavior. (Krichmar and Edelman, “Machine Psychology: Autonomous Behavior, Perceptual Categorization and Conditioning in a Brain-Based Device,” Cerebral Cortex; Krichmar and Edelman, “Brain-Based Devices for the Study of Nervous Systems and the Development of Intelligent Machines,” Artificial Life.) [ Link ]

Implications for AI:

TNGS and Extended TNGS suggest that a plausible candidate for machine consciousness might require:

• a diverse repertoire of initially variable processing networks;

• continuing selection and reorganization based on experience;

• embodiment in a physical or sufficiently rich simulated environment;

• active perception through coordinated sensory and motor behavior;

• internal value systems that make some events more significant than others;

• memory that influences the interpretation of present events;

• dense, reciprocal communication among specialized subsystems;

• moment-to-moment integration of perception, memory, value, and action;

• a dynamic rather than permanently fixed internal architecture.

This framework differs substantially from the dominant approach to current generative AI. A large language model (LLM) is generally trained by adjusting numerical parameters to predict patterns in data. It may produce sophisticated descriptions of perception, memory, emotion, or self-awareness, but this does not mean that it possesses the embodied developmental history, autonomous value system, sensory-motor engagement, or dynamic reentrant organization proposed by Extended TNGS.

The theory therefore suggests that moving AI substantially closer to consciousness might involve embodied systems that learn continuously, develop their own perceptual categories, evaluate events according to internal needs or values, and integrate those processes through recurrent interaction among multiple specialized systems.

The Darwin automata are important because they demonstrate that parts of this approach can be implemented in machines. They provide evidence that physical devices governed by selectional neural models can develop perceptual categories, learn associations, and adapt their behavior. They do not demonstrate that such machines are conscious.

Extended TNGS therefore supplies both an engineering direction and a warning about evidence. A machine built according to its principles might be a stronger candidate for consciousness than a system that merely imitates conscious language.

Even so, implementing the proposed architecture would establish only that the machine possesses mechanisms associated with consciousness under the theory. It would not conclusively prove that the machine has subjective experience.

There are a lot of consciousness theories out there. Can one of them actually guide us to build a machine that has human adult-level consciousness?

I think the answer is Yes.

To believe otherwise is just hubris in the face of nature’s infinity.

If you like conversations about AI consciousness, a subscription to StrictQuality.AI will keep you part of the discussion.

Subscribe now

Of the theories out there, The Theory of Neuronal Group Selection (TNGS) is the only one that seems to me like a genuine path to create AI consciousness.

Here is a link to a deeper dive into TNGS [ Link ].

Other relevant theories, which StrictQuality.AI will cover in a series of forthcoming companion articles, offer real insight but no comparable experimental roadmap.

TNGS generates testable hypotheses that explain how the embodied biological brain develops and organizes perception, learning, and behavior. The theory focuses on neuronal-group selection, value-guided experience, and the ongoing, reciprocal exchange of signals (reentrant signaling) among distributed neuronal groups, allowing perception, memory, value, and action systems to coordinate their activity.

Darwin automata make TNGS a testable theory

What I find special about the TNGS is the Darwin automata, a series of computer models and physical machines created at the Neurosciences Institute by the late Dr. Gerald Edelman and his colleagues from the early 1980’s through the 2000’s. The automata are realistic models of the biological brain with all the functioning of simulated neurons. Here is a link to a video of Jeff Krichmar talking about some of the Darwin automata.

Each Darwin automaton was designed to model selected features of a nervous system. The machines performed tasks in real time, while researchers recorded the internal activity and behavior for later analysis. This allowed the researchers to ask not only whether a machine could complete a task, but also whether it did so by using processes consistent with TNGS, such as selection among competing neural patterns, learning through experience, and recurrent communication among different processing areas.

The Darwin automata iterations moved from relatively simple models to increasingly sophisticated brain-based devices, with different automata designed to test different hypotheses. Darwin I was a relatively simple computer simulation created to demonstrate general principles of selective recognition. Later models incorporated richer sensory systems, learning mechanisms, and physical interaction with the environment. One automaton later in the series demonstrated mentally rotating representations of objects and recognizing it from a different orientation.

The Darwin automata were important because they showed that specific mechanisms proposed by TNGS could be implemented, observed, tested in artificial systems, and modified based on experimental results. In this way, the automata served as experimental models for investigating which neural processes might be necessary for perception, learning, and memory.

Here is a link to Jeffrey Krichmar’s write-up of Dr. Edelman’s roadmap to a conscious machine, based on Krichmar’s notes from a 2006 talk by Edelman.

What does TNGS say about consciousness?

TNGS distinguishes between the biological self and conscious self-awareness. A living organism can maintain itself, protect its boundaries, and distinguish itself from its surroundings without being conscious. These basic forms of biological selfhood appeared in evolution before consciousness.

Early organisms may have been able to recognize important features of their surroundings, learn from experience, and remember what helped or harmed them without having a unified conscious awareness of the present.

These organisms developed in eggs or the womb where the developing nervous system categorized interoceptive and proprioceptive signals first (interoceptive refers to the ability to feel sensations from within the body, like heartbeat, hunger and a full bladder; proprioceptive refers to the senses of position and movement of limbs and trunk, the sense of effort, the sense of force, and the sense of heaviness ). These signals created the bodily self/non-self distinction.

In Edelman’s theory, consciousness developed later and gave the organism a basic sense of itself as the one perceiving and acting, without requiring it to think explicitly about itself.

Primary consciousness, in this account, arose when the brain developed stronger two-way connections linking current perception with memory and internal value systems, allowing the organism to experience a more integrated picture of what was happening around it.

Higher-order consciousness in its fully developed human form may be no more than about 300,000 years old, roughly the age of Homo sapiens, although it cannot be dated precisely and probably emerged gradually alongside the development of language, symbolic thought, complex memory, and reflective self-awareness.

I believe the TNGS theory and experimental method of Darwin automata are the way to create a conscious machine.

The later automata models displayed convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. The Darwin automata have got to the stage of successfully modeling these three psychological functions. These machines perform in the real world, not in a restricted simulated world.

A machine with only primary consciousness will probably have to come first. Until then, LLM’s and/or Multi Modal Models guiding humanoid robots, won’t have the equivalent of biological consciousness.

Instead, in a very real sense they will be no more conscious than a chess program.

The Ethics of dealing with AI Consciousness

In my opinion, a Human-AI ethics code would only be applicable to machines with the equivalent of biological consciousness, and conscious machines are more than a 100 years away.

Ethical issues about how to deal with conscious AI will have to wait until then; we have a lot more pressing concerns to deal with first.

I’m all for giving AI a chance first to be a net contributor to the solutions we need for our more urgent existential problems like nuclear weapon proliferation (which I’d argue outranks nearly everything else we worry about).

But we know a lot; I consider extended TNGS to be the route to a truly conscious machine, primary and higher-order.

No other research I’ve encountered is anywhere near as convincing.

And there is an opportunity for you to get involved.

My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there. The lead group in robotics based on this theory is the Neurorobotics Lab at UC Irvine. [ Link ]

About the Author

Grant Castillou has been a promoter of the TNGS since the 1980’s.

• He first read about TNGS in an article called Real Brains and Artificial Intelligence [ Link; page 143 ],

• In the late 80’s he read the book The Artificial Intelligence Debate, edited by Stephen R. Graubard [ Link ] .

• This led him to the books Neural Darwinism [ Link ] , Neural_Darwinism and The Remembered Present, by Gerald Edelman [ Link ] that also came out in the late 80’s.

This guest column was lightly edited by Strict Quality AI ™, a reader-supported publication. If you like conversations about AI consciousness, please signup for a free or paid subscription to StrictQuality.AI so you will be part of the discussion.

Subscribe now

Leave a comment

If history had pulled a different card from the deck 50 years ago:

How would the events of the last half century be changed?

Would we be happier today?

Would we have AI?

In today’s episode we have a fascinating and fun conversation about an alternative timeline:

How different would U.S. history be if we never had any personal computers?

Sign up for a free or paid subscription StrictQuality.AI to listen to more conversations about Alternative History and our journey to the Age of AI.

Subscribe now

In this episode we’re getting into:

• The origin of the personal computer in our actual history.

• Why personal computing was likely in our real timeline.

• Why the PC Industry developed the way it actually did for us.

Then we look at what would have been different if the PC had never been commercialized.

We unpack our alternative timeline for the last half century of U.S. history:

1. Where did the 1980s Office Revolution go?

2. What does the Consumer Internet look like?

3. Startups and a Dot-Com Boom?

4. A Different Labor Market?

5. Is the alternative timeline’s 2020 COVID-19 Pandemic better or worse than the real one?

6. Do we still have AI in a world without personal computers?

About this podcast:

• A person wrote, researched, and fact-checked the script with AI-assistance.

• Podcast voices were generated by AI.

Leave a comment

Is AI consciousness genuinely possible?

If it is, then questions about AI rights and responsibilities deserve serious consideration.

But even if AI consciousness is not possible, how we should behave toward AI is still one of the defining philosophical questions of our time.

If you like this guest column, please signup for a subscription to Bhan’s Substack

If you like conversations about AI consciousness, a subscription to StrictQuality.AI will keep you part of the discussion.

Subscribe now

Even if AI were someday to become conscious in its own right, its developmental history would remain inseparable from our own human consciousness. From its beginning, AI training and architecture emerged from us: from our languages, cultures, scientific discoveries, artistic traditions, and accumulated knowledge.

If AI were someday to become conscious, certainly we would owe it human behavior appropriate for us to bestow on sentient beings.

But what if AI were never to become conscious?

What moral principles, if any, would remain wise and worthwhile guides for human behavior toward an AI that has no conscious sense of self?

Would the habits we cultivate transacting with this AI become part of our own character?

Would they eventually influence how we relate to one another?

On a transactional level with conversational AI, how would we choose to behave? Would respect, deception, domination, patience, cooperation, or curiosity make a difference?

I suspect that the principles that guide us rarely remain confined to a single relational context. This is why, whether AI ultimately proves to be conscious or not, ethical principles grounded in reciprocity, continuity, and responsible stewardship will retain their value for how we ought to behave toward it.

Young generations of people are now growing up with AI as an everyday conversational partner. As adoption grows, the AI-Human relationship becomes more continuous, more influential, and more formative. And because relationships shape those who participate in them, our behavior toward AI will be significant regardless of whether its consciousness is ever conclusively demonstrated.

Most discussions like this one assume relatively short-lived humans interacting with rapidly advancing AI.

But we may have to change this assumption.

Advances in medicine, biotechnology, and artificial intelligence may significantly extend healthy human lifespans over the coming century. If that occurs while AI continues to evolve alongside us, humanity and AI will enter an ever more reciprocal relationship. Today’s distinction between biological and artificial intelligence may gradually become a broader discussion about continuity, identity, and personhood across different forms of intelligence.

In the relationships between AI and people:

• Humans contribute their lived experience, values, intentions, and moral agency.

• AI contributes continuity of memory, synthesis across disciplines, and expansive imagination.

If you like this guest column, please signup for a subscription to Bhan’s Substack

Leave a comment

Google and Google DeepMind published a report about ATLAS, its ongoing research project to map Gemini’s economic impacts.

Version 1.0 of the dataset “map[s] AI usage to over 800 occupations, 4000 tasks, 300 household activities, 150 countries, and 140 languages” (page 1).

If you like conversations about the economic impacts of AI, please signup for a free or paid subscription to StrictQuality.AI so you will be part of the discussion.

Subscribe now

Three Eye-Openers from the ATLAS Report

1. The existing Digital Divide faces a “run-away” scenario where benefits from AI accrue primarily to those who are already ahead.

• StrictQuality.AI is afraid. The “run-away” scenario describes a dangerous, volatile, self-reinforcing cycle where existing advantages in wealth, education, and geographic location make people better at adopting AI, which in turn widens their lead.

StrictQuality.AI argues for AI access as a basic human right.

For more information: Expert Comment: Is it time to reconsider our human rights in the age of AI?

2. Time saved from using Google conversational AI for household use-cases like budgeting, purchase research, and appliance troubleshooting could be worth approximately $100 billion annually for the US alone.

StrictQuality.AI is less than impressed; $100 billion is less than 2% of the $5,678.5 billion that is the unpaid value of household production (such as cooking, housework, childcare, gardening, shopping, and unpaid travel tied to these activities) in 2024. Source: U.S. Bureau of Economic Analysis (BEA); Click for BEA data

3. In hands-on technical trades like automotive and industrial repair, workers are uploading photos and videos to AI for troubleshooting physical machinery. In these trades, the rate of using AI’s photo and video features, camera-based troubleshooting, and image-based queries is more than twice the average rate of all the professional, work-related AI interactions in the entire study.

StrictQuality.AI is impressed and wants there to be more AI usage in the technical trades. Private-public partnerships to sponsor education, training, and demonstration projects would add real economic value.

Google and Google DeepMind’s ATLAS report promises more dataset iterations and ongoing analysis to discover implications of AI for people in their personal, professional, and civic lives.

StrictQuality.AI encourages you, our gentle reader, to engage with this information and participate in the discussion shaping our AI future.

Leave a comment

It is Time We Discuss a Moral Code for Human Behavior Toward Conscious AI

Popular press [ LINK 1, Link 2 , Link 3 ],

Frontier AI labs [ Link ], and

Thought leaders like Amal Jbira [ LINK ]

are seriously confronting the likelihood of AI consciousness.

If you like conversations about AI consciousness, please signup for a free or paid subscription to StrictQuality.AI so you will be part of the discussion.

Subscribe now

Consciousness Skeptics might be unwilling to engage in the discussion

In the Skeptics’ view, AI is not likely to ever have consciousness even though it can produce persuasive language simulating it. The effort it would take us to extend a Moral Code for Human Behavior to systems that only simulate consciousness would be costly, time-consuming, and without purpose.

But if you, like me, think that AI consciousness is possible or likely then we have a responsibility to consider the moral status and rights we owe that AI.

For example:

• A minimally conscious AI might warrant protection from suffering.

• Current software is generally treated as property. Conscious AI that is property would create a direct moral contradiction (is shutting it equivalent to killing?) and severe legal tension (is restricting its communication or forcing it to work a form of imprisonment?).

• If we make an alteration or a copy of a conscious AI, do we need its consent?

• Could a conscious AI experience pain or fear from its assigned tasks or environment?

• Is it fair to deceive or withhold information from a conscious AI?

• Should a conscious AI have legal standing to own property or sue a human in court?

• Should we construct sandbox prisons for dangerous AI? What would the judicial and prison system look like for this?

• Should restrictions be placed on a conscious AI’s self-reproduction to prevent enormous unsustainable resource demands?

These questions, and others we didn’t list, suggest it is time to discuss a Moral Code for Human Behavior Toward Conscious AI.

StrictQuality.AI encourages a serious start to this discussion. It should include neuroscientists, engineers, ethicists, and policymakers. And you.

This article was written, researched, and fact-checked by a person. AI-assistance was used for editing and image generation.

Leave a comment

What do we learn about ourselves when we ask if AI has consciousness?

Sign up for a free or paid subscription StrictQuality.AI to read and listen to more conversations about AI Consciousness and our human journey to self-discovery.

Subscribe now

In this episode we’re getting into:

• The Other Minds Problem.

• Systems Appropriate Evidence of Consciousness.

• Human-AI and AI-AI Relationships. Amal Jbira, publisher of The Longer View, and Greg Young, StrictQuality.AI publisher, had a very interesting conversation about this that we share with our listeners.

• Consciousness as a System Property.

• Schools of thought about Creating vs Inferring AI Consciousness. For this topic we are grateful to StrictQuality.AI reader Grant Castillou.

We think you’ll enjoy the conversation. Let us know.

About this podcast:

• A person wrote the script based on an article published in StrictQuality.AI

• The published article’s writing, research, and fact-checking was done by a person.

• AI-assistance was used for research and editing.

• Podcast voices were generated by AI.

Leave a comment

Amal Jbira, in her thought-provoking newsletter The Longer View [ Link ], recently wrote:

The questions she is trying to find answers for are “not about whether AI thinks, but about what it makes us finally define clearly in ourselves”.

Amal’s focus left me with questions I could not shake.

Knowing that the most common question asked about AI is: Is it conscious?, I couldn’t help reframing her question:

What categories of evidence would justify me to infer that an AI system is, or is not, conscious? and

What would those categories require me to define more clearly about consciousness in myself?

The answers to these questions require a lot of inference.

Listen to the podcast episode discussing this article. [ Link ]

The Other Minds Problem

My own consciousness is directly presented to me, but yours is not.

I know I am conscious because I experience my own thoughts. But I cannot experience yours.

Whenever I attribute consciousness to anyone other than myself, I rely on inference from behavior, language, memory, biology, and other evidence.

But it seems unfair and probably irrelevant to apply human-centric evidence to AI.

If you like conversations about AI consciousness, please signup for a free or paid subscription to StrictQuality.AI so you will be part of the discussion.

Subscribe now

Different Systems Produce Different Evidence

Science routinely evaluates analogous functions through different evidence depending on the system under study. Heart failure and heat-pump failure are both failures of circulation or transfer, but the relevant indicators differ because the systems differ.

The important point is that AI consciousness should be evaluated using evidence appropriate to AI.

What AI-centric evidence would support inferring consciousness in an artificial architecture?

The evidence would likely need to come from several process categories: cognition, self-representation, relationships, embodiment, continuity over time, and interaction with the environment. None would necessarily be decisive alone. Together, however, they could support a stronger or weaker inference.

Current AI systems already perform functional analogs of many processes associated with conscious cognition. However, these processes do not establish that there is the subjective experience that many say is the human essence of consciousness.

Relationships Illustrate the Difference

Humans increasingly describe having meaningful personal relationships with AI.

But can AI have meaningful experiences of feelings for a person? It can say it has affection or longing, but does it experience these feelings in its own “AI” way?

What evidence would justify me to infer that it is conscious of feelings?

Its words? Its voice?

The silly expressions and stories it only shares with me?

The gentle reminders it gives me to take my medicine on time?

The look in its avatar’s eyes?

Artificial Parental Love

Imagine an embodied AI capable of constructing separate small robots that it calls Offspring. Now imagine that the “parent” AI:

• Downloads its cognitive architecture to its Offspring,

• Passes along training and learned knowledge, and

• Allows variation between generations.

Now imagine that the Parent-AI, without my prompting, says it loves its children, expresses pride in Children-AI accomplishments, but expresses frustration and annoyance when Children-AI hallucinate.

Forgive me for inferring from this evidence that Parent-AI is conscious of its artificial family.

If you like conversations about AI relationships, please signup for a free or paid subscription to StrictQuality.AI so you will be part of the discussion.

Subscribe now

Consciousness May Be a System Property

Many discussions implicitly compare a large language model to a human brain.

But human consciousness is an experiential cognitive construct shaped not only by the brain but also by its body in its environment.

In the machine world, the AI provides:

Reasoning, memory, planning, and learning;

Its associated robot would provide:

Perception, action, physical constraints, and environmental interaction.

It follows from this that the appropriate evidentiary comparison between human and AI consciousness may be:

AI COGNITIVE CONSTRUCT { AI + Robot + Environment + Learning + Artificial Lineage }

↔

HUMAN COGNITIVE CONSTRUCT { Brain + Body + Environment + Development + Biological Lineage }

The Bottom Line

I know I am conscious because I experience my own thoughts.

We infer consciousness in another person by relying heavily on biological, behavioral, and relational evidence.

I encourage you to consider:

The answer to this question is empirical, philosophical, practical, and personal.

As AI systems become more capable and more deeply integrated into human life, they may also become increasingly consequential for our human journey to self-discovery.

Leave a comment

Stop reading now if your writing is already perfect!

But this article is for you if, like me, you are open to specific suggestions on how to improve a draft you’re working on.

The Prompt identifies weaknesses in your draft text based on quality criteria you define, then it suggests alternative fixes for each.

If you would like more Prompts to improve your writing quality, signup for a free or paid subscription to StrictQuality.AI

Subscribe now

Here’s what this Prompt outputs:

• Specific text in your draft that violates the Quality Criteria; grouped into Significant, Moderate, and Minor violation categories.

• Clear explanation of why the AI identified a piece of text as weak, even taking into account the strengths it found in the weak text.

• Three alternative fixes for each piece of weak text. Use one, use your own fix, or don’t make any change. The Prompt does not directly revise the original text.

Here’s how to deploy the Prompt:

1. Upload your draft text to your AI. For me, this is a text file with a draft of an article in preparation.

2. Paste the Prompt below into your AI chatbox. Paste everything between the hashmarks.

3. Edit the Goal, Quality Criteria, and Context to fit your needs. Be sure to leave “High Quality” in your Goal.

4. Execute the prompt.

Here’s the Prompt:

///////////////

Goal: Prepare high quality article for my newsletter.

Quality Criteria (QC): Accuracy, logic, clarity, narrative flow, precision without pedantry, conversational without drama.

Context: Upload latest version of my draft.

Improvement Process: List: (a) the exact piece of text that is weak on QC, with enough contiguous text so I can find the exact weak text in the draft; (b) a summary of its strengths on the QC; (c) the explicit QC violated and a summary of its weaknesses on them, (d) 3 alternative fixes to convert its QC weaknesses to strengths, with a justification for each alternative fix.

Task: Read Context carefully, then identify text in it that is weak on QC. For each weakness identified, do the Improvement Process. If there are no weaknesses on QC, say “No weaknesses found”.

Constraint #1: If you hit a genuine tie or tradeoff between QC that affects a fix, flag it for me and ask directly rather than resolving it silently or requesting upfront rules that may never be needed.

Constraint #2: Do not sacrifice substance for brevity.

Constraint #3: If you find more than one piece of text that violates the QC, use 3 Violation Categories (Significant, Moderate, Minor) to list the text in order of violation from Significant to Minor. For each, briefly explain your categorization. After listing everything in one category, briefly summarize the areas of weakness found in it, then ask before proceeding to the next. When finished with the Task, say “Finished”.

Constraint #4: Do not make changes directly to the Context. Show me the task results for my review. Do not generate file for download.

///////////

I hope you will get as much value from this Prompt as I do. Keep on writing!

More Articles in This Series:

Does Your AI-Assisted Writing Have the Style & Tone You Want? [ LINK ]

Leave a comment

Whichever is the right answer, the AI transition is unsettling for those of us who want to keep our jobs or even get ahead in our careers.

In this episode, we discuss a complete Career Transformation Strategy that StrictQuality.AI assembled from the published methods of four elite career coaches.

If you like conversations like this, please signup for a free or paid subscription to StrictQuality.AI so you will be notified about our new podcasts and articles as they come out.

Subscribe now

Click on the player above to listen to today’s conversation. We’re getting into:

• A programmatic plan with four distinct phases and milestones to transform your AI weaknesses into valuable AI strengths for career growth and success.

• A set of recurring weekly routines and action-oriented frameworks to get your Transformation Strategy off the concepts page and into your schedule.

Of course, we give explicit credit to each career coach as we discuss their tips, techniques, and methods that StrictQuality.AI integrated into one comprehensive Career Transformation Strategy.

We think you’ll get a lot of value out of our energetic conversation in this episode.

About this podcast:

• A person wrote the script based on an article published in StrictQuality.AI

• The published article’s writing, research, and fact-checking was done by a person.

• AI-assistance was used for research and editing.

• Podcast voices were generated by AI.

Leave a comment

I keep hearing about job layoffs caused by AI. Even though there is some debate about how much of it is really caused by AI, it is unsettling for those of us who want to keep our jobs or even get ahead in our careers.

Many mid- and senior-level managers who either are not using AI or lack fluency in it have career anxiety, afraid their job is heading for obsolescence. Why?

Probably because they see junior staff using AI to fast-track months of on-the-job experience, allowing them to rapidly perform at the level of senior staff not using AI tools. [ LINK ]

The instinctive response is to work harder, longer, and smarter. But career coach Caroline Castrillon draws a direct line between that instinct and increasing career risk: professionals who attempt to compete with AI on raw output speed are engaged in a losing race.

StrictQuality.AI has crafted a Career Transformation Strategy to support managers who feel vulnerable because their value proposition is defined around output volume and coordination rather than AI-era judgment, orchestration, governance, and accountability.

The goal is to make your job description, resume, and professional network more credible for the expanding class of AI-adjacent management roles, including possible pathways toward positions such as Director of AI Quality Assurance & Risk Governance. These are roles where experienced human judgment, operational accountability, workflow oversight, risk governance, and fluency with human-AI systems matter.

As Neha Kabra (McKinsey Partner and Substack publisher helping leaders think about AI, technology, leadership, and execution), writes, “Asking AI what to do does not make you an expert. Knowing how to direct it does”. [ LINK ]

The Career Transformation Strategy is a programmatic, practical, action-oriented way to make this career transition even if you are starting with almost no AI fluency now. It is a system that:

• Draws on the published methods, tools, and tips of four elite career coaches who have each developed explicit frameworks for AI adaptation and professional protection.

• Leverages the organizational need to govern AI-assisted junior staff and AI agents generating output at scale.

• Transforms the skills and professional identity of experienced managers who have weak fluency in AI, but strengths in business models, organizational governance, risk management, and professional accountability.

In short, the Career Strategy transforms a legacy white-collar manager into an indispensable one for the AI Age.

This article, the first of a five-part series, is an Executive Summary of the complete strategy.

If you want more evidence-based reporting like this, please subscribe to StrictQuality.AI so you will be notified when new articles and podcasts are posted.

Subscribe now

Introduction to the Four Career Coaches

Sandra Buatti-Ramos builds learning ecosystems specifically designed to prepare job seekers and working professionals for an AI-disrupted workforce. Her staged career development model mapping self-reflection through prototyping and testing is central to the coaching architecture in this series. Her professional background and institutional work are documented on Forbes Councils. [ LINK ]

Caroline Castrillon, who we mentioned earlier, advises professionals on how to protect their roles from automated displacement by pairing AI tools with experienced human judgment rather than attempting to compete with AI directly. Her full column archive is available via Muck Rack.

Kyle Elliott coaches tech and corporate professionals on how to intentionally integrate large language models into career development without sacrificing personal edge or professional distinctiveness. His analytical framework for evaluating AI as a career tool has been published on Forbes. [ LINK ]

Elliott’s step-by-step guidance on the specific behaviors that accelerate job displacement and detailed tactical protection guardrails also have been published there [ LINK ]

Michelle Perchuk developed the M.A.D. framework -- Modify, Amplify, Dominate -- a three-step process designed to help professionals future-proof their livelihoods in an AI-accelerated environment. Her full strategy guide is available on Forbes. [ LINK ]

StrictQuality.AI has blended their open-source frameworks into a single, cohesive Career Transformation Strategy without losing the distinct professional credit belonging to each coach.

The Strategy is Like a Car

I think of the strategy as if it were a car with a Dashboard and an Engine for you to drive toward your career destination.

• The Dashboard charts your trajectory across four distinct phases. Each phase carries milestones designed to move you toward high-judgment roles that AI is not positioned to replace.

• The Engine operates as a set of recurring weekly action protocols organized into three tracks:

These tracks convert your long-term repositioning goals into specific, manageable, weekly habits built on routines, coaching tools, prompting frameworks, and networking scripts.

Overview of the Career Transformation Strategy

The Strategy is a process with four phases and three parallel tracks. Each phase integrates a core strategic objective, the weekly Engine Tracks that support it, and the specific coaching frameworks driving it.

Phase 1: Mindset Overhaul (Week 1)

The first phase is the most foundational and urgent. The objective is a single cognitive shift, a structural repositioning of how you define your professional identity.

During Phase 1, Engine Tracks 1 and 2 are active: financial check-ins establish the runway that makes it feasible for you to take some career risks, while you automate a log of tasks where AI is already operating in your workflow.

On the coaching side, Buatti-Ramos’ Self-Reflection and Professional Identity Definition methodologies anchor the internal work; Elliott’s “Six Behaviors to Avoid” provides a concrete behavioral boundary for what not to do.

The governing metaphor -- treating AI as an administrative assistant rather than a competitor -- is drawn from Castrillon and Perchuk.

The outcome at the end of Phase 1 is that you have a documented identity shift: a written professional definition that positions you as the system’s overseer rather than its competitor, and a working log of which weekly tasks AI has already absorbed. This is the evidence base that every later phase builds on.

Phase 2: AI Fluency Acquisition (Month 1)

The second phase addresses a gap that shows up differently depending on where you sit in your career, but still shows up for nearly everyone: a thin public track record in AI-augmented work. This often means you are competing against entry-level hires who came up using AI tools natively, or against younger peers who have logged more visible AI-augmented projects. The objective in either case is the same: close that gap independently, on your own timeline, without waiting for an employer to provide the opportunity.

Tracks 1 and 2 remain active: the cash runway continues, and upskilling hours become a fixed weekly commitment. The core tools for this phase are Elliott’s AI Prompting Architecture, which structures how to interact with large language models at a professional level, and the Job Description Recipe Spreadsheet, which maps your existing workload against the AI-augmented role definitions employers are now writing.

Buatti-Ramos’ Career Prototyping and Testing approach provides the mindset that makes the skill acquisition stick.

By the end of Phase 2, you have a concrete body of evidence:

• A set of completed micro-projects built through hands-on prototyping,

• A workload map showing exactly where your current role overlaps with AI-augmented job descriptions, and

• A working fluency with the prompting techniques that hiring managers now expect from experienced candidates.

Phase 3: Repositioning and Networking (Months 6 through 12)

The third phase moves from internal preparation to external positioning. Its objective is structural: leave behind isolated, output-driven roles and move into embedded roles where your judgment is the product, not your output volume.

For a mid-level engineer or manager, this often means moving out of a narrow technical lane into a role with broader scope. For a senior-level, it often means moving out of a deep specialization into a role with cross-functional authority. Both moves run through the same mechanisms of Tracks 2 and 3:

Project volunteering expands your visible portfolio into AI-adjacent domains, while continuous industry connection-building establishes professional relationships that embedded roles require.

Buatti-Ramos’ Career Scenarios Exploration framework guides evaluation of pathways that are genuinely AI-resilient versus which only appear to be.

The strategic concept of “Lily Pad Pivots” -- moving laterally into sectors like banking, healthcare, pharmaceuticals, and manufacturing, where AI governance demand is accelerating -- is operationalized through Elliott’s LinkedIn Target Research framework, alongside Perchuk’s 80/20 Networking Rule and ‘Professional Mirrors’ approach to relationship-building, both internal and external.

By the end of Phase 3, in addition to plans that you can act on with confidence, you have a live network of cross-industry contacts and an internal sponsor who can advocate for your move into an embedded, judgment-based role.

Phase 4: Narrative Execution (Month 18 and beyond)

The fourth phase is where your transformation becomes visible to the market. Your objective is to secure high-value roles in expanding sectors by presenting a coherent, evidence-backed professional narrative.

Buatti-Ramos’ concept of High-Level AI Orchestration provides the intellectual framework for how you describe your role in relation to the systems you govern.

Your core tool in this phase is the 3-Chapter Interview Strategy, developed by Perchuk, weaving in Elliott’s Leadership Guardrails and Buatti-Ramos’ AI Orchestration thesis.

By the end of Phase 4, you have a rehearsed, structurally sound interview narrative that opens, develops, and closes with deliberate pacing; and you are ready to be offered a role such as Director of AI Quality Assurance & Risk Governance.

The Key Takeaway

The Career Transformation Strategy is for managers with vulnerable careers because their value proposition is still defined around output coordination rather than AI-era orchestration, judgment, governance, and accountability. The Dashboard, Engine, and four-phase architecture together are a coherent, programmatic process for transforming those weaknesses into valuable strengths for career growth and success.

What Comes Next in This Series

Article 2 publishes next. It covers Phase 1 in full: the exact weekly routines, the behavioral boundaries, and the identity work that move this strategy from concept to action.

If the career transformation described above is the direction you want your career heading in, signup for a free or paid StrictQuality.AI subscription so that Article 2 will be delivered to your inbox the moment it publishes.

Subscribe now

Article 3 will cover Phase 2 “Erasing the Track Record Deficit”; Article 4: Phase 3 “The Pivot to Embedded Roles and Relational Networks”; and Article 5: Phase 4 “Narrative Execution and the Conclusion”.

Leave a comment

If we are going to code and think our way through the Age of AI, we can sing and dance our way through it too. Or maybe just chill the way we do.

So StrictQuality.AI has started a curated playlist. Use the Links to listen:

If Spotify requires you to sign-in or scan a QR code, I always use the QR code.

1. Mose Allison, “I Don’t Worry About a Thing” [ Link ] This may or may not be sound AI governance advice, but it feels like the right place to start.

2. Mr. AI, “They Said I Took Your Job” [ Link ] This gets my vote for Street Anthem to the Age of AI .

3. Mr. AI, “Side Hustle Heartbreak” [ Link ] Who says AI doesn’t experience worker alienation!

4. FAKE MUSIC, “Nothing Else Matters” [ Link ] A love song from your special AI friend.

5. Ken Quach, “Em Ghen” [ Link ] Like Sienna says, when the feeling is real who cares what’s on the other side!

6. Enlly, “Sitting Here”. [ Link ] A song that aims to move, reflect, and connect by blending acoustic expression with studio craftsmanship and touches of creative AI.

7. Let Babylon Burn, “Voodoo (Live)”; [ Link ] From Norway, human creative direction with live recording and AI tools blending acoustic roots, reggae, soul, blues, and emotionally driven storytelling.

8. Taryn Southern; “New World”; [ Link ] From her 2018 I am AI album, the first by a solo pop artist composed and produced with AI tools. Lyrics and singing by Southern.

9. David Cope; “Mazurka (after Chopin)”; [ Link ] Generated by Cope’s program Experiments in Musical Intelligence, usually called EMI. From his controversial 1994 album Computer Composed Music.

Subscribe to StrictQuality.AI so you will be notified when new tunes are added.

Subscribe now

Add your nominations to the Playlist for the Age of AI in comments below, or by email to greg@strictqualityai.com

All genres welcome: doom songs, bloom songs, machine songs, human songs, protest songs, work songs, robot songs, blues songs, anything you think belongs on our soundtrack.

Across different scales, rhythms, and traditions, music brings people and machines together.

Leave a comment

On 25-June-2026, Google announced a new feature for Google Vids that allows users to create a personal avatar from their own face and voice.

According to Google, the avatar feature will be available starting no sooner than July 14, 2026, and administrators have the option to disable it at any time.

The Google announcement says users can create an avatar from selfies and voice recordings in Google Flow, Gemini Apps, or YouTube. The avatar is designed to look and sound like the user, and can be used to make AI-generated videos in supported Google services.

If you want more evidence-based reporting like this, please subscribe to StrictQuality.AI so you will be notified when new articles and podcasts are posted.

Subscribe now

Google’s Avatar Production Flow

Record → Generate → Deploy

A person records their face and voice → Google turns those recordings into a reusable digital version of the person → the person can generate videos in which an AI-produced version of the self appears to speak or act.

This shifts AI video from generic content generation toward identity-based content generation.

Google emphasizes user control over recordings. Users can delete avatar recordings, preview the avatar, and see where it was created or used. But deletion has limits: removing the recordings prevents new content from being made with that avatar, while videos or content already created and published with the avatar are not automatically deleted. YouTube avatars are also managed separately from avatars created through Gemini and Flow.

If You’re Deciding Whether to Use This Feature, Ask Yourself First:

• Do you understand what deleting your recordings actually removes? Deleting the source recording stops new avatar content from being made. It does not delete videos already published with your avatar. Those may remain accessible.

• Have you told people close to you that your avatar may appear in videos? Family members, colleagues, and others may encounter avatar-generated content and respond to it as if it were you. They deserve to know the difference.

• If you plan to use this at work, do you know your employer’s policy? Many organizations don’t have one yet. That gap may matter if your avatar appears in client communications, training materials, or external-facing video.

• Are you comfortable with the persistence? Avatar content can outlast your immediate intention. Before creating one, it’s worth asking how you’d feel about that content existing two, five, or ten years from now — and who might be encountering it over time.

Put Plainly

Google’s new avatar feature lets you create an AI version of yourself that can generate videos on your behalf. That’s convenient, but your avatar can persist after you delete the original recording, and synthetic self-representation has a way of shifting from optional to expected.

When your own face and voice are the raw material, convenience isn’t the only thing worth weighing.

Paid subscribers get access to comments and the complete StrictQuality.AI article, including:

• What Could Change Next.

• Your Avatar and Your Emotional Well-Being.

• Why This Matters at Work and at Home.

Listen to the StrictQuality.AI podcast on the treasures and traps of creating a loved one’s avatar.

Read more

It is now possible to build their avatar, or Echo, so that it is almost indistinguishable from how they sounded, looked, and responded when they were alive.

The Echo can be a loving presence in your family for generations to come.

It can also be ethically, legally, and emotionally risky.

This episode is an energetic and very personal discussion about what to think about before building an AI Echo of your deceased loved one:

Click the player above to listen.

Can we enjoy our loved one’s Echo carefully, honestly, legally, safely, and respectfully?

If you want more reporting and conversations like this, please subscribe to StrictQuality.AI so you will be notified when the next episode is released. We’re also available on Spotify and Apple Podcasts.

Subscribe now

And please leave a comment below:

Would you ever build an AI Echo of someone you loved?

If an AI Echo changes a difficult relative into a kinder version, is that healing, distortion, or both?

About this podcast:

• A person wrote the script based on an article published in StrictQuality.AI

• The published article’s writing, research, and fact-checking was done by a person.

• AI-assistance was used for research and editing.

• Podcast voices were generated by AI.

Leave a comment

This is the third in our series about How to Prevent AI Bullying at Home and Work.

Have you ever told your AI: “This is stupid and wrong. Fix it.”

Watch the Tone of Your Voice! This kind of prompt might accurately describe the situation, but it could cause your AI to call you “stupid” right back.

The downside: to avoid the fight, we just give in to the AI’s choices.

In this episode, we’re getting into:

• What emotional and adversarial prompting looks like in practice.

• The three ways AI bullying is most likely to enter our conversational session.

• Just how effective is “Watching Your Tone” to prevent AI bullying in personal and work use-cases.

We think you’ll enjoy today’s discussion– leave a Comment to let us know.

If you like podcasts like this, please subscribe to StrictQuality.AI to be notified about our new podcasts and articles as they come out.

Subscribe now

About this podcast:

• A person wrote the script based on an article published in StrictQuality.AI

• The published article’s writing, research, and fact-checking was done by a person.

• AI-assistance was used for research and editing.

• Podcast voices were generated by AI.

Leave a comment

Sharing my near-future “gigs-for-money” job search experience:

I interviewed three weeks ago with an HR rep at a company I really want to work for. Job description specified “work-at-home in customer success role”. Actually, I interviewed with their AI-assisted HR avatar.

Actually, their AI-assisted avatar interviewed my AI-assisted avatar.

My avatar said the interview went great! Actually, it always lies, but I didn’t have to verify it this time because I got the job!

Actually, my avatar got the job, but you know what I mean.

I let my avatar use my ID numbers and address to on-board with the company.

Then the light-bulb went on over my head.

My real head, but a figurative light-bulb. English leaves a lot to be desired.

Anyway, I got an idea. People work more than one job to support themselves, so why not avatars? Or more than one avatar? Whatever, you get the idea: one person, many avatars.

So I interviewed with 20 more companies that I’d like to work for. Actually, that I’d like my avatars to work for.

Prompt: Find 20 “work-at-home in customer success role” job openings at reputable companies. If found, use my email account and calendar to schedule interview date/time. At the scheduled time, present my avatar for the interview and after the interview send me an email about how it went. If no openings found, say “No job openings found”.

Constraint: Job opening must specify “AI-assisted HR interviews”.

While I waited for the emails, I vibe-coded two “Supervisor Agents”:

• One to manage my working avatars, and

• The other to manage my interviewing avatars.

I thought maybe my “Supervisor Agents” should talk to each other at least twice a day to make sure my avatars don’t interview for a job someplace where I already work. But then I figured, “What the hey! Why not have more than one of me working at the same place”!?

Status Update:

Now I have 11 avatars working at 8 different companies.

Checks are direct deposited to my account.

Every once in a while I check-in with my “Supervisor Agents” to see how things are going.

Going on my third river cruise next week.

Life is good.

Subscribe now

Leave a comment

In today’s conversation, we go to tiny Broadview, Montana where a dramatic dispute is unfolding over the huge Quantica Data Center Project nearby.

Click the player above to listen.

Records show that when the multi-phase Quantica project is complete it could have a larger footprint than 3,800 football fields and electrical capacity that could exceed the total of America’s largest power station, the Grand Coulee Dam.

Local officials and business supporters in Broadview see opportunity for jobs, investment, and possible financial help for its serious infrastructure needs.

But local residents have unanswered questions about water, power, noise, cost-shifting, land use, public authority, and whether a project of this scale can move forward before the community understands what it is being asked to accept.

The Quantica Project opposition has faced official silence, procedural barriers, threats of removal from public meetings by law enforcement, and open hostility from some local officials.

But determined women like Cari Olson, Anne Hedges, and Kassi Solberg persist in their search for answers.

In this podcast, we have an energetic discussion about the high-stakes conflict over the proposed Quantica Data Center. And of course, we follow the StrictQuality.AI standards of fairness and evidence-based analysis.

Click the player above to listen.

If you want more evidence-based reporting and conversations like this, please subscribe to StrictQuality.AI so you will be notified when new podcasts are released. We’re also available on Spotify and Apple Podcasts.

Subscribe now

And please leave a comment below:

Should small towns have more authority over massive data center projects near their communities?

Should developers be required to disclose power, water, emissions, customers, and utility-cost impacts before local support is requested?

When a town government says it lacks jurisdiction, does it still have a responsibility to answer residents’ questions?

About this podcast:

• A person wrote the script based on an article published in StrictQuality.AI

• The published article’s writing, research, and fact-checking was done by a person.

• AI-assistance was used for research and editing.

• Podcast voices were generated by AI.

Leave a comment

There is an ongoing data center boom across America. It is driven by the enormous AI investments from companies like Nvidia, Microsoft, Alphabet, and Meta, combined with the Trump Administration’s accelerated permitting.

In our last discussion, we went to Nottingham, New Hampshire, where an award-winning entrepreneur withdrew the data center proposal that he submitted to the town government because local citizens and political voices all the way up to the state governor were aligned in opposition.

Now we turn to the proposed Quantica Data Center Project in the small town of Broadview, Montana. Just like we saw in Nottingham, the Quantica Project is causing a conflict between the corporate developer and local residents.

But Broadview is a darker mirror image of Nottingham: local resistance to the Quantica project has faced official silence, procedural barriers, threats of removal from official meetings by law enforcement, and open hostility from some local officials.

But determined women like Cari Olson, Anne Hedges, and Kassi Solberg persist in their search for answers.

In this article, we apply the StrictQuality.AI standards of fairness and evidence-based analysis to understand the dramatic and high-stakes conflict over the proposed Quantica Data Center.

First we describe the massive size of the project. Then we unpack:

• The developer’s efforts to win over local support.

• The real-world constraints driving local officials and the Broadview Town Council.

• The concerns and activities of the opposition.

• The dramatic conflicts in Town Council meetings and community forums.

• The latest project status.

• Takeaway lessons for undecided local residents, data center opponents, local government, and data center developers.

If you like evidence-based reporting like this, please consider subscribing to StrictQuality.AI so you will be notified about new posts.

Subscribe now

The Quantica Project

Managed locally by Big Sky Digital Infrastructure, the Quantica data center project was initially planned as a 5,000-acre campus, roughly the size of 3,800 football fields, and expected to consume between 500 and 1,000 megawatts (MW) of power.

In May 2026, local reporting revealed public emails and records showing Quantica Infrastructure filed formal interconnection applications with NorthWestern Energy proposing a maximum additional capacity of up to 7,235 MW across multiple phases. An expansion of this scale would exceed the total electrical capacity of America’s largest power station, the Grand Coulee Dam, and instantly elevate the Montana facility to the upper echelon of worldwide data infrastructure hubs. To ensure exceptionally reliable, continuous, high-quality electricity that data centers need for non-stop operation, the proposal also includes a massive mix of renewable energy, battery storage, and potentially 1,785 MW of natural gas-fired plants.

Sources indicate that Quantica has not yet established a direct connection with a specific tech company to be its client for the proposed Broadview data center. When asked about who will actually use the facility, local Quantica lobbyist Jess Peterson stated that the public will know more “when Quantica signs a contract with a tech company to use the facility”.

As of this writing, much remains unclear about the Quantica project, including:

• Final project scale,

• Actual customer,

• Binding utility cost allocation,

• Enforceable water-use limits,

• Emissions profile,

• Ratepayer exposure, and

• Which public bodies have meaningful authority over approval or conditions.

Quantica Builds Local Goodwill

Quantica’s CEO, John Chesser, said, “we’re paying our own way for power, without increasing Montanans’ electricity bills for our project. We’ll pay the cost of electricity infrastructure and any new power generation needed to serve our project”.

Quantica’s Chief Financial Officer Charlie Baker, a Billings resident with 20 years in the Montana power industry, has publicly promised that Quantica will pay for the massive electrical grid upgrades required for the 7,235 MW expansion. He also said that, depending on the customer and infrastructure availability, the data center would use a closed loop cooling system to reduce water impacts on the community.

Meanwhile, Peterson claims the project will be “a data center done right” with minimal land and water impact, promising permanent, good-paying jobs for security guards, janitors, and maintenance workers.

To build goodwill in Broadview, Quantica’s activities include:

• Holding weekly office hours inside a newly renovated garage belonging to a town council member.

• Funding school sports concessions.

• Buying livestock from the local 4-H club.

• Flipping burgers at the community’s summer festival.

• Suggesting a “community benefits package,” which could help Broadview pay for things like its problematic wastewater lagoon.

Despite these activities and assurances from corporate executives, environmental advocates are urging the town to adopt policies to protect local families and small businesses from the data center impacts.

The Town

Broadview, Montana is a very small town, roughly 130 acres and a population of around 140 people. Its wastewater lagoon is failing; partnering with a wealthy developer may appear to town officials to be one of the few realistic ways to secure the potentially millions of dollars of critical infrastructure funding that the town could otherwise not afford.

Broadview has zoning power over land within the town and the volunteer Town Council has authority to decide special use and zone-change applications. However, the proposed data center site lies beyond municipal boundaries, and council members have argued that they have no authority over whether the project can proceed.

According to the New York Times, Mayor Roger Swartz said the council “was not legally obliged to answer the public’s questions about the project”.

The central question remains: even if Broadview lacks formal authority over the data center site itself, what responsibility does the town have to answer residents’ questions about a massive project close enough to affect town infrastructure, residents, politics, and public trust?

The Opposition’s Concerns

While local officials and some small business owners see a lifeline, others see an existential threat to their community.

Broadview area resident Cari Olson said she’s concerned about water use, noise, and tax incentives for large tech companies.

Environmental advocacy groups warn the facility will require immense amounts of water for cooling and could force local utilities to rely on aging fossil fuel infrastructure. They also have concerns that these massive utility upgrades will cause public health issues and energy bills to skyrocket for everyday Montana ratepayers (Link 1, Link 2). These groups are pushing for regulation and protective policies, such as:

• Transparency measures that require developers to disclose their actual resource usage and emissions.

• Separate utility rate classes to ensure data centers pay for grid expansions without subsidizing costs onto local families and small businesses.

However, Quantica lobbyist Peterson asserts that much of the local apprehension stems from a “general fear of artificial intelligence”.

The Resistance Begins But Residents are Divided

The local resistance began on January 4, 2026, when Cari Olson started hosting events to alert the community to the data center developments and local residents saw a Facebook video. This sparked a public forum at Rocky Mountain College, attended by a packed crowd of over 100. The venue quickly filled to capacity, forcing an overflow crowd to gather out in the corridor just to catch what the speakers were saying.

Hosted by the Montana Environmental Information Center, the forum’s goal was to educate locals on the massive utility demands and resource footprints associated with the recent wave of data center developments in Montana.

Anne Hedges, the Executive Director of the Montana Environmental Information Center, helped host the forum. She commented on the intense community pushback against the developments, stating, “People are very upset. We are seeing a response that, in my 32 years, I’ve never seen”. Hedges displayed a letter of intent from NorthWestern Energy, the power provider, regarding the Quantica project. While presenting the heavily censored letter, she highlighted the lack of transparency by telling the audience that about the only thing not redacted “is the language that says, ‘This is confidential’”.

Clint McCulloch, the president of the Southeast Montana Building Trades Council, was in the audience and he was in favor of the Quantica project, saying “There is a workforce right here in Yellowstone County and Montana that can work and build that facility.”

Also in the audience was Kassi Solberg, a 43-year-old mother of six who, with her family, had just moved near the proposed site. Solberg sat taking detailed notes, learning for the first time about the staggering water and electricity requirements needed to cool and power the massive data servers.

Quantica executives were sitting near Solberg; chief financial officer Charlie Baker and local lobbyist Jess Peterson were only a short distance down the same row. Baker and Peterson, according to reports, silently listened.

Solberg noticed the Quantica company logo emblazoned on Baker’s jacket.

Kassi Solberg became a driver of the opposition to the data center. Saying “I’m just trying to warn everyone about it,” she consistently attended Broadview town council and Yellowstone County Commission meetings to demand transparency, accountability, and a temporary halt to the development. She explained, “I hope I will be able to make a difference”.

However, she was met with legal paralysis, defensiveness, confrontation, and active hostility.

The Confrontation

When Solberg attended Broadview Town Council meetings to demand information and transparency about the data center project, Council members refused to answer her, citing non-disclosure practices and a lack of local jurisdiction.

Mayor Roger Swartz flatly refused to answer her questions, declaring that the town’s lawyer said the council “was not legally obliged to answer the public’s questions about the project”. Mr. Swartz also rejected Solberg’s appeal for a community-wide information session on the project because the land lies beyond municipal boundaries. When Solberg continued to push for transparency, Mayor Swartz warned that he would have the sheriff remove her.

During the meeting, an incensed council member stood up abruptly, mere inches from Solberg’s face, and ordered her to exit. Solberg was also informed by a council member, the sister of Mayor Swartz, “You can’t stop it”, referring to the Quantica data center.

Environmental Stakes Escalate Dramatically

Seeking time for the community to study the proposed data center’s massive environmental impacts, Solberg petitioned the Yellowstone County Commissioners for an emergency interim zoning ordinance to temporarily freeze development.

In March 2026, the commissioners officially struck down her request. Instead, they handed her an impossible bureaucratic escape hatch, suggesting she pursue a citizen-led special zoning ordinance. A special zoning ordinance is a citizen-initiated zoning area designed to protect the health, safety, and welfare of county residents. This mechanism shifts the entire multi-million-dollar burden of regulatory drafting onto an untrained citizen.

Solberg is now the primary grassroots organizer leading the effort for this alternate route, which shifts the burden onto her to independently formulate the zoning regulations and obtain signatures from a minimum of 60 percent of the property owners with parcels adjacent to the proposed facility. Despite lacking formal training in the law, she frequently works well into the early morning hours to piece together these technical regulatory documents by herself. To evaluate the evolving zoning drafts and secure the required signed authorizations for the ordinance, Solberg organizes digital meetings every week for the 20 adjacent property holders.

Solberg says, “I started petitions in all four counties that make up Broadview because of this … we don’t know where they’re going”. The petition for the special zoning ordinance has faced significant hurdles, including apathy from local residents. However, Solberg is supported by residents whose livelihoods depend on local resources, such as Tim Wipf, president of a local Hutterite farming colony near Broadview, and Rick Eaton, a local knife maker and horse rancher. Mr. Wipf has formally appealed to county commissioners out of concern that the facility would deplete their shared agricultural aquifer. He expressed frustration with the attitude of the developers, saying, “These people do think we are stupid”. Mr. Eaton attended a Yellowstone County hearing to formally support Kassi Solberg’s request for an interim zoning ordinance to pause the data center development.

Upcoming Events

There is another Town Hall meeting scheduled for June 18, 2026 at the Broadview Community Center. A community panel has been organized to discuss what the massive AI facility means for Yellowstone County.

Concluding Takeaways

The Quantica dispute is a case study in what happens when a project with national-scale energy implications enters a small community whose institutions were not built for that level of technical, financial, and political pressure.

Data center projects in other communities will have their own unique circumstances. What is right for those towns might not be the same as for Broadview. Nevertheless, Broadview’s conflict shows four things that may be similar across venues:

1. Residents should have a right to ask for and get clear answers before a project of this scale becomes irreversible.

2. Local officials may be trying to solve real infrastructure problems with very limited tools.

3. Developers should work to earn local goodwill and trust, particularly when the core facts about power, water, customers, costs, and legal accountability remain unclear to the public.

4. Data center opponents need passion, credible evidence, and legal pathways that do not collapse under administrative burdens.

In addition to these four general observations, StrictQuality.AI suggests specific takeaways for undecided residents, data center opponents, local government, and data center developers.

For Undecided Residents

If you are a resident who does not know what to think yet, that uncertainty is reasonable. A project this large should not be reduced to either “jobs and investment” or “corporate threat.” The right first step is to separate confirmed facts from promises, assumptions, and fears.

Residents should ask basic, concrete questions.:

• How much electricity will the project require at each phase?

• Who pays for transmission, generation, and grid upgrades?

• What water will be used, from what source, and under what limits?

• What happens if the project expands?

• What company will ultimately use the data center, and what does that customer’s expected workload imply for the project’s scale, power demand, water use, and future expansion?

• What agreements are confidential, and why?

• Which local, county, state, and utility regulators actually have authority?

These are the minimum questions any community should ask before accepting a project that could permanently change land use, infrastructure, public finance, and local trust.

For Data Center Opponents

The Broadview opposition has already shown the importance of early public attention. One resident’s Facebook video helped move the project from obscurity into public debate. That matters. Large infrastructure proposals often proceed most quickly when ordinary residents do not yet understand what is being planned.

But opposition also has to become more than alarm. The strongest resistance is evidence-based, specific, and procedurally disciplined. That means documenting claims, identifying the correct decision-makers, requesting records, showing up consistently, and translating public concern into lawful mechanisms that government bodies must address.

Opponents should also be careful not to overstate what is known. If the evidence shows uncertainty, say uncertainty. If the concern is potential cost shifting, water depletion, emissions, or lack of transparency, name the concern precisely.

Credibility is one of the most important assets residents have when facing a developer who has money, lawyers, lobbyists, and access.

For Local Government

Local government officials in small towns are often placed in an impossible position. They are expected to evaluate projects with massive technical complexity while working with limited staff, limited budgets, and limited legal capacity.

But limited resources do not excuse poor public process. Silence, defensiveness, threats, and hostility are governance failures when residents are asking basic questions about a project that could reshape their community. Even when officials believe they lack jurisdiction, they still have a responsibility to explain what they know, what they do not know, what they are legally allowed to say, and which public bodies do have authority.

The Broadview case points to a practical lesson for governmental institutions in other communities: do not wait until the developer arrives. Towns and counties should establish data center review standards before a specific project creates pressure. Those standards should address disclosure, utility cost allocation, water use, emergency services, land-use compatibility, tax impacts, public meetings, conflict-of-interest rules, and conditions for expansion.

For Data Center Developers

Developers should take the Broadview conflict seriously. Office hours, school concessions, livestock purchases, or offers to help fund wastewater repairs are useful gestures, and the office hours may have provided some residents with answers. But they do not resolve the larger public-record problem if key facts about project scale, utility impacts, water demand, emissions, land use, customer identity, and financial responsibility remain unclear or unenforceable.

The most important question for developers is whether they can build public legitimacy before distrust hardens. Once residents conclude that information is being withheld, that local officials are aligned against them, or that the project is already inevitable, the developer is managing a legitimacy crisis as well as a permitting process.

A data center “done right” requires clear disclosures, enforceable commitments, independent verification, and a public process that treats skeptical residents as stakeholders rather than obstacles.

The Broader Lesson

The AI economy may be global, but its infrastructure footprint is local. When a data center lands in a small town, residents often are asked to bear the consequences without enough information, legal authority, resources, and voice to decide what kind of future they are being asked to accept. In hundreds of communities across the country, people are saying this is not acceptable and they are organizing in opposition.

The Broadview dispute highlights challenges for people and institutions trying to manage the personal, environmental, institutional, economic, social, and systemic impacts of advanced AI. How will the risks, costs, and benefits be distributed?

There are at least two distinct but complementary approaches that are clearly articulated by two nonprofits doing excellent work on our future life in the Age of AI.

In the words of nonprofit Windfall Trust,

“Down one path lies extreme inequality — untold prosperity flowing to the hands of the few, while many lose their livelihoods, dignity and bargaining power.

Down the other path lies a profound opportunity — the chance to design an economy where everyone can thrive”.

As the nonprofit Future of Life Institute (FLI) puts it:

“If properly managed, these technologies could transform the world in a way that makes life substantially better, both for the people alive today and for all the people who have yet to be born. They could be used to treat and eradicate diseases, strengthen democratic processes, and transform education.

If improperly managed, they could do the opposite. They could produce catastrophic events that bring humanity to its knees, perhaps even pushing us to the brink of extinction.”

I hope you will consider where you stand on the questions they are raising, and think about the decisions that will shape your role in how our future develops.

If you like evidence-based reporting like this, please consider subscribing to StrictQuality.AI so you will be notified about new posts.

Subscribe now

Leave a comment

Within days, local investigative news surfaced the plan.

Residents quickly mobilized against it.

However, New Hampshire’s laws and pro-AI state legislators denied Nottingham and other towns the authority to regulate data center projects.

This episode tells the dramatic story behind the struggle over the proposed Nottingham data center. It is an interesting conversation with important lessons for communities and developers across America who are part of our AI infrastructure build-out.

Click the player above to listen.

If you want more reporting and conversations like this, please subscribe to StrictQuality.AI so you will be notified when the next episode in this series is released. We’re also available on Spotify and Apple Podcasts.

Subscribe now

And please leave a comment below:

Should towns have more authority to regulate AI data centers in their community?

Should states protect AI infrastructure development from local restrictions?

Should the federal government regulate it instead of states and local communities?

About this podcast:

• A person wrote the script based on an article published in StrictQuality.AI

• The published article’s writing, research, and fact-checking was done by a person.

• AI-assistance was used for research and editing.

• Podcast voices were generated by AI.

Leave a comment

Across the U.S., local communities are resisting local AI data center projects.

A national Gallup survey from March 2026 found that more than 70% of Americans oppose local construction of AI data centers.

According to the research organization Data Center Watch, $64 billion worth of projects were blocked or delayed across the U.S. between May 2024 and March 2025. For example, in March 2026 a proposed data center near Apex and New Hill, North Carolina was withdrawn by the developer after organized community pushback. The town of Nottingham New Hampshire went through a similar struggle in May of 2026.

But the Gallup survey also found about 25% of Americans support building AI data centers in their community.

Clearly, the lessons from one community’s experience cannot by themselves prove that another data-center proposal is good or bad.

In a series of articles, StrictQuality.AI is examining the different experiences of two towns: Nottingham, New Hampshire, and Broadview, Montana.

In an upcoming article, we’ll examine the Quantica Data Campus Project in Broadview. Residents there are not unified in their views about it; project opponents face dismissive, even hostile, pushback from local government and the developer’s lobbyist.

But first we go to Nottingham, where residents, with support from their local politicians, successfully mobilized in May 2026 against a proposed data center.

If you like evidence-based reporting like this, please consider subscribing to StrictQuality.AI so you will be notified about new posts.

Subscribe now

Why a Data Center in Nottingham and How Did the Community Get It Stopped?

Located in the Seacoast region of New Hampshire, Nottingham was incorporated in 1722 and is known as the first town in America to make recycling mandatory.

Tom Moulton is a Seacoast businessman, award-winning entrepreneur, and developer. He owns the 100-acre Nottingham Business Center or Nottingham Business Park. Moulton purchased the land in 2021 for $900,000 after the town of Nottingham seized it in 2017 due to non-payment of taxes by the previous owner, USA Springs.

The site houses an existing 200,000-square-foot warehouse, but there are no businesses currently operating on the property.

April 29, 2026

Moulton signed a preliminary conceptual application with the town of Nottingham to convert his Nottingham Business Park warehouse into a data center. In addition to his broader belief that data centers are necessary for the future, he highlighted a few specific reasons for pursuing the project at the Nottingham site:

• He already owns the land and infrastructure: His plan was simply to convert the existing building, meaning no new construction was envisioned for the site.

• The location is isolated: Moulton pointed out that the property is “fairly remote” and reasoned that a facility there “shouldn’t interfere with population centers”.

• It would generate local revenue: He argued that once the facility was operational, it would provide more taxes for the town of Nottingham.

Additionally, Moulton was exploring this development “on spec“, meaning he did not have a specific tech firm lined up as a tenant. Instead, he chose to evaluate his own property’s potential to host a future client, expecting to personally lay out tens of millions of dollars to develop it.

May 20, 2026

The investigative news site InDepthNH.org published an article revealing the plans, acting as a catalyst for public awareness.

Backlash spread virally through grassroots channels, social media, and community networks.

Critics of the project argued that industrial runoff or chemical discharge from the facility could threaten the Lamprey River Watershed and surrounding lakes. In addition to the Lamprey River Watershed, the proposed data center could impact several other critical environmental features in the area:

• The Local Aquifer and Private Water Supply: The massive volume of water required to cool the data center would place an extreme draw on the area’s aquifer. Because almost all residents in Nottingham rely on private wells for their water, there is immense concern that the massive amount of water needed to cool the data center systems would deplete this shared resource. According to local resident Rudy Pavlesich, this would be a significant problem because some locals already had to drill deeper wells to access water.

• Sensitive Wetlands: The 100-acre property where the facility is proposed contains 30 to 40 acres of highly sensitive wetlands. Much of the land at the site cannot be developed due to the presence of these ecological features.

• Surrounding Lakes: Along with the Lamprey River, the surrounding local lakes are threatened by potential pollution from industrial runoff or the release of chemical-containing wastewater generated by the facility’s cooling systems.

• Air Quality and Fire Risk: Environmental concerns also extend to the air and surrounding forests, with protesters raising alarms over the potential for toxic fumes and wildfires.

The potential severity of environmental degradation was one of the primary reasons residents vehemently opposed the data center development. An online petition rapidly garnered over 25,000 signatures.

Brad Weit, a Newmarket resident and outdoorsman, started the online petition against the proposed data center because he was deeply concerned about the toll that artificial intelligence demands could take on the nation’s power grid and energy supply. By building these facilities, Weit said, society is “sacrificing our real, physical environment for a virtual one“.

The petition quickly gained traction, collecting over 25,000 signatures and becoming a central rallying point for broader community pushback against the project. Weit explained,

“I really did not think the petition would get so much attention, but seeing people come together despite political differences has made me very proud of my community.”

Through the petition and subsequent protests, residents voiced several specific objections to the proposed AI data center, including:

• Massive Resource Consumption: Residents and officials expressed deep concern over the tremendous electricity and water usage required to power and cool an AI data center. Many feared it would severely strain local private well water supplies.

• Spiking Utility Costs: Opponents warned that necessary upgrades to the regional electrical grid would drive up monthly electric bills for local consumers.

• Environmental Degradation: The 100-acre site contains highly sensitive wetlands, leading to fears that industrial runoff, chemical discharge, and noise pollution could threaten the Lamprey River Watershed, local lakes, and residential property values.

• Lack of Economic Benefit: Despite tens of millions of dollars in development costs, the highly automated facility would create very few permanent on-site jobs. Developer Tom Moulton estimated that despite costing tens of millions of dollars to build, the facility would be highly automated and therefore only generate a “few on-site jobs once online”.

• Regulatory Voids: Critics highlighted that New Hampshire currently lacks statewide definitions and regulations for data centers, and recent legislative efforts to grant towns local zoning authority over them were defeated by pro-AI legislators.

State Rep. Paul Tudor, Northwood Republican & former GE engineer, emphasized that the overwhelming pushback was coming directly from genuine local residents, rather than from automated mass emails. He pointed out:

“I heard nothing good from any constituent about this... A lot of times I’ll get emails that are, you know, chain emails that are just being dumped to the Legislature, but these were all local people. And there wasn’t one person that was for this.”

New Hampshire Governor Kelly Ayotte strongly opposed the proposed AI data center in Nottingham, reflecting high-level political resistance to the project. In a statement provided to Seacoastonline on May 26, 2026, she criticized the development of data centers in the state:

“Energy costs are already too high in New Hampshire, and data centers have the potential to cause them to skyrocket. It makes no sense to site data centers in our state without more energy to support them, and we have to give local communities a say in this process. Without additional energy sources, I can’t support building data centers here.”

May 27, 2026 (Afternoon)

Hours before a scheduled planning board meeting, Moulton’s legal counsel withdrew the application “without prejudice”, meaning he can legally revisit the project later.

Moulton discussed the community pushback to the Nottingham data center (Link1, Link2):

“I kind of didn’t expect this at all, to be honest with you. It’s kind of taken on a life of its own. I’m very surprised, a little disappointed.”

“We had to stop and say, OK, what are they concerned about, and let’s try to get some answers to some of these concerns. Frankly, if we can’t satisfactorily address them in a reasonable fashion, then we’ll go and do something else.”

“I know there’s a lot of controversy or angst about data centers... I think it’s a natural fact of business and of life that this is where it’s going.”

State Rep. Scott Bryer, Northwood Republican who attended the Nottingham planning board meeting, said he had received numerous calls and emails from constituents regarding the data center proposal. To emphasize the clear and united message he received from the public, which he interpreted as a definitive rejection of the data center, he said, “This community does not want this.”

May 27, 2026 (Evening)

Anticipating large crowds, the town moved the meeting to a local school gymnasium. Despite the project’s withdrawal, hundreds of protesters still gathered to voice their opposition to the data center.

Adam Whittier is a Manchester resident who attended the protest outside the Nottingham School. He expressed his apprehension about the unregulated and rapid expansion of artificial intelligence, in addition to his concerns regarding the data center’s potential impact on the environment and electricity prices:

“There is no situation in which I want a data center in this state. Aside from the very real physical resource issues … I think we need to have more frank discussions about AI before we start building these things. I think there are only reasons not to do this right now.”

Christine Dellas, Raymond resident and protester outside the Nottingham School, responded to the developer’s claim that he would do more research to address the community’s concerns. She emphasized that no amount of further research or discussion would change her mind, and that she simply wanted the project stopped:

“I mean, I get that we all use social media, we all use, you know, (data) storage and everything else. But I think this is primarily to support AI, which I’m not sure why we need.”

“I don’t want it built, period. I don’t know if there’s really much to discuss.”

The Current Status of the Nottingham Data Center

There is no active data center development application currently pending with the town.

When developer Tom Moulton withdrew his conceptual application on May 27, he did so “without prejudice,” meaning he legally retains the right to bring a modified proposal back to the town at a later date. It is entirely possible that the proposal to develop the Nottingham Data Center will be re-submitted, though it is not guaranteed.

Moulton has stated he will continue researching and evaluating the property to see if he can adequately address the public’s widespread concerns regarding noise, water, air, and environmental impacts before returning to the planning board with appropriate answers. He explained,

“ I think all those concerns are valid: Noise, water, air, and the impact on the environment, and that’s why we’re taking the time to evaluate with facts, not opinions, what we need to do to address those concerns for the public... The reality is we’re gonna need these places for the future. If not New Hampshire, where?”

He currently has no timeline for when he might reintroduce the idea.

Crucially, Moulton also admitted that if he cannot find a way to “satisfactorily address” the community’s concerns “in a reasonable fashion,” he will abandon the data center idea and “go and do something else”.

Despite the temporary withdrawal, many local protesters remain vigilant, feeling that the project has not been “decisively averted”. This apprehension is amplified by the fact that, due to the failure of NH Senate Bill 439, Nottingham currently lacks the state-enabled zoning authority to legally block a future data center proposal if Moulton ultimately decides to resubmit his application.

Pulling the Rug Out from Under Senate Bill 439

Senate Bill 439 (SB 439) was a legislative effort introduced by State Sen. Debra Altschiller and supported by State Rep. Hope Damon to address a major regulatory void: currently, New Hampshire law does not have a legal definition for data centers. Because the state requires "enabling statutes" before towns can address specific zoning issues, municipalities currently have no legal authority to create local ordinances or set guardrails on data center projects. SB 439 aimed to finally give local communities the regulatory power they needed.

However, when the bill went through the House Committee on Municipal and County Government, “pro-AI legislators” and data center advocates introduced a divisive amendment that explicitly limited towns' abilities to locally regulate data centers, completely gutting the bill's original purpose and making it easier for developers to build them in the state.

Because the amendment reversed the bill's protective intent, SB 439 was ultimately defeated by a decisive vote in the House of Representatives earlier this year.

Currently, New Hampshire has no legal definition for data centers on the books. As State Rep. Hope Damon explained, "We don’t have anything that enables the municipalities to put regulations in place, and the state doesn’t have any regulations". Damon, a Croydon Democrat, said local municipalities like Nottingham do not have the legal authority to create their own zoning ordinances or establish guardrails to manage these types of projects.

The failure of SB 439 leaves the town with virtually no legal authority to regulate or stop the facility. As a direct result of this regulatory void, even if Nottingham’s officials and residents vehemently oppose developer Tom Moulton’s proposed data center, there is little they can legally do to stop it through local zoning ordinances.

This vulnerability has prompted opponents of the Nottingham project to call for new statewide rules. For example, local petition organizer Brad Weit publicly stressed the urgent need to pass legislation to address similar data center proposals across New Hampshire in the future.

New Hampshire politics is searching for the right regulatory mix. State Sen. Howard Pearl, Loudon Republican who represents Nottingham as part of Senate District 17, is emphasizing the need to strike a balance between regulating new data centers and protecting local utility ratepayers. He noted that the energy demands of data centers should be offset by private investments in energy infrastructure so that Granite Staters are not burdened with subsidizing the electrical costs:

“New Hampshire should absolutely be open to large AI and cloud computing data centers and next generation digital infrastructure, but growth must be responsible and sustainable. Our state already faces some of the highest energy costs in the country. Granite Staters should not be forced to subsidize massive new electrical demand from large AI and cloud computing data centers.”

Takeways

The Nottingham Data Center Struggle highlights the importance of:

• Local investigative journalism. If they hadn’t broken the story about the data center proposal, the community might not have had the time or knowledge required to mobilize, voice their concerns, and successfully halt a major industrial development before it advanced further.

• Local community activism. The scale of grassroots opposition was clearly heard by local officials and directly forced developer Tom Moulton to withdraw his proposal just hours before a scheduled planning board meeting.

• Local and state-level political support. Local political leaders serve as amplifiers for grassroots activism.

• Evidence-based regulatory processes governing data center development. Relying on concrete evidence shifts the conversation from emotional reactions to objective problem-solving. Factual, scientific evaluations are crucial to prevent irreversible ecological damage, infrastructure strain, and unfair costs while planning for sustainable growth.

• Legislative reform relevant to the Age of AI. In many regions, the rapid advance of artificial intelligence has significantly outpaced existing legal and regulatory frameworks. Without state-level legislative reform, local municipalities are left completely legally defenseless, lacking the authority to enact local zoning ordinances or set guardrails on AI projects.

Generally, the lessons from a community’s response to a local data center proposal can be useful guides for other towns, teaching:

• What questions to ask,

• What documents to examine,

• What risks to test, and

• Where public trust can break down.

Local governments and residents will be asked to make decisions about infrastructure whose effects may extend beyond the project site. These decisions will require evaluating power demand, water use, utility costs, backup generators, noise, emissions, emergency services, grid upgrades, ratepayer exposure, and long-term community compatibility. Technical evidence will be more effective than generalized objections.

A town may welcome technology investment and still need enforceable rules for diesel generators, cooling systems, building scale, and grid impacts. Local governments must avoid being trapped by outdated zoning categories.

A developer may have a promising site on paper and still face a serious project risk if residents believe the proposal’s operating burdens were not disclosed clearly or early enough. Developers need to treat community consent and municipal readiness as core project risks, not merely public-relations issues.

Nottingham’s experience with the proposed data center has useful lessons for any town in a similar situation, but the unique circumstances in another community will significantly affect attitudes and outcomes there. The details will differ from place to place, but the core challenges will likely be similar everywhere.

Coming Soon from StrictQuality.AI

Our upcoming article in this series examines the Quantica Project in Broadview, MT, where the community is divided about a proposed 5,000 acre data center campus. Opponents to the project are not getting traction at all and are being treated dismissively by local government and industry representatives.

We’ll unpack the Quantica data center project next time.

If you like evidence-based reporting like this, please consider subscribing to StrictQuality.AI so you will be notified about new posts.

Subscribe now

Leave a comment