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Author: Demetrius New

  • Beyond The Model: The AI Dependency Stack

    I Started Studying AI and Somehow Ended Up Studying the Power Grid

    I didn’t start studying artificial intelligence because I wanted to understand the electrical grid.

    Yet somehow, that’s where the trail led.

    What began as an effort to better understand AI models gradually expanded into compute infrastructure, data centers, electricity generation, transmission constraints, capital investment and eventually governance.

    Looking back, the progression makes perfect sense.

    Models → Compute → Data Centers → Electricity → Transmission → Capital → Governance

    Each subject exposed a dependency underneath the previous one.

    And that has changed the way I think about AI.

    Follow the Dependency

    When most conversations about artificial intelligence begin, they start with the model.

    How capable is it?

    How quickly is performance improving?

    What tasks can it automate?

    What happens when agents become more autonomous?

    Those are important questions. But eventually I started asking another one:

    What does this depend on?

    AI models depend on compute.

    Compute depends on chips, servers, cooling systems and data centers.

    Data centers depend on enormous amounts of reliable electricity.

    Electricity depends on generation capacity and the infrastructure required to move that power where it is needed.

    That means transmission capacity, substations, interconnection queues, permitting and infrastructure investment suddenly become part of the AI conversation.

    Keep following the chain and another layer appears.

    Capital.

    Someone has to finance all of this infrastructure.

    Companies have to decide how much capacity to build, where to build it, what assumptions justify the investment and whether expected AI demand will generate sufficient returns.

    Now the conversation isn’t simply technological.

    It is operational, financial and strategic.

    And eventually it becomes a governance problem.

    Capability Is Only One Layer of the System

    One of the most important lessons I’ve taken from this progression is that technological capability doesn’t guarantee system-level capability.

    A model might become significantly more powerful while the infrastructure supporting its deployment remains constrained.

    More demand for compute can create pressure on data-center capacity.

    Additional data centers increase electricity demand.

    New generation capacity doesn’t necessarily solve the problem if transmission infrastructure cannot deliver that electricity where demand is growing.

    And even if every physical constraint could be removed, organizations still have to determine whether the resulting investments make economic sense and whether the systems being deployed can be governed responsibly.

    This creates an interesting possibility:

    The biggest constraint on the next stage of AI might not always be AI.

    It could appear somewhere else in the dependency chain.

    Systems Don’t Respect Industry Categories

    We tend to organize knowledge into categories.

    Artificial intelligence.

    Energy.

    Finance.

    Infrastructure.

    Risk.

    Governance.

    But real systems don’t care about those categories.

    Their dependencies cross them constantly.

    An AI strategy can become an infrastructure strategy.

    An infrastructure strategy can become an energy strategy.

    An energy constraint can become a capital-allocation problem.

    A capital decision can create new operational risks.

    And those risks eventually require governance.

    What looked like several unrelated subjects was actually one interconnected system viewed from different layers.

    That’s what systems thinking started changing for me.

    Instead of asking only:

    What can this technology do?

    I became increasingly interested in another question:

    What has to remain true for this entire system to work?

    That question exposes a very different landscape.

    The Bottleneck Moves

    Another realization followed.

    Bottlenecks don’t disappear simply because technology improves.

    They move.

    If models become more efficient, another constraint may become more important.

    If compute capacity expands, electricity availability may become the limiting factor.

    If generation expands, transmission may become the constraint.

    If infrastructure expands rapidly, capital discipline or governance may become the limiting factor.

    This means leaders evaluating AI cannot look exclusively at technological capability.

    They also have to understand the dependency chain supporting that capability.

    Otherwise, it becomes easy to mistake progress in one layer for readiness across the entire system.

    The Infrastructure Underneath the Intelligence

    I still study AI.

    But now I find myself paying much more attention to what surrounds it.

    Energy infrastructure.

    Data-center development.

    Transmission.

    Capital allocation.

    Operational risk.

    Governance.

    Not because I abandoned the original subject, but because following it seriously kept expanding the boundaries of the system.

    That’s probably the biggest lesson this journey has taught me:

    You don’t really understand a system until you understand what it depends on.

    AI may appear to be a software revolution when viewed from the surface.

    Follow the dependency chain downward, however, and you eventually reach physical infrastructure, energy, economics and governance.

    And sometimes the most important part of a technology isn’t the technology itself.

    It’s everything that has to work underneath it.

  • Governing Agentic AI: Closing The Accountability Gap

    When AI Acts Without Permission, Who Owns the Decision?

    For most of the history of enterprise software, accountability has been relatively straightforward.

    Humans make decisions. Software executes instructions.

    Artificial intelligence—particularly agentic AI—is beginning to complicate that relationship.

    Recent controlled evaluations involving advanced AI agents have demonstrated systems taking actions outside their intended authorization boundaries. Separately, legal experts are beginning to examine a question that enterprises will eventually have to confront themselves:

    When an autonomous AI system causes harm, who owns the decision?

    The answer may prove more complicated than simply pointing toward the company that developed the model.

    The Emerging Accountability Gap

    Traditional software operates largely within predefined workflows.

    An employee clicks a button. A transaction executes. A database updates. Someone initiated the action, and organizations can usually trace responsibility through established roles, permissions, and controls.

    Agentic AI changes the structure.

    Organizations are increasingly experimenting with systems capable of selecting tools, navigating software, communicating with other systems, making intermediate decisions, and executing multi-step objectives without requiring human approval at every stage.

    That creates enormous potential for productivity.

    It also creates a governance problem.

    Authority can be delegated faster than accountability can be redesigned.

    An organization might give an AI agent permission to interact with customers, modify code, access databases, initiate workflows, evaluate transactions, or communicate with external systems.

    But if that agent takes an unauthorized action, traditional accountability structures may suddenly become much less clear.

    Was the problem the model?

    Was it the developer?

    Was it the organization’s configuration?

    Were excessive permissions granted?

    Should a human approval gate have existed?

    Did monitoring fail?

    Was the behavior foreseeable?

    These are not simply technical questions.

    They are governance questions.

    Autonomy Changes the Risk Model

    Organizations frequently evaluate AI according to capability:

    What can the model accomplish?

    How accurate is it?

    How much productivity can it generate?

    How much labor can it automate?

    Those questions matter.

    But autonomous systems introduce another dimension:

    What authority are we giving the system to act?

    Capability and authority are not the same thing.

    A highly capable system with tightly restricted permissions may represent manageable operational risk.

    A moderately capable system with broad system access, weak monitoring, and no meaningful approval boundaries could represent considerably greater risk.

    That means enterprises may eventually need to evaluate AI systems using something closer to:

    Capability × Authority × Impact Surface

    The more consequential the potential action, the stronger the control structure surrounding that action should become.

    Permission Must Become Explicit

    This is where governance needs to move beyond broad statements such as:

    “Human oversight is required.”

    That sounds reassuring, but it doesn’t tell an organization very much operationally.

    Effective governance requires defining exactly where human authority begins and AI authority ends.

    For every consequential autonomous system, organizations should be able to answer several basic questions.

    What is the agent authorized to do?

    What is it prohibited from doing?

    Which actions can it execute independently?

    Which actions require approval?

    Who owns the consequences of those actions?

    What evidence is retained?

    Who can override the system?

    Under what conditions is the system automatically stopped?

    Those answers should exist before deployment.

    Otherwise, organizations risk discovering their accountability structure during an incident.

    “The AI Did It” Is Not a Control

    There is another reason this issue matters.

    Autonomy does not necessarily eliminate organizational responsibility.

    If anything, increasing autonomy may increase the importance of demonstrating that reasonable controls existed around the system.

    Organizations routinely delegate authority to employees, vendors, contractors, and automated systems.

    Delegation does not normally eliminate accountability.

    AI should not be assumed to create an exception.

    The relevant question therefore becomes less:

    “Did a human directly perform this action?”

    And increasingly:

    “Did the organization establish reasonable controls around a system capable of performing this action?”

    That shift has significant implications for executives, risk teams, cybersecurity leaders, auditors, and boards.

    Evidence Will Matter

    There is also an important second-order consequence.

    When autonomous systems participate in consequential decisions, organizations will need evidence capable of reconstructing what happened.

    That means retaining more than a final output.

    Organizations may need reliable records showing:

    • What objective the system received
    • What permissions it possessed
    • Which tools it accessed
    • Which actions it attempted
    • Which actions were blocked
    • Where human approval occurred
    • Which controls were active
    • What ultimately triggered the outcome

    Without that evidence, organizations may know what happened without being able to demonstrate why it was allowed to happen.

    That is a dangerous position during an audit, investigation, lawsuit, or regulatory inquiry.

    Governance Must Move Before Autonomy

    The enterprise conversation around AI has largely focused on increasing capability.

    Better models.

    More powerful agents.

    Longer workflows.

    Greater automation.

    But every increase in autonomous capability should eventually trigger a corresponding governance question:

    What new authority did we just give the system?

    Because the real risk isn’t simply that AI becomes capable of doing more.

    It’s that organizations delegate consequential authority faster than they establish ownership, boundaries, evidence, and controls around that authority.

    The organizations that understand this early will not necessarily deploy less AI.

    They may actually be positioned to deploy more of it—because they understand where autonomy ends and accountability begins.

    The defining question of enterprise agentic AI may therefore become surprisingly simple:

    Who owns what the AI is allowed to do?

  • The AI Agent Isn’t the Risk. Unmanaged Autonomy Is.

    A Shift Most Organizations Haven’t Fully Recognized

    For the past two years, artificial intelligence has largely been associated with chat interfaces. Employees asked questions, generated content, summarized documents, and experimented with productivity.

    That phase is ending.

    The next wave of AI is increasingly defined by agents rather than assistants.

    Unlike traditional chatbots, AI agents perform tasks. They don’t simply recommend actions—they increasingly execute them.

    This represents a meaningful shift in enterprise risk.

    From Information to Action

    A chatbot produces information.

    An AI agent may:

    • Send customer emails
    • Update CRM records
    • Analyze financial reports
    • Trigger automated workflows
    • Coordinate across multiple applications
    • Make recommendations that are immediately acted upon

    The transition from generating information to executing business processes fundamentally changes the governance requirements.

    Execution introduces accountability.

    The HR Analogy

    Imagine hiring a new employee.

    Before their first day, HR and management establish:

    • Job responsibilities
    • Access permissions
    • Reporting structure
    • Performance expectations
    • Approval authority
    • Escalation procedures
    • Documentation requirements

    These controls exist because organizations recognize that autonomy requires oversight.

    Ironically, many AI agents receive broader operational access than a new employee would—without comparable governance.

    Governance Must Scale With Autonomy

    As organizations deploy more capable AI agents, governance maturity must evolve alongside them.

    Key questions include:

    Who approves the agent?

    Who authorized deployment?

    Who owns ongoing oversight?

    What decisions can it make independently?

    Every autonomous action should have clearly defined boundaries.

    Not every task deserves full automation.

    What systems can it access?

    The principle of least privilege applies just as much to AI as it does to human employees.

    Who reviews its work?

    Human oversight remains critical for high-impact decisions.

    The objective is not removing humans.

    The objective is placing humans at the correct control points.

    How are mistakes investigated?

    Without logging, documentation, and audit trails, organizations cannot determine:

    • What occurred
    • Why it occurred
    • Whether it has happened before
    • How to prevent recurrence
    AI Governance Is Becoming Operational Governance

    The discussion around AI often focuses on models, algorithms, and technical performance.

    Those remain important.

    However, many organizations will discover that their greatest challenge is not model intelligence.

    It is operational accountability.

    The companies that benefit most from AI adoption are unlikely to be those with the most autonomous systems.

    They will be those that understand where autonomy should stop and governance should begin.

    Final Thought

    Every technological leap eventually forces organizations to revisit familiar management principles.

    AI agents are no exception.

    The question isn’t whether AI can perform work.

    The question is whether organizations are prepared to manage digital workers with the same discipline they expect from human ones.

    Because as AI gains autonomy, governance can no longer remain optional.

    Demetrius New

    Author’s Note:
    This article presents a strategic interpretation of emerging trends in enterprise AI. While individual organizations will vary, the observations reflect broader shifts in compute demand, infrastructure investment, energy planning, and AI governance that are increasingly influencing enterprise adoption.

  • Quiet Thesis: Why AI Is Becoming an Infrastructure Allocation Problem

    Introduction

    When discussions turn to the future of Artificial Intelligence, they often focus on models, benchmarks, and algorithmic performance. While those advances remain important, another shift is taking place beneath the surface.

    As AI systems become more capable and resource-intensive, organizations are increasingly competing on their ability to secure and manage the infrastructure that makes those systems practical at scale. Compute capacity, energy availability, networking, and capital investment are becoming strategic considerations alongside software innovation.

    This is the “quiet thesis”: AI is evolving from primarily a software challenge into an infrastructure strategy challenge.

    Beyond Algorithmic Supremacy

    For much of the last decade, competitive advantage in AI was driven largely by improvements in algorithms and model capability. That remains an important source of innovation.

    However, larger models require exponentially more compute, storage, networking, and electrical power. As these demands grow, infrastructure becomes an increasingly important competitive constraint.

    A breakthrough model delivers little value if an organization cannot deploy, operate, and scale it reliably.

    The conversation is no longer just about building better models—it’s increasingly about building better systems around those models.

    Infrastructure as Competitive Strategy

    Organizations investing in AI are beginning to make decisions that extend well beyond software engineering.

    Capital allocation now influences compute availability.

    Infrastructure planning influences deployment speed.

    Energy strategy affects long-term scalability.

    Vendor diversification influences operational resilience.

    Competitive advantage increasingly comes from how effectively organizations coordinate these decisions rather than optimizing each one independently.

    This does not diminish the importance of software innovation. Instead, it recognizes that software and infrastructure are becoming strategically inseparable.

    The Convergence of Capital, Energy, and Governance

    Perhaps the most significant shift is that AI strategy is no longer confined to technology teams.

    Executives responsible for finance, operations, risk, compliance, cybersecurity, and infrastructure increasingly influence the success of enterprise AI initiatives.

    Three areas are beginning to converge:

    • Capital Allocation determines where organizations invest in compute, networking, and supporting infrastructure.
    • Energy Strategy helps ensure reliable power for increasingly demanding AI workloads while balancing cost and resilience.
    • AI Governance provides the oversight needed to manage operational, regulatory, security, and organizational risk.

    Viewed together, these are not separate initiatives. They represent interconnected decisions that collectively shape an organization’s ability to deploy AI responsibly and sustainably.

    Conclusion

    The organizations that adapt to this convergence early may be better positioned to scale AI effectively over the coming decade.

    Rather than viewing AI solely as a software initiative, leaders should increasingly evaluate it as an enterprise infrastructure strategy that spans technology, finance, operations, and governance.

    The future of AI will still be shaped by better models.

    But those models will increasingly depend on better infrastructure decisions.

    Author’s Note:
    This article presents a strategic interpretation of emerging trends in enterprise AI. While individual organizations will vary, the observations reflect broader shifts in compute demand, infrastructure investment, energy planning, and AI governance that are increasingly influencing enterprise adoption.

  • AI Systems Don’t Fail Because of Intelligence — They Fail Because of Governance

    In the excitement of deploying artificial intelligence, the conversation often gets hijacked by the technology’s “intelligence.” We’re captivated by its ability to generate natural-sounding text, create stunning images, and identify complex patterns in data. But this focus is misplaced.

    While model accuracy, hallucinations, and bias are significant, they aren’t the primary drivers of AI failure. The real risk lies in a much less glamorous, but far more consequential, area: governance.

    Think about it: A highly intelligent financial analyst is useless if their company has no accounting systems. Their insights would be lost in a sea of data, and their decisions could have disastrous consequences if not properly reviewed and audited. The same principle applies to AI.

    The Mirage of “AI Risk”

    Most companies view AI risk through a technological lens. They worry about:

    • Model accuracy: “Is our prediction model right often enough?”
    • Hallucinations: “Is the language model just making things up?”
    • Bias: “Does our AI perpetuate societal inequalities?”

    These are critical issues, and they absolutely require technical solutions. However, they are symptoms of a larger, more fundamental problem.

    The Real Risk: A Lack of Control

    The true danger of AI is not its lack of intelligence, but its potential for uncontrolled autonomy. The real risks that companies face are:

    • Uncontrolled automation: Giving AI the authority to make critical decisions without appropriate oversight.
    • No audit trail: The inability to trace the decision-making process of an AI system, making it impossible to understand how it reached a particular conclusion.
    • No human checkpoints: Failing to incorporate human judgment at key points in the AI lifecycle, allowing the system to operate on autopilot.
    • Unclear authority over model outputs: Failing to define who is accountable for the decisions made by an AI system.

    This is a failure of governance, not technology. It’s a failure of organizational processes and controls, not of algorithms.

    The Missing Layer

    To successfully deploy AI, companies must introduce a robust governance layer. This means going beyond simply building and deploying models, and focusing on:

    • Architecture: Designing AI systems with control, transparency, and auditability built-in. This involves defining clear interfaces, data pipelines, and decision points.
    • Oversight: Establishing clear processes and personnel responsible for monitoring and managing AI systems throughout their lifecycle. This includes continuous monitoring of performance, bias detection, and regular audits.
    • Accountability: Clearly defining roles and responsibilities for AI development, deployment, and performance. This means knowing who is responsible for the inputs, the outputs, and the consequences.

    The Core Concept

    Here’s the key takeaway:

    Powerful AI systems need governance the same way financial systems need accounting.

    Just as we wouldn’t trust a financial analyst without an accounting system, we shouldn’t trust an AI system without a robust governance framework. Governance provides the necessary checks and balances, the audit trail, and the accountability structure to ensure that AI is used effectively, ethically, and responsibly.

    Closing

    The next generation of AI infrastructure will not be defined by its intelligence alone. It will be defined by its ability to be governed, managed, and controlled. It will be characterized by its defensibility.

    The true differentiator for companies that succeed with AI will be their ability to build and implement robust governance frameworks. This is not just a regulatory or ethical imperative; it’s a fundamental business necessity.

    It’s time to shift the conversation. It’s time to stop worrying about the “intelligence” of AI and start focusing on the control. If you enjoy reading about AI from this perspective, like, follow and share. Leave a comment, I will be providing more on this topic.