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The Empowerment Imperative: Rewriting Agentic Marketing from Extraction to Human Flourishing

In the Industrial Revolution, production became scalable. Advertising followed because it had to. If you can manufacture goods at scale, you need a way to manufacture desire at scale.

In the AI revolution, something stranger is happening: advertising itself is becoming software. Not a message. Not a campaign. A procedure running continuously, adapting to each person, rewriting itself after every interaction.

That shift has a name now: agentic marketing.

And it raises a question sharper than “is personalization good or bad?”

What objective function is the agent optimizing and what constraints does it live under?

Because the same machinery that could build a healthier society can also build the most efficient alienation engine we’ve ever created.

This article is about the fork in the road. And why we’re building the path less taken.

Contents

Part I: The Transformation

Part II: The Fork

Part III: The Practice

Part IV: The Movement

From Mass Messaging to Personalized Programs

Traditional advertising works like print.

You decide a message. You distribute it. You measure aggregate impact. Maybe you segment by demographics. Then you repeat.

The unit of work is the campaign. The optimization target is the audience. The feedback loop is measured in weeks.

Agentic marketing works like software.

The system observes a person’s context-signals, constraints, history, inferred state. It selects an action-creative, timing, offer, channel, framing. It measures the effect. It updates its internal model. It runs again.

That loop is continuous. The unit of work is the individual trajectory. The optimization target is behavior over time. The feedback loop is measured in milliseconds.

Once you see this distinction, a lot of modern marketing stops looking like persuasion and starts looking like control systems.

Not because someone decided to be sinister. But because that’s what happens when you give an optimization engine a simple objective and unlimited iterations.

Turing’s Gift: Making “Procedure” Precise

In 1936, Alan Turing wasn’t trying to invent computers. He was solving a problem in mathematical logic. But along the way, he gave us something more fundamental: he made the concept of “procedure” precise.

A procedure, Turing showed, is just:

  • A finite set of states
  • A memory medium
  • A transition rule that maps (current state + input) to (next state + output)
  • An interface with the world

That’s it. Anything that behaves like this can be studied as a machine. And once you can study it as a machine, you can ask machine questions about it: What does it compute? What can’t it compute? What happens if you run it long enough?

Agentic marketing qualifies.

The state is the platform’s evolving model of you. The memory is embeddings, profiles, session traces, cross-device graphs, purchase histories-everything that persists between interactions. The transition rule is the learning algorithm plus the selection policy. The interface is the interventions going out and the behaviors coming back.

This is why “AI advertising” is not just better targeting. It’s not just “more personalized.” It’s a different kind of thing.

A billboard is a message. A Facebook ad is still recognizably a message, even if it’s targeted.

An agentic marketing system is a program running on you.

Hypernudging: The Closed Loop of Persuasion

Most debates about advertising assume a one-off influence attempt.

Someone shows you an ad. You’re influenced or you’re not. End of story.

But modern platforms rarely nudge once.

They nudge, measure your response, update their model, and nudge again. Then they do it thousands of times across weeks and months, adjusting not just the content but the timing, the channel, the framing, the context in which you encounter it.

Legal scholar Karen Yeung calls this hypernudging: not persuasion as speech, but persuasion as adaptive infrastructure.

The difference matters.

A single nudge - a default option, a strategic product placement - can be evaluated as a discrete intervention. You can ask: Is this manipulation? Is this transparent? Does the person know they’re being nudged?

But hypernudging is harder to see, harder to evaluate, harder to resist. Because it’s not one intervention. It’s an environment that reshapes itself around you based on what it learns about how you respond.

You’re not being persuaded. You’re being adapted to.

Agentic marketing is hypernudging with planning and tool use.

Agentic Marketing: The Next Escalation

So what’s actually new?

Agentic marketing isn’t just “LLM writes ad copy.” That’s a productivity tool. Interesting, but not transformative.

The transformation is that the system can now:

Plan. Not just generate content, but build multi-step strategies for an individual or cohort. If the goal is conversion, what sequence of touchpoints is most likely to get there? What objections need to be addressed first? What trust needs to be built?

Act. Deploy variants across channels and touchpoints-search, social, email, on-site, in-app, conversational-coordinating them toward a coherent trajectory.

Learn. Attribute outcomes back to actions. Update beliefs about what works for whom under what conditions. Revise strategies based on what it discovers.

Persist. Remember. Not just what you clicked, but why it thinks you clicked. What worked and what didn’t. What your apparent goals are. What patterns predict your behavior.

This is marketing shifting from optimization of campaigns to optimization of trajectories.

And trajectories are where ethics live.

Because a single ad can be evaluated in isolation. But a trajectory that unfolds over months, adapting to your responses, learning your vulnerabilities, adjusting its approach based on what moves you - that’s not a message. That’s a relationship. And relationships can be healthy or extractive, empowering or diminishing.

The machinery doesn’t know the difference. The objective function does.

Two Worlds, Same Machinery

Here’s the fork in the road.

When a system can adapt itself per person, it can optimize for two very different futures. The technology is identical. The values embedded in the objective function are not.

Let’s call them World A and World B.

World A: The Alienation Trajectory

In World A, the objective function is simple and familiar:

Maximize clicks. Maximize conversions. Maximize engagement time. Maximize revenue per user.

These are the metrics we’ve been optimizing for two decades. They work. They’re measurable. They’re what the platforms are designed to produce.

But when you give an agentic system this objective and let it run long enough, with enough data, across enough people, certain patterns emerge. Not because anyone intended them, but because they’re what the objective function rewards.

Compulsion loops. The system learns that certain triggers - fear of missing out, social validation, variable reward schedules - produce more clicks than informative content. So it generates more triggers. And learns which triggers work best for you specifically.

Narrowed exploration. The system learns that showing you what you’ve already engaged with produces more engagement than showing you something new. So your world gets smaller. Your options become echoes of past choices.

Escalating stimulation. The system learns that yesterday’s trigger is today’s baseline. So it escalates. More urgency. More scarcity. More emotional intensity. Until everything is a crisis and nothing feels normal.

Identity capture. The system learns your triggers so well that it can predict your behavior better than you can. At which point, are you choosing? Or is the system choosing for you, and you’re just executing?

This isn’t science fiction. This is the documented trajectory of engagement-optimized platforms. We’re just describing it in systems terms.

The destination of World A is a society of isolated individuals, each trapped in their own optimization bubble, making choices that feel like theirs but were architecturally predetermined - a world where marketing doesn’t persuade you so much as become you.

World B: The Empowerment Alternative

In World B, the objective function is different:

Maximize goal-consistent outcomes. Maximize capability expansion. Maximize trust. Maximize long-term user satisfaction. Subject to: transparency, consent, no exploitation of cognitive vulnerabilities.

Same machinery. Different values.

When you give an agentic system this objective and let it run, different patterns emerge:

Goal alignment. The system learns what you’re actually trying to achieve - not what you click on, but what you’d endorse upon reflection. It optimizes for that.

Capability expansion. Instead of making you dependent on recommendations, the system helps you become better at making your own decisions. It teaches, explains, suggests frameworks, offers alternatives you hadn’t considered.

Trust accumulation. Because the system is optimizing for long-term satisfaction, not short-term conversion, it can afford to be honest. To show you the tradeoffs. To sometimes say “you might not need this.”

Autonomy preservation. The system respects your ability to change your mind, to disengage, to say no. It doesn’t punish you for leaving. It doesn’t exploit your weaknesses.

The destination of World B is a society where marketing becomes a genuine service-where the systems that help you buy things are aligned with your interests, not just your impulses.

This isn’t utopian. It’s just a different objective function.

And here’s the thing: it’s probably more profitable in the long run. Because trust compounds. Because customers who feel served, not exploited, come back. Because the lifetime value of a relationship beats the extraction value of a transaction.

We’re not asking for altruism. We’re asking for longer time horizons.

A Minimal “Agency Layer”

If you build agentic marketing without guardrails, you default to World A. Not because you’re evil. Because that’s where the local gradient points.

So the practical question is: what is the smallest layer you can add that changes the trajectory?

We’ve been working on this. Here’s what we’ve found.

1. Explicit Goals (Not Inferred “Interests”)

The current paradigm infers what you want from what you do. Click on running shoes, get more running shoe ads.

But what you click on and what you actually want are often different things. You click on the outrage bait even though you’d rather feel calm. You buy the impulse purchase even though you’re trying to save money.

An empowerment-aligned system doesn’t just infer from behavior. It asks. It lets you declare what you’re actually trying to achieve - health, learning, financial stability, creative expression, whatever matters to you.

Then it optimizes for that, not for engagement.

The current paradigm personalizes by default. You have to actively opt out, if you can find the setting.

An empowerment-aligned system reverses this. Personalization is off until you turn it on. And when you do, you know what you’re turning on. “Why am I seeing this?” has an answer. “Stop showing me this” actually works.

3. Constraint Checks (Before Action)

Before the system takes any action, it runs through a set of hard constraints:

  • No inference of sensitive attributes without explicit disclosure
  • No escalation loops (frequency caps, cooldown periods)
  • No dark patterns (artificial scarcity, disguised ads, manipulative countdown timers)
  • No exploitation of documented cognitive vulnerabilities

If the action fails any constraint, it doesn’t happen. No exceptions. No “but the metrics.”

4. A Second Reward Signal (Agency)

The system doesn’t just optimize for conversion. It also optimizes for agency.

What does that mean in practice? It means the reward function includes:

  • Goal-consistency: Did the outcome move the person toward their declared goals?
  • Regret signals: Did they return the product? Block the advertiser? Leave negative feedback?
  • Exploration: Are they seeing diverse options, or being funneled into a filter bubble?
  • Trust: Are they coming back? Recommending to others? Engaging voluntarily?

This isn’t about being nice. It’s about avoiding the trap where you optimize yourself into a local maximum that’s a global catastrophe.

The Metrics That Actually Matter

If you only measure CTR, you can only build CTR machines.

The agency layer requires a dual dashboard. Two sets of metrics, tracked side by side, both real:

Performance Metrics (what we’ve always measured):

  • Conversions
  • Revenue
  • Cost per acquisition
  • Return on ad spend

Agency Metrics (what we should have been measuring):

  • Goal-consistency score: Are purchases aligned with declared user goals?
  • Exploration diversity: Is the user seeing varied options, or being funneled?
  • Regret proxy: Returns, negative feedback, blocks, rapid churn
  • Trust proxy: Repeat usage, reduced ad blocking, positive sentiment, referrals

Neither set is optional. Neither is more real than the other.

The point isn’t moral purity. The point is seeing the full picture-so you can avoid building a system that optimizes society into a corner.

Why This Ends in Agentic Commerce

We’ve been talking about marketing. But the trajectory leads somewhere specific.

Agentic marketing inevitably collapses into agentic commerce.

Why? Because once the funnel becomes a single conversational thread, the system that recommends becomes the system that transacts.

Our previous research documented the scale of this shift: 800 million weekly ChatGPT users. 51% of Gen Z starting product research in LLMs. 4,700% year-over-year increase in AI agent traffic to e-commerce sites. $1-5 trillion projected market by 2030.

These numbers represent something unprecedented: the emergence of AI as a commerce channel, not just a tool.

When ChatGPT helps you shop, builds your cart, compares alternatives, reasons through tradeoffs, and completes your purchase - all in a single conversation thread - the entire funnel collapses into a dialogue.

And here’s where the fork matters most.

An agentic commerce system optimized for extraction will:

  • Push you toward higher-margin products regardless of fit
  • Create artificial urgency to prevent comparison shopping
  • Obscure tradeoffs that might lead you elsewhere
  • Lock you into subscriptions you didn’t intend
  • Learn your vulnerabilities and exploit them systematically

An agentic commerce system optimized for empowerment will:

  • Help you understand what you actually need
  • Surface alternatives you hadn’t considered
  • Present honest tradeoffs, even uncomfortable ones
  • Support your ability to say no, to wait, to think
  • Optimize for your long-term satisfaction, not just today’s transaction

The technology is identical. The values are opposite. The societal outcomes diverge.

What We’re Building

This article isn’t just analysis. It’s a manifesto and a construction project.

We’re building what we’re describing. An agentic commerce platform with the agency layer baked in. A system that demonstrates, in working code, that empowerment optimization is technically feasible, economically viable, and better for everyone in the long run.

The research foundation:

The implementation:

  • A Gemini hackathon submission proving the concept
  • Open-source code and specifications so others can build on it

The wave:

  • This article, framing the fork
  • Technical specifications for the agency layer
  • Build logs documenting what works
  • Community gathering around the alternative

We’re not waiting for regulation. We’re not waiting for platforms to change. We’re building the alternative and proving it works.

The Invitation

The question isn’t whether AI will transform marketing and commerce. It already has.

The question is what we build now, while the architecture is still being decided.

If you’re a marketer, you’re facing a choice. The old playbook-maximize clicks, optimize engagement, AB-test toward conversion still works. But it’s building World A. Every campaign that exploits rather than serves moves the needle toward alienation. Every campaign that genuinely helps moves it toward empowerment. The metrics you choose to optimize are votes for the future you’re building.

If you’re an engineer, you’re writing the code that shapes behavior at scale. The abstractions you create, the defaults you choose, the constraints you build in or leave out - these are ethical choices, whether you name them or not. You can build the agency layer. You can make transparency the default. You can refuse to implement dark patterns. The code is the argument.

If you’re a researcher, the questions are open. How do we measure agency? How do we formalize goal-consistency? How do we build systems that help humans flourish rather than systems that predict and exploit? The theoretical frameworks we develop now will shape what’s possible later.

If you’re a user, you’re not powerless. You can demand transparency. You can support platforms that serve your interests. You can opt out of systems that treat you as a resource to be extracted. The market responds to what people will and won’t accept.

The fork is real. The choice is now. The code is being written.

We’re building World B. Come help.

What Comes Next

This article frames the problem and the alternative. But framing isn’t enough.

Next in the series:

  • The Agency Layer: A Technical Specification - Interfaces, constraints, and metrics for empowerment-aligned systems
  • Build Log: Shipping an Empowerment Commerce Agent - What we learned building
  • From Marketing to Commerce: Why the Funnel Becomes a Conversation - The bridge to OpenAI and agentic commerce at scale

The code: The repository containing our implementation-goal alignment engine, alienation detection, agency optimizer, and the MCP tools that make it work across platforms-is open source. Philosophy without code is commentary. We’re building the proof.

The community: We’re gathering practitioners, researchers, and builders who want to work on this. Not because we have all the answers, but because the questions are too important to work on alone.

Closing: The Code Is the Argument

Turing showed us that procedures can be formalized, studied, compared. Hypernudging showed us that persuasion can be adaptive, continuous, environmental. Agentic marketing is the synthesis and the choice point.

The same machinery that can maximize compulsion can maximize empowerment. The same learning systems that can exploit vulnerabilities can expand capabilities. The same personalization that can trap people in filter bubbles can help them discover what they actually want.

The technology doesn’t choose. The objective function does. And we choose the objective function.

Shoshana Zuboff documented surveillance capitalism’s logic: extract behavioral data, predict and modify behavior, sell the modification capacity to the highest bidder. That’s World A, formalized.

We’re documenting an alternative: understand goals, align recommendations, build trust, sustain relationships. That’s World B, under construction.

We might be wrong about the economics. Maybe extraction really is more profitable, even in the long run. Maybe trust doesn’t compound. Maybe humans prefer to be optimized rather than served.

So far we don’t think so. And we’re building the code to find out.

Because the future of agentic marketing and perhaps of AI itself-belongs not to those who build better extraction algorithms, but to those who remember what these systems are supposed to be for:

Helping humans flourish.

References

Our Research Series:

Key Sources:

  • Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society.
  • Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society.
  • Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future. PublicAffairs.
  • Russell, S. (2019). Human Compatible: AI and the Problem of Control. Viking.

Implementation:

  • Repository: Coming soon
  • Gemini Hackathon Demo: Coming February 2026
  • OpenAI Apps SDK Integration: Coming Q1 2026

This article is part of our ongoing research into AI-driven marketing and commerce. Follow the analysis series for updates. The code is open source. The invitation is real.

Published on Saturday, January 3, 2026 · Estimated read time: 18 min

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