Marketing is quietly crossing a threshold.
Not because we can “make more content” with AI.
Because every time the cost of thinking drops, the amount of experimentation and personalization you can afford explodes. And that changes the shape of marketing from campaigns you launch to systems you operate.
I’ve spent decades teaching small businesses to stop chasing shiny tactics and install a marketing system that creates clarity and consistent growth. That “system-first” mindset is exactly what this moment demands, except now the system can learn faster than your team can type.
The “Moore-ish” Law of Marketing: When the Cost of Thinking Drops, Experimentation Explodes
Table of contents
- A “Moore-ish” law for marketing
- Three forces changing marketing right now
- What this means in the near term
- The role shift: from makers to operators of systems
- What to do right now: a near-term playbook
- How this fits the Duct Tape Marketing system
- FAQs
A “Moore-ish” law for marketing
Here’s the framing:
When the cost per marketing experiment keeps falling, the number of experiments you can afford rises dramatically.
If it gets 2x cheaper, or 2x faster, to generate, quality-check, and deploy a new variation, you do more of them. Over time, that pushes marketing from campaign-centric (big launches, big bets) to system-centric (continuous learning, continuous improvement).
The unlock is not “AI content.” It’s collapsed time-to-learning.
When time-to-learning collapses, the limiting factor shifts:
- Not “can we produce it?”
- But “can we measure what’s true and decide well?”
That is the big idea in one sentence:
Execution moves from human throughput to machine throughput, while humans move up the stack to judgment, strategy, constraints, narrative, offers, positioning, and ethics.
Three forces changing marketing right now
1) Cost per experiment is falling
The ability to create variations is no longer scarce. What is scarce is the ability to run clean tests, protect the brand, and decide.
2) Time-to-learning is collapsing
Shorter loops mean you can improve messaging, creative, and offers continuously instead of waiting for a quarterly campaign post-mortem.
3) Coordination work is becoming automatable
Most marketing teams spend more time coordinating work than creating leverage. As tools integrate into work apps, AI can draft, route, repurpose, tag, schedule, and execute multi-step workflows under human supervision.
What this means in the near term
1) Content shifts from assets to streams
The near-term change is simple to say and hard to implement:
Your team stops “shipping one landing page” or “one email sequence” or “one ad set.”
You ship:
- A message system
Positioning, proof, objections, tone rules, prohibited claims, and brand constraints. - A modular content library
Claims, examples, stories, CTAs, offers, proof points, and objection-handling modules. - A generation and QA pipeline
A workflow that continuously produces variants, checks them, deploys them, measures performance, and feeds learnings back.
Role impact
- Copywriter becomes editor-in-chief plus conversion strategist
Owns voice, truth, persuasion, compliance, and performance feedback. - Designer becomes system designer
Builds templates, components, motion rules, and brand constraints. - Content lead becomes content operations lead
Owns workflow, governance, QA, and measurement loops.
2) Personalization shifts from segments to situations
Segmentation is still useful, but the economics are changing.
When personalization gets cheaper, you stop asking only:
- “Which segment is this?”
And start asking:
- “What situation are they in right now?”
- “What job are they hiring us for?”
- “What objection is active?”
- “What constraint is binding: budget, time, risk, internal approval?”
- “What is the next best step that fits their reality?”
Role impact
- Campaign manager becomes journey architect
Owns triggers, decisioning, orchestration, and next-best-action paths. - Marketing ops becomes decision ops
Owns data quality, identity, measurement, guardrails, and evaluation standards.
If you want one practical takeaway: the “unit of personalization” is shifting from a persona to a moment.
3) Agents start eating coordination work
Most marketing teams spend more time coordinating work than creating leverage:
- Creating briefs
- Routing approvals
- Repurposing content
- Tagging and organizing assets
- Scheduling and posting
- Producing “version 14” of a variation
- Summarizing results and sharing updates
As AI integrates with workplace tools, these coordination tasks can be automated or semi-automated with human checkpoints.
Role impact
A new role emerges, especially in teams that want scale without chaos:
Marketing agent wrangler
The person who builds repeatable agent workflows, monitors outputs, tunes prompts, sets permissions, and makes sure “automated” never means “unaccountable.”
The role shift: from makers to operators of systems
If change keeps accelerating, the safest career position is not “the fastest maker.”
It is:
The person who can design the system that produces outcomes repeatedly.
Here’s a simple mapping.
Roles that shrink (execution throughput)
- “Write 20 posts”
- “Make 30 ad variations”
- “Draft 10 nurture emails”
- “Create first-pass briefs”
These become machine-default, especially for first drafts and variant generation.
Roles that grow (judgment, leverage, trust)
- Positioning and offer design
What to say, to whom, and why it’s true. - Creative direction
Taste, narrative, cohesion across channels. - Performance strategy
What to test, what to stop, what to double down on. - Marketing operations and governance
Permissions, QA, brand safety, compliance, evaluation. - Customer research synthesis
Turning messy reality into usable direction.
What to do right now: a near-term playbook
If you want a practical playbook that fits this Moore-ish acceleration, focus on four builds.
1) Build a truth layer
Your team needs a single source of truth that answers:
- What claims can we make?
- What proof supports each claim?
- What is disallowed legally, ethically, or brand-wise?
- What language do we never use?
- What industries, customer types, or outcomes require extra care?
This is how you prevent fast nonsense.
AI without a truth layer produces confident randomness. AI with a truth layer produces scalable clarity.
2) Standardize a production pipeline
A healthy pipeline looks like:
Brief → generate → fact-check → brand-check → legal-check (if needed) → deploy → measure → feed learnings back
Notice what’s missing: polish endlessly.
If the system is meant to stream variants, your job is not perfection. Your job is controlled learning.
3) Create an evaluation habit
The question is not “did AI write it?”
The question is:
Did it move the KPI while protecting the brand?
This is where many teams will break. If you cannot evaluate, you cannot scale.
At minimum, define:
- Your primary KPI by channel
- Your guardrail metrics (complaints, unsubscribes, refund rate, brand sentiment indicators)
- Your decision cadence (daily for ads, weekly for emails, monthly for site and SEO)
- Your stopping rules (when to kill a test quickly)
- Your doubling rules (when to scale a winner)
4) Reskill around leverage
Train marketers to do the work that scales:
- Design experiments
- Write constraints
- Critique outputs
- Interpret results
- Orchestrate tools and workflows
- Document learnings so the system improves over time
Many teams will run more tests, but fail to compound the learning. The habit of documenting what worked and why becomes a strategic advantage.
How this fits the Duct Tape Marketing system
This moment does not replace strategy. It punishes teams who try to skip it.
Duct Tape Marketing has always been rooted in the idea that marketing is a system, not a pile of tactics, and that clarity beats chaos.
AI acceleration rewards that approach because:
- A system gives you the message constraints that prevent garbage at scale.
- A system gives you the customer journey structure that makes personalization meaningful.
- A system gives you a measurement discipline so “more output” becomes “more learning,” not “more noise.”
Or said another way:
AI makes tactics cheaper. It also makes strategy more valuable.
If you want to future-proof, build the machine, but lead it with principles:
- Strategy before tactics
- Truth over hype
- Consistency over novelty
- Learning over launching
FAQs
1) Is this just about creating more content faster?
No. The advantage is not volume. The advantage is iteration, testing loops, and faster time-to-learning. Volume without evaluation just creates more waste.
2) What is the biggest risk as experimentation gets cheaper?
Scaling bad assumptions. If your truth layer is weak, you will publish confident errors, drift off-brand, and damage trust faster than ever.
3) What should small businesses do if they do not have a data science team?
Start simpler. Use AI to increase iteration on high-leverage assets where measurement is clear, like ads, landing pages, and email subject lines. Keep the loops tight and focus on one KPI per test.
4) How do we prevent brand inconsistency when AI is generating variants?
Operationalize brand constraints, not just guidelines. Build templates, component rules, disallowed language lists, and a review checklist that enforces your standards.
5) Do we need “agents” right now?
Not to start. Begin with a standardized pipeline and a truth layer. Agents become valuable when you have repeatable workflows worth automating, and clear checkpoints for approvals and measurement.
6) Which roles should we hire or promote for this shift?
Look for people who can design systems, run experiments, and make decisions with incomplete information. “Taste plus rigor” becomes a premium combination.
7) How does personalization change first for most teams?
You move from broad segments to situational messaging on the same core journey. Think objection-based variants, industry-context variants, and stage-of-awareness variants, all measured and refined continuously.
8) How do we know we are using AI in a way that drives growth, not just efficiency?
If your AI program only measures time saved, you are still in productivity mode. The shift is tying AI-enabled workflows to business outcomes, with clear accountability for impact.
Next step: If you share your context (agency serving SMBs, in-house B2B, local service business, SaaS, ecommerce), I’ll translate this into the three highest-leverage workflows to automate first, the roles to redesign, and the metrics that keep the machine honest.
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