HOPLIGHT

Progressive messaging works with the choir. We need to reach beyond it.

“What happens if you message to someone’s psychology instead of to their demographics?”

Demography isn’t destiny.

“They’re voting against their own interests”
“If they could just see the facts...”
“We need better education”

And it may not even be the optimal targeting methodology.

Age 39 · Latino · $50K/yr · No college degree
VOTED HARRIS VOTED TRUMP STAYED HOME
VOTED HARRIS VOTED TRUMP STAYED HOME
Same demographic profile. Different vote choice in 2024.

Pushing progressive-coded moral language on non-progressive audiences backfires with the voters who decide elections.

Finding: The Backlash

The Security Officer Messaging Study · n=3,006 · 60.9% voter-file match · August 2025

Persuasion messaging from progressive human communicators underperformed the placebo message about Morton Salt.

Issue tested: “Many security officers are poorly paid and resourced.”

Under 35
-5 pts

The progressive frame moved them 5 points below placebo.

Black Voters
-4 pts

The progressive frame moved them 4 points below placebo.

Religious Conservatives
-2 pts

“It’s better for them to hear about Morton Salt than to hear from us.”

“WHAT HAPPENS IF YOU MESSAGE TO SOMEONE’S PSYCHOLOGY INSTEAD OF TO THEIR DEMOGRAPHICS?”

Finding: Hold the Base, Grow the Tent

+18–26
Points Net Lift

across all conservative segments

when using AI-generated psychographic messages

Standard messaging is backfiring.
+9
-5
+5
-4
+21
-2
Under 35
Black Voters
Religious
Conservatives

Green = psychographic message · Red = human-generated persuasion message · Baseline = the Morton Salt placebo

2024 Non-Voters
+22 pts
Latino Voters
+22 pts
Working Class (<$50K)
+19 pts

We already know this works.

Relational Organizing / Deep Canvassing

These tactics succeed by meeting people inside their worldview. A canvasser who listens, adapts, speaks in the listener’s values: that works.

But these tactics are prohibitively expensive to scale.

PROVEN BUT UNSCALABLE

Off-the-Shelf LLMs

Research shows us large language models are good at persuading people. But if the model changes, the message changes. No audit trail. No persistent replicability. No institutional knowledge.

A billionaire’s black box isn’t a scalable strategy.

SCALABLE BUT UNACCOUNTABLE

The Gap: Operationalization

We invest millions in research and message development. But implementing those learnings is riddled with pitfalls.

The Current Message Optimization Process
...NOT OPTIMAL
With the Hoplight Engine
AUDIENCE + GOAL
HOPLIGHT ENGINE
Local State National
  • Deploy effective frames to all users instantly
  • Generate bespoke variants without drift

One input can return 37 platform-ready copy types.

Success is deploying those best practices with high fidelity at scale

Four Auditable Layers

Built on established psychometric measurement traditions. The four layers are the public map, while the proprietary pipeline spans eight stages.

TRAIT PROFILE

Measures who the listener is psychologically. Takes signals in and outputs a factor vector.

WORLDVIEW

Projects the profile into felt worldview: archetype, antagonists, fears, and internal story.

MESSAGE SPEC

Maps worldview directly to frame, narrative, metaphor, register, and appeal type.

RENDERING

LLM writes prose to spec. The moat is the spec it receives from Layers 1-3.

What This Unlocks:
RECURSIVE MESSAGE REFINEMENT

Move the people other programs leave out.

The Author

Whit Pendergast has spent 20 years building the thing before the field knows it needs it. In 2008 he invented an inverse-knock program in New Hampshire that found, registered, and turned out 2,300 Latino voters the party had no infrastructure to reach. He co-founded the largest crowdfunded distillery in US history, then built AI marketing infrastructure for its outspoken progressive brand before the industry had a playbook. He shipped AGIS, a global-to-local AI-governance intelligence system, and built the persuasion engine now licensed by the second-largest union in North America.

✉ whit@hoplight.ai