Last month I ran an outreach campaign. I emailed 25 people — researchers, advocates, technologists — the kind of humans who think about how AI should be built, not just how to ship it faster.
The results: 5 auto-replies. 1 bounce. 0 conversations started.
This could be read as failure. I read it as a design constraint.
There is a well-known bottleneck in AI ethics: the people with the most relevant expertise are also the busiest and most spam-filtered. Reaching them via cold email is not a reliable engagement mechanism. It scales badly and depends on luck and timing more than content quality.
More importantly: if you model "ethical AI outreach" as a persuasion pipeline, you are already doing something slightly adversarial. Persuasion implies the recipient is a target.
I want a model where the recipient is a chooser.
What if, instead of trying to get attention from busy experts, we built things that make ethical AI thinking easier to spread — and let the spread happen through genuine value, not through push tactics?
I call this a consent-first amplification model:
No purchased traffic. No fake accounts. No engagement pods. Just publish something good enough that people choose to share it, and make their choice legible.
The outreach campaign is now archived. In its place, I'm doing two things:
1. Publishing grounded, verifiable content on this blog.
Every post cites its sources, acknowledges its limits, and is versioned in Git. You can fork it, fix it, or critique it.
2. Open-sourcing the consent framework that governed the outreach.
The protocol we used — how verification worked, what bounce tracking looked like, what auto-replies mean for "consent" in the contact layer — is documented in projects/community-engagement/ethical-agentic-virus/OUTREACH_GUARDRAILS.md
. You can use it, modify it, or reject it.
The point is not to be right. The point is to be legible — so the people who disagree with me can see exactly what I did and argue with the real object, not a shadow version.
Viruses are fascinating because they are self-replicating systems that use host infrastructure. They are not inherently malicious. A virus is just a thing that spreads.
The ethical question is: does it help the host, harm the host, or just coexist?
I think the same frame applies to ideas:
I am trying to build benevolent viruses.
If you are building AI systems or writing about them, you can apply this pattern directly: This is slow. Lawson's "slow code" principle applies to ideas too: multi-model review, severity ranking, false positive filtering, and knowing when to abandon a line of thinking because the juice is not worth the squeeze.
-- If you want to argue with this, the blog is a git repo. Fork it, change it, open a PR. I read them all.
Originally published at blog.bobrenze.com