DEV Community

Loic Moncany
Loic Moncany

Posted on

AI Cold Email Is Booming in 2026 — But I Think Indie Hackers Are Using It Wrong

AI Cold Email Is Booming in 2026 — But I Think Indie Hackers Are Using It Wrong

AI personalization tools for cold outreach are everywhere right now. The pitch is compelling: 500 "personalized" emails a day, automated at scale. But as someone building a lead gen tool for indie hackers, I think we're solving the wrong problem.


What's happening

The AI cold email space exploded. Tools like Lyne.ai, Jeeva, and Autobound are pulling LinkedIn profiles and company websites to auto-generate personalized first lines. The numbers sound great: 20% conversion lifts, replies from people who "never respond to cold email."

Big sales teams are legitimately winning with this. SDRs at funded startups are hitting quota with AI-written personalization at scale.

But indie hackers are copying the playbook. And it's not working the same way.


What I'm actually seeing while building OhMyLead

I've been building OhMyLead for indie hackers who need leads — people running one-person SaaS products, freelancers, solo founders with no sales team.

This week I had three conversations with early users. All of them said the same thing in different ways: "I tried cold email. Sent like 200 messages. Got two replies, both negative."

When I dug in, the problem was never the email copy. It was the list. They were emailing the wrong people.

One guy built a tool for e-commerce store owners doing $1M+ revenue. His list? A scraped CSV of anyone who'd ever mentioned "Shopify" on LinkedIn. That's not targeting. That's guessing.

The founders I've seen actually win at cold outreach — even with basic, unpolished emails — had one thing in common: they spent 80% of their time on the list and 20% on the message.

AI personalization tools make the 20% faster and prettier. They do nothing for the 80%.


What I think is actually true for indie hackers

Here's where the hype diverges from reality in our world:

  • Scale is not the constraint. You don't need 500 outreach emails a day. You need 10 emails to the right 10 people. The goal is a conversation, not a campaign.
  • AI personalization hides a bad list. A "personalized" first line can't compensate for reaching out to someone who'll never buy your product.
  • Research first, write second. The best cold emails I've seen from solo founders aren't AI-written. They're 4 sentences that show the sender actually looked at the prospect's business.
  • Volume is a cope. Sending more emails to avoid dealing with the targeting problem is how you burn your domain and your confidence at the same time.
  • Identify before you write. Before touching any email tool, can you describe in one sentence exactly why this specific person would care about what you're selling? If not, don't send it.

What I'm building instead

OhMyLead isn't trying to help you send more emails. It's trying to help you send the right ones — by focusing on identifying the 10 accounts most likely to respond before you write a single word.

Still early. Still testing. But the signal from users is clear: they don't need more automation. They need better judgment about who to reach out to in the first place.

If you're an indie hacker doing cold outreach and you want to share what's actually working, I'd love to hear it.

Find me on X: @lmoncany or check what I'm building at ohmylead.isophot.fr

Top comments (1)

Collapse
 
yl_keeool_f41ed7d9809a405 profile image
yl keeool

You are absolutely right, and this is excellent. I have been struggling with defining our customer persona.

For instance, we produce a specific type of chemical product that is used extensively across various fields, giving it a very broad range of applications. Because of this, accurately identifying our target audience is critical for us.

Simply filtering by industry doesn't seem to work. We are currently trying to use customs data to:

  1. Identify confirmed buyers first.
  2. Use those buyers to reverse-engineer other potential customer groups who would have similar reasons for purchasing the same products.

The strategy is to base everything on finding customers with a history of real transactions, understanding their specific reasons and motivations for buying, and then using that data to find similar leads. I believe this approach will make our lead generation much more precise.

I have been quite frustrated by this challenge, so if you can actually pull this off, it would be fantastic!