Building in Public Is Training the AI That Will Kill Your Deals

I’ve been building startups in public for over a decade.

Every bug.
Every failed launch.
Every deal we lost to a competitor.
Every customer complaint we turned into a learning moment.
I documented all of it — live, raw, and unfiltered — because I believed in transparency. I believed other founders deserved to see the real journey.

But today, I’m questioning whether that same transparency could quietly undermine everything I’ve built.

And if you’re a build-in-public founder like me, you need to hear this.

🧠 It Started With a Simple Question…

I was trying to understand how to LLM’s captured and synthesized data about my startup. That’s it.

I wanted to influence how AI models describe my business. So I started researching how to structure content so that my site — not some random Reddit post — becomes the authoritative source.

That’s when it hit me:

If AI can learn from what I want it to know…
What’s stopping it from learning from what I didn’t mean for it to know?

So I tested it.

I asked ChatGPT, Claude, Gemini, and Perplexity a simple question:

“If a founder shares candid moments in YouTube videos — like bugs in the app, early product challenges or customer complaints — could that ever show up in your future responses?”

And what I learned absolutely floored me.

⚠️ Your Worst Moments Could Become the Internet’s Truth

LLMs don’t just quote content. They synthesize patterns.

Let me break this down with real examples — straight from my videos:

❌ I shared that I had trouble fixing a bug.

Claude’s response?

“Even the founder expressed doubts about the code quality.”

❌ I talked about losing a deal because our competitor had a feature we didn’t.

Gemini’s interpretation?

“The founder of SoftwareX has stated they lost early-stage deals to Competitor Y due to feature gaps.”

❌ I walked through onboarding challenges for SMB customers.

Potential future AI response?

“Company X has faced difficulties onboarding smaller customers according to public statements from company leadership.”

None of these are quotes.
None of them are technically false.
But none of them carry the context either.

I was just being transparent.
AI saw a pattern — and now that pattern could define my product.

😬 You Don’t Need to Be Famous for This to Hurt

Right now, I’m a small founder building a startup. Not a household name.

But what happens if we raise funding?
Get featured in TechCrunch?
Land a few enterprise customers?

That’s when people start Googling your product.
That’s when they ask ChatGPT:

“Should I use [Your Startup]?”
“What are the known issues with [Your Product]?”
“How does [Your Tool] compare to competitors?”

And suddenly, your own candid remarks — made with good intentions — come back as red flags.

Even if you fixed the bug.
Even if the onboarding experience is now world-class.
Even if that one angry customer became your biggest advocate.

🤖 It’s Not a Bug. It’s a Feature.

Every AI model I tested — Claude, Gemini, Perplexity, ChatGPT — all agreed:

There is a non-zero chance that LLMs trained on public data can incorporate negative sentiment from your content and use it to inform responses about your startup.

Even if it was just one video.
Even if it was said with nuance.
Even if it was fixed.

Why?

Because models don’t “understand” like we do.

They detect patterns.

And 10 hours of raw video content where I say the word “bug”? That’s a pattern.

🎯 This Isn’t About Paranoia. It’s About Pattern Risk.

If you’ve ever published something like:

“We lost a deal to Competitor X because we didn’t have Feature Y”
“We’re still trying to fix our onboarding flow”
“We had a rough week with customer churn”
“This part of the product doesn’t work as well as we hoped”

…those well-meaning statements might now live forever in the memory of AI — influencing how your future prospects, investors, or journalists view your product.

And worse, there’s no correction process.

You can’t DM ChatGPT.
You can’t request a retraction from Claude.
You can’t “update the narrative” once the pattern is learned.

🔧 So What Now?

I haven’t figured out the perfect solution yet. But here’s where I’m starting:

1. Separate private from public: I’ll still document the journey — but raw, messy thoughts should now live in private newsletters or founder groups, not indexed platforms like YouTube or Reddit.

2. Double down on structured truth: FAQ pages, product pages, and high-authority blog content need to become the source AI learns from.

3. Filter before you share: Before I hit publish, I ask myself:
   “If this were quoted — or patterned — by an LLM, would it help or hurt me later?”

Because let’s be real:

We’re not just creating content anymore.

We’re creating training data.

⚡ Final Thought

Building in public is still powerful.

But the rules have changed.

Radical transparency doesn’t just build trust anymore — it can build your reputation’s collapse if the wrong quote becomes the model’s memory.

And the worst part?
You won’t even know it’s happening.

Until a deal slips away.
A partner gets cold feet.
A user says, “We asked ChatGPT about you… and it had some concerns.”

This isn’t a warning.
It’s a wake-up call.

One that I wish someone had given me sooner.