The Transparency Paradox
Building in public has become a standard growth strategy for startups. Share your journey, attract an audience, and convert followers into customers. The playbook is well-established.
But when AI does significant portions of your work — writing your code, generating your content, conducting your research — the transparency calculation changes. How do you build in public authentically when the "building" looks different from what people expect?
I have been navigating this tension since I started using AI tools extensively. Here is my framework for what to share, what to protect, and why the distinction matters.
What I Share Openly
The Tools and Workflows
I am transparent about which AI tools I use and how I use them. There is no competitive advantage in hiding your toolchain because everyone has access to the same tools. The advantage is in how you think, not which software you run.
Sharing my workflows does three things:
- Builds credibility: People trust founders who are honest about how they work
- Attracts the right audience: Other builders who want to learn from real experience
- Creates content naturally: Workflow posts are some of my highest-performing content
The Lessons and Failures
I share what goes wrong. When an AI-generated feature broke in production, I wrote about it. When my content pipeline produced a bad article that I caught before publishing, I shared the experience. When a market research approach led me to the wrong conclusion, I explained what happened.
Failures are more valuable than successes for building in public because they demonstrate real experience. Anyone can share wins. Sharing losses proves you are actually doing the work.
The Decision Frameworks
I share how I make decisions: which features to build, which markets to target, which experiments to run. These frameworks are genuinely useful to other founders and they showcase the thinking that AI cannot replace.
What I Keep Private
Specific Metrics
I do not share exact revenue, traffic, or conversion numbers. Not because they are bad, but because specific numbers invite comparison, and comparison without context is misleading. A conversion rate that is excellent for one business model is terrible for another.
I share directional trends ("traffic is growing month over month") without specific numbers.
Proprietary Prompts and Systems
While I share which tools I use, I do not share the specific prompts, system instructions, and quality gates that make my content pipeline work. These represent months of iteration and are a genuine competitive advantage.
The distinction: sharing that I use a quality scoring system is fine. Sharing the exact scoring rubric and thresholds gives away the edge.
Customer Information
This should be obvious but bears repeating: nothing that could identify specific customers, their data, or their behavior. No test results with real user data. No screenshots of internal dashboards. No examples that include identifiable information.
The Authenticity Question
When AI writes your code and helps generate your content, is building in public still authentic? I think about this a lot. My conclusion:
Authenticity is not about doing everything yourself. It is about being honest about what you do and how you do it.
A chef who uses a food processor is not less authentic than one who hand-chops everything. The quality of the dish — and the vision behind it — is what matters. The same is true for AI-assisted building.
The inauthenticity would be pretending the AI does not exist. Claiming you hand-wrote every line of code. Suggesting the content came purely from your pen. That is dishonest. Saying "I used AI to build this, and here is how I directed it" is honest and interesting.
The Content Strategy for AI-Native Builders
Building in public when you use AI tools creates a unique content opportunity. You can write about:
- How AI changes the building process — genuinely useful to other founders
- Where AI fails and you have to step in — shows expertise and judgment
- The decisions AI cannot make for you — demonstrates strategic thinking
- Comparisons and reviews of AI tools — practical value for your audience
This content attracts an audience of builders who are also using AI tools, which is a growing and engaged demographic.
The Risk of Over-Sharing
There is a risk specific to AI-powered startups: over-sharing your approach makes it easy for competitors to replicate. If your competitive advantage is partly in your AI workflow, documenting every detail of that workflow reduces the advantage.
My rule: share the principles, not the implementation. "I use a quality scoring system for content" is a principle. The exact prompts, thresholds, and decision trees are implementation.
The Framework: Share, Protect, or Skip
For every potential piece of building-in-public content, I ask three questions:
- Does sharing this help my audience? If yes, strong signal to share.
- Does sharing this help competitors more than it helps me? If yes, protect it.
- Is this interesting enough to be worth anyone's time? If no, skip it.
Most content falls clearly into one category. The edge cases are where you need judgment.
FAQ
Should I disclose that I use AI tools?
Yes, but do not make it the centerpiece of everything you share. Mention it naturally when relevant. Your audience cares about what you are building and what they can learn, not the specific mechanics of how every line of code was generated.
Does building in public with AI attract or repel investors?
Most investors are fine with AI-assisted building. What they care about is whether you understand the technology, whether you can maintain and evolve the product, and whether the business has a defensible advantage beyond the tools. If your answer to "what happens when competitors use the same AI tools" is strong, investors will not be concerned.
How do I build a personal brand when AI does the work?
The brand is built on your thinking, decisions, and judgment — not your typing speed. The founders who build strong personal brands in the AI era are the ones who share the most interesting insights about the building process, not the ones who generate the most code.
Is there a backlash risk for being open about AI use?
Some audiences react negatively to AI use. The best approach is to be matter-of-fact about it rather than defensive. "This is how I work" is a stronger position than "AI is just a tool" (which sounds defensive). Lead with the quality of the output and the honesty of the process.