Building in public means sharing a company's progress, decisions, metrics, and lessons openly as it grows. It earns trust and attention by giving the audience a stake in the journey, and it turns the company's own work into content. For AI startups, whose audiences value substance, it is a durable distribution channel when done with a cadence and boundaries.
Why Does It Work for AI Startups?
Because the audience rewards substance and transparency. Developers and technical buyers distrust polished marketing and respond to candid accounts of what is being built and why. Building in public supplies that candid material continuously, without inventing content. Our guide to technical content distribution covers how the output spreads.
What Should You Share?
The journey: decisions and their reasoning, lessons from failures, milestones, benchmarks, and non-sensitive metrics. Each gives the audience something real to follow and something to learn from, which is what earns attention over time. Our guide to benchmark content covers one especially credible format.
What Should Stay Private?
Anything that could harm the business: detailed financials, unreleased strategy, customer data, and information that gives competitors an edge. Transparency should not create competitive or legal exposure. The line is between sharing the journey and handing over the map. Our guide to AI crawler access covers related content-control decisions.
How Do You Avoid It Becoming a Distraction?
By treating it as a channel with a cadence, not a constant performance. Regular, substantive updates work; real-time broadcasting consumes the time meant for building. The discipline is to post deliberately, not reactively.
How Does It Connect to Founder Brand?
Tightly. Building in public is often the engine of a founder's brand, giving them a consistent narrative to share. The two reinforce each other: the brand amplifies the updates, and the updates build the brand. Our guide to AI startup founder brands covers the broader playbook, and the founder personal brand guide covers the general approach.
How Does It Reach Beyond Followers?
Through the communities and platforms the audience uses. Stack Overflow's 2025 Developer Survey shows developers gather across multiple community platforms, so building in public content should be distributed where those audiences actually are, not only on one channel.
Machine legibility is standardizing: llms.txt is now published by OpenAI, Anthropic, and Gemini for their developer docs, and documentation platforms generate it automatically.
Why Does Legibility Decide Who Gets Cited?
AI answers are assembled from content the model can cleanly read and trust, so legibility is the new ranking factor. Clear definitions, structured data, and a machine-readable site map make a product easy to include; ambiguity makes it easy to skip. The llms.txt proposal exists for exactly this, and it is now published by OpenAI, Anthropic, and Gemini for their developer docs, with documentation platforms generating it automatically. Legibility is not a trick — it is the difference between being represented accurately in AI answers and being invisible to them, and it compounds with the quality of the underlying content.
Invest in assets that compound — docs, open source, community — rather than one-off campaigns. Hootsuite's 2026 Social Trends research notes content volume already exceeds human-written supply, so durability wins.
Diagnose the distribution gap before adding content: legibility, docs, and community usually matter more than volume. llms.txt is now published by the major AI labs, which shows how legibility is standardizing.
How Conbersa Extends Build-in-Public Content
Conbersa runs distribution across a fleet of real physical smartphones, one identity per device, so a founder's updates and lessons reach multiple platforms and communities without shared signals. See how it works at conbersa.ai.