I Built an AI That Doesn't Give Advice. Here's What I Learned
I spent years publishing content.
Blogs.
Books.
Ideas.
Almost nobody cared.
A few months ago I stopped asking:
"How do I get more traffic?"
and started asking:
"Why do people avoid taking action even when they know what to do?"
That question turned into a side project called TruthLoop.
The idea is simple:
Instead of giving advice, the AI keeps asking follow-up questions to uncover the behavioral pattern behind a problem.
Not the symptom.
The pattern.
One thing surprised me while building it:
Most users don't lack information.
They already know what they should do.
They're avoiding something.
A difficult decision.
A conversation.
A risk.
A fear.
A contradiction.
The challenge wasn't building the AI.
The challenge was getting the AI to stop repeating itself and actually go deeper with each loop.
Still early.
Still tiny.
Still figuring out distribution.
But building a product has taught me more about human behavior than years of publishing content.
For founders building in public:
What's the biggest thing your product taught you that you weren't expecting?
The "they already know what to do, they're avoiding something" insight is the most underrated thing in this space. I build automation for small businesses and hit the same shape: the bottleneck is almost never that they don't know a task should be automated — it's that they don't trust handing it over. So the product became less about clever
automation and more about earning trust: show its work, fail loudly, let them keep a hand on the wheel. On your repetition problem — what helped me get an AI to go deeper instead of looping was forcing it to summarize the pattern-so-far before each new question, so it can't re-ask what it already knows. Have you tried that between loops?
Not exactly.
The loops aren't pre-written.
Each question is generated in real time from the user's input and the behavioral patterns uncovered so far.
The system isn't trying to reach a predefined answer.
It's trying to follow the pattern until the explanation no longer holds.
This is a big problem, the repetition that can lead to exhaustion... sometimes it really feels like we're in a continuous loop, and sometimes it almost forces us to reset... the human mind plays tricks on us, what we don't need is an assistant, AI, playing repeated and looping tricks on us... The TRUTH is often uncomfortable and we don't like it, so I'm quite curious to see what results you'll get with this tool and what paths it can lead us down. Good luck with what's to come, always with TRUTH and without LOOPS...
Well said.
The uncomfortable part is rarely the insight itself.
It's the contradiction the insight exposes.
That's where hidden patterns tend to reveal themselves.
The technical challenge you mentioned about preventing the AI from repeating itself and driving deeper into the loop is massive. When I was building my project, I experienced something similar, we expect users to want the fastest path to an answer, but my biggest surprise was that building for friction is sometimes better than building for speed. If a tool makes things too easy, the user doesn't value the output or engage with the actual problem. How are you structuring your system prompts or state management to ensure the AI recognizes it’s hitting an emotional roadblock rather than just a technical one?
Interesting observation.
Technical blockers disappear when the environment changes.
Emotional blockers tend to follow the user into the new environment.
That's usually the signal that the loop is deeper than the task itself.
Most of the language learners, including me, learn languages primarily focusing on grammar and vocabulary and completely forget about the core part which is speaking -after some learning. And most of the language learning apps doesn't provide the opportunity to speak. That's how LangSpeak was born.
It let's the learners speak in their preferred language. Because application of what we know returns more than just focusing on theory only.
As you said, "They're avoiding something" they're sitting in their comfort zones instead of speaking.
Well said.
Grammar wasn't the bottleneck.
Speaking was the exposure point.
That's often where hidden resistance becomes visible.
Answering your question directly: building an AI daily planner taught me that the friction isn't in making the plan — it's in the gap right after the plan is made. Users would create a detailed, organized schedule and then not return until the next day to plan again. The planning itself had become the comfort behavior, a substitute for action rather than a trigger for it. It completely reshaped what I focused on: instead of helping people plan better, the more useful thing was building nudges that help them actually start on what they'd already planned. Your framing of people "avoiding something" rather than "lacking information" is exactly right — it's the same pattern showing up in a different context.
What stands out is that the obstacle moved.
It wasn't in the planning phase.
It was hiding in the transition from clarity to action.
That's where hesitation loops tend to become visible.
the insight about users already knowing what to do hits hard
i noticed the same thing validating my own product — people don't have an information problem, they have a friction problem. they know their notion workspace is a mess. they just never fix it because starting feels overwhelming.
the product that removes the first step wins, not the one that gives more advice
what does a typical loop look like before a user hits the pattern you're looking for?
Interesting distinction.
What I've noticed is that users rarely begin with the real constraint.
They begin with the explanation.
The loop keeps following the hesitation until the explanation stops changing and the underlying protection pattern becomes visible.
the explanation stops changing" — that's a sharp way to put it
so the loop is essentially waiting for the user to run out of new reasons and hit the actual thing they're protecting
that's not a chatbot. that's closer to therapy infrastructure
Maybe.
I think it's closer to pattern recognition than therapy.
The loop isn't trying to explain the user.
It's trying to make the hidden pattern visible.
One thing I’d be careful about:
It’s not about understanding value or taking action.
The real constraint is what users must believe is true before they trust the system enough to engage at all.
That underlying assumption often decides whether the product feels obvious or confusing.
That's a useful distinction.
The question isn't only what people are avoiding.
It's what assumption makes the avoidance feel reasonable in the first place.
Behavior usually makes sense once the hidden belief becomes visible.
The "already know, still avoiding" pattern shows up in the AI tool space too.
A lot of founders and consultants know they should be using AI to process contracts and research docs. They have the knowledge. But a specific friction stalls them: what happens to their files once they upload them? Most tools train on everything by default.
The avoidance isn't about capability. It's about a concrete risk they can't see resolved.
I've seen that shift with goffer.ai, a private document vault that doesn't train on your files. Once that concern is off the table, adoption moves fast. The product is basically "absence of a problem" rather than a feature list.
Your framing about the behavioral pattern underneath a problem is useful here. The blocker is almost never what people first say it is.
Exactly.
People rarely resist the action itself.
They resist the consequence they expect from the action.
Once the perceived risk changes, behavior often changes much faster than expected.
One thing I'd be careful with:
The challenge may not be whether people lack information or even whether they're avoiding action.
The harder decision could be what users need to believe is happening before they trust the questioning process enough to continue.
That sounds subtle, but it can quietly shape who the product resonates with and how it's evaluated.
That's a good point.
A question can create insight.
But only if the user believes the next layer is worth uncovering.
Trust is part of the loop too.
Possibly.
The reason I'd still be careful is that trust can mean several different things while appearing validated by the same behavior.
That's one of those decisions I'd want confidence in before building too much around it.
I wouldn't try to unpack that properly in a thread.
If you're curious, drop your email and I'll put together the tighter version.
Agreed.
Questions create insight.
But only when people believe the process is helping them discover something, not forcing them toward a conclusion.
Trust isn't separate from the loop.
It's part of the loop itself.