Most AI tools help you execute. Almost none help you decide.
On the train right now. Four people across the aisle have been debating AI for twenty minutes. Mostly about presentation slides.
I've been building with AI constantly too. Writing tools, coding agents, assistants that can ship a feature in the time it used to take me to make coffee.
But something kept bothering me.
These tools are very good at helping you move fast. What they struggle with is telling you what was actually worth doing in the first place.
That distinction might sound subtle. In practice, it's where most of the waste happens.
Before any task, there are a few questions most people never ask out loud:
- Why is this the right thing to do right now?
- What do I think will happen if I do it?
- What am I giving up to make space for it?
Those are decisions. And they're mostly invisible.
And speed...makes this worse, not better.
When execution is cheap, it becomes easy to move confidently in the wrong direction. I've caught myself building things that didn't need to exist, simply because the barrier to building them was so low.
There's also no record of why a decision was made, what alternatives were considered, or what outcome was expected at the time. A few weeks later, all you see is what happened. The reasoning is gone or reconstructed from memory.
Without that original context, you can't learn anything reliable. You end up repeating patterns without realising it.
What changed for me
About a year ago I started logging decisions in Notion. Not tasks. Decisions.
Before doing something, I'd write what I was about to do, why it made sense, and what I expected to happen. Then a few weeks later I'd go back and write what actually happened.
It felt slow at first. Almost unnecessary.
But after a few weeks, patterns appeared. Decisions that felt reasonable in the moment but consistently led nowhere. Others that looked small but reliably moved things forward.
What made this useful wasn't the writing. It was having a reference point. A record of what I actually believed before outcome bias kicked in.
The learning started to compound. Each decision did two things: it produced an outcome, and it improved my future decisions.
Without that, everything gets rewritten by hindsight. You remember decisions as smarter than they were when they worked, and blame external factors when they didn't. You can't learn from a version of events your memory has already edited.
What this means for AI tools
The current generation assumes the task is correct and helps you finish it.
That's useful. But it's the easier half of the problem.
The more valuable version would act as a counterbalance. Something that creates clarity before you act, and feedback after. Imagine this: you decide to spend three hours on something, and instead of helping you do it faster, a system tells you "you've made similar decisions seven times, and five of them didn't lead to meaningful outcomes."
That kind of feedback is uncomfortable. It's much easier to feel productive by finishing tasks than to question whether those tasks should have existed at all.
But it's what's missing.
- Most tools optimise for: help me do this faster.
- Almost none address: should this be done at all?
The first makes you feel productive. The second makes you effective.
That thinking, over time, turned into Monti. A system that remembers what you decided, why you decided it, what you expected to happen, and what actually happened. So you don't have to rely on memory or intuition alone.
The real advantage isn't speed anymore. It's learning from your own decisions, consistently.
Really agree with the execution vs. decision distinction. There's a clear market gap here.
The tools that help you decide require you to externalize your reasoning—forcing you to articulate assumptions you haven't consciously examined. That friction is valuable but uncomfortable. Most people default to execution tools because they feel productive.
I think the real unlock is AI that can say 'wait, have you considered...' before you commit, not just after. Pre-mortem built into the workflow.
Agreed. That friction is exactly the point, but it’s also why most people avoid it. Writing down assumptions forces clarity, and clarity removes the comfort of vague optimism.
On the pre-mortem point, that’s already something Monti has in its workflow. Before committing to a decision, it pushes you to articulate expected outcomes and surfaces risks based on similar past decisions. In some cases, it can flag patterns like “you’ve made a similar call before and it didn’t land.”
That moment, before you act, is where most of the leverage sits because the learning is there, fresh, in that moment. After that, you’re just managing consequences and well, it might be too late by then.
Using AI to accelerate execution without a decision log is like driving a supercar into a dead end; you just get stuck faster. Documenting your reasoning creates a commit history for your strategy, protecting your roadmap from the trap of building cheap but meaningless features. This approach turns the fog of hindsight into a sharp architectural guide that ensures you are fixing the logic, not just the code.
What was the last feature you shipped that felt like a win during the build but turned out to be a total ghost town?
That analogy is uncomfortably accurate. I ran into this while building ARAMA. Early on, I shipped features around pricing experiments that felt directionally right during the build, but later saw they were barely used or didn’t change decisions in the way I expected. The issue wasn’t execution, it was that I never made the underlying assumptions explicit or revisited them properly. That gap is what pushed me to start logging decisions more rigorously. Once you see a few of those “ghost town” features in hindsight, you stop trusting gut feel alone.
Clarifying assumptions is truly the only way to keep a roadmap from turning into an endless string of expensive mistakes. To avoid that dreaded 'ghost town' scenario, we’ve been forcing ourselves to make decisions based purely on real user data before we even start building Bunzee.ai.
Speaking of which, if you’re open to it, could I ask for your brutally honest critique on our team's very own 'ghost project'? I'd love to see it through your sharp lens!
The product gets much stronger the moment it stops feeling like an AI assistant and starts feeling like decision memory.
That’s the real wedge here.
Most tools help people produce more.
Very few help them stop repeating expensive patterns.
If Monti can reliably show:
what you decided
why you decided it
what you expected
what actually happened
…then this stops being productivity software and starts becoming judgment infrastructure.
That’s a much stronger category.
Also, worth pressure-testing the name.
“Monti” feels softer than the actual product.
Useful for a notes tool, lighter than what this becomes if it works.
If this is really decision memory, the brand likely needs more weight over time too.
Absolutely. The shift from “helping produce” to “preventing repeated mistakes” is exactly where the value compounds over time.
What I’ve seen in practice is that most teams already generate enough output; the real leakage comes from decisions being made without a clear expectation and then never being revisited with context.
Once you make that loop explicit, decision to expectation to result, patterns become hard to ignore, and that’s where behavior actually changes, not just productivity.
Exactly.
That loop is the product.
Decision
expectation
result
pattern
Once that becomes visible, teams are not just remembering notes. They are seeing their own judgment improve or repeat mistakes.
That’s why I’d be careful with the brand layer.
“Monti” feels approachable, but the product you’re describing is heavier than that.
If this becomes decision memory, the name has to carry trust, weight, and seriousness.
Otherwise people may read it as another personal AI notes tool when the real category is much stronger.