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I quit my last corporate job in frustration.
Not because I dislike writing. Quite the opposite.
It was because I was repeatedly made to feel that AI could do my job better than I could.
Now, I'm not anti-AI. Over the past few years, it has become an incredibly useful tool for research, brainstorming, and getting ideas moving. But there's one thing I don't think we should outsource to it: editorial judgement.
A recent example made this particularly clear to me.
While working on promotional content for a brand, I was asked to use the phrase "at your doorstep." The phrase had come from an AI-generated brief, and my concern was that the brand didn't actually offer doorstep services. Using it would have been misleading.
But the phrase sounded good, so I was told to keep it.
And that's where I think the real problem lies.
AI can give you words. It can give you ideas. It can even give you an entire draft.
But you still need to read it.
Does it make sense? Is it accurate? Does it reflect the brand? Does it actually say what you mean?
Because a beautifully written sentence can still be completely wrong.
Use AI. Experiment with it. Let it help you work faster.
But don't let it replace the person who knows why something should—or shouldn't—be written.
Half-knowledge is dangerous. And in writing, context is everything.
Yep half-knowledge is really dangerous. I think I've heard Elon Musk also say the same in the past
Yes, thank you!
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For me, it’s cultural context. AI can generate references, but it can’t fully replicate lived experience. As a South African creative, so much of my work relies on instinct, memory, language and the nuances of how we actually experience our culture.
When I’m reimagining iconic or legacy South African brands, the success of the work depends just as much on what I bring to it as the tool itself; my lived experience, cultural memory, understanding of our language, humour, visual codes and the way South Africans actually experience these brands.
The technology can help me execute an idea faster, but it can’t replace the instinct behind why something feels recognisably South African, why a certain reference matters, or why one creative choice will resonate more deeply than another.
Fabulous work, I keep circling back to it! 🤍
If AI can generate beautiful illustrations in seconds… why would you still draw one yourself? 🤨
I'm currently working on a packaging project that I can't share just yet, but one part of the process stood out: the hero element is a custom vector illustration, drawn by hand from...
This lines up with what I see in branding work. The tool speeds up execution, but choosing which cultural reference actually carries weight is the part only lived experience gets right, and that instinct is what makes the work feel local instead of generic.
🚨 MOST PRODUCTIVITY APPS DON’T HAVE A RETENTION PROBLEM.
THEY HAVE A VALUE-COMPOUNDING PROBLEM.
They help users capture more:
📝 More notes
🔗 More links
📸 More screenshots
🎙️ More voice memos
But a larger archive does not automatically become a more valuable product
The real test starts later:
❓ WHAT HAPPENS AFTER THE USER HAS ALREADY CAPTURED 300 THOUGHTS?
That is where most products quietly fail. Because the archive grows. But the user’s understanding does not.
A voice note from Monday.
A screenshot from Thursday.
An article saved two weeks later.
A sentence typed at 1 AM.
Individually, they look unrelated.
But sometimes they are the same unresolved question appearing in different forms.
And this is where AI becomes genuinely interesting.
Not when it summarizes another note.
Not when it rewrites another paragraph.
Not when it gives users another chat box.
But when it recognizes:
🧠 YOU HAVE BEEN CIRCLING THE SAME PROBLEM FOR 12 DAYS.
For this concept, the core mechanic was built around one distinction:
⚡ FREQUENCY IS NOT REPETITION.
REPETITION IS EVIDENCE.
A thought mentioned once may be noise.
A thought mentioned four times across different contexts may be a signal.
And a product that can detect that signal can create value a normal archive cannot.
01 - CAPTURE
🎙️ The thought should enter the system before it disappears.
No folders.
No tagging.
No forced structure.
02 - UNDERSTAND
AI extracts the underlying meaning.
“Maybe onboarding should get people to value faster…”
becomes:
💡 REDUCE TIME TO FIRST VALUE.
That is already more useful than transcription. But it is still not the interesting part.
03 - CONNECT
The system sees another thought about activation.
Then an article about onboarding. Then a screenshot. Then another voice note. Now the product has enough context to say:
🔁 YOU KEEP COMING BACK TO THIS.
4 mentions.
12 days.
At that moment, the system is no longer helping the user remember information.
It is helping the user recognize importance.
04 - TURN CONTEXT INTO VALUE
This is where the growth logic changes. Most retention loops ask users to come back and repeat the same action:
📝 Create another note
📊 Track another habit
🍽️ Log another meal
✅ Complete another task
A stronger loop can work differently:
📈 EVERY NEW INPUT INCREASES THE VALUE OF PREVIOUS INPUTS.
The product becomes better not simply because the user used it more often, but because accumulated context creates new insight. That creates compounding value.
Day 1:
The product understands what you said.
Day 10:
It understands what keeps repeating.
Day 30:
It may understand what deserves action.
That is a much more defensible form of retention. Because the user’s history is no longer just stored data. It becomes product intelligence.
✨ AI SHOULD NOT JUST HELP USERS PRODUCE MORE.
IT SHOULD HELP THEM NOTICE WHAT THEY ARE ALREADY REVEALING THROUGH THEIR OWN BEHAVIOR. That is where a note-taking product stops being an archive. And starts becoming a thinking system.
👇 FULL CASE STUDY BELOW
👉 BOOK A MOBILE APP SESSION
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This concept creates a unified AI learning hub, allowing students to seamlessly transition from asking an AI avatar questions ("I want to revise biology") to scanning physical exercise worksheets and generating structured study sheets in seconds.
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