Still February 19th, 2026.
I’ve been blogging a bit more freely lately. Less polished. More thinking out loud( Thanks ChatGPT for helping make my thoughts more grammatically correct and readable .
Today I was reflecting on two things:
- A past post I wrote about running chatGPT while jogging.
- A short YouTube clip where a GenAI company leader said something along the lines of:
“Why did we need developers? We spent a year building this solution. I was able to generate it with AI in an hour.”
I’m too lazy to find the
https://www.youtube.com/@ThePrimeTimeagen
She got lightly roasted for that take.
Not maliciously — but thoughtfully.
And the reason is important.
The Illusion of “One Hour of Work”
On the surface, her claim sounds impressive:
- A team spent a year solving a hard problem.
- AI generated the solution in an hour.
Order-of-magnitude improvement, right?
Not exactly.
What was skipped in that statement was the most valuable part:
The year of understanding the problem.
That year wasn’t wasted time.
It was a problem definition.
It was false starts.
It was wrong assumptions.
It was architectural pivots.
It was constraint discovery.
It was learning what not to build.
AI didn’t replace the year.
It compressed the final implementation step.
The Real Pattern I’m Seeing
I work across different products and use cases. And I’ve noticed something very consistent:
Sometimes it takes:
- A year of friction
- A year of incidents
- A year of edge cases
- A year of “this doesn’t quite work”
Before I realize:
“Oh. We’ve been thinking about this wrong.”
And once that realization clicks?
With a few focused prompts, I can build something incredibly fast.
Not because AI is magical.
But because the thinking is done.
Prompting Is the Output of Experience
There’s a misconception that AI replaces expertise.
In reality:
AI amplifies clarity.
If you deeply understand:
- The constraints
- The trade-offs
- The failure modes
- The operational realities
You can feed that into a prompt and generate something powerful.
But without that context?
You generate a clean-looking wrong answer.
That’s the gap I saw in that leadership statement.
It wasn’t an hour of work.
It was:
1 year of research → distilled into 1 hour of decisive prompting.
This Applies to LLMs Themselves
Take large language models.
Technically speaking, if you had enough GPUs and enough data, you could reproduce something similar to OpenAI’s models.
The hardware isn’t the only moat.
The real moat is:
- Years of iteration
- Billions of training signals
- Reinforcement tuning
- Evaluation pipelines
- Feedback loops
That intellectual compounding is the asset.
It’s not just “run GPUs and get intelligence.”
It’s:
Run GPUs for years with the right objective functions.
Why Open-Source IDE Tools Struggle (For Now)
I like open tooling. I really do.
But let’s compare:
- A community-driven coding IDE
- A product like Cursor
The challenge isn’t just interface design.
It’s:
- Access to frontier models
- Model fine-tuning
- Institutional feedback loops
- Evaluation at scale
An open IDE can be great at orchestration.
But if it doesn’t control the model layer, it inherits its limitations.
That doesn’t mean open tooling won’t improve.
It just means:
The hard part isn’t the UI.
It’s the compounding intelligence layer underneath.
A Wild Idea: Distributed Model Training
Here’s a thought experiment.
Bitcoin uses distributed compute to secure money.
What if engineers pooled GPU power to train an open coding model?
- Solve real coding problems.
- Score outputs.
- Feed reinforcement signals back into a shared model.
- Iterate publicly.
The challenges?
- Incentive structures
- Reward alignment
- Non-deterministic output scoring
- Funding infrastructure
- Governance
It’s not trivial.
But it’s interesting.
And it would shift the “model moat” conversation.
The Leadership Gap
The real lesson from that video wasn’t about AI speed.
It exposed something deeper:
There’s often a gap between:
- Leadership perception of effort
- The invisible cognitive labor behind expertise
From the outside, it can look like:
“You built this in an hour.”
From the inside, it’s:
“I’ve been wrestling with this system for a year.”
AI makes the last mile visible.
It makes the final step dramatic.
But the invisible year still matters.
My Core Take
AI doesn’t eliminate expertise.
It converts expertise into leverage.
The better you understand a system,
The faster you can build with AI.
The weaker your understanding,
The faster you can build the wrong thing.
So when someone says:
“Why did this take a year? AI can do it in an hour.”
My response is simple:
It took a year so AI could do it in an hour.
And that distinction matters.