Startups often chase the dream of the A-team, thinking that the best talent will make scaling easier and faster.
But sometimes, having the A-team creates more problems than it solves.
We’ve all heard the classic startup mantra:
- Yes, you’ll launch faster.
- Yes, you’ll use the latest and greatest tech.
But there’s a pattern I’ve seen over the years, even back before the boom of LLM (Large Language Models). It still holds true today.
The Hidden Problem With the A-Team
A client approached me recently with an A-team lineup you’d dream of:
- Top-tier data engineer
- Full-stack expert
- Cloud guru
- Clear technical vision
Now, at first glance, this sounds like a recipe for success, right? The best talent, the best tools, the best tech. So what went wrong?
Here’s where the hidden pattern kicks in.
When you have the best team, you get:
- Complex, overly-curious systems.
- Unnecessary over-engineering.
Everything seems perfect... until the real test happens—when traction hits. When real-world conditions and real user load start to push the system to its limits.
The Technical Setup: Optimized, But Costly
They were processing 20,000 transactions per minute on PostgreSQL. Everything was optimized to perfection:
- Indexing
- Partitions
- Read replicas
- Materialized views
- Spatial indexing
It was a well-oiled machine. But there was one problem… the cloud costs were out of control.
They scaled like pros. They optimized like pros. But cost? It was still bleeding them dry.
The Real Problem: The Questions Nobody Asked
When I stepped in, I didn’t add more tools or fancy infrastructure. Instead, I started asking the right questions:
- Why was 80% of the processing happening in real-time?
- Did every single piece of data really need to be perfectly accurate?
- Could we rethink when and how data was processed?
Sometimes, it’s the simple questions that lead to the most powerful solutions.
The Fix: A 50-line Code Change
The solution? A 50-line logic shift.
Yes, just 50 lines of code, and the results were staggering:
- Non-critical calculations were approximated during off-hours.
- Real-time processing was reduced to just the essentials—only 20% of the processing was real-time, down from 80%.
Here’s the outcome:
- Delivery speed jumped 5x.
- Costs dropped by $60,000/month.
- Accuracy? Went from 20 to 19.8—and users didn’t even notice.
As we all know, it happened again—Intel laid off 20,000 people. This story will continue to repeat. I’ve released a video on YouTube that you might be interested in.
Check it out here: https://youtu.be/LeINdqbzMpk
The Takeaway: Great Engineering Isn’t About More
Here’s the most important takeaway: Great engineering isn’t about knowing more—it’s about seeing the problem from a simpler perspective.
It’s easy to get lost in complexity. The best engineers often get too focused on being precise, too rigid, and too stuck in the weeds. But the real challenge is to keep thinking like a beginner. To simplify.
Sometimes, the most impactful decisions come from stripping things down to the basics. From stepping back and asking, “What if this could be simpler?”
How to Avoid Over-Engineering and Scale with Confidence:
To help you avoid falling into the over-engineering trap and scale with more clarity, here are some key questions to ask your team:
- Are we overcomplicating the problem?
- Do we really need real-time processing for everything?
- Is the pursuit of perfection costing us more than it’s worth?
- Can we identify the essential features?
- Are we scaling our team and tech too quickly?
- Have we planned for failure?
- How will this affect long-term scalability?
- Are we making decisions based on assumptions or data?
Reflect: When Was the Last Time Simplicity Turned Everything Around for You?
If you’re building something right now—whether a product, a team, or a business—think about that one decision, that simple shift, that had the biggest impact. When was the last time a simple decision made all the difference?
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Hope you found this helpful.
Chat soon,
Rohan 👋
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