How software complexity taxes products and prevents scalability (and what to do instead)
The "Technical Bureaucracy" Tax: Why your R&D is actually a maintenance sinkhole.
Most founders mistake complexity for the "natural cost of growth." It’s not. It’s a systemic tax on your capital that never shows up on your balance sheet until it’s too late.
When you scale without structural modularity, you trigger an operational levy. We’ve all seen it: a simple 2-day feature ends up taking 3 weeks because of "interdependencies." That’s the Sync Tax in action.
The Cold Logic: Multipliers vs. Drag
In a lean, high-performing system, you have a Value Multiplier. $1 of effort/capital flows through the org and generates $>1$ of market value.
But in an overly complex product, that same dollar gets consumed by a sort of Technical Bureaucracy. Instead of shipping, your engineers are:
- Managing fragile data alignments.
- Dealing with "Syncs of Death" (trying to keep legacy data and new services from breaking each other).
- Sitting in "Coordination Meetings" just to ensure that fat bureaucratic machine doesn't explode.
You aren't investing in the future of your product anymore; you’re just paying interest on your structural debt.
Automation Won't Save You
The biggest mistake I see is founders trying to "automate the mess." Or worse, moving to a distributed system (Microservices) without modularizing the logic first.
- Automation of a mess is just faster chaos.
- Premature Distribution just adds a network-level "Sync Tax" to your existing logic tax.
The Strategy of the Cut
The only way out is enforcing structural modularity. You have to identify when the decoupling point is due and act accordingly. If you don't prioritize this strategy change, you’ll hit a plateau where the cost of maintenance exceeds the value of anything new you ship.
Your run-rate stays high, but your velocity hits zero. That's the death of a startup.
The Reality Check:
Look at your last month of dev tickets. How much of that R&D budget actually created new market value, and how much was spent just "keeping the lights on" inside a complex codebase?
At what point do you stop the feature rat race and start untangling the mess just so you can actually get things out the door again?
The framing of complexity as a tax rather than just "tech debt" is really useful. In my experience building API-based tools, the biggest trap is that complexity compounds silently — each new integration or edge case adds a tiny bit of overhead that seems harmless until suddenly 60% of your sprint is spent on maintenance. The "automation of a mess is faster chaos" line is spot on. I've seen teams reach for orchestration tools and CI/CD pipelines to paper over architectural problems when they really needed to step back and simplify the domain boundaries first.
Hey @alisherVerifly are you a CEO/founder? If so, I'd like to ask you a few questions about your experience. I'm trying to learn what type of product/service I should provide to help in this erea.
That two-day feature turning into three weeks really hits home, honestly. I’ve been stuck in that sync tax myself.
I spent eight months building a SaaS that never got a single paying customer. Part of the reason was that I ignored how messy the codebase was, slowing me down.
Here are some tips to help you prioritize which parts to modularize first when everything feels tangled:
Start small with decoupling: Pick one pain point and modularize that first. This will help you get a sense of what’s working and what’s not.
Don’t rush automation until your core logic is clean. It just spreads chaos faster.
Regularly review dev tickets to spot maintenance vs. new value split early. This will help you make sure that you’re not wasting time on unnecessary work.
How do you prioritize which parts to modularize first when everything feels tangled?
I compare overly complex systems with highly taxed bureaucracy because you don't know exactly why the "tax" is so high.
Once you analyze the situation, it becomes more like credit card debt.
Now you know what bill has the highest interest.
What's the recommendation of any financial advisor? Pay up first the debt with the nastiest interest.
Here it's the same, which is why I disagree with your first step. It's not about getting a sense but reducing the deficit in a sustainable way.
But I agree with your other steps, you can't automate an inefficient system, or you will just accelerate a loss.
About reviewing tickets, well I guess that's a daily tasks for engineers, product managers, and engineering managers.
Here I'm talking to business owners, non-tech people who have to make decisions that have negative consequences if the technical aspect is not well understood and not well assessed by the tech department.
You're right - I should clarify. "Start small" doesn't mean start easy. It means start with the one module that's causing the most pain and is still achievable.
The credit card analogy works, but only if you actually know which debt has the nastiest interest. In messy codebases, that's not always obvious without some diagnostic work.
I've seen teams jump straight to "fixing everything" and burn months with zero progress because they misidentified the bottleneck. Starting with one high-impact area lets you validate your approach and build confidence before the bigger surgery.
But fully agree: automate nothing until the logic is clean, and non-tech founders need better signals to assess this tax before it kills velocity.
Thanks for clarifying. And yes, spot on, learning which debt has the nastiest interest is key and a real challenge. It's one of the things I'd like to solve.
I’ve seen teams assume slowing velocity is just “growth pain” when it’s actually accumulated structural debt quietly eating execution speed. The tricky part is founders often don’t notice it until simple changes start requiring multiple teams, meetings, and workarounds.
Totally agree on automation not fixing the root issue. If the underlying boundaries aren’t clean, automation just helps you ship complexity faster. In my experience, periodically stepping back to simplify architecture (even when roadmap pressure is high) tends to pay off more than pushing another feature into an already tangled system.
Curious how you personally decide the timing for that “cut” moment — is it metrics-driven (cycle time, bugs, delivery lag) or more intuition from engineering leadership?
Stepping back to simplify architecture, especially when roadmap pressure is high, is easier said than done, not to say that's most of times impossible.
The key here is that the CEO or business leadership must be involved. Reducing complexity must be part of the roadmap, not a task you perform behind the curtain, trying not to be discovered by your manager.
Something I used for some time was similar to this, add some related complexity removal to the design of new features, so the feature would be a bit more expensive but because we were also removing complexity. But that's not sustainable and especially it's not transparent. This method does not help to have a good relationship with the business leaders.
What's the main problem? That the business leaders do not even know that cost exists! They ask for something, thinking it will only cost the number of hours engineers work on it. Tech also doesn't help. At least in my experience, it's really hard for tech to communicate to business the actual cost of complexity, especially in the future. So, the actual problem is that the business doesn't know there is a high tax that will be paid, and the more they add, the higher the tax becomes.
So, about metrics, I'm exploring this topic in depth trying to come up with some metric that can help CEOs to make more informed decisions. However, that's just a metric. It would be great to have a way to estimate that cost.
This matches my experience. Complexity isn’t just technical debt, it’s decision debt. Every extra layer forces assumptions you then have to defend forever. Keeping scope brutally small early has been the only reliable way I’ve found to stay honest about what a system actually does.
Ok that's smart. If you only have code that adds high value to the product and avoid bloating it, that can help you stay stable and the impact of added features would be lower.
However, let's say you do something that makes your product viral, everyone signs up and buys, you get thousands of orders per day. That's awesome, congratulations.
Now, those customers start demanding features and flexibility. Simultaneously, other competitors attracted to the same market because of your success, start offering those features you don't have yet.
How do you deal with skyrocketing demand? I can assure you, writing efficient code as an engineer is no longer enough. Now the way changes are implemented and shipped needs to be part of the business strategy, not just being careful as engineer.
Are you a CEO/founder? Let's talk if you're interested, I'm exploring this topic in depth and asking relevant people to learn what type of product/service I should offer.
That inflection point is exactly what I’ve been designing around lately.
Most people don’t realize scale is a governance problem before it’s a performance problem.
Could you explain what you mean by scale being a governance problem?
Thanks for the question.
When demand spikes, infrastructure usually isn’t what breaks first.
Decision flow does.
Who approves scope changes?
Who says no to feature requests?
Who protects core simplicity when revenue pressure rises?
At small scale, those choices are informal.
At larger scale, if decision rights aren’t explicit, velocity turns into chaos.
That’s what I mean by governance.
Thanks for clarifying. That's what I call ownership, but yeah, I agree. One of the first checks in the list is that every part of the system has an owner who is responsible for it.
But what would you say should be the next move from there?
This makes sense.
I’ve seen the same pattern. The decision looks simple in isolation, but complexity compounds in layers most people never see.
I wonder if part of the issue isn’t just visibility, but incentive structure. If leadership is rewarded for shipping features, complexity becomes someone else’s problem later.
Metrics help, but only if they change behavior.
Are you thinking DORA-style metrics as a translation layer for non-technical founders?
Unless the CEO wants to kill his company for some reason, I'm sure as soon as they learn the real future cost of their decisions today, they would immediately start asking questions and change their minds.
A business leader with engineering background might realize what they are doing, but those who don't understand what's inside that black box called tech department, they will just continue to squeeze it as much as possible.
I'm exploring a few metrics and yes, I'm partly inspired by DORA metrics, but of course, measuring how much more it will cost to develop something with the current level of complexity, interdependence, etc.
Does that make sense to you?
I’ve seen ownership help a lot, but feature pressure still sneaks in when revenue is on the line.
How do you actually protect simplicity when growth is pulling the other direction?
This is what I'm exploring right now. I don't think CEOs are well-informed. Especially if they are non-technical founders, they are completely blind to the actual cost of their decisions. So, they see a "great opportunity" that costs X to implement and that's it, all clean revenue. Not true. Usually in an interdependent product that new feature would introduce more complexity to the system and it would also be more complicated to implement because it has to be plugged into a big system. To do that, lots of meetings to coordinate and to not forget anything that breaks the whole system need to take place. Then that new feature needs to be added to the regression tests because everyone is so afraid of making that monster unhappy. If the CEO knew the real cost, the story would be different in terms of resource allocation. He would probably say, ok and how much would it cost to reduce that complexity so adding a feature is not so wasteful?
I'm exploring literature but also trying to come up with metrics (DORA style) but for CEOs so they can have visibility over the future cost of what they want to do.
Does that make sense?