Most business leaders think that the best way to beat the competition is to push their development teams harder and demand faster delivery. I've seen the opposite happen many times.
When you prioritize "shipping fast" and "getting to market first," you often end up taking the longest time to succeed, because your team must spend months, sometimes years, addressing the problems caused by your haste. On the surface, things appear to be improving, but internally, they can feel overwhelming. You will notice this impact on your staff.
This is the harsh truth about rushing IT development:
Here's what really happens in the codebase when you tell your team to "just get it done fast": you don't do proper input validation and sanitization because you say, "We'll add that later." And then you have to deal with SQL injection attacks and data breaches for months. This wasted time could have been avoided by using simple parameterized queries and validation frameworks.
In 2024, the average cost of a data breach was $4.88 million. 73% of these breaches require more than 200 days to resolve. You only code for the happy flow, but real users submit incorrect data, experience network timeouts, and encounter failures with third-party APIs.
Your app crashes more than it should because you didn't set up proper error handling, or circuit breakers, or graceful degradation patterns. I know these take time to implement, but what would you rather have? Customers abandoning it?
Businesses lose an average of $5,600 per minute when their systems go down, and e-commerce sites can lose up to $300,000 per hour during busy times. Instead of fixing the root causes of problems, you just patch them up with quick fixes. Instead of proper garbage collection, that memory leak gets a band-aid restart script. Instead of being optimized, the slow database query is cached.
Soon, you will find yourself struggling to keep your building intact.
To keep up with technical debt, companies usually have to spend 23–42% of their total IT budget each year.
You don't do full testing because "writing unit tests takes longer than manual testing." This approach does not include load testing, test-driven development, or integration testing. Your first real test is when you have paying customers in production. Companies that don't test their software properly have 60% more bugs in their products and spend 40% more time fixing them than companies that do.
You start without being able to properly monitor and see what's going on. There are no logging frameworks, no application performance monitoring, and no health checks in place. When things go wrong—and they will—it's difficult to figure out what's amiss. Without proper monitoring, it takes an average of 4.5 hours to find and fix IT problems. With full observability tools, it only takes 45 minutes.
It's easy to see that every shortcut you take today will cause two new problems tomorrow. Each of those problems makes two more. You're going to be in a lot of trouble with technical debt, security holes, and unstable systems soon. All because you were in a hurry to meet some random deadline.
The true cost of rushing in those "move fast and break things" success stories is often overlooked. You don't guarantee a quick time to market when you rush code to market. You're just making sure that failure to market happens quickly. Remember that most Silicon Valley break-movers lose millions, but you never read about those; you only read about the 1 in 350 VC-backed companies that make it. That is a staggering 0.29%. I would not bet on that strategy just yet.
Because code that is rushed doesn't just break once. It breaks all the time. In production. This issue arises when dealing with real customers. At the worst times. Your developers are putting out fires instead of adding new features. Instead of adding the features that the customer asked for, they're fixing race conditions at 2 AM. They're patching vulnerabilities in dependencies rather than creating the next version.
According to research, developers in environments with a lot of technical debt spend 42% of their time on maintenance and bug fixes, while those in well-architected systems spend only 23% of their time on these tasks. Bad code drives up your infrastructure costs by requiring more servers to handle the same load. Your database runs slower because no one took the time to make the right indexes or make the queries run faster. Unoptimized applications typically require 3 to 5 times more infrastructure resources, directly impacting your cloud computing and operational costs.
The costs of getting new customers go up because products that are rushed have higher churn rates. People stop using apps that crash a lot or don't work well. For example, 53% of mobile users will stop using an app if it takes longer than 3 seconds to load. It costs 5 to 25 times more to get a new customer than to keep an old one.
In the meantime, what about your competitor who took an extra month to set up proper error handling, security controls, and performance optimization? They're growing smoothly while you're still working on the base.
Let me tell you a myth that is costing you millions: The race isn't about speed unless you're in a real winner-take-all market with huge network effects. It's about lasting.
There is usually room for more than one winner in most markets. Your real job isn't to be the first to market; it's to still be there when the "fast movers" fail because they owe too much money. The businesses that are the biggest in their markets aren't usually the first ones there. They are the ones who took the time to use excellent software engineering practices from the start. They used well-known security frameworks like the OWASP guidelines to make their systems safe, set up the right authentication and authorization patterns, and made sure their APIs were designed with security and resilience in mind from the start.
Companies that have good security practices have 76% fewer security incidents and save an average of $1.76 million for every breach they avoid. They wrote code for failure scenarios using patterns like retry logic with exponential backoff, circuit breakers to stop failures from spreading, and bulkhead isolation to keep problems from spreading.
They set up full logging and monitoring so they could find problems before customers did. Systems that are built well and have the right resilience patterns are up 99.9% of the time, while systems that are built quickly are up 95% to 98% of the time. While you may believe that 95% to 98% uptime is an acceptable figure to agree to, take a moment to consider what that actually translates to in terms of downtime for your availability metrics. Remember that you should only calculate the times you really want to be available. This is due to the fact that any unavailability during your downtime is not taken into account. But failures do not take your opening hours into consideration.
Successful companies used domain-driven design to get the business requirements right, made complete API documentation, and built automated testing suites that found regressions before deployment. Companies that do a lot of testing deliver features 2.5 times faster and with 50% fewer bugs after deployment.
They made sure that their environments were always the same by using infrastructure as code, setting up the right CI/CD pipelines with automated security scanning and regression testing, and planning for horizontal scaling from the start.
Companies that have mature DevOps practices deploy 208 times more often and have lead times that are 106 times faster, all while being more reliable.
The truth is that your development schedule isn't about meeting deadlines. The purpose is to create systems that function effectively when real people use them in real-life situations with actual data and at a large scale. If your code crashes under load because you didn't use the right caching strategies or database connection pooling, it doesn't matter how fast it is to market.
If you neglect to conduct security code reviews and utilize static analysis tools, the likelihood of hacking increases significantly.
Think about the return on investment: putting in an extra 20–30% up front for the right architecture, security, and testing usually cuts the total cost of ownership by 60–80% over the life of the application.
The first "delay" of 2 to 4 weeks for proper engineering practices saves 6 to 12 months of fixing technical debt later on.
You have a simple choice: either take the time to follow excellent software engineering practices now, or spend the next two years telling customers why your system is down again while your competitors take your market share. The companies that last and eventually take over choose quality engineering over random speed. I leave it up to your imagination as to what multi-trillion-dollar company immediately comes to mind.
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