24 months, from writing the first line of code to bankruptcy: The common pitfalls an architect saw in 47 "failed" projects

Running fast doesn't necessarily mean running steadily.

Many startups don't fail on the path of market competition, nor do they run out of money. Instead, they are trapped by their own accumulated code and chaotic architecture due to the inability to scale their products, ultimately falling into a desperate situation of slow death. In the past three years, an architecture consultant reviewed the codebases of 47 startups that sought help at the "product scalability" juncture and astonishingly found that they almost all stagnated along the same timeline.

Startup Technical Debt: The Timeline to Death

Meir Avimelec Davidov, the founder and CEO of Gliltech Software, specializes in "putting out fires" when startups encounter technical crises. Davidov clearly points out that these startups usually come to him not because they "burned through their money," but because of the scalability crisis in their codebases and technology stacks: "The product can't be scaled at all, but they don't know why."

He found that in such cases, the pattern leading to failure always repeats itself without exception:

Months 1–6: Everything goes smoothly. The pace is fast, there are frequent releases, customers are happy, and life is good.

Months 7–12: The progress starts to slow down. Strange bugs appear, and "fix it later" becomes the team's slogan.

Months 13–18: Integrating any new feature almost always affects three existing features. Every deployment is extremely stressful.

Months 19–24: Three more engineers are hired, but they are just maintaining the existing mess, and no one is working on new things.

After 24 months, reality leaves only two choices: either rewrite the system from scratch or watch the system die slowly.

He can make such a generalization because the "foundational problems" he saw in the 47 codebases are highly similar.

The most glaring issue is at the database level. "About 89% have absolutely no database indexes. None at all." The reason the application is slow is not due to some strange bugs. Every request scans through 100,000 records. The resource side is also equally eye - catching. About 76% of the companies "bought eight times the number of machines" in the cloud, with an average utilization rate of only 13%. "You pay for a hundred machines but only use thirteen," burning through $3,000 to $15,000 a month for nothing.

The security side is also very vulnerable. Nearly 70% of the systems have authentication vulnerabilities that would give security engineers a "heart attack." As for quality assurance, the most fatal problem is often not the low coverage rate, but the complete absence: 91% of the teams have no automated tests. So every deployment is like playing Russian roulette, and no one can "click a button to confirm that nothing else is broken."

If calculated in terms of cost, estimating an engineer's annual salary at $120,000, Stripe's research shows that developers spend 42% of their time dealing with bad code. So for a 4 - person team, it's a waste of over $600,000 in three years. On top of maintaining the junk code, add the reconstruction cost of $200,000 - $400,000 and the income loss for 6 - 12 months during the reconstruction period. The total loss for each company is $2 - 3 million.

Many founders don't realize this until the 18th - 24th month. At this time, they have just completed their Series A financing with a beautiful growth curve, but they don't realize that "the growth is about to fall apart."

"Two - Week Architecture, 18 - Month Stability"

So what can really avoid these problems?

Davidov's method is very simple: Make the most cost - effective investment before writing the first line of code—spend two weeks on architecture. He said bluntly, "I know it's boring, and you want to release the product quickly, but these two weeks can save you 18 months of hell."

During these two weeks, you should maintain a scaling mindset from the start. First, ask "what will break when there are 10,000 users" instead of "can it run with 100 users." Key paths such as database queries, file uploads, and background jobs should have the capacity to handle 100 times the load from the first day. At the same time, automated testing should be implemented from Day 1. If you can't "click a button to confirm that nothing else is broken," every deployment is a gamble. It's better to choose a "boring" technology stack. React/Node/Postgres may be boring, but they are easy to recruit for, have answers on Stack Overflow, and won't suddenly "die" at 2 am.

External architecture review should also be brought forward to the first week. In his words, "Find someone who has done this kind of thing to review your architecture in the first week. Don't wait until the 12th month; it'll be too late then."

The reason for doing this so early is because of a harsh but true statement: "Most technical co - founders and the first batch of engineers are good at writing code, but they've never designed a scalable architecture. It's like being an excellent chef but never having managed a restaurant kitchen during the dinner rush."

The principle of "Move fast, break things." may work for companies like Facebook that can burn money indefinitely. But in your startup, it's suicide. Therefore, every technical startup team should conduct a self - check: Where will the system break when the user volume increases by 10 times? Do you have automated tests? Can the database handle 100 times the query volume? If the user growth is 10 times, will the infrastructure cost skyrocket to $50,000 per month?

If the answer to any of these questions is "I don't know," you're building a house on quicksand.

The Same Problems, One Startup After Another

Although the pitfalls pointed out by Davidov seem "basic" (and thus have been questioned), many front - line practitioners in the comment section recognize their universality.

One reader said bluntly that his work is similar to Davidov's, and these pitfalls are "just the tip of the iceberg," especially after the explosion of AI, as anyone who can use tools like Claude Code (referring to AI coding tools) is quickly launching products.

"The seemingly shiny products are actually full of untested code, no one takes responsibility, and there's a 'no need for documentation' attitude. There's basically no architecture design." He sighed that the most valuable things in engineering are often ignored. "Both research and practice say don't avoid testing," but many startups "don't think so."

Another developer also provided on - site testimony saying "I've seen it too."

He witnessed a company spending three years on a product that should have been completed in six months. Due to poor initial design, the finished product had to be scrapped and redone. He also saw a software project that was very likely to be a big hit, but the team stopped it before launch—not because of market issues, but because the architecture led to high per - user costs. "For the same functionality, a bad architecture requires 50 times more servers." His summary is very realistic:

• Doing engineering right from the start isn't necessarily more expensive;
• Maybe just 3 strong engineers can work faster and more steadily than 20 cheap outsourced workers;
• Conversely, "cheap but inefficient" outsourcing will pile up a mountain of technical debt over the years, which will ultimately be worthless.

Some people think this is just the reality, nothing profound. It all boils down to basic skills and self - discipline.

Bro, this is the best article I've read here in the past few weeks, seriously—every word is a scar of real founders, not just armchair theorizing. But to be honest, most early - stage founders still won't listen: they'll continue to tape features together, saying "we'll refactor later," and then cry when a two - second query blows up their $30k AWS bill.

The most absurd thing is: this isn't even some profound technical advice; it's just basic skills and self - discipline—write tests, name tables properly, add indexes to queries, and don't chase shiny new frameworks. I've seen people bragging about "scalability issues" when they only have 500 users, but the truth is: they don't even use background task queues or cache layers.

Someone in the comment section also raised a thought - provoking question: "Great writing. What role does ambient coding play in all this? How much of the code you're auditing now is written by AI?" This hits the current key point. AI has lowered the threshold of "getting things up and running" to an unprecedented level and has also advanced the arrival of "slow death" significantly. The code generated by models often "seems usable," but it accumulates technical debt faster and makes it harder to judge the quality.

The creativity and destructiveness of LLM coexist: it can quickly turn ideas into code, but it may also mistake a temporary scaffold for a foundation, and the cost often doesn't become apparent until the 18th month.

Reference Link: https://old.reddit.com/r/Entrepreneur/comments/1o4jup6/i_audited_47_failed_startups_codebases_and_the/

This article is from the WeChat official account "InfoQ", author: Tina. Republished by 36Kr with permission.