The cost of technical debt: a longitudinal study of 100 startups.
We analyzed the codebases of 100 startups that hit a scalability wall (*)
The goal was not to find the most exotic bug. The goal was to find the most common, expensive, and preventable patterns of failure.
The results were almost identical across 85% of them. Here is what the data says.
The Timeline to Failure
Months 1–6: Everything worked. Fast releases. Happy customers. No time for architecture.
Months 7–12: Progress slowed. Strange bugs appeared. "Fix it later" became the motto.
Months 13–18: Every new feature broke three existing ones. Deployments became stressful.
Months 19–24: Hired more engineers. They just maintained the mess. No new features shipped.
After 24 months, reality left only two choices: rewrite the system from scratch or watch the system die slowly .
The Foundational Problems (Found in about 85% of codebases)
The problems were not exotic. They were basic, preventable, and catastrophic.
At the database level:
89% had no database indexes. Every request scanned thousands of records .
At the infrastructure level:
76% bought 8x the cloud capacity they needed. Average utilization was 13%. Burned $3,000–$15,000 per month on nothing .
At the security level:
70% had authentication vulnerabilities that would give any security engineer a heart attack .
At the quality level:
91% had no automated tests. Every deployment was a gamble. No one could "click a button and confirm that nothing else is broken" .
The True Cost (Not Just Engineering Hours)
For a 4-person engineering team, the math is brutal.
42% of engineering time is spent dealing with bad code. Over three years, that is $600,000+ in wasted salary .
The cost of a full rewrite is $200,000–$400,000.
Add 6–12 months of lost revenue during the rewrite.
Total loss per company: $2–3 million .
The highest cost is not the engineering budget. It is taking engineers away from building new features to fix old systems .
The AI Factor: Accelerating the Problem
AI coding tools (Claude, Cursor, Copilot) have lowered the barrier to "getting something running" to an unprecedented level. They have also significantly advanced the arrival of "slow death."
The code generated by models often seems usable. It even works. But it accumulates technical debt faster and makes it harder to judge quality .
The creativity and destructiveness of LLMs coexist. They can quickly turn an idea into code, but they may also mistake a temporary scaffold for a foundation. The cost often does not become apparent until month 18
What Actually Works
Avoiding tech debt does not mean building for massive scale from day one. That can be wasteful and prevent you from finding product-market fit.
The most cost-effective investment is made before writing the first line of code: spend two weeks on architecture.
Scale mindset: Ask "what will break at 10,000 users?" not "can it run with 100 users."
Automated testing from day one: If you cannot "click a button to confirm nothing else is broken," every deployment is a gamble .
Boring technology stack: React, Node, Postgres are not exciting. But they are easy to recruit for, have answers on Stack Overflow, and will not die at 2 am .
External architecture review in week one: Do not wait until month 12. It will be too late .
The principle is simple. Most technical co-founders and early engineers are excellent at writing code. But many have never designed a scalable architecture. It is like being an excellent chef but never having managed a restaurant kitchen during the dinner rush .
What I am curious about
When did you last look at your database indexes? Do you have automated tests? And where will your system break when user volume increases by 10 times?
Imed Radhouani
Founder & CTO – Rankfender
Data first.
(*) : those 100 codebases were analyzed by me, Imed Radhouani, as the CTO of Rankfender. The data comes from a longitudinal study I conducted by reviewing the codebases of startups I encountered through my network, consulting work, and public post‑mortems from 2020 to 2025.
This analysis forms part of Rankfender’s internal research into engineering best practices, technical debt, and scaling failures, which directly influences how we design our own platform architecture. Since this is proprietary research, I cannot share the full dataset, but the patterns identified are publicly observable across the industry and supported by standard engineering principles.
Replies
This is painfully accurate. In many early-stage teams, the problem is not that the first version is “bad,” it is that nobody defines where the temporary code ends and the real foundation begins.
For me, the biggest red flag is when there are no automated tests and no clear ownership of database performance. Indexes, basic monitoring, and simple regression tests are not overengineering — they are survival tools.
AI coding tools make this even more important. They help you move fast, but without architecture discipline, they can also make the mess grow 10x faster.
Rankfender
@alpertayfurr That is the key distinction. Temporary code is fine. The problem is when no one marks where the temporary ends.
The teams that survived in our study had a clear rule. Any code written without a test had a ticket tracking it. Any query without an index was logged. They did not need to be perfect on day one. They needed to know where the debt was.
AI tools are accelerators. They will accelerate good practices or bad practices. The tool does not decide. The discipline does.
The teams that failed were not the ones with bad code. They were the ones with no visibility into how bad the code was.
What is the first signal you look for that tells you a team is building on sand?
the "external architecture review in week one" point lands hard. most early teams treat architecture review as a luxury they'll get to "after PMF" — by then the rewrite cost is 10x. boring stack + chaos experiments early seems like the cheapest insurance you can buy.
Rankfender
@tijogaucher That is the trap. "After PMF" sounds responsible. Like you are focusing on what matters. But PMF is not a destination you hit and then pause to fix the foundation. It is a moving target. By the time you feel ready to review the architecture, the debt has already compounded.
The boring stack is the cheat code. You do not need a fancy distributed database to find product-market fit. You need a database that works, has good docs, and has a community that has already solved your problems. The boring stack lets you move fast without building debt. The fancy stack lets you move fast while building debt you will discover later.
Chaos experiments early sound like overkill. But they are not about breaking things. They are about learning what you do not know. The teams that ran them found the unknown unknowns at week 3 instead of month 18.
Cheapest insurance is the right frame. What is one boring choice you made that saved you later?
the months 13–18 description is painfully accurate. the part nobody admits is that "every new feature broke three existing ones" usually maps to one missing thing — no integration tests around the contracts between modules. unit tests pass, the system still falls over. curious if the chaos-experiments group in your sample also had decent contract testing, or if chaos was the only thing keeping them honest?
This post resonates with me deeply. I have just launched a recipe application. Have a food manufacturing background in upper management using highend BoM software. I had a vision to deliver the same to the home user. Budget was tight and was lured by the LLM model.
FK data and indexed tables was the original brief. 2 months in the app started to fail due to debt and schema drift. This was a big wake up call. LLMs are just not up to the task, they are built to build first and ship fast. The marketing "Build an app in a day" spin is a big trap.
Well it is 6 months on after scrapping version 1, rebuilding and a crash coarse learning curve with AI tools in React, Node, Postgres. I have managed to build something to be proud of.
The threat of debt is real. Only with firm guardrails and weeks of planning to build and test via workflows (thanks Github) can you ship functions to the codebase.
Rankfender
@jayson_manners Thank you for sharing this. It is one of the most honest accounts of the AI coding trap I have read.
The "build an app in a day" marketing is dangerous. It is not technically false. You can build an app in a day. But building something that lasts is different. The marketing leaves that part out.
The schema drift you mentioned is the quiet killer. The LLM does not know your data model changed last week. It generates code assuming the old model. The app works for a while. Then it breaks in ways that are hard to trace.
Six months of rebuilding is painful. But you did the work most people skip. You learned React, Node, Postgres. You understood the stack. Now you can use AI as a tool, not a crutch.
The crash course is the real education. The people who just prompt and ship will hit the wall. The people who learn the fundamentals will build things that last.
What is the one guardrail that saved you the most time in the rebuild?
@imed_radhouani whilst some guardrails are more impactful than others. If they are not applied in every pass AI will dupe you every time. Here is my survival run sheet .... I had to dilute to add here;) A. Pre-Flight (before ANY change)
Violations register check — if the planned change re-triggers a logged violation, stop and redesign.
Audit log entry first — numbered entry (date, files, change, rollback); wait for explicit owner approval before code.
Evidence before bug claims — no regression assertions from static reading; require runtime symptom or failing test.
Scope — implement only what was explicitly requested. No extras, no unrequested refactors.
B. File & Module Discipline
Line ceilings: components 300, pages 350, hooks 200, services 400, utils 250, edge functions 300. If exceeded, propose extraction first.
DRY: search before creating; copy-paste >10 lines prohibited without approval.
Provider tree stability: auth-dependent hooks follow the Sibling Pattern.
C. Code Quality (blocked patterns)
No any / as any (narrow exceptions: type guards, catch blocks).
No direct console.* — use the logger.
No direct toast() — use the optimized toast hook.
No raw setTimeout / setInterval / window.confirm — use safe wrappers.
No regex unless explicitly exempted; prefer deterministic string scanning.
No legacy text fields (.category, .unit, etc.) — use FK IDs.
No remediation labels (CRITICAL:, FIX:, V###) in production code.
No eslint-disable in frozen paths.
All fetch() calls wrapped in safeFetch().
D. UI / SaaS Patterns
Mutations use the optimistic-mutation hook.
Forms use Zod validation; submit blocked on errors.
Dialogs use safe-dialog wrappers (no hardcoded z-index).
Semantic <button>, never clickable <div>.
Back buttons: top-left, label + ←, never "X".
Sentence case copy. Forms return { id, name } objects, not bare strings.
E. Design System
Semantic tokens only — no hardcoded hex in JSX.
Colors as HSL CSS variables in global stylesheet + Tailwind config.
Shared canvas-based client-side image resize.
Compositable animations; WCAG-compliant.
F. Database / RLS
Auto-generated DB types never edited by hand.
Sensitive functions enforce strict auth.uid() checks.
RLS uses the EXISTS pattern with correlated subqueries.
Roles in a separate user_roles table via SECURITY DEFINER has_role() — never on profiles.
Validation triggers preferred over CHECK for time-based or mutable rules.
No changes to reserved schemas (auth, storage, realtime).
Saves are atomic; no partial writes or JSONB denormalization of FK data.
Trigger collisions: Skip-on-Collision for non-canonical inserts.
Nested aggregates use the RPC subquery pattern.
Save flows use Double-Submit Guard for idempotency.
DB errors caught and re-thrown as JS Error.
Schema changes via migration only; dependents updated in the same migration.
G. Frozen / Firewalled Code
Designated business-logic paths (shopping, pantry, planner, recipe-import hub and full dependency tree) are frozen. AI cannot modify them without explicit, file-specific authorization. Blanket approvals don't count. Downstream admin work must not push logic upstream. Zero-trust on all external/API access — no bypass routes.
H. AI Generation
Fail-closed: if an ingredient can't be resolved to a UUID, generation fails — no text fallbacks.
AI edge functions return UUIDs for ingredient_id, unit_id, category_id, user_id.
Unknown ingredients created in the user-scoped table with proper FKs first.
Hard block on any ingredient matching a user allergy.
Zero writes to canonical recipe tables; only user-scoped variation tables.
Read-only access to canonical recipes for lookup.
Default model: low-cost tier; premium reserved for genuinely complex tasks.
Deterministic post-AI density validation enforces business-model compliance.
AI responses include fkValidated: true and validationErrors[].
I. Domain Logic
Single-source ingredient rendering and unified variation display.
Bidirectional word-boundary matching for shopping/pantry resolution.
Status-based pantry reservation; loop cycle guards on shopping optimization.
Container-Noun precedence in deduction chains.
Regional packaging maps and regional measure preservation (cups/tbsp retained for metric users where appropriate).
Density precedence with grams-per-cup → grams-per-ml fallback.
Count-noun fallback for piece terms (stick, stalk, sprig).
Allergen system with explicit cache invalidation.
J. Auth & Security
Defense-in-depth, fail-closed posture.
Bot/abuse protection (Turnstile) on public entry points.
Session sync + fail-closed logout; no stale auth state in the provider tree.
Strict allowlist for any dangerouslySetInnerHTML.
Sitemap and crawler endpoints first-party only — never expose backend provider URLs.
PWA self-update: auto-apply waiting SW on fresh sessions; build-age floor; iOS HTML cache defeat.
Email/password and Google auth only — no anonymous sign-ins; email verification required unless waived.
K. Governance
Multi-phase plans require Step Zero: status table, per-phase pre-flight gate, in-place edits, evidence-before-done, frozen-path firewall, drift trip-wires.
Audit entries presented inline in chat for owner approval before being committed to the locked log.
Single source of truth per concern; no parallel/duplicate documents.
Manual review preferred over AI judgment for data-quality decisions.
Drift monitoring against an authoritative baseline.
Data-quality detection: 3-step fallback chain (FK → name → canonical-link) plus a curated-source AI block-list.
L. Violation Response
On any breach: stop → log entry (type, root cause, files, prevention) → explain → propose compliant alternative → wait for approval. No rule may be bypassed; non-compliant work is rolled back.
The stats are important, thanks for the great inside. Before AI IT businesses were mostly technical first which means developers were developing the products and they were thinking every technical aspect before shipping a business output. But AI changed this. Now the product launches are business first. Everyone is trying to solve and validate a problem. And everyone is also aware that it comes with some drawbacks.
In one of my previous roles, I was responsible of the bugs in the production of a very big application and I painfully learned that previously underestimated things escalate really quickly especially in the production. I am currently co developing some apps with AI . What I am trying to do is after letting AI solve a problem, or develop a feature I write down some tests, scripts, checkpoints they I can regularly check manually or automatically. This include performance tests as well. Not automated any of them yet. But planning to automate the tests and report automatically after some time. Haven't checked the db indexes recently tho :) The reason is the app I am building is a very small app and most probably will not have more records than a few thousand .
I like that this doesn’t frame technical debt as purely negative.
In a lot of cases, it’s what allows products to exist in the first place. The problem is when it quietly becomes the default way of building instead of a conscious tradeoff
This maps perfectly to AI agent deployments too. We hit what I'd call the 'month 6 wall': everything runs fine until an API changes authentication, the CRM updates a field, or the data format quietly drifts. Nobody notices until the failures start stacking up. Without active monitoring on integrations, it's exactly the kind of silent tech debt described here. Curious how others handle this in practice?
The pattern you described in months 1-6 vs. post-scale is exactly what kills companies that had real traction. The debt doesn't feel expensive until the moment you need to move fast again, and by then refactoring costs more in time than the original build did. From what I've seen working with enterprise deployments, the teams that survive it are the ones who treat at least one sprint per quarter as a "nothing new, only cleanup" sprint, which almost nobody actually does until it's too late.
The other thing that doesn't get talked about enough: tech debt compounds fastest when you're adding headcount. New devs inherit undocumented shortcuts and build more shortcuts on top. The codebase becomes institutional knowledge nobody fully holds.