Let’s say the quiet part out loud: yes, bad development can “increase LTV”—if by LTV you mean how long a client keeps paying because they can’t escape.
But that’s not real customer lifetime value. That’s dependency-based revenue. It’s fragile, it’s stressful, and it usually ends with a rewrite, a hard breakup, or a reputational crater.
This article breaks down:
- Why “bad code = higher LTV” can appear true
- What’s actually happening economically
- Why it collapses over time
- How to build healthy LTV without turning your projects into a trap
What People Mean by “Bad Dev Increases LTV”
In SaaS, LTV is typically a positive metric: customers stay because they get consistent value.
In services/agency work, people sometimes misuse LTV as:
- “How many months can we bill this client?”
- “How hard is it for them to switch?”
- “How many change requests will this mess generate?”
That’s a different metric. Call it what it is:
Trap LTV (dependency LTV)
Revenue created by complexity, opacity, and technical debt that makes leaving expensive.
It’s not “loyalty.” It’s “switching cost.”
Why Bad Development Can Increase Billing Duration
Here are the main mechanisms that make bad work look profitable short-term.
1) Lock-in through undocumented complexity
If the system has:
- no documentation,
- no tests,
- no clear architecture,
- “magic” behaviors,
…then every small change becomes risky. The client sticks around because only the original team “knows” how it works.
Result: more hours billed per change.
2) Tech debt creates recurring incidents
Bad code produces:
- regressions,
- flaky integrations,
- unstable deployments,
- unexpected edge-case failures.
Result: ongoing “maintenance” becomes a permanent subscription—just not the good kind.
3) Feature velocity slows, but change requests never stop
When the codebase is fragile, every feature costs more time:
- more manual QA,
- more hotfixes,
- more “it worked on my machine.”
Result: the calendar fills up even if progress doesn’t.
4) Misaligned incentives: hourly billing without quality gates
If you’re paid by time and there’s no clear definition of done:
- quality becomes optional,
- refactoring becomes “later,”
- deadlines push hacks into production.
Result: the system accumulates future billable pain.
5) Stakeholder opacity hides the damage
If the client doesn’t track engineering outcomes, they only see:
- “stuff is happening”
- “devs are busy”
- “we shipped something”
Result: the mess survives long enough to keep billing.
The Real Cost: Why This “LTV” Is a Time Bomb
Bad development doesn’t create value. It creates liability. That liability eventually becomes visible.
1) Trust decays faster than revenue grows
Clients forgive delays.
They don’t forgive:
- repeated outages,
- broken releases,
- “we need another week” forever.
Once trust is gone, your “LTV” becomes a countdown.
2) You lose high-margin growth paths
Healthy LTV comes from:
- upsells,
- expansions,
- referrals,
- multi-project relationships.
Trap LTV kills those because clients don’t recommend a team that hurts.
3) You invite audits, rewrites, and replacements
Sooner or later, one of these happens:
- new CTO joins,
- external audit runs,
- security review comes,
- investors ask uncomfortable questions.
Then the client discovers the truth: cost of change is insane.
The outcome is usually:
- rewrite,
- vendor switch,
- internal rebuild,
- or “please hand over everything and goodbye.”
4) Your team burns out
Working in a chaos codebase is brutal:
- firefighting never ends,
- planning becomes fake,
- people leave,
- quality drops further.
That’s how a “profitable” client becomes a team killer.
“Vibe Coding” as a Business Pattern
“Vibe coding” isn’t inherently evil. It’s a tool. The problem is where you use it.
Good vibe coding
- early prototype
- proof of concept
- UI demo
- internal tool with low risk
- timeboxed experiments
Dangerous vibe coding
- payments
- fiscalization/receipts
- authentication/permissions
- CRM sync logic
- inventory/order flows
- anything that can cause money loss or legal exposure
If you vibe-code something that touches money or compliance, you’re basically selling future incidents.
How to Tell If Your “LTV” Is Actually a Trap
Here’s a quick diagnostic. If your revenue grows mainly because:
- ✅ “Every change takes ages”
- ✅ “Only we can maintain it”
- ✅ “Client is afraid to touch it”
- ✅ “No one understands the system”
- ✅ “We do lots of fixes, not progress”
…then it’s not retention. It’s captivity.
Real healthy LTV looks like:
- ✅ faster delivery over time
- ✅ fewer incidents over time
- ✅ predictable releases
- ✅ clear roadmap outcomes
- ✅ client expands scope willingly
Metrics That Expose the Truth (Track These)
If you want to stop debating philosophy and start being factual, track:
Delivery & change metrics
- Lead time for changes (commit → production)
- Deployment frequency
- Change failure rate (how often releases break something)
- MTTR (mean time to recovery after incidents)
Quality & maintainability metrics
- Bug rate per feature
- Cycle time per ticket
- Test coverage where it matters (not vanity coverage)
- Time spent in refactoring vs firefighting
Business-facing metrics
- Cost of change (hours per “small” change)
- Time to onboard a new developer
- Churn risk signals (escalations, “we’ll get back to you,” scope freezes)
If your “LTV” rises while these get worse, you’re building a trap.
The Ethical Angle (Because It’s Not Optional)
Intentionally building a system that’s hard to leave is basically:
- exploiting asymmetry of knowledge,
- selling uncertainty as a product,
- creating avoidable dependency.
That might work in the short run. Long run it destroys:
- brand,
- hiring,
- partnerships,
- deal flow.
If you want to play long, you optimize for outcomes, not confusion.
How to Build Healthy LTV (Without Weaponizing Tech Debt)
Here’s the playbook that actually scales.
1) Sell reliability as a feature
Make quality visible:
- release notes
- uptime reports
- incident postmortems
- performance improvements
Clients pay longer when they can see improvement.
2) Define “done” with quality gates
Minimum gates for production code:
- basic automated tests on critical flows
- linting/static checks
- structured logging
- rollback plan
- code review checklist
3) Document only what matters
You don’t need a novel. You need:
- architecture overview (1 page)
- how to deploy
- env variables & secrets handling
- integration points (CRM, payments, webhooks)
- “common failure modes” notes
4) Build for replaceability (ironically increases retention)
When clients know they could leave, they stay because:
- switching isn’t scary,
- you’re not a risk,
- you feel like a partner.
That’s real LTV.
5) Package maintenance like a product
Stop selling “hours.” Sell outcomes:
- response time SLA
- monthly improvement budget
- performance/SEO improvements
- quarterly refactoring milestones
- security patch cadence
Clients understand products. They hate endless “dev time.”
Practical Checklist: Stop Trap-LTV Before It Starts
Use this before you ship anything serious:
- One clear owner for architecture decisions
- Written definition of “done”
- Tests for money/compliance flows
- Log + monitoring basics
- Deployment documented in 10 steps or fewer
- No “magic” config hidden in random places
- A new dev can run the project locally in under 60 minutes
- Top 10 risks listed and mitigations chosen
If you can’t check these, you’re not shipping a product—you’re shipping future billing.
Conclusion: Yes, Bad Dev Can Inflate LTV—But It’s the Wrong LTV
Bad development can increase billing duration by raising switching costs and creating ongoing breakage.
That’s not customer value. That’s technical debt monetization.
If you want sustainable growth, optimize for:
- speed with stability,
- transparency,
- predictability,
- and outcomes the client can measure.
That’s the version of LTV that survives audits, new CTOs, and market cycles.
FAQ
Does “vibe coding” always mean low quality?
No. It’s a method. It becomes a problem when it replaces engineering discipline in critical systems.
Why do clients stay in bad systems for so long?
Because switching is risky, expensive, and unclear. They’re not loyal—they’re stuck.
Is it ever acceptable to ship quick-and-dirty code?
Yes—when it’s timeboxed, isolated, and planned to be rewritten or hardened before it becomes core infrastructure.
What’s the fastest way to reduce trap dependency?
Documentation + tests on critical flows + clearer architecture boundaries. Make change safe.