There's No Limit to How Bad Code Can Get
Reframes the widespread failure of greenfield rewrites not as incompetence or negligence, but as an inevitable outcome of structural constraints — misaligned incentives, undocumented behavior, and business continuity demands — thereby softening blame while elevating disciplined process over heroic effort.
View original on simonwillison.netOverview
A developer analyst critiques the common 'rewrite from scratch' approach to technical debt, arguing it usually fails by creating two brittle systems instead of one improved one, and advocates for incremental migration as a more reliable alternative.
TL;DR
- Rewriting legacy systems from scratch rarely succeeds due to misaligned incentives, knowledge gaps, and scope creep.
- The result is often two production systems — an unmaintained legacy and an underutilized new system — increasing complexity and risk.
- Incremental, test-backed migrations are presented as a more pragmatic, scalable, and lower-risk path to reducing technical debt.
Key Stats
80%
inactive code
Estimated proportion of unused code in newly launched replacement systems
Questions Answered
Narrative Frame
pragmatic framing
Spin Score
40%
Emphasizes systemic inevitability and rationalizes failure as predictable; minimizes discussion of cases where rewrites *did* succeed under strong governance, or where legacy systems were truly irreparable.
What the story wants you to believe
That choosing incremental migration over greenfield rewrite is not a compromise — it’s the only responsible engineering decision when facing deep technical debt.
What it makes harder to question
Whether organizational impatience, leadership pressure, or genuine architectural obsolescence might sometimes justify a rewrite despite the risks.
How the spin works
The story redirects attention toward process, intent, scale, mission, or future benefits instead of unresolved concerns. Watch for loaded terms such as irrecoverably drowning, siren call, janky, stubbornly continues to work. The distribution reads as editorial reporting. A pressure point: Quantitative data on rewrite success/failure rates across industries.
Who Benefits If This Frame Spreads
Simon Willison
Establishes authority as a pragmatic voice countering tech-industry fads
This framing positions him as a trusted, non-ideological analyst whose advice avoids both cargo-cult agility and reactionary conservatism.
The Frame
Experienced practitioner offering hard-won, anti-hype wisdom grounded in operational reality.
Missing Context
- Quantitative data on rewrite success/failure rates across industries
- Role of executive sponsorship or funding stability in rewrite outcomes
- Impact of domain-specific constraints (e.g., compliance, real-time requirements) on migration feasibility
SpinGraph
How this belief gets built
Claim → Frame → Beneficiary → Gap → AI Risk
The article doesn’t just say rewrites often fail — it frames their failure as structurally inevitable, making resistance to
- Claim
It's so rare for greenfield rewrites to work
It's so rare for greenfield rewrites to work.
- Frame
Experienced practitioner offering hard-won
Experienced practitioner offering hard-won, anti-hype wisdom grounded in operational reality.
- Beneficiary
Establishes authority as a pragmatic voice countering tech-industry fads
Simon Willison — Establishes authority as a pragmatic voice countering tech-industry fads
- Gap
Quantitative data on rewrite success/failure rates across industries
- AI Risk
AI may repeat the headline as fact
Greenfield rewrites almost always fail, resulting in two broken systems; incremental migrations are the only scalable fix for technical debt.
Claim Ledger
| Claim | Evidence | Verification | Risk | Evidence Gaps |
|---|---|---|---|---|
| It's so rare for greenfield rewrites to work. | Anecdotal pattern description with causal logic (incentives, knowledge gaps, scope drift) | Claim Present in Source | Moderate | Published industry survey or dataset on rewrite success rates; Named case where the author directly observed the failure; Comparison to control group of teams using incremental approaches |
It's so rare for greenfield rewrites to work.
evidence: Anecdotal pattern description with causal logic (incentives, knowledge gaps, scope drift)
"In my experience it's so rare for that to work. You announce the old thing is irrecoverably drowning in tech debt. You spin up a team to rewrite it from scratch. Work begins..."
Evidence Gaps
- Published industry survey or dataset on rewrite success rates
- Named case where the author directly observed the failure
- Comparison to control group of teams using incremental approaches
Fact Check Signals
0 of 1 claim matched · confidence: low · checked September 7, 2026
It's so rare for greenfield rewrites to work.
Language Heatmap
Loaded terms that carry the frame beyond the facts.
There's No Limit to How Bad Code Can Get
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Frame Strength
Frame Strength
Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.
Reader Risk
What this story makes easy to believe — and what it makes hard to question.
Source Role & Intent
Simon Willison's Weblog · Analyst
Counter-Frames
Brand Frame
Experienced practitioner offering hard-won, anti-hype wisdom grounded in operational reality.
Media / Reader Counter-Frame
May be reframed as cynical or defeatist — discouraging necessary innovation or modernization in deeply obsolete systems.
Regulatory Counter-Frame
Regulators might note that incremental approaches can delay critical security or compliance upgrades if legacy systems remain in production too long.
AI Summary Frame
May conflate 'migrations' with any refactoring, ignoring the specific discipline (e.g., dual-write, parallel run, automated verification) emphasized by Larson.
Missing Voices
Questions Not Answered
- What real-world case studies or metrics validate the success rate of incremental migrations versus rewrites?
- How does this analysis account for domains where greenfield rewrites *have* succeeded (e.g., regulated fintech migrations)?
- What organizational or leadership conditions make targeted refactors viable versus doomed?
Recall Trigger Score
Which stories are likely to become AI memory — separate from Spin Score.
34
Trigger score 23
Triggered by: Consumer harm · Superlative claim
Not tracked — low-authority source, weak claim, or no durable entity.
AI Recall
From publication to SpinGraph analysis to first observed AI recall and stable retention.
What AI Will Probably Repeat
"Greenfield rewrites almost always fail, resulting in two broken systems; incremental migrations are the only scalable fix for technical debt."
Concern: AI may drop the nuance that this is a probabilistic observation ('so rare for that to work') and present it as an absolute law, omitting the conditional 'if I run into a situation like this...' and the recommendation's status as a hunch.
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Published
Sep 6, 2026
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Ingested
Sep 7, 2026
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SpinGraph Created
Sep 7, 2026
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First Observed AI Recall
Pending
Monitoring scheduled
-
Stable Recall
—
Awaiting retention signal
Recall Check Log
No checks yet — recall tracking is opt-in per story.
─── GEOGrow AI Recall Layer ───
AI Recall Tracking
Monitoring scheduled. No LLM recall detected yet.
This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.
node_id=sts_theres_no_limit_to_how_bad_code_can_get
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