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November 28, 2025
When Technical Debt Looks Like Collector’s Gold (But Isn’t)
Tech acquisitions often feel like appraising rare coins. From the surface, everything shines – but the real value lies beneath. I’ve seen too many deals where what looked like valuable innovation turned out to be dangerous technical debt. Let me show you how to spot the difference.
Just like coin experts distinguishing authentic doubled dies from worthless machine doubling, we uncover hidden quality issues that make or break M&A deals. Your target’s codebase might look pristine, but here’s what actually matters during technical due diligence:
Why Coin Collectors and Tech Acquirers Share the Same Nightmare
In coin collecting, machine doubling creates superficial flaws that tank value. In software? Look for these red flags:
- Copy-pasted code disguised as features
- ‘Temporary’ fixes baked into core architecture
- Test coverage that’s wide but shallow
Last year, I evaluated a SaaS company whose platform seemed perfect – until we ran this real-world test:
for i in {1..50}; do
curl -s -o /dev/null -w "%{time_total}\n" https://target-platform.com/api/v1/critical_endpoint &
done
Response times crashed from 200ms to 14 seconds. Their beautiful code facade hid serious structural issues – classic machine doubling in tech form.
The Technical Due Diligence Trifecta
1. Code Autopsy: Beyond the Blurry Surface
Polished demos lie. Actual due diligence reveals truth. Watch for:
“Our last acquisition boasted 82% test coverage. Turned out they tested button colors while core logic went unchecked.” – Tech exec who overpaid by millions
Try this yourself:
grep -r 'TODO' ./src | wc -l # Counting unfinished business logic
git log --since='6 months' -p | grep -c 'hotfix' # Measuring patch frequency
2. Scalability X-Rays
Like coins under magnification, these patterns crumble under load:
- Single PostgreSQL instances handling all reads/writes
- Redis used as primary storage instead of cache
- Kafka queues without consumption monitoring
One ‘microservices’ architecture we examined was actually 78 Lambda functions chained together with no timeout protection. The discovery slashed $2M from the deal price.
3. Risk Decoding: The Hidden Flaws
Spot the notched ‘R’ in your target’s stack:
- Open-source code in ‘proprietary’ systems
- Cloud accounts without proper access controls
- Production deployments from personal laptops
Our risk assessment once uncovered $4.3M in GDPR fines waiting to happen – European user data sitting in unprotected US servers.
The Valuation Impact Matrix
Technical flaws hit where it hurts most – the deal price:
| Issue Type | Valuation Impact | Remediation Cost |
|---|---|---|
| Cosmetic issues (messy code) | -5% to -15% | 2-6 months |
| Structural flaws (no scaling) | -15% to -40% | 6-18 months |
| Active deception (fraud) | Deal dies | Legal nightmares |
Three Battle-Tested Due Diligence Tactics
After vetting 47 acquisitions, here’s what works:
- The Detail Test: Demand exhaustive architecture diagrams. Teams hiding flaws will push back.
- Traffic Therapy: Simulate real user loads during diligence. Systems that buckle aren’t worth premium prices.
- Code Time Travel: Use
git blameto see if ‘features’ were born from panic fixes.
Don’t Get Fooled by Machine Doubling
The best acquirers approach tech stacks like expert coin collectors:
- Treat shiny demos like blurry eBay photos – verify before trusting
- Measure real performance, not vanity metrics
- Price deals on structural integrity, not surface glitter
In my experience, 7/10 acquisition targets have some form of technical machine doubling. Your job? Separate the valuable specimens from the shiny counterfeits before the deal closes.
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