How Historical Token Design Teaches Modern M&A Teams to Spot Code Quality Red Flags

When one tech company acquires another, a deep technical audit is required. I’ll explain why a target company’s approach to this specific problem can be a major red flag or a green light during an M&A deal. As an M&A due diligence consultant, I’ve learned that historical parallels—like the design, production, and subvariants of 19th-century anti-slavery tokens—can teach us invaluable lessons about evaluating modern tech stacks. Just as those tokens revealed the values, craftsmanship, and attention to detail of their creators, a company’s software architecture, code quality, and technical decisions expose its priorities, risks, and long-term viability. In this post, I’ll break down how lessons from historical token design directly apply to code quality audits, scalability assessment, and technology risk analysis in M&A transactions.

1. The Design Philosophy Behind Code Quality or Why “Good Enough” Isn’t in a Tech Due Diligence Playbook

The 1838 “Am I Not a Woman and a Sister” anti-slavery token was not merely a fundraising tool—it was a statement of values. The American Anti-Slavery Society (AASS) didn’t just slap a generic image on a coin; they invested in meaningful design, intentional messaging, and a direct reference to British abolitionist tokens. This attention to detail mirrored their commitment to a cause. In M&A, the same principle applies: code is a statement of intent.

Code as a Manifesto: What Your Codebase Says About Your Culture

During a recent due diligence engagement, I audited the codebase of a SaaS startup claiming to be “cloud-native” and “developer-first.” Their marketing was slick. But their code? A patchwork of copy-pasted Stack Overflow solutions, undocumented dependencies, and a lack of proper version control. The code quality audit revealed that their actual practices didn’t align with their stated values—like the token’s message, their code was a manifesto, but it was one of corner-cutting.

Key indicators of a healthy codebase:

• Clean, modular architecture (e.g., microservices, well-defined interfaces)
• Comprehensive documentation (READMEs, API docs, deployment guides)
• Test coverage ≥ 70% (unit, integration, E2E)
• Automated CI/CD pipelines with linting and static analysis

If a company’s code is a “kneeling woman” asking for dignity, but the codebase is a legacy monolith with 10-year-old dependencies, it’s a red flag—not just for technical debt, but for organizational misalignment.

Actionable Takeaway: Use a Code Quality Scorecard

Create a code quality scorecard for every target. Score each project on:

• Maintainability Index (MI) (ISO 25010 standard)
• Code duplication % (tools: SonarQube, PMD)
• Technical debt ratio (estimated refactoring time vs. development time)
• Number of critical security vulnerabilities (OWASP Top 10)

Example: A target with 80% test coverage and a 1.2 technical debt ratio (1.2 months refactoring for 1 month of dev) is a green light. A 30% test coverage and 5.0 ratio? Red flag.

2. Subvariants and Forks: The Hidden Scalability Risks in Your Tech Stack

Remember the HT-81A token variant? Slightly smaller planchet (27mm vs. 28.3mm), opposing rim irregularities, and a strongly struck second “8” in the date. These subtle differences weren’t just quirks—they were production artifacts that collectors now use to assess rarity, authenticity, and value. In M&A, think of these as code forks, microservices, and legacy modules.

Subvariants = Technical Debt in Disguise

I once audited a company with 12 microservices, all built by different teams over five years. Three used Node.js v12 (deprecated), four used Python 2.7 (EOL), and one had a custom fork of a Redis library with no upstream sync. This was their “HT-81A”—a subtle but critical divergence from best practices.

During scalability assessment, I found:

• Database contention due to unoptimized queries in the legacy services
• API incompatibility between services (e.g., JSON vs. XML payloads)
• No centralized logging/monitoring (each service had its own ELK stack)

The result? Their system could handle 50K users—but at a cost of 300ms latency and 50% CPU utilization. Not sustainable.

Actionable Takeaway: Map Your “Token Variants”

Create a technology variant map for every target. Use a tool like libraries.io or WhiteSource to:

1. Identify all open-source dependencies and their versions
2. Flag deprecated or EOL libraries
3. Map internal forks and custom patches
4. Calculate the “variant risk score” (e.g., 1 point per deprecated lib, 2 points per custom fork)

Example: A target with 5 deprecated libraries and 3 custom forks gets a score of 11—high risk. A score of 0? Green light.

3. Production Artifacts: The Scalability Test You Can’t Skip

The HT-81A token’s rim irregularities and strike variations weren’t flaws—they were evidence of production. In M&A, your target’s scalability assessment should focus on the same: how their system behaves under real-world conditions.

Stress Testing: The “Panic of 1837” for Modern Apps

The Panic of 1837 triggered the rise of Hard Times Tokens—a market response to economic collapse. Similarly, your target’s system will face its “panic” during M&A integration, traffic spikes, or regulatory changes. Did they plan for it?

During a diligence, I reviewed a company’s load tests. They claimed to handle 100K concurrent users. But their stress test only simulated 10K—and even then, they used a single-region AWS setup with no auto-scaling. Their “production” was a prototype.

Key scalability red flags:

• No auto-scaling or load balancing
• Single-point-of-failure (SPOF) architectures (e.g., one database, one cache)
• No disaster recovery plan (RTO/RPO > 24 hours)
• Latency > 200ms at peak load

Actionable Takeaway: Run a “Token Production” Stress Test

Simulate real-world conditions:

1. Load test with 2x peak traffic (e.g., if peak is 50K, test 100K)
2. Chaos engineering (e.g., randomly kill nodes, simulate network latency)
3. Failover test (e.g., simulate AZ outage in AWS)
4. Data migration test (e.g., simulate acquiring company’s data schema)

Example: A target that maintains <100ms latency and <1% error rate during stress tests is a green light. A 2-second latency and 15% error rate? Red flag.

4. The Hidden Risks in “Restrikes”: Legacy Systems and Technical Debt

The 2010 restrike of the 1838 anti-slavery token is a modern reproduction—authentic in design, but not in history. In M&A, this is your target’s legacy system: it may look functional, but it’s not built for the future.

Legacy Code: The “Restrike” That Costs Millions

I once audited a company with a 10-year-old monolith. It looked “stable”—but the technology risk analysis revealed:

• No CI/CD pipeline (manual deployments, 2-hour downtime)
• No automated testing (QA team manually tested every feature)
• No cloud migration plan (all infrastructure on-prem)
• Key developers had all the knowledge (no documentation, no onboarding)

The cost to modernize? Estimated at $2.5M and 18 months. Deal breaker.

Actionable Takeaway: Assess the “Restrike Risk”

Use a legacy system checklist:

1. Age of the system (e.g., > 5 years = higher risk)
2. Modernization roadmap (e.g., containerization, cloud migration)
3. Knowledge silos (e.g., number of employees who know the system)
4. Integration complexity (e.g., APIs, data formats)

Example: A legacy system with a 12-month modernization plan and 50% automated testing is a yellow flag. A system with no plan and 0% automation? Red flag.

Conclusion: Your Tech Due Diligence Checklist

Just as the anti-slavery tokens reflected the values, craftsmanship, and risks of their era, your target’s codebase, architecture, and processes reveal its true potential. Here’s your M&A tech due diligence checklist:

• Code Quality Audit: Scorecard for maintainability, test coverage, and security.
• Scalability Assessment: Stress tests for traffic, failover, and data migration.
• Technology Risk Analysis: Variant maps for dependencies and legacy systems.
• Legacy System Review: Checklists for modernization, knowledge silos, and integration.

In M&A, the devil is in the details—just like in token collecting. A single subvariant, a rim irregularity, or a poorly struck “8” can change the game. Spot the red flags, celebrate the green lights, and always remember: code is history in motion.

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