Mapping the Full Cost of a Technology Choice
When engineers evaluate technology, they typically focus on direct technical costs: performance benchmarks, feature lists, API design, and maybe licensing. But the Innovation Tokens model forces you to account for the full cost — the iceberg below the waterline.
Here is a more complete cost model for any technology choice:
Visible Costs (above the waterline):
├── Licensing / hosting fees
├── Development time to integrate
└── Learning curve for initial implementation
Hidden Costs (below the waterline):
├── Hiring: Can you find people who know this?
├── Onboarding: How long until a new hire is productive?
├── Debugging: What happens when it breaks in production?
├── Tooling: Do monitoring, backup, and deployment tools exist?
├── Upgrades: Is there a tested migration path between versions?
├── Security: Are CVEs tracked? Are patches timely?
├── Community: Are questions answered? Is documentation maintained?
├── Interoperability: Does it integrate with your existing stack?
├── Bus factor: How many people on your team truly understand it?
└── Opportunity cost: What did you NOT build while wrestling with this?
The hidden costs almost always exceed the visible costs. A technology that saves you 20% on compute but requires a dedicated engineer to operate is not saving you anything — it is costing you an entire salary plus the opportunity cost of what that engineer could have built instead.
The "Two Databases" Trap
Dan McKinley specifically warns about the cost of running multiple technologies that serve the same purpose. If you run both PostgreSQL and MongoDB, you do not just pay the cost of each database — you pay the cost of the interaction between them:
- Two different backup strategies to maintain and test
- Two different monitoring configurations
- Two different failure modes to understand
- Two different upgrade cycles to manage
- Every engineer needs to understand both
- Data consistency between them becomes your problem
This is why the Innovation Tokens model is not just about individual technology choices — it is about the combinatorial complexity of your entire stack. Each new technology does not add complexity linearly; it multiplies it. A stack with five technologies has not five operational concerns but something closer to five-factorial interaction surfaces to understand and debug.
The Bus Factor Amplifier
Bus factor is the number of people on your team who would need to be "hit by a bus" (leave the company, go on vacation, or become unavailable) before a critical system becomes unmaintainable. For boring technology, the bus factor is effectively the entire industry — anyone with standard skills can step in. For exciting technology, the bus factor is often one: the engineer who championed the adoption and learned it deeply.
When that engineer leaves — and eventually, they always do — the exciting technology they championed becomes a liability. The team inherits a system they do not understand, cannot debug confidently, and cannot upgrade safely. This is one of the most common sources of "legacy system" horror stories: not that the technology was bad, but that it was novel when adopted and the knowledge never spread.
Key Point: When evaluating whether something costs a token, ask: "If this breaks at 3 AM and our most experienced engineer is on vacation, how long does it take the next person to fix it?" If the answer is "they can't," that is not a one-token cost — that is a business continuity risk. The 3 AM test is the single most clarifying question in technology evaluation. It cuts through all the benchmark noise and forces you to confront the operational reality of your choice.