The hidden costs of engineering decisions. Written by Lloyd Moore, former CTO at Blockdaemon.
A CTO mapped her time: 88% coordination overhead, 12% genuine judgment. AI agents eliminate the coordination tax but not the need for judgment. The three-person company operating at 4% of traditional headcount is not an anomaly. It is the new cost structure your competitors will use against you.
AI collapsed the economics of apprenticeship. One company trained a junior engineer across three technical domains in fifteen months—a trajectory traditional mentorship would spread across three years. The senior's cost fell from $50,000 annually to under $5,000.
On-call costs most companies $331,000 annually in hidden attrition and productivity loss. Blockdaemon designed a system that cost $39,000 and generated zero staffing problems. The economics of paid, voluntary on-call.
A Series B company with 75 engineers rejects 90% of qualified candidates. Trial-based hiring produces 71% more successful hires per candidate while costing $30M less annually. The economics of hiring false negatives.
A VP of Engineering measured new engineer productivity for three months. The CFO estimated $800K annual cost. The reality was $6.5M. Why senior engineers take twelve months to reach full productivity—and how to compress it to seven.
Information flow is necessary but not sufficient. The companies that retain senior engineers don't just communicate better—they restructure incentives so that executive success depends on retention. Six interventions that change the economic calculus.
Engineering metrics begin as coordination tools but become artefacts for presentation. Story points rise, incidents fall, yet momentum slows. The illusion of control achieved through measuring motion instead of meaning. When metrics become targets, they distort the very reality they were meant to clarify.
A company lost five senior engineers in 18 months. Exit interviews cited compensation. The real problem: information does not flow upward through organizational hierarchy. By the time problems reach executive level, they have metastasized into resignation decisions made months earlier.
Every engineering org contains a system that nobody will touch. The original author has departed. It works—mostly. Changing it risks catastrophic failure. The cost of this fear manifests as every feature that did not ship because it would require touching the untouchable.
Cloud computing promised infinite elasticity. What it delivered was companies discovering their limits during production outages and receiving surprise infrastructure bills. Capacity planning did not become obsolete. It became invisible—until its absence became expensive.
Hiring a full-time executive takes six months and costs $200,000 annually. For companies with immediate technical problems, this timeline is incompatible with survival. Fractional leadership corrects a market inefficiency by separating expertise from time.
Technical due diligence reveals what investors truly care about. It is not test coverage or velocity. It is whether your technology can deliver what your business promises, whether your team can build what comes next, and whether you understand the risks.
When servers crash from full disks, the problem is not mysterious bugs but basic observability. A client was paying three times what they needed for compute, just to store logs. The fix was straightforward. The lesson is universal.
The move-fast mentality that built your startup becomes its biggest liability at the 20-person inflection point. Understanding why coordination costs explode—and what to do about it.
Staging environments promise safety but deliver false confidence. How production's complexity, scale, and chaos expose the fundamental gap between testing theatre and reality.
When deployments became background processes, companies gained speed but lost something more valuable: the collective judgment that catches disasters before they happen.
The economics and org dynamics of technical intuition versus formal review, and why a senior engineer's judgment is both invaluable and potentially catastrophic.
A payment processor discovered a critical bug that had throttled their system for 18 months. When they fixed it, downstream services collapsed within hours. Sometimes the most dangerous thing you can do is make your code work perfectly.
A company optimized a database query from 200ms to 50ms—a 4x improvement. Three hours later, system throughput dropped 80%. The counterintuitive reality of how local optimization destroys global performance.
An engineering manager opens the monthly AWS bill: $127,000 for CloudWatch logs, $41,000 for EC2 compute. The application exists to serve customers, but the logs cost three times more than the application itself.
The engineers who introduce the most bugs are often the most valuable. Not because they are careless, but because they ship more code working on harder problems.
A weekly architecture meeting everyone complained about prevented $500,000 in incidents. The counterintuitive economics of coordination versus execution in engineering.
A company's deployment frequency metric showed 10x improvement. The actual deployment count had not changed. Teams learned to game the metric. When metrics become targets, they stop being good metrics.
A company rewrote their order processing system to eliminate technical debt. The new system was clean, well-architected, and missing 47 critical edge cases. Sometimes the mess is the documentation.
Two startups raised identical funding and built competing products. One chose cutting-edge technology. The other chose boring, proven tools. Five years later, the boring stack had won decisively. The counterintuitive economics of technology selection.
A company with 127 incidents per quarter appeared less reliable than a competitor with 12 incidents. Until due diligence revealed the truth: the first company detected problems, the second one did not. The paradox of observability maturity.
A company spent $180,000 annually running an API endpoint they believed was unused. Every attempt to deprecate it failed because no one could prove what depended on it. The economics of fear versus the economics of waste.