Tech Economics

Writing new code is glamorous. Keeping old code alive is where the real money goes. Here's the economics behind that gap.

Paying top-of-market for engineering talent feels reckless until you price out what bad hires actually cost.

Market share and market value don't scale together. The gap between first and second place is almost always far larger than the numbers suggest.

Salesforce and Rackspace sold software in the same era. One became worth hundreds of billions. The difference was hiding in a single line of the income statement.

The real value of a strong engineering hire often arrives before they commit a single line of code. Here's where the returns actually come from.

Market leadership looks great on press releases. The economics often tell a different story, and the second-place player is usually the one quietly making money.

Amazon cut 27,000 jobs and kept hiring. The contradiction isn't confusion — it's a deliberate, if costly, workforce strategy.

A product generating real revenue can still be a net drain on the company. Understanding why requires rethinking what 'cost' means in software.

A competitor dropping to $0 doesn't automatically destroy your pricing. It forces you to answer a question your customers were never asking before.

Being first costs more than it pays. The companies that dominate tech markets usually got there second, with better timing and someone else's tuition bill.

The compute bill is the headline. The human labor underneath it is the real story that AI companies don't want to discuss.

Winning a market and profiting from it are different things. The economics of tech competition consistently reward the runner-up more than the leader.

Technical superiority rarely decides market outcomes. Distribution, timing, and switching costs matter more than most product teams want to admit.

Informatica didn't lock in its enterprise customers with contracts. It did it with data pipelines, trained workflows, and ten years of institutional memory baked into a single vendor.

The math on engineering talent is almost always done wrong. Salary is the least important number in the equation.

When a venture fund stops deploying capital, its portfolio companies don't just lose a backer. They enter a slow-motion crisis most founders never see coming.

The most productive engineers aren't the ones writing the most code. They're the ones deciding what not to build.

A dormant app isn't free to own. The ongoing costs of keeping one alive are real, largely invisible, and almost never factored into shutdown decisions.