Writing on software economics.

A ranked feed pays for reaction rather than usefulness: audited against each user's own chronological feed, Twitter's algorithm amplified anger by 0.47 standard deviations, and users rated the posts it chose lower than the ones it passed over.

When the cost of building software collapses, markets too small for a software vendor become worth serving, and the scarce input moves to the person who owns the problem and a place to run the software every day.

When custom software gets cheaper to build than a platform is to configure, money that used to flow to implementation services redistributes rather than disappears.

When the cost of building software collapses, more niche markets become worth serving, not fewer — the outcome is more builders and more products, not a shrinking industry.

A feature added to please an entire customer base is a liability, not an asset — it's whichever capability was chosen deliberately for one workflow that holds its value.

Software designed for millions of users carries built-in compromises — attention-driven engagement, data collection as subsidy, feature bloat — because that's what mass-market economics requires.

Across past technology waves, the most economic value went to whoever adopted a breakthrough intensely and applied it to unserved industries, not to whoever invented it.

The only question worth asking when evaluating AI for a task is whether it produces the outcome cheaper than a human would, including the time spent reviewing its work.

A system that personalizes by inferring what you want from your behavior optimizes for whoever controls the algorithm, not for you.

Whether autonomous AI agents can actually generate revenue depends on separating what's shipping from what's still hype — most of what gets called an autonomous agent today is a demo, not a business.