Sora 2 is the perfect example: a dopamine machine disguised as progress. It doesn’t solve real problems; it just creates endless engagement loops to inflate “usage” numbers for investors.
The identical logic drives AI erotica — maximize attention, claim it’s “user engagement,” and call it growth. When the core product stops evolving, they start monetizing loneliness.
It’s not about what people need, it’s about what still moves the charts.
Exactly! software is the only engineering discipline where failure has no liability and degradation has no visibility.
Bridges collapse once. Software collapses silently, one abstraction at a time. The incentives are inverted: short-term growth gets rewarded, long-term stability gets deprecated.
When there’s no physical consequence for failure, “good enough” becomes the design philosophy.
Completely agree, software’s “materials” haven’t improved, only the scaffolding around them.
We’ve industrialized the process without industrializing the discipline. The result is mass-produced code built on shaky abstractions, fast to assemble, and faster to decay.
Linux and curl weren’t built on sprints or OKRs. They were built on ownership, long time horizons, and the idea that stability is innovation when everyone else is optimizing for speed.
That’s spot on - and it’s amplified by how the market rewards velocity signaling over craftsmanship.
Teams are optimized for output volume, not outcome quality. Hiring pipelines favor those who can “ship fast,” while the systems they ship into grow exponentially more complex. The result: shallow competence at scale.
AI just poured fuel on it - it lets everyone look 30% more productive while compounding the same underlying brittleness.
Each layer promises efficiency but adds hidden coordination cost. Ten years ago, a web app meant a framework and a database. Now it’s React → Electron → Chromium → Docker → Kubernetes → managed DB → API gateway - six layers deep to print “Hello, world.”
Every abstraction hides just enough detail to make debugging impossible. We’ve traded control for convenience, and now no one owns the full stack - just their slice of the slowdown.
I’ve recently been dealing with scaling one of those “framework and a database” web apps for a company that’s growing fast and hit scaling limits. You know what the easiest way to scale it is? Containerize it and deploy it on Kubernetes with a managed DB.
If you don’t recognize that, it may be because you don’t work with applications that need that scale. In that case, you might get away with simpler approaches. But if you expect to grow significantly, you can save a lot of money and pain by designing the app to scale well from the beginning.
Exactly. “Tests pass” has replaced “software works.”
We measure coverage instead of correctness, and AI-generated tests just made it worse, they validate syntax, not behavior. The illusion of safety lets teams ship faster while silently compounding technical debt.
The real regression isn’t missing tests, it’s that we stopped thinking during them.
Good question. The difference is in scale and tolerance.
Crashes used to be localized, one app, one machine. Now a missing field in a config file can take down 8.5 million Windows systems globally. Spotify leaking 79GB of RAM isn’t a “bug,” it’s normalized waste.
The signal isn’t that bugs exist, it’s that catastrophic ones no longer trigger process change. We’ve accepted systemic failure as normal because hardware and cloud budgets hide the cost.
This hasn’t been true since shortly after the internet became commercially used. Microsoft rollouts have been crashing machines worldwide since the 2000s at least.
One of the main differences now is scale. You’d have to work much harder to show that quality is actually getting worse.
That’s fair, but the difference isn’t about whether we have linters or not. It’s about outcomes.
In the ’90s, inefficiency meant slower code. Today it means 32GB RAM leaks in calculator apps, billion-dollar outages from a missing array field, and 300% more vulnerabilities in AI-generated code.
We’ve automated guardrails, but we’ve also automated incompetence. The tooling got better, the results didn’t.
Yeah, the irony is that this is the only thing still propping up the U.S. economy.
Real growth drivers are gone — manufacturing is flat, tech productivity has stalled, and GDP is now inflated by the promise of future AI miracles.
Companies aren’t creating new value; they’re monetizing hope — issuing debt against models that don’t yet work and counting that as “growth.”
It’s not innovation anymore. It’s financial theater dressed as progress.
