Auto-grading decade-old Hacker News discussions with hindsight

One thing this really highlights to me is how often the "boring" takes end up being the most accurate. The provocative, high-energy threads are usually the ones that age the worst.

If an LLM were acting as a kind of historian revisiting today’s debates with future context, I’d bet it would see the same pattern again and again: the sober, incremental claims quietly hold up, while the hyperconfident ones collapse.

Something like "Lithium-ion battery pack prices fall to $108/kWh" is classic cost-curve progress. Boring, steady, and historically extremely reliable over long horizons. Probably one of the most likely headlines today to age correctly, even if it gets little attention.

On the flip side, stuff like "New benchmark shows top LLMs struggle in real mental health care" feels like high-risk framing. Benchmarks rotate constantly, and “struggle” headlines almost always age badly as models jump whole generations.

I bet theres many "boring but right" takes we overlook today and I wondr if there's a practical way to surface them before hindsight does

"Boring but right" generally means that this prediction is already priced in to our current understanding of the world though. Anyone can reliably predict "the sun will rise tomorrow", but I'm not giving them high marks for that.

I'm giving them higher marks than the people who say it won't.

LLMs have seen huge improvements over the last 3 years. Are you going to make the bet that they will continue to make similarly huge improvements, taking them well past human ability, or do you think they'll plateau?

The former is the boring, linear prediction.

>The former is the boring, linear prediction.

right, because if there is one thing that history shows us again and again is that things that have a period of huge improvements never plateau but instead continue improving to infinity.

Improvement to infinity, that is the sober and wise bet!

The prediction that a new technology that is being heavily researched plateaus after just 5 years of development is certainly a daring one. I can’t think of an example from history where that happened.

Perhaps the fact that you think this field is only 5 years old means you're probably not enough of an authority to comment confidently on it?

Claiming that AI in anything resembling its current form is older than 5 years is like claiming the history of the combustion engine started when an ape picked up a burning stick.

Your analogy fails because picking up a burning stick isn’t a combustion engine, whereas decades of neural-net and sequence-model work directly enabled modern LLMs. LLMs aren’t “five years old”; the scaling-transformer regime is. The components are old, the emergent-capability configuration is new.

Treating the age of the lineage as evidence of future growth is equivocation across paradigms. Technologies plateau when their governing paradigm saturates, not when the calendar says they should continue. Supersonic flight stalled immediately, fusion has stalled for seventy years, and neither cared about “time invested.”

Early exponential curves routinely flatten: solar cells, battery density, CPU clocks, hard-disk areal density. The only question that matters is whether this paradigm shows signs of saturation, not how long it has existed.