cs702

I figured as much :-) Day-suddenly-night is a pretty good way to describe it.

Maybe it will happen again. I have no idea; maybe with AI it is different this time.

> when investors pulled out

Nothing spooked them before then, not even the head of the Fed warning about "irrational exuberance" on the front page of every newspaper.

There was a change in behavior. It was sudden, and it surprised everyone -- well, maybe not everyone, but many.

Otherwise, I agree there quite a few parallels with the present.

It's more than that, though. Anecdotally, in my experience, many smart people who own stocks but are not traders haven't given much thought to how these markets work.

I edited my comment to convey more clearly what I meant:

Sometimes, simple narratives cannot truly explain these events.

Everyone wants a simple narrative that explains the drop and fits in their head. Sometimes there isn't one.

Who are the buyers and sellers of the stock? How have their process, mindset, motivations, fears changed?

I don't think it's easy to answer those questions.

I edited my comment so it better conveys my thoughts.

Sometimes there isn't one. Answering precisely why the price went down requires understanding the process, mindset, motivations, fears, and finances of every buyer and every seller everywhere. To this date, there is no consensus on the causes of the crashes of 1929, 1973-74, 1987, 2000-2002, and 2007-2008, although there's no shortage of simple narratives claiming to explain the causes.

Seemingly small changes in the behavior of buyers and sellers can have a disproportionate impact on market capitalization, because share prices are set at the margin by the last trade executed, between a particular buyer and a particular seller. Billions of shares of a stock could trade hands at $100/share, but if one share then trades hands at $1/share, the company's market capitalization at that moment is 99% lower, even though only one dollar changed hands. This sort of thing was in fact seen during the great crash of 1929, when there were occasionally no normal bids for some stocks, and tiny orders to buy them for less than $1/share suddenly were filled, temporarily sending market caps to near zero.

EDIT: I made many changes to this comment so it better reflects what I meant to write the first time around.

According to page 6, year to date in 2026, operations generated $127B in cash, and capital spending on property and equipment was $132B, so year to date, the business consumed $5B.

According to page 8, in the last quarter, operations generated $42B and capital spending was $51B, so over the past three months, the business consumed $9B.

Those figures don't include any capital spending made through off-balance sheet arrangements. No idea what those might be, or for how much Alphabet could be on the hook.

I'm curious to see if all that money invested in infrastructure will pay off. I hope it pays off, for everyone's sake, but hope is not certainty.

Those holdings are carried in the books at valuations that arguably could be justified in the past but are no longer realistic.

Private equity firms are basically sitting on them, unable to sell them or list them at current valuations.

It's not that surprising, given that these models generate each new token by sampling from a context-determined probability distribution.

If you run the same prompt repeatedly many times, it becomes increasingly probable that a tiny percent of the generated sequences will go off in, ahem, unusual directions.

Lots of theories seeking to explain this reality, but that's all they are: theories. Maybe some will be proven right. Maybe all will be proven wrong.

As of right now, the economic reality lags far behind the hype. But down the road... I wouldn't place a bet either way.

> In the years ahead, this may cost American companies tens if not hundreds of billions of lost revenue.

The question is, if European governments and businesses conclude they cannot trust non-European software and hardware, what can they do about it?

On the software side, it seems to me the only realistic alternative they have is to build upon existing open-source projects.

On the hardware side, their options seem more limited to me, but I'm not an expert on the subject.

Modern best practices for a medical team (standard roles, responsibilities, consequences, processes, etc.) have evolved through trial and error, since the advent of civilization, to prevent costly human error.

In hindsight, it's not too surprising that the same best practices can be applied by teams of AI agents to minimize costly AI error.

It's still incredible. We sure live in interesting times!

Thank you to the author for writing and to the poster for sharing it on HN :-)