20 years ago: https://en.wikipedia.org/wiki/DRAM_industry_price_fixing

> According to the one-count charge filed in San Francisco's federal court on Thursday, Park conspired with unnamed employees from other memory makers to fix the price of DRAM sold to computer makers from April 1, 2001, to June 15, 2002. The government says the move directly affected sales to U.S. computer makers Dell, Hewlett-Packard, Compaq, IBM, Apple Computer, and Gateway.

etc

and your quote:

> This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other.

Both sound like the GAN approach that was popularized a decade ago and kinda the start of the "genAI" boom.

The one I went to last week had a banner advertising a $20/hr starting wage. Clearly they'd like to hire more.

Getting promoted at a FAANG is like a micro-effect. One percent of one percent of one percent. Generalizing our experience to 72 million Americans ain't it boss.

And our experience is subject to dozens of selection bias. For example, most of the best people I know at my current job are that way in part because I trained them at my last job at a university. And then they referred me into their employer networks. Beyond the hurdles of getting into a flagship state school, having those connections matter. Nobody I know from undergrad had any employment at FAANG when I was laid off a decade ago, I just got lucky enough to have a second cohort contacts in the form of student employees at a different flagship state university doing internships, landing jobs and looking for referral bonuses. Last I checked LinkedIn I'm the only engineer from my alma mater at my employer.

Like if you manage a call center and set up KPIs around average call time, reps will start hanging up on customers. Employees could always have done that, and the causal link was always there, there was just no reason to.

IMO the problem is executives want (and perhaps need) their directs to report and track one big number month over month. If you give them five metrics they'll never know if you're making progress or just oscillating between a few local minima. And if each of their ten directs has five metrics, you now have 50 numbers and no idea what time it is[1].

[1]: https://en.wikipedia.org/wiki/Segal%27s_law "A man with two watches never knows what time it is"

It's complicated, but really worth learning how prometheus and grafana heatmaps combine if you want dashboards for real time service data. Multimodal distributions are basically the expected outcome given all the caching done in distributed systems.

Thats a pretty strong outcome. It implies that not only are GPUs that power AI too expensive long term, but they cost too much to operate even if they were free.

Seems to me the more likely outcome is a wave of dotCom style bankruptcies wiping out equity holders for companies who contracted to buy chips and datacenters at MSRP, and a second wave for the groups that step in after to operate whats left without the absurd financing charges and lower capex.

Paradoxically, wholesale electric costs are negative during the day here in California[1], especially in the Salinas region. In theory you could get paid to tumble dry clothes.

[1]: https://www.caiso.com/todays-outlook/prices

- there's no seasonal pattern to the matches, they happen sorta randomly.

- they drive increased query traffic in the hour or so before the game

- then during the game usage drops, sometimes to below "normal" depending on time of day and who's playing

So... now the accuracy of your forecasting tool depends on correctly predicting when world cup matches happen, and also who wins them!

edit: and this is just one recent example. others involve severe weather, national gameshows, earthquakes, and when you celebrate christmas.

Did you watch it actually fold that shirt? Super slow and bad at it.

> Deliberately avoid a heavyweight "alert governance" process; the lightest recurring check that keeps FP-rate honest is the right dose.

And one for load bearing:

> Five open questions still stand; the load-bearing two are the runbook-AC contradiction (ratify "high-priority set only") and pinning the "high-priority set" definition + SLO source-of-truth before Milestone 3 (small-sample noise on a low-traffic fleet).