I know because I bought a house in 2013 where the builder delighted in using a dozen weird fixtures and the cheapest bulbs they could find and I spent a lot of 2015 doing just that.

There are lots of things that LLMs are genuinely good at, searching by image isn't something we need LLMs for. I asked Google's LLM when google image search launched and it reported

> Google officially launched its "Search by Image" feature—allowing users to upload a picture or image URL to find related content—in 2011

It's pretty good, I actually like it because it's very thorough and catches real things.

On a recent PR though it very confidently pointed out an "error" and suggested a "fix." Now I authored this code by hand and the "error" was it going "this one doesn't look like all the other ones" and I'm a relatively consistent person so it's not the kind of mistake I would make, which means I probably put thought in and the difference was intentional.

I looked at it's suggestion carefully and my original code was correct, the "fix" would have broken the logic. Not a huge deal all things said.

But I'm looking back at it's original report, it's comprehensive, confident, and ends with "Reply 'fix this for me' and I will fix it" and it made me think about more junior engineers.

I double checked it because I had written the code by hand, I understood the context, I also have enough experience to know that if I wrote this one function differently there was probably a good reason. But if I were earlier in my career, with less experience, would I have just clicked the easy button?

Probably. Especially if everyday I'm clicking the easy button.

Highway engineers used to think it was a good idea to make highways as straight as possible, people going in a straight line is easiest right? They realized that if you didn't put some curves in the road that people would just disengage and a perfect straight "easy" highway was much more dangerous than one with curves. I feel like AI is an "easy" highway

Memory is already this squishy thing and then when you go back to reminisce about {meaningful event} and go pull up your edited photos, what are you even looking at? Google used to play these ads constantly of editing stuff out, changing how the sky looks, etc.

It's present day you effectively gaslighting future you. What's the point of memorializing something and then immediately destroying the truth of what happened with a bunch of edits.

Reality doesn't matter. If it gets in the way of money and or ads, we can just change it!

For a while every economic advance seemed to mean more and better jobs for horses. But then the automobile comes along and there's no more need for horses and we can see what happens to an animal that has no economic reason to exist.

We still have a much smaller number of horses for the few economically viable roles a horse can fill and as toys for the wealthy.

The question is if labor will follow the same path.

In other words, I have no idea where all the white collar workers are supposed to go.

Yes, but those were horses. Now substitute the word 'human'.

But yeah, the robot armies don't need grain so why hike up the price of bread? Lack of grain makes those people resentful which means you need to deal with their anger. Sure, it can be dealt with but it's just cheaper to give the humans grain so they are docile. This is basic governance 101 that goes back to the romans (and further).

They also didn't slaughter all horses immediately. You can't eat that much horse meat anyways. It happened piece by piece.

The only good reason for an abrupt mass culling of the 99% (for a coldly calculating rich person with no empathy) would be game theory, i.e. them not being a contender for power any more. If there are no humans, there is nobody who can question the control of the 1%. It would be thus less about economics and more about power.

I am really rooting for the bottom 99%, myself being a part of it, but I really don't know what will happen to us.

This could be anything from "we put a prompt in front of chat-gpt that says 'you are an expert in marketing econometrics'" to "we built a model trained exclusively on marketing econometrics material"

No matter what they actually did, the agent (assuming its an LLM) doesn't understand marketing econometrics, instead it's tuned to produce output tokens that I suppose make more sense when the topic is marketing econometrics.

I'm not an LLM detractor, but I find the kind of thinking that's become prevalent to be so squishy. Humans are great anthropomorphizers and it seems today that no one is attempting to hit the brakes on that instinct. The models don't understand anything, in the way that we commonly use the term understand.

It seems we've confused ourselves because the box that doesn't understand marketing econometrics can produce marketing econometrics analysis and when we ask it why it came to such and such conclusion it can produce convincing explanations.

As an aside, I also feel like I've heard this for 30 years about marketing. Marketing is everywhere, the surveillance economy tracks our every move in more and more invasive ways every day, and still companies go "Aw shucks, we just can't make sense of this data." It reminds me of a time when I was working for Abercrombie and Fitch and there was this massive report our team was partially responsible for generating. 500+ pages, generated everyday, sent to a high speed printer from a COBOL job every morning at 5am so that 10-15 copies could be made for the executives. It had *all the data* and each executive had their own little ritual around which bits they thought were important.

Throughout my career as an engineer I've been asked to get more data, more data, more data. Process the data, analyze the data, create some graphs and tables and help people understand the data.

One thing I've realized is that the people demanding all this data, all this insight, all this analysis, are unlikely to actually need it or use it when available. They are tasked with making decisions, and decisions are scary because you can make the wrong one. They would love to not make the decision and maybe you can find enough data that the choices get cut down to just one. Then if it ends up wrong they aren't to blame, the data made them make that decision.

So all of this surveillance, all of this analysis, all of this data is likely just to make some person feel a bit more comfortable about making a decision.

Obviously it's partial (or else there would be a billion lines) but it gives a good broad view of what things have gotten more or less expensive.

- TVs, toys, software, and cellphone services are cheaper.

- Clothing, funishings, and cars roughly flat.

- Healthcare, education, childcare, food, and housing are all more expensive by more than 50%.

So this is the moment we are in, we can certainly find things that were cheaper but your average consumer buys a TV once every few years, they buy food and pay for housing every day.

I don't think people are ignorant of the upsides of this deal, they are just capable of also recognizing the downsides.

Grocery inflation is not nearly as bad as the food inflation overall, which is driven by food-away-from-home just absolutely skyrocketing.

Billions of words have been spilled about housing, so I will boil it down simply. It is a mixture of policy and preference. It doesn't have to be the way it is, we just need to collective will to change things.

The less said about the mess that is American health care, the better. It is the one area where monopoly effects plague almost every part of the system. Whether it is pharmaceutical companies charging monopoly prices for new drugs (knowing that consumers don't directly chose, or directly pay for, what is prescribed)... or often monopolistic hospitals conjuring up obscenely high prices, billed often deliberately confusing and obtusely, designed for insurers to negotiate down... or insurers (a quasi monopoly, since it's what your employer choses, and your employer has limited choice) themselves coming up with confusing plans with a myriad of exceptions, where even a fully insured person can end up bankrupt after a major heath scare.

In practice, the "Bennett hypothesis" (the idea that increased generosity of financial aid leads to higher tuition) is the most likely explanation that I see for high education inflation. Perhaps a symptom of just how loose things were could be seen with the "ITT Technical Institute" type institution that spent far more time recruiting students via slick advertisements and taking their student loan money, versus working with businesses and developing a solid education program that created employable students. A working system would've never let ITT Technical Institute last for as long as it did. I think the expanded loans were made with good intentions, but they unfortunately were not clamped down stringently enough.

It would be interesting to see this chart repeated for other countries. Many of them probably don't have the issue with higher education and health care that the US does, but perhaps one could find other interesting issues. Some of which is not controllable... and perhaps some of which is.

They are moving to cooler waters but the cooler waters won't have the food supplies they need. So it's either stay where the food is and overheat or go to cooler water and starve.

According to OECD data, US healthcare spending in 2023 was 28% from government schemes, 55% from health insurance, 11% out-of-pocket, and 5% from other sources. For most countries, the health insurance category is further split into compulsory and voluntary categories, but that distinction does not really exist in the US.

All US health insurance spending is reported in the compulsory health insurance category. Probably because the bulk of the spending is from employment-based insurance, which is effectively mandatory. (You usually can't opt out and take cash instead.) Naive aggregators then combine government spending and compulsory insurance and report that as public spending.

The terminology is used to talk about languages that have async and sync functions where you declare (or color) the function as either async or sync.

In these languages it's pretty common for the language to enforce a constraint that async functions can only call other async functions. Javascript / Typescript, Python are popular examples of languages with colored functions.