freefaler
Aren't the 99% received salary for their work and produced this after being fully paid what they agreed voluntary for their work?
If my house was build by 10 people, can they just get the house back after I've paid them?
If you can do something for your client, then you get to eat what you both voluntary agreed the deal is worth.
Does a plumber work for Altman, or for himself? Why this need to have a boss and not live by the direct fruits of your own labor?
In a free market economy, you get from the butcher the meat only if you can give the butcher something in return. It might be cleaning the store, or throwing out the smelly bloody stuff out.
It's not pretty, but life is not pretty.
It is great to find a way to swap for other's needs what you're good at and enjoy, and sometimes it's possible.
However, since you need to solve another's person problem to barter with from something, it's by default not something you like, but something the other person needs. And this frequently is not what you would like to do at the moment.
So basically, this is life, sometimes we can write beautiful code, sometimes we need to fix a messy code our client uses every day.
And sometime we don't have the pleasure to work on the computer at all, because AI took our job away, and we end up cleaning the store.
They constantly are poking at the requirements to understand what's the purpose and they find a better, simpler or sometimes hackier (for temporary stuff) solution, but they are on time, they solutions work and they get shit done.
I've seen some really crafty "artisanal" ones that debate for hours and rewrite 10 time the release scripts... Not the most efficient way to spend their time.
The good ones are increasingly producing good solutions much faster than before.
The not so good ones, are doing the same bullshit they did before, but now the don't even bother to check it. For example one requirement to "not implement it in the frontend" led to 3 times PR rejected, because it still has been implemented (even if it's partially there) in the frontend.
I'm not sure what's the reason, but I assume it's
- inability to understand architecture choices and their consequences
- "don't bother me with shit, just pay me a salary" attitude
but the AI lead to amplification of the good and the bad in people.
And I'm much more worried if the government forces these providers to share my data so they can enforce automatically and cheaply the next "save the children" law, which is mostly "no tax sheep left unharvested" law.
(after a short research your LLM usage is currently used to build your profile)
It's hard to expropriate from (on average) the most active members of society that have build their own businesses and have reaped benefit based on free market economics and are mobile geographically, and not risk averse.
The bigger problem is not the tax, but how it is used. If you have a leaky bucket, no matter how much water you put in, it won't hold.
With all the technologies, the way to improve the society is through more transparency on how the money is spent, breaking over regulated sectors with inherent oligopolies and let the people keep what they earn.
That wealth has been taxed already when the profits were realised and will be taxed again on consumption.
The CEO hasn't build shit, he has been a manager all his life working for a salary and a bonus, so he might not understand the psychology of a person who risks a lot and gets paid voluntary by the value he provides to his customers.
I can paint my house and I can change the oil in my car, but I prefer not to do it. I might host something personally, but self-hosting comes with cost, uptime requirements and bug fixing (by the AI and how would I verify the fix and possible regressions?).
A lot of B2B services exist to provide complete solutions. The business pays so it won't spend time/management resource/risk to nurse that solution.
With AI the cost of software will go down, no more 15$ per seat plans, but it won't go to 0 or self-host everything.
This makes the technical skills required but not sufficient part of the whole business and there are many businesses (like restaurants) that are bad businesses, because many people are willing to open a restaurant as a life-style choice even though most of them are not a good business. This influx of new competitors, make the current restaurant owners profit to go down, because of the increased competition that isn't purely market driven.
In the software world that means "a small product" won't have any moat & pricing power, and your competitors might be wiling to subsidise it for years as a side project, making the whole market suffer from profit erosion (an economic term) leading to zero profit long-term equilibrium.
For example try charging per message (as previously charged by SMS/MMS) in the era of subsidised messenger apps with free calls and messages.
This makes the unique position of SAAS software with 9$/month subscription highly doubtful in the future.
The approach that works for me is using Justin's Skycak methods he mentions in his books:
https://www.justinmath.com/books/
Check the shorter "Advice on upskilling" or "The Math Academy way" for well researched approach.
Basically, using the books as the source for the LLM to limit itself by RAG-ing the text and then generate the required tests, questions and explanation of the concepts.
The biggest problem wasn't the workload itself, but how inefficient learning was back then. It was the early days of the Internet, no coursera, no Google indexed uni sites, no LLMs, no online tutors. I only had a mediocre textbook and some notes I borrowed from friends when I couldn't attend lectures or labs. It took ages. I’d burn hours trying to parse two paragraphs in the textbook, only to ask mates who didn't understand it either.
Now, we have an incredible amount of information & tech at our fingertips: LLMs, Google NotebookLM, online tutors, dozens of textbooks, and sites like Anna's Archive packed with high-quality "free" material. With all this technology, the time it takes to understand, prepare, and master a topic is a fraction of what it used to be. It still takes work and discipline, but man... you can feed textbooks into an LLM and use targeted prompts to debug your exact knowledge gaps on the practice test you get from the LLM.
So I reckon it's easier now than before, simply because learning can be done so much more efficiently. After all these years, I still remember the hardest classes and the most stringent professors with great affection - even though I hated those bastards back then. My Group Theory prof once invalidated a definition I wrote because of a single missing word. At the time, I thought it was an injustice, but later I realized that word was crucial.
University is where young people must face hard work and reality, because it teaches them what true meritocracy is. It should be hard, it's the same way a gym workout should be hard or it’s ineffective.
The approach that works for me is using Justin's skycak methods he mentions in his books:
https://www.justinmath.com/books/
Check the shorter "Advice on upskilling" or "The Math Academy way" for well researched approach.
So what works for me
- open a project in ChatGPT/Notebook LM - dump all the relevant and highly cited materials (textbooks, papers) - dump the advice on upskilling text or a short summary I've written for the LLM
- create "Learning Goals", that contain what I want to learn, and how to estimate is my level good enough
1) Ask it to create a learning path from the materials, following the approach. Give that to an adversarial LLM for cross check. (just for sanity check)
2) Ask it to create an "entry test" to check what I do know and what I don't
3) Iterate step by step on each module/submodule from the learning path that intermingles the approach of: small theory step + small practical task + small test. Log what's missing/wrong in my dept log. Give the dept log at the end of the session to the LLM to incorporate/create another test/task.
What I have found useful in this approach is that it will generate a lot of practical tests/tasks for me and it will explain a concept in many ways until I understand it. Also it finds some prerequisites I might miss, but based on my tests and debt log unexpected things I thought I understood surface.
So with the limits of LLM and while building a mental map of the relevant parts it's usually enough to spot the hallucinations, but if you apply structured approaches these are minimal. And it's super good, because the number of practice tests and explanations is endless.
The interfaces are a bit clunky, but current multimodal LLMs are ok with images or even hand writing.
I will recommend that structured approach.
The modern societies run via those devices and the enforcement will move to the mostly free Internet that was "a long time ago, when it didn't matter as much".
The policy shift came around 2017 when the government understood that subsidies and closed market created lazy automakers (e.g. check the US market for the same dynamic in trucks). In 2018 they removed subsidies for cars less than 150km of range and changed the requirements to receive them tying them to battery density.
After Tesla came and subsidies were reduced, most of the inefficient companies went bankrupt. It was the free-market competition that killed them. The reason why it changes was the competition not the subsidy. The subsidy was somewhat important, but as many of us knows, nothing government run produces good consumer products, you need market and free choice to get productivity. So it was the free-market and subsidy cutting that created the BYD, Li Auto, Xpeng and Nio. That's the proof that without free market it's impossible to have strong companies. And without subsidies you can have strong companies, even if it takes more time.
In EU and USA subsidies didn't create a lot (Northvolt?), because there were not enough free-market driven demand and too much regulation to jumpstart the free-market forces that will start the engine of innovation.
So the answer is not "capitalism and free-market doesn't work and governmental industrial policy is the answer", but it's the market that creates the strong companies. You can speed it up with venture/government capital, but without the brutal competition in the marketplace where people vote with their wallets you won't make it work.
After Tesla came with special laws allowing it to work without Chinese co-ownership, but with a mandate to source from Chinese suppliers, this created the ecosystem needed for the next iteration, by the capitalist urge of Elon to get into the market and by free-market economy of suppliers to Tesla that are up to the standards to supply it.
The government set up the battlefield, and let the free-maket filter the strongest to survive.
In the Western countries the government and regulations are frequently the bane of the car companies. Good luck opening a mine or a battery factory in Germany, regulation, unions, green mandates, 36 hr. week. You can't fight with 1 leg tied.
https://www.youtube.com/watch?v=HUEvRyemKSg
As the internet become the place where people do a lot of things, no government (and especially no security services) will be able to keep themselves from trying to control it or at least monitor it. And with the new LLM features they can automatically do much more than before.
Human nature is a constant and when the government sees an easy way to enforce something, many more bureaucrats will try to do it.
I have a measure for all content I consume, quality/hr of reading/listening. If it's just a long video that has 2-3 questions that has caught my attention I'd be listening only those. If it's a long text that I might find something interesting I'll ask the LLM to summarize the main ideas as a filter before I decide to dive in.
Books, and their audiobooks version have on average much more bang per hour than random podcasts, because they're structured, authors had spend more time on them and you can cherry pick from a structure.
I also have caught myself using sloppy content as excuse not working on planned tasks with excuses like "this might be useful", or watching "productivity porn" videos. I think LLMs are good as a pre-filter for that.
But when it goes over the safe limit it should be enough to decide to ventilate.
https://www.youtube.com/watch?v=HUEvRyemKSg
As the internet become the place where people do a lot of things, no government (and especially no security services) will be able to keep themselves from trying to control it or at least monitor it. And with the new LLM features they can automatically do much more than before.
Human nature is a constant and when the government sees an easy way to enforce something, many more bureaucrats will try to do it.
There is a whole class of tricky documents. A decent (if you ignore the marketing bias) post about this problem can be found here:
Even though the house is really old, it has been taken care of. There is no mold, the doors are still the same original ones, the fireplaces with decorative tiles are still there, and the wooden fireplace parts are still in good condition. I don't know how they did it, but it was built rather well.
Interesting tidbit: on the ceiling there was something like a Star of David. After asking LLMs what it was, one of them said that when Australia became a country, the Federation Star had only six points, denoting the newly incorporated states, but later a seventh point was added. Gemini told me that the frieze details were typical for the 1901-1910 period, and this helped me date the house.
This took about 10 minutes. Before, it would probably have taken me at least several hours of Googling.
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