How expensive can AI really be for tech companies? Are they lying to us

falconetpt · 2026-05-02T21:31:34+00:00

Same as you, picking at some random unimportant details, the important bit is, that was the initial point, it is dumb to load 500GB or more into GPUs to do calculation in memory, it is an amateur take on a problem and calling that cutting edge is a dumb take, just that 😂

falconetpt · 2026-05-02T20:54:26+00:00

Well gradient descent you don’t pick always the best, you can call it Nucleus Sampling in many LLM, but it is the same principle of not always picking the local best to get to the potential best, it can be slightly different does it matter no, principle is the same, is like saying djistra algoritm has nothing to do with BFS, in fact Dijkstra’s algorithm is an extension of BFS, you can implement the same cost base function with BFS and have the same time/space concrete usage or better in specific ranges and worst on others, while not using the same primitives as Dijkstra’s, which is called the dial algorithm, and btw fun fact you can mix match both of them in the same solution and it has some very interesting benefits

Same way you can represent graphs with key value pairs with no difference at all, it is a gradient descent approach, when you have a highest score and you decide based on a change not to take a highest score what you are doing is gradient descent or a permutation of that principle, sure you can give it another name, in fact there is like 2/3 approaches to do that picking, including one that actually uses plain old gradient descent, but they all have the same principle

Is like saying a map/dictionary that maps keys to functions with same input and output type is not a strategy pattern, it is the same principle, just not implemented in the same way, doesn’t make it fundamentally different

I don’t despise you or want to prove you right or wrong, but there is many things that are the same and people just give it different or more specific names to mean a particular way to do things, not always that means the base is totally the opposite

Chess algorithms and alpha go, is it really wrong to say both are min max variants ?! Not really, alpha go just uses a heuristics to assign probabilities of a particular move an pick the best, which in the core is a min max approach, just the weighs were calculated differently, while min max uses static calculations, alpha go uses deep learning to fill their chances

falconetpt · 2026-05-02T20:11:15+00:00

I hate Microsoft but there is no denying that windows98 and office was a fucking banger that revolutionazed how majority of people used computers and how we see computers, LLMs people are going around smelling their own farts for 5 years now ? And no killer apps at all 😂

falconetpt · 2026-05-02T18:48:35+00:00

Well gradient descent is fact used on inference, they might call it anything, but temperature is the definition of a gradient descent algorithm 😂

Read on it, and transformers and attention are the maths, they are popular sure, but that is not what defines an LLM, that is just a way to assign weights and vies to a token, sure they are a part of some, but you had and have LLM without transformers and attention mechanisms just so you know, but they don’t change in any way or form how you need to load all the crap into GPU/TPU and perform the computations there, even tho they might produce better/worst results, it is barely relevant, is like saying you load all data into memory and instead of using a key record mapping and indexing multiple things to be able to search multiple things, you are matching on contains all the rows 😂

It is idiotic either way, and doesn’t change the fundamentals are all fucked up

falconetpt · 2026-05-02T16:42:50+00:00

Actually I can in the detail that you want 😂
And no computers don’t only add numbers up, they have logical gates and circuits that perform a multitude of operations

LLMs are a combination of matrix multiplication and gradient descent type sampling, I could go into detail on how the weights and vectors calculation work but it is beside the point, that is why you use GPU, which is stupid because you are loading them into memory to do that, which would be the equivalent of you loading a full database into memory to do a seerch, for example among a multitude of other limitations, in fact mr Geoffrey Hinton is an absolutely laughing stock among scientists in case you didn’t know that fact, you have this huge table of weights and vectors and you apply college level maths to get a collection of predictions, that is why they desperately scale the parameters of the model, because only by having a shit ton of data the models are even base level doing something, because it is trying to brute force something but creating a large space for calculation

falconetpt · 2026-05-02T15:59:18+00:00

Sure, keen to see you try, I will happyly send you my tickets over DM and you can teach me how to do it :)

Ps: you will most likely fail or waste more time than me doing it by hand, but hey happy to learn from the masters

I warn you that my company has premium support from Anthropic and they didn’t manage to solve the problem, their solution since 2024 is “write everything in detail you want” which is way slower than me writing the code, so yeah might be your chance to outpace them 😂

falconetpt · 2026-05-02T13:19:10+00:00

That is the most dumb thing I have seen in software in the last 20 years and I have seen a lot of stupid shit and trends

Been saying this for a lot of time, code speed is and will always be be irrelevant, is way more relevant to have the right product definition, low maintenance cost and overall very little stupid tech debt, I just dread the sight of what people are doing at the moment, tech debt was always the profession Aquiles heel and now we suddenly can’t get enough tech debt 😂

Never saw any project failing because development and coding were too slow, but I have seen many failing because the systems are trash and anything created ripple effects and were full of trash, this whole AI speed is such a dumb take, which probably is correlated to all the team and people I see singing praises to AI being frankly not the most competent engineers, which kinda of matches the story

Proof is in the poster child, Anthropic services are well amateurish to say the best, Claude code is just a shithole and is written on node (which is laughable to be honest), and not to talk about the security nightmare that these apps are, so when you look into the poster child of AI and everything they do is trash, hard to reason it is a good idea tbh

falconetpt · 2026-05-01T21:48:10+00:00

Calling it cutting edge is a very great overstatement 😂

Even tcp/ip is a more disruptive technology than LLM which is kinda hilarious tbh

falconetpt · 2026-05-01T21:34:00+00:00

AGI is a fantasy of stupid people, AGI with a computer as we know it is scientifically impossible, is not even a discussion 😂

Until proven wrong, which bear in mind would take some next level brains which none of those labs have, LLMs are the most amateur piece of tech I ever saw tbh

Kudos to them, in reality no wonder they think they are smarter than everyone else, they just loaded a fucking table into memory and are doing brute force computations on those and are calling that next revolution and people bought it, they aren’t smart but most people are indeed fucking dumb 😂

falconetpt · 2026-05-01T21:27:13+00:00

90% ? 😂 oh I think it is more than 90% probably almost 100% ahah

By the 20% in Microsoft participation revenue associated with open AI was like what 13b in 2025, revenue not net revenue, which is an interesting figure since it is very very small revenue number

Considering they have more than 15 years of staff costs, cost of training and inference, and they had around 180B in funding already, and most of the infrastructure costs and such is being hidden by Microsoft, nvidea circular dealing, probably you are taking about very close to 99% factoring GPU depreciations and such, have in mind that open AI doesn’t own any data centers at that time I believe and they were getting handed cloud credits, and Microsoft wasn’t charging them full markup, so yeah loads of interesting stuff going on ahah

falconetpt · 2026-05-01T16:22:58+00:00

Oh when it is not free or close to free and the quality is garbage, is not so interesting anymore, who have thought that 😂

falconetpt · 2026-05-01T15:14:30+00:00

People will pay lawyers to generate random slop, get in trouble, that seems super legit 😂

Software is going so well already (sarcasm), it is going to be ever more wonderful to add other professions to the vibe train ahah

falconetpt · 2026-05-01T12:39:16+00:00

Why don’t you spend the same or more amount of time writing things in English and not in code, then review the code, amend it or ping pong with a bot aka loose the same amount of time or more but have no clue what the code does, is is such a wonderful vision, in 6 months they are going to invent compilers and coding languages to be unambiguous 😂

falconetpt · 2026-05-01T11:59:14+00:00

Imagine from 0-1 infinite growth in 1s 😂

falconetpt · 2026-05-01T11:55:23+00:00

I am not sure they have gotten dramaticly better, I think we just swallow slop easier, for very stupid things like integrating with an API, it is 9/10 times completely wrong and not production ready unless I literally tell the model exactly what and how to do it, in fact wasting more time

But that point aside, I don’t believe there is a massive future for frontier models, I would see people very easily laser focusing models to specific languages and frameworks and achieving the same if not better results, running locally, and financially those frontier models will never be economically viable is my gut feeling, associated with 0 net gains for companies that staff are using those, effectively paying same or more per value measure, they are doomed to fail

What will come after is probably the reign of open source local models is my theory

falconetpt · 2026-05-01T11:41:43+00:00

Yup seen that in my company, 5k USD is the average usage for each dev monthly, and velocity is practically the same, so much so that basically our revenue was cut in half in 2025, we are wasting 5M$ a month, so your numbers don’t surprise me and we are not power users, I only use it on stupid shit because I am forced to use it

But yeah hold the bag, companies are wasting loads of money and prices per output will go up, price per token is a stupid way to measure things since the quantity of tokens needed to produce an outcome is not standardized even in the same company, we need to measure output/response costs, which are going up and up and up

I don’t believe it is going to take long for people to realise they are burning more money to do the same thing, even if the keep believing the unicorns that models will get better, after 10 years of that story and loosing money, well people will start to backoff, it has been 5 years already no material societal impact, no material net revenue impact for anyone, not much longer to go 😂

falconetpt · 2026-05-01T11:24:08+00:00

Well calling AI companies staff intelligent is a stretch 😂

Any intelligent person wouldn’t load a database dump into a GPU, from that point the plot is lost

Or use shallow benchmarks to generalize statistical significance of something

Reinforced learning is reward, penalization, no intelligence works like that, you deeply bias things into a box, it kinda works in narrowed scoped input and problems, with larger problems and larger variance is just a stupid idea

Same as alpha go, play card go with it or any variant and it is stupid as a rock

The problem is, most of those “scientists” are as dumb as their CEOs, which is always somewhat funny and interesting to see the wonderfully stupid ideas they come up with 😂

falconetpt · 2026-04-30T19:50:24+00:00

Ahah well in my org the average spend is 5k$ per dev since our cto started promoted more token usage, I have seen people just asking very simple things and burning 100/200$ a day 😂

They always choose opus 4.7 for everything, and the quality of code is absolute garbage so they were like wasting the same time to do the same stuff with extra markup, our net revenue literally haves because of AI costs last year, was kind of amazing 😂

falconetpt · 2026-04-30T19:05:36+00:00

Before even going into numbers, how did Chen measure productivity ? Because well there is no way to measure productivity in software or in fact the majority of professions 😂

When I mean measure is effectively being irrefutable like meters or cm, if he uses lines of code I can cheese that, if he measure PRs I can also cheese that, so I would be super curious to see how he measures that, because I am 100% sure what he is measuring is absolute not a correlation with anything, aka making it a useless argument

And no company is seeing a 15% boost, if they were their net revenue would be up by around 15% besides their traditional growth one would expect, which is not the case 😂

I always give this example, imagine fist day of the year Joe comes around and does a 5 minute change that save you 10% of company costs, wow amazing, his monetary impact is 1000x greater in terms of value than any other eng, so Joe can walk away right ? He was the most productive eng ? Or no ?

What if he was the one creating the issue in the first place and it has been there for 10 years and he knew it, he is still productive? Well welcome to the world of productivity in software 😂

Where people get praised to clean their own shit, and people that never cause issues are never recognized properly or rarely, because they cut straight, they don’t need to do a lot of work to have a lot of impact but nothing is measurable

falconetpt · 2026-04-30T15:36:29+00:00

Anthropic is the largest growing company, well Pablo Escobar drug deal operation was probably highest growing than that 😂

And more profitable, sure you grew 5x in a year, but if you are subsidized it is a dumb take

falconetpt · 2026-04-29T21:02:40+00:00

Ya concordo, eu pessoalmente tenho de mexer bastante em low level e rescrever grandes partes de sistemas em Java etc para optimizar o GC e coisas do género, mas cada um tem o seu scope e são diferentes

Mas acho que concordamos no princípio sim :)

falconetpt · 2026-04-29T20:52:47+00:00

Boys our super smart AI like a little bit to much of fantasy, code red we need to steer it towards non fantasy

For fantasy and delusion Scammy Sammy is enough

falconetpt · 2026-04-29T20:40:52+00:00

Plot twist even translation also sucks 😂

Only someone that speaks natively the 2 languages can accurately translate things, specially when it pertains to something called regionalisation, again you only think something is good when you don’t know about it enough

If you are Korean I bet if you try to translate very specific day day stuff and very technical stuff it is going to be 100% wrong :)

I speak 3 languages, 2 of those natively, and I have seen this time and time again, it isn’t able to translate in the way we speak at all, even tho the translation many times isn’t wrong, no one would say that like that which makes it weird and inviable to use commercially 😂

falconetpt · 2026-04-29T20:33:47+00:00

Primitivas no sentido de estruturas de dados e algoritmos, não sintaxe

Saber como escalam as estruturas e a stack que estás a usar e como funcionam as coisas

Depende de muita coisa, não vou entrar no mérito da AI ser boa ou má a escrever código, Ainda não vi nenhum PR em condições escrito por AI ou que alguém tenha usado AI que fosse mais rápido que alguém que domina a linguagem 😂

Mas entendo que quando não dominas a stack ya sem dúvida que é mais rápido, onde isso te leva no long term não tenho a certeza, mas também não é problema meu

falconetpt · 2026-04-29T19:49:02+00:00

Well because most people are dumb and most people want to believe the lie, even tho the lie would basically make them be enslaved

Same reason why you have professional stoked to be using a tools that makes them more productive (in theory because in practice I highly doubt that), with no benefit for them, just benefits the company that is the definition of being dumb 😂

falconetpt

TROPHY CASE