The world’s top two AI startups are ignoring requests by media publishers to stop scraping their web content for free model training data, Business Insider has learned.
OpenAI and Anthropic have been found to be either ignoring or circumventing an established web rule, called robots.txt, that prevents automated scraping of websites.
TollBit, a startup aiming to broker paid licensing deals between publishers and AI companies, found several AI companies are acting in this way and informed certain large publishers in a Friday letter, which was reported earlier by Reuters. The letter did not include the names of any of the AI companies accused of skirting the rule.
OpenAI and Anthropic have stated publicly that they respect robots.txt and blocks to their specific web crawlers, GPTBot and ClaudeBot.
However, according to TollBit’s findings, such blocks are not being respected, as claimed. AI companies, including OpenAI and Anthropic, are simply choosing to “bypass” robots.txt in order to retrieve or scrape all of the content from a given website or page.
A spokeswoman for OpenAI declined to comment beyond pointing BI to a corporate blogpost from May, in which the company says it takes web crawler permissions “into account each time we train a new model.” A spokesperson for Anthropic did not respond to emails seeking comment.
Robots.txt is a single bit of code that’s been used since the late 1990s as a way for websites to tell bot crawlers they don’t want their data scraped and collected. It was widely accepted as one of the unofficial rules supporting the web.
It’s just how machine learning has been since ever.
We only know the model’s behavior by testing, hence we only know more or less the behavior in relation to the amount of testing that was done. But the model internals has always been a black box of numbers that individually mean nothing and if tracked which neurons fire here and there it’ll appear just random, because it probably is.
Remember the machine learning models aren’t carefully designed, they’re just brute-force trained for a long time and have the numbers adjusted again and again whenever the results look closer or further away from the desired output.
If the models are random then we shouldn’t be trusting them to do anything, let alone serious applications. If any other type of software told us that it’s based on partially random results we’d say “get that shit out of here, I want my software to work first time, every time”.
“Statistically good enough” works for some applications but not for others. If a LLM finds a formula that has an 80% chance to be the cure for cancer or a new magical fuel or some amazing new material that’s cool, we’re not going to look the gift horse in the mouth.
But using LLM to polute the web with advertising texts that are barely inteligible, and using it as a pretext to break copyright in the process, who does that help? So far the only readily available commercial application for LLMs has been to spit out semi-nonsense so that a bunch of bottom-crawling parasitic industries can be enabled to keep on pinching pennies and shitting up everything they touch.
Which, ironically, it will help them to hit bottom all the faster, so in a strange way it’s a positive return, but the problem is they’re going to take down a lot of useful things with them.
If the models are random then we shouldn’t be trusting them to do anything, let alone serious applications.
That’s not the reason we shouldn’t be using them for anything other than generating lorem ipsum style text or dialogue for non quest critical NPCs in games.
The reason is that, paraphrasing Neil Gaiman, LLMs don’t generate information, they generate information shaped sentences.
Specifically, an LLM takes a sequence of characters (not a word or text; LLMs have no concept of words, or text, or anything else for that matter; they’re just an application of statistics on large volumes of sequences of characters; no meaning or intelligence involved, artificial or not)… as I was saying, an LLM takes a sequence of characters, pushes it through its model, and outputs the sequence of characters most likely to follow it in the texts its model has been trained on (or rather, the most likely after discarding the ones its creators have labelled as politically incorrect).
That’s all they do, and they’ll excellent at it (or would be if it weren’t for the aforementioned filters), but that’ll never give you a cure for cancer unless there already was one in their training data.
They take texts written by humans, shred them, and give you their badly put back together dessicated corpses, drained of any and all meaning or information, but looking very convincingly (until you fact check them) like actually meaningful or informative texts.
That is what makes them dangerous. That and the fact that the bastards selling them are marketing them for the jobs they’re least capable of doing, that is, providing reliable information.
(And that’s while they can still be trained on meaningful and informative texts written by humans — inasmuch as anything found on reddit, facebook, or xitter can be considered to be meaningful or informative —, but given that a higher and higher percentage of the text on the internet is being generated by LLMs soon enough it’ll be impossible to train new models on anything but 99% LLM generated garbage, at which point the whole bubble will implode, as anyone who’s wasted time, paper, and toner playing with a photocopier or anyone familiar with the phrase “garbage in, garbage out” will already have realised… which is probably why the LLM peddlers are ignoring robots.txt and copyright laws in a desperate effort to scrape whatever’s left of the bottom of the barrel.)
LLMs don’t generate information, they generate information shaped sentences
That is besides the point. A random number generator is more or less random but it still has applications.
The problem is not them being random, it’s hiding that they are being random so they can be used for applications where randomness is not a feature.
