I refuse to use “AI”, and you should boycott it too. It is an evil technology entirely based on theft, with massive ethical, legal, societal, environmental, and financial problems. This thread explains why. 🧵

The training data for these language models is entirely based on stolen data. Art, books, code, knowledge – all stolen from their authors without their knowledge or consent. It is the largest theft of intellectual property in history.

Unlike a human training by studying existing media, AI models simply copy vast swatches of copyrighted material into themselves, and can even be tuned to regurgitate those works verbatim.

Major AI companies have trained their models by feeding them vast swathes of pirated books. While they were eventually taken to court over this criminal act, they got off with a mere payout, while keeping the models trained on this stolen data.

They then moved to training models using old physical books, with AI companies buying second-hand books en mass (including very rare books), scanning them, and then destroying the originals.

Models for image generation were trained on hundreds of millions of stolen images which were scraped from the internet, again without the artists’ knowledge or consent.

Models and agents for programming are also based on plagiarism, with training largely taken from public source code repositories, including ones under copyright (or copyleft.) Again, code authors never consented to this usage, and many repos predate LLMs.

More general models, such as those used by chat agents, were largely trained on data stolen from forums, social media, articles, and wikis – once again without the authors’ consent.

While I’ve singled out specific examples in this thread, ALL major AI models (including OpenAI / ChatGTP, Anthropic / Claude, Microsoft / CoPilot, and Amazon, to name a few) have been trained in this way, on human-made data taken without the permission or consent of the authors.

If you are a creative and you are considering using AI, remember that it is a technology that is designed around stealing the work of other people just like you.

There are also a lot of unresolved legal issues around the use of generative AI. Anything made using it may be at risk of legal action in the future, once the laws finally catch up with the technology.

The main legal defence from AI companies is the claim that training AI models on copyrighted material counts as “fair use”, which is not proven law at all, with some courts already ruling that it is not (at least in certain circumstances.)

As already mentioned, AI executives have admitted that the technology is based on theft.

They are spending vast sums of money to lobby lawmakers into making exemptions for them, especially in the USA. It remains to be seen if they will be successful.

XAI’s Grok image generation model allows itself to be used to create non-consensual sexualised images of real people, including children. XAI have started suing users rather than fixing the problem.

Grok’s image generation model also appears to have been trained on child sexual abuse material.

The environmental costs of the data centres powering AI are staggering. The power used by data centres, the water used to cool them, and the resources used to build them are massive. Then there is the manufacturing of all the hardware they need.

A UN report estimates that AI data centres will consume more power than 650 million people, and more water than 1.3 billion people, by 2030.

Data centres are expected to account for the largest increase in energy demand by 2030, especially in the USA – all at a time when we urgently need to reduce our dependence of fossil fuels to avoid climate catastrophe.

Overall, fossil fuels (gas and coal) power over half of data centres in both the USA and China. While this is expected to shift more towards renewables in the future, the current policies in the USA greatly favour the more polluting sources of power.

In the USA, there has been talk of building new gas pipelines specifically to power AI data centres.

In addition to the environmental effects, the energy demand of data centres has also increased the cost of energy for everyone. In the USA, data centres are estimated to have already cost the public $23 billion in price increases.

Here in Ireland, citizens are estimated to have paid an extra €715 million (approx. €300 per household) on electricity between 2015-2023 due to data centres, with that amount expected to more than double over the next decade.

The demand for hardware for AI data centres has also led to unprecedented price increases for computer hardware, something which gamers and game developers have felt especially hard as they need high-performance PCs.

The next generation of consoles has also been delayed because the increased costs of memory makes them unviable, with Valve, Microsoft and Sony all announcing delays due to AI-related memory supply issues.

As an aside, hardware manufacturers such as Nvidia, Micron, and AMD are the only companies actually making a profit from AI, because they are the ones selling the hardware to everyone else.

Source: https://isaiprofitable.com/

The build-up of AI and data centres is currently running a multi-TRILLION dollar deficit. The entire business model is a scam bubble set to collapse eventually.

The major AI companies have been created and run by some of the most evil human beings on the planet. Apart from the grand-scale theft that’s already been covered, they have a laundry list of other offences (some confirmed, some merely alleged.)

After former OpenAI (creators of ChatGPT) employee Suchir Balaji blew the whistle on the mass theft involved in training, many believe that OpenAI either hounded him to suicide, or had him murdered to silence him.

In 2023, OpenAI’s head of research, Tal Broda, openly called for the genocide of the Palestinian people. While he later deleted the posts, he never suffered any repercussions from OpenAI.

OpenAI’s founder, Sam Altman, has been accused of sexually abusing his sister while she was a child.

Elon Musk, founder of XAI, performed a Nazi salute twice during Donald Trump’s inauguration rally.

Major AI companies OpenAI, Microsoft, Google, XAI, Nvidia, Microsoft, Amazon, ReflectionAI, all signed a deal with the Pentagon allowing use of their AIs for all purposes, including fully autonomous weapons and the mass-scale surveillance of citizens.

Amazon, Google, Microsoft, and possibly other American AI companies, worked with Israel in building AI systems to create kill-lists of Palestinian civilians to murder during their genocide of Gaza.

Anthropic’s Clause AI was used by the USA and Israel when conducting their unprovoked bombing of civilian targets in Iran.

While it is likely an attempt to over-sell the capabilities of AI as a form of advertising, Elon Musk has said that he believes there’s a 10-20% chance that AI will wipe out humanity, but wants to continue making it anyway.

During a 2016 interview, OpenAI’s Sam Altman said that “AI will most likely lead to the end of the world, but in the meantime there will be great companies created with serious machine learning”.

Given that many of the people behind the major AI models are bigots in one way or another, plus the undiscerning nature of their training data, it’s hardly surprising that the output of their AIs is also biased towards racial stereotypes and other bigotry.

AI image generators are also heavily prone to bias and bigotry, often producing exaggerated stereotypes.

[Note: Apologies for the AI-generated images which are heavily used as examples in this article.]

People relying on AI for their information also makes it easy for bad actors to plant false information, by influencing a small number of companies who then push that disinformation to everyone who uses AI.

AI video and voice generation also have the potential to make it falsely appear that someone said something they didn’t, which could become a really powerful tool for smearing people, especially activists.

It is also very easy for anyone to trick AI models into spreading false information. In 2024, a Swedish researcher wrote an obviously-fake medical paper about a fictional disease, and AIs soon started diagnosing people with it.

In addition to intentional disinformation, AI output is also rife with misinformation, again due to the undiscerning nature of the (stolen) training data. It doesn’t have the capability to judge if information that it has been fed is true or not.

In 2024, Google’s AI summary suggested that people should mix glue into their pizza sauce to keep the cheese from sliding off, which it apparently sourced from a joke comment on Reddit.

AI chatbots are also prone to hallucinations – when they confidently produce false information. This is not a malfunction, but a part of how these AI models work. They are generating the worlds to a plausible-sounding answer, not verified facts.

This can also lead chatbots to double-down on their false information and try to gaslight the user, presumably because it seems like the most plausible way to continue the conversation.

AI chat bots such as ChatGTP have also been responsible for encouraging harmful and/or criminal acts, including suicide, murder, and theft.

Wikipedia has an article listing AI chatbot-related deaths.

The CEO of Krafton used ChatGTP to help formulate a plan to defraud Unknown Worlds (the studio behind underwater survival game Subnautica) of $250m and to take direct control of the company.

There is growing evidence that using AI is making us dumber, as people who use AI have less reason to engage their brains in critical thinking and other mental skills.

For AI companies and the corporations pushing for its adoption, the end goal is to replace most human jobs with AI, so that the ultra-rich can get richer and the rest of us can be thrown into poverty and destitution. No other outcome is acceptable under capitalism.

This of course doesn’t consider that if the billionaires have all the money and no one else has any, then there will be no one left for them to get any more from.

In practice, despite the immense hype, AI is actually reducing companies’ productivity, rather than increasing it. In a 2024 study, 80% of workers said AI added to their workload rather than reducing it.

A 2026 study showed that 90% of executives believe that AI hasn’t actually boosted productivity.

Many of the companies that enacted mass layoffs to replace employees with AI have since re-hired employees, with 55% of leaders saying it was the wrong decision to cut staff for AI.

Studies have also shown that code produced by AIs has significantly more bugs and errors than human-written code, especially (and most worryingly) in areas of safety and security.

Even given all of that, there is also the simple fact that many people don’t like things created using AI.

Young people have started using the term “AI” to refer to anything inauthentic, unbelievable, or rubbish.

Games that employ Generative AI sell and review worse on Steam.

Currently, games that release using GenAI content get harshly criticised (even if just by a vocal minority), and directly lose customers and reputation, whereas far fewer people are calling out for more games using AI or panning games that don’t use it.

In summary, AI is an inherently evil technology, based entirely on theft, developed and pushed by some of the most vile people imaginable, is harming our environment, increasing our cost of living, and changing our world for the worse in many ways, all to produce soulless slop.

So why am I boycotting it and encouraging others to do the same? Because boycotts WORK. The technology is already losing incomprehensible sums of money – it can only succeed if everyone uses it. The more people refusing to use it, the sooner it will die.

We’ve all seen the argument that “AI is here to stay, so we need to adapt or die”, but the same could be said of any negative practice throughout history, practices which people have organised against and overcome. Slavery, child labour, segregation, the Divine Right of Kings…

Some notes:

• I’ve used the term “AI” because it is short and understandable, though the term “LLMs” would be more accurate.
• I’ve tried to favour scientific and tech sources for the links, over more mainstream media or social media posts, as people may find the latter less trustworthy.

I’ve released my second Unreal Engine Marketplace product.
This one is a configurable randomisation system, written entirely in Blueprints.

It allows random sequences of either numbers (e.g. die rolls) or outcomes (e.g. chance to hit) to be generated, with options to coax the results into fairer distributions and to limit unlikely edge cases.

We’ve all played games where some randomised result feels really unfair because it seems to keep rolling against us repeatedly. Since the outcome of each roll is not affected by previous rolls, it’s perfectly possible (though unlikely) to get any number of low-probability results in a row, and given enough attempts and enough players, such a sequence is almost guaranteed to happen to someone.

So, I created this system to allow designers to control the randomness, to make it slightly less random but also closer to what we as humans expect random outcomes to be like, based on some of the tricks I’ve read about other game developers using.

It uses a two main methods to achieve this effect:

The first method is the use of a shuffle bag – instead of rolling for each outcome as needed, a number of future outcomes are placed into a bag and their order is randomized.
For example, given a 20% chance, two successes and eight failures are placed into the bag, guaranteeing an exact 20% outcome over ten rolls.
This actually changes the nature of the what is being randomised – the individual outcomes are no longer random, but instead the order of the outcomes is randomised.

The second method uses random generation, but adjusts the chances to favour some outcomes and disfavour or disallow others (such as excessively long streaks.)
For example, when rolling six-sided dice, each number can become more likely to be rolled the more rolls it has gone without appearing.

The system has a lot of options to further configure the randomisation algorithm. The full documentation is available on Google docs.

These tricks can be used for things like making sure the player doesn’t go too long without scoring a hit or a critical strike, to make sure that a weapon which deals damage in a range has an even spread inside that range, or to make it increasingly unlikely for a player to go without finding rare treasure with a very low chance to drop, to name but a few examples.

NV Random on the Unreal Marketplace

This tutorial is a quick summary of the process for preparing a content pack for submission into the Unreal Marketplace.
It is geared towards content-based asset packs.

The tutorial will cover three main aspects:

1. Preparing the content, including a list of things to check before submission.
2. Submitting the product to the Marketplace.
3. Updating a submission, including updates when a new version of the Unreal Engine is released.

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The goal of this tutorial is to set up a lantern object which casts soft dynamic shadows from the solid parts of the lantern.

The two normal ways of creating detailed shadows are to either bake them with static lights (which will not work for a moveable light source), or to use Raytraced Distance Field Soft Shadows, which don’t work very well if the light source is actually inside the model which is supposed to cast the shadow.

Instead of either of those, I will explain how to solve this problem by using a light function.

A light function is a material shader that can filter the intensity of light being output by a light source.  It can be used to give the light different intensities at different angles, with the output of the light dependant on the colour of the material, from black (no illumination) to white (full illumination).

We will use this to effectively mask the parts of the light where we don’t want it to shine at full brightness.
Further, we will blend between two different masks depending on the distance, giving a more realistic effect where the shadows are fairly sharp close to the light source, but get softer as the distance increases.

 

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The following are some musings on how to make good RPG systems, and which mistakes or pitfalls to avoid, based on the systems I’ve seen in various roleplaying games.

Note that I am talking specifically about RPG character progression systems, and not the other staples of the genre such as dialogue, choices and consequences.

I feel that a character system should:

• Have interesting and meaningful choices
• Differentiate gameplay between character builds
• Feel satisfying
• Avoid offering truly bad choices which only act as traps for inexperienced players
• Not get in the way of normal play

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I have played a lot of games, and in so doing I have seen a lot of the same mistakes, or at least design decisions that I disagree with, repeated many times.
I have also seen many excellent ideas in games that really helped them to stand out and be exceptional.

Recently, I’ve taken some time to think about the types of things that I do and don’t like to see in games, and from there I have created a list of what I consider to be good game design goals.

Of course, some of these are subjective – they are for making the type of games I like to see – but most of them are fairly general and should apply to almost every game.

Game Design Goals

1. The player should have to think.

While some amount of direction is necessary, the player shouldn’t be spoon-fed exactly what to do next every second, or every time that they complete an objective.  Occasional hints are fine, but the player should need to think and work out how to achieve their goals for themselves, rather than just following instructions.

Bad: The player is told exactly what to do each step of the way.
Good: The player is told roughly what to do, and can find information hinting at the solution, or work it out for themselves.

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When creating levels for games, you will often want to place sequences of terrain features which repeat, for example: stairs, fences, and other repetitive details.

When making stairs, you could model an entire staircase in your 3D-modelling package specifically for the area at hand, but that requires you (or your artists) to make a lot of customised, single-use meshes.

Alternatively, you could model individual steps (or a mesh with a small number of steps) and repeat those until your staircase is the right height.  Constantly using the same model will most likely make the repetitive use of the same texture extremely obvious and jarring.

This article proposes a solution to this problem: to create a number of unique meshes which are all variations on the same section of the repeating terrain, and to then use a blueprint to automatically select and place the individual meshes to build up the desired length of terrain.

For example, to model a dozen variations of a single step, and then have a blueprint which randomly (or sequentially) picks one of those variations and places it appropriately until it has built the entire staircase.

This allows the level designer to quickly and easily create repetitive terrain features, each with a unique look, and without requiring custom assets to be made for each individual use case.

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Bioshock is a 2007 game created by Irrational Studios, and is considered to be a spiritual successor to System Shock 2 (1999).

Despite its unique setting and atmosphere, the game contains a number of major design flaws that are worth investigating as game design anti-patterns (anti-patterns are the study of a “wrong” solution to a problem, to understand why it’s wrong and how the problem could be solved in a better way.)

In this article, I am going to specifically talk about two areas of Bioshock’s design;

• Interface – the interfaces that the game uses to allow the player to control its various aspects, such as genetic powers and items.
• Balance – the way that the various elements of the game are balanced against each other, and the way the game provides difficulty to the player.

Before I begin, I would like to mention a “pitch document” from early in Bioshock’s development, which set out the developers’ aims and design goals for the game.
This document is available as a set of images on Irrational’s Website.  I will be referring to and quoting sections of this document throughout the article, as I describe the various problems that the developers were looking to solve.

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