The Future of Everything is Lies, I Guess: Safety Software LLM The Future of Everything is Lies I Guess 2026-04-13 New machine learning systems endanger our psychological and physical safety. The idea that ML companies will ensure “AI” is broadly aligned with human interests is naïve: allowing the production of “friendly” models has necessarily enabled the production of “evil” ones. Even “friendly” LLMs are security nightmares. The “lethal trifecta” is in fact a unifecta: LLMs simply cannot safely be given the power to fuck things up. LLMs change the cost balance for malicious attackers, enabling new scales of sophisticated, targeted security attacks, fraud, and harassment. Models can produce text and imagery that is difficult for humans to bear; I expect an increased burden to fall on moderators. Semi-autonomous weapons are already here, and their capabilities will only expand.

Alignment is a Joke Well-meaning people are trying very hard to ensure LLMs are friendly to humans. This undertaking is called alignment. I don’t think it’s going to work.

First, ML models are a giant pile of linear algebra. Unlike human brains, which are biologically predisposed to acquire prosocial behavior, there is nothing intrinsic in the mathematics or hardware that ensures models are nice. Instead, alignment is purely a product of the corpus and training process: OpenAI has enormous teams of people who spend time talking to LLMs, evaluating what they say, and adjusting weights to make them nice. They also build secondary LLMs which double-check that the core LLM is not telling people how to build pipe bombs. Both of these things are optional and expensive. All it takes to get an unaligned model is for an unscrupulous entity to train one and not do that work—or to do it poorly.

I see four moats that could prevent this from happening.

First, training and inference hardware could be difficult to access. This clearly won’t last. The entire tech industry is gearing up to produce ML hardware and building datacenters at an incredible clip. Microsoft, Oracle, and Amazon are tripping over themselves to rent training clusters to anyone who asks, and economies of scale are rapidly lowering costs.

Second, the mathematics and software that go into the training and inference process could be kept secret. The math is all published, so that’s not going to stop anyone. The software generally remains secret sauce, but I don’t think that will hold for long. There are a lot of people working at frontier labs; those people will move to other jobs and their expertise will gradually become common knowledge. I would be shocked if state actors were not trying to exfiltrate data from OpenAI et al. like Saudi Arabia did to Twitter, or China has been doing to a good chunk of the US tech industry for the last twenty years.

Third, training corpuses could be difficult to acquire. This cat has never seen the inside of a bag. Meta trained their LLM by torrenting pirated books and scraping the Internet. Both of these things are easy to do. There are whole companies which offer web scraping as a service; they spread requests across vast arrays of residential proxies to make it difficult to identify and block.

Fourth, there’s the small armies of contractors who do the work of judging LLM responses during the reinforcement learning process; as the quip goes, “AI” stands for African Intelligence. This takes money to do yourself, but it is possible to piggyback off the work of others by training your model off another model’s outputs. OpenAI thinks Deepseek did exactly that.

In short, the ML industry is creating the conditions under which anyone with sufficient funds can train an unaligned model. Rather than raise the bar against malicious AI, ML companies have lowered it.

To make matters worse, the current efforts at alignment don’t seem to be working all that well. LLMs are complex chaotic systems, and we don’t really understand how they work or how to make them safe. Even after shoveling piles of money and gobstoppingly smart engineers at the problem for years, supposedly aligned LLMs keep sexting kids, obliteration attacks can convince models to generate images of violence, and anyone can go and download “uncensored” versions of models. Of course alignment prevents many terrible things from happening, but models are run many times, so there are many chances for the safeguards to fail. Alignment which prevents 99% of hate speech still generates an awful lot of hate speech. The LLM only has to give usable instructions for making a bioweapon once.

We should assume that any “friendly” model built will have an equivalently powerful “evil” version in a few years. If you do not want the evil version to exist, you should not build the friendly one! You should definitely not reorient a good chunk of the US economy toward making evil models easier to train. ...

The Future of Everything Is Lies, I Guess: Safety