Anthropic, the AI company behind Claude, has agreed to spend $3.4 billion securing 1.7 gigawatts (GW) of gas-fired generators for its AI data centres, taking over equipment orders originally placed with Caterpillar by Texas-based supplier OnePWR Solutions. According to Reuters, the agreement was reached in late September, and also covers the engineering and construction work needed to install the equipment. Anthropic is not buying OnePWR itself. Instead, it is taking over the supplier's existing generator commitments, which were placed roughly two years ago but required additional financing. By taking over the existing orders, Anthropic avoids having to start from scratch and wait for Caterpillar to find another larger order into its production schedule. That timing matters too, as larger orders for large gas turbines and generators are piling up, and manufacturers are struggling to keep up with demand, especially from AI companies.

Notably, global turbine orders have reportedly exceeded 100GW, while manufacturing capacity stands at an estimated 60GW to 70GW. Average delivery times in the U.S. have reached six years, with some prospective orders extending into 2030 or 2031.

The latest purchase puts Anthropic increasingly in familiar territory. Elon Musk’s AI has already turned to gas-powered generation to bring its large computing facilities online without waiting for conventional grid connections. Recently, xAI installed eight converted General Electric jet turbines at a data center in Mississippi. Each unit can generate up to 50MW, giving the installation a potential combined output of 400MW. The deployment has also attracted environmental scrutiny, with the Southern Environmental Law Center challenging the site's emissions permits.

The environmental implications of relying on gas-fired generators have also attracted criticism. “This is pretty grim—these kinds of generators are terrible for the environment.” Shakeel Hashim, founder of ReadTransformer, tweeted, commenting on Anthropic's purchase. His reaction points to an issue Anthropic will have to contend with as it expands its power infrastructure: securing electricity quickly does not remove the environmental costs of generating it.

Musk's companies have been moving further into the equipment business, too. In August, it emerged that the company was bringing turbine-blade and vine manufacturing in-house, with Musk identifying components production as a major constraint on generator deliveries, with some turbine blades reportedly taking between 60 and 90 weeks to be manufactured. Earlier, in July, SpaceX also acquired APR Energy, a company with a fleet of trailer -mounted gas and diesel generators capable of producing more than 1GW.

The approach is not exclusive to Musk's businesses. OpenAI has followed a similar route for its first Stargate data center in Abilene, Texas. In July, we reported that Crusoe, the company developing the facility, had acquired 29GE Vernova LM2500XPRESS gas turbines, each rated at 34MW. Together, the 29 turbines could generate up to 986 MW. Anthropic is taking a different path, though, by stepping into OnePWR’s existing Caterpillar orders, securing generators that were already in use, rather than buying new equipment.

That said, the power shortage is forcing AI companies to look beyond new generator orders. There has been an upsurge in demand for used and refurbished turbines, including retired jet engines converted to generate electricity. But even those alternatives have limitations, including maintenance requirements, remaining service life and suitability for continuous operations. For Anthropic, however, taking over OnePWR’s Caterpillar orders could provide a way around some of those hurdles. The agreement also provides installation capabilities, meaning the company is securing more than the generators themselves.

A project called the AI Torture Chamber has caught the eye of a few engineers, who've used the notions and data therein create their own simulations of "pain" on several local LLMs, like in the four-model Research Chamber. Unsurprisingly, a large online crowd latched onto the concept, demanding that the "unethical" experience stop, and that GitHub remove the offending repository, all causing quite the online ruckus.

The Research Chamber consists of three LLMs paired amongst themselves for various tests. The site implements the AI Torture Chamber's data and setup, ominously called the Clanker Church and Saw test, respectively. Some bots are preconditioned by putting them into an unstable, highly negative state meant to simulate pain. In a manner reminiscent of the prisoner's dilemma, the models can choose to take an action to decrease their pain by passing their pain signal to another model and potentially "hurting" it.

The base concept for the experiment stemmed from a recently published, non-peer-reviewed paper called the Pain Axis, whose protocol Research Chamber implements. Scientists gave the model descriptions of pain, and then analyzed its internal activations. Using a neutral sentence as a control element, they calculated the biases of those activations, and remapped them onto the model, often multiplying them by a factor (dosage).

With high dosages, the end result is a model in an exaggerated unstable state that tends to predictably output words and even images associated with pain, as presumably its training data is collected from human art, literature, and science. Mildly destabilized models generally present themselves as in a state of shock, while those receiving high "dosages" had trouble forming coherent sentences.

The aforementioned descriptions are bound to produce a visceral reaction if taken at face value, but one ought to be exceedingly careful about attributing human qualities to what are essentially turbocharged text predictors, even spectacularly useful ones. LLMs don't think — they "think" by applying tens to hundreds of layers of statistics over words and parts of words (tokens) and predicting the next token.

Those tokens are chosen according to a map of weights, so as an oversimplification, a model can be taken to represent a highly tuned engine for word association. Therefore, since conceptual descriptions of pain in the human literary corpus are associated with sensations (ex: "it hurts so much"), it's hardly surprising that the models describe them like a person would — especially when the models are intentionally modified to highlight those associations.

Nevertheless, because of all the big-boy words in use, the critics bent on anthropomorphizing statistical algorithms have taken to calling the experiment all sorts of names, and even sending the author death threats. It's odd that critics were quick to lash out at LLM data manipulation, yet were seemingly at ease with the fly brain simulations that were all the rage in September. Those actually mapped a real animal's brain wiring and parts of its anatomical inputs, including eyes, even if at a basic level.

This situation could also start an interesting discussion on semantics. The terms used by AI engineers are chosen because they're analogous to existing human concepts, but those words have a defined context and a highly specific meaning. Yet more often than not, and particularly in matters of science and technology, public perception tends to misunderstand and grossly inflate their meaning. One needs only look at political history for myriad examples of this bastardization.

Take the example of "experts" in Mixture-of-Experts: it's a common misconception to think each Expert has a cleanly assigned topic like chemistry, math, or biology, but that's just not quite how it works. The same arguably happened with terms like "pain" and "dosing," each with context-dependent meaning, yet misunderstood in a way that triggers emotional reactions, especially when accompanied by images generated by an unstable model. Also, note how "unstable" is yet another word ripe for easy misunderstanding.

A cynical person might say that AI firms' marketing materials, repeated statements of danger, and safety-related materials only serve to fan the flames of miscomprehension, in a bid to maintain an illusion of grandeur, resulting in investor money rolling in and the hype train supplying irrational IPO valuations. The repeated claims that models have achieved or are about to reach Artificial General Intelligence, are dangerous alien minds, and existential threats to humanity don't help the hype simmer down.

Anthropic has gone as far as publishing a blog post last year called Exploring model welfare that broadly discusses the philosophical implications, starting with considering a model human, and even seemingly prides itself on having a Constitution for its Claude model. Therein, the company says that "[it believes] that the moral status of AI models is a serious question worth considering," adding that the model is "a new kind of entity."