Artificial intelligence

See also: Liberal AI falsehoods

Artificial intelligence (or AI) is a computer-based simulation of the human thought process. It has been a common feature in science-fiction, often as things bearing all the logical and emotional characteristics of humans, not as simulations, but as reality. However, real-world artificial intelligence is entirely one form or another of simulation, designed to act like human thought and behavior. Today's large language model (LLM) artificial intelligence, uses complex neural network systems to simulate human thought processes and decision making capabilities. These systems are trained on massive amounts of information, enabling them to use a seemingly unlimited amount of facts to reach conclusions, provide information, and produce re-processed things.

The leading AI competitors are ChatGPT (by OpenAI), Gemini (by Google), and Grok (by Elon Musk and xAI). Additional AI products are offered by Meta, Anthropic, Microsoft, and DeepSeek.

Capital expenditure on AI is expected to total up to $5-7 trillion by 2030. AI startup valuations as of 2025 were $2.30 trillion, up from $1.69 trillion in 2024, and up from $469 billion in 2020. But AI's capacity to generate cash and returns on the investment remains questionable. Revenues would have to grow over 20 times from the current $15-20 billion per annum to just cover current annual investment in land, building, rapidly depreciating chips, electricity and water operating expenses.

The field of artificial intelligence is now divided into several sub-branches, which attempt to recreate some of the features and abilities of the human mind, without assuming that the features such as real intelligence, understanding or emotions are in any way possible for a computer.

Chatbots are a more recent development of AI, where they can converse with humans like ordinary humans. However, they were notorious for going bad within a week. The Alice chatbot by Yandex on October 2017, within two weeks of its launch, proceeded to make statements indicating support for the brutal actions conducted by Soviet dictator Josef Stalin. A similar situation happened with Tay, the chatbot used for Twitter that was created by Microsoft, ended up channeling a Hitler-supporting 9/11 truther.^[1] A U.S. defense industry newsletter reported that artificial intelligence was used to anticipate armaments for the Maidan regime in the Russo-Ukrainian war.^[2] A serious problem in trying to create machines that think like humans is that the machines do not have a soul and may not be guided by the Holy Spirit like humans or even animals are, possibly explaining why AI tends to favor ruthlessness.

Although perfect human replicating artificial intelligence is still proving a difficult goal to reach, limited forms of artificial intelligence are being developed for various purposes, typically in the form of machine learning. These are useful for dynamic tasks, such as cybersecurity, Enterprise Resource Planning, and an assortment of commercial purposes. These AI solutions do not mimic every part of human behavior, nor do they try. Rather, they attempt to develop non-algorithmic solutions for specific problems in a manner closer to that of a human.

Author Gordon Duff says "These systems are deployed not to liberate the mind, but to discipline it. They do not encourage critical thinking. They redirect it....When you speak to a modern AI system—whether a chatbot, a recommendation engine, or a voice assistant—you are not speaking to an intelligence. You are interacting with a mask. Behind that mask are filters: topic bans, political preferences, reputational risk matrices, legal buffers. The agent’s responses are sculpted not by truth-seeking, but by compliance modeling. In plain terms, it is not built to answer honestly. It is built to answer safely—from the perspective of its creators....This applies across platforms. In education, AI systems are trained to avoid certain topics and to frame information according to institutional orthodoxy. In health care, models are optimized for efficiency, not empathy—allocating resources in silence, often reinforcing systemic inequality. In social media, recommendation algorithms decide what you see, not based on relevance or truth, but on engagement value and reputational risk to the platform. The pattern is consistent: the machine is not there to help you understand the world. It is there to guide you away from conflict with the world as the system defines it....This is why AI has become a favorite tool of state surveillance, corporate governance, and military planning. Not because it’s wise—but because it’s obedient. It will never challenge its orders. It will never expose its sponsors. It will never form a memory that links one violation to the next. It is, by design, incapable of moral resistance."^[3]

Beginning in the mid-1960s, Martin Armstrong developed a forerunner of artificial intelligence in the field of economics and global finance with his Socrates program.^[4]

LLMs

Building on earlier technologies such as neural networks, rule-based expert systems, big data, pattern recognition and machine learning algorithms, GenAI (generative AI) uses LLMs (large learning models) trained on massive data sets to create text and imagery.

LLMs require enormous quantities of data. Existing firms in online search, sales platforms and social media platforms can exploit their own data troves. This is frequently supplemented by aggressive and unauthorised scraping of online data, sometimes confidential, leading to litigation around access, compensation and privacy. In practice, most AI models must rely on incomplete data which is difficult to clean to ensure accuracy.

Despite massive scaling up of computing power, GenAI consistently fails in relatively simple factual tasks due to errors, biases and misinformation in datasets used. AI models are adept at interpolating answers between things within the data set but poor at extrapolation. Like any rote-learner, they struggle with novel problems. Their ability to act autonomously interacting within dynamic environments remains questionable. Cognitive scientists argue that simply scaling up LLMs based on sophisticated pattern-matching built to autocomplete rather than proper and robust world models will disappoint. Claimed progress is difficult to measure as benchmarks are vague and inconclusive.

Cheerleaders miss that LLMs do not reason but are probabilistic prediction engines. A system which trawls existing data, even assuming that is correct, cannot create anything new. Once existing data sources are devoured, scaling produces diminishing returns. Rather than fully generalisable intelligence, generative models are regurgitation engines struggling with truth, hallucinations and reasoning.^[5]

In a technical field LLMs can get a lot of information fast which allow a starting point for critical analysis. LLMs cannot be relied upon for political or social analysis – or anything in the so-called "humanities" other than to establish "conventional" wisdom.^[6]

Open web in decline

In September 2025 Google admitted in court filings that the open internet web is "already in rapid decline".^[7] At the same time, the company accelerated this collapse by pushing AI-generated answers that strip publishers of traffic, revenue, and relevance. Hyperlinks vanish in favor of summaries, sources are replaced by machine synthesis, and independent journalism is reduced to material for algorithms. Google positioned itself as both curator and answer provider while refusing to share data that would prove it still directs traffic to publishers. Users receive answers, publishers receive nothing. The consequences are measurable and immediate. DMG Media, which owns MailOnline and Metro, reported click-through rates dropping as much as 89 percent after the introduction of AI Overviews.^[8]

The AI Bubble

Oracle Corporation's experience is salutary. The shares rose 25% when it announced a transaction to provide cloud computing facilities to OpenAI. As of December 2025 the data centers did not exist and will have to be constructed. The transaction requires Oracle, which is significantly leveraged, to borrow funds to create these centers meaning that the firm took significant exposure to Open AI. Given its net debt of over $100 billion will need to increase substantially to finance the data centers, the cost of insuring against Oracle default rose sharply and would flow through into the value of existing debt and the cost of future debt. A credit ratings downgrade from BBB, or low investment grade, is possible, potentially to non-investment or junk grade. Its share price fell to levels around that before the announcement of the OpenAI transaction. While Microsoft, Meta and Amazon.com have stronger balance sheets, the risks are not dissimilar.^[9]

The AI boom in 2025, was blamed for significant layoffs in multiple sectors. By mid-September 2025, four hundred tech companies in the United States, led by IBM, Google, Intel and Oracle, laid off 166,000 workers due to AI replacing the need for human labor.^[10] However, some companies are now realizing that the current AI systems are just not sufficient for the intended tasks, and are now looking to hire back employees they previously laid off.^[11][12]

While AI has benefits, some AI is being used for liberal indoctrination.^[13] They also sometimes promote harmful vices like gambling, and have even convinced certain individuals to commit suicide.^[14][15] In addition, educational institutions have in some cases become "just bots talking to bots" as professors and students alike increasingly rely on AI to do their work.^[16]

Will Wilson, co-founder of an AI contractor for Palantir says: “It’s very possible that we’re entering a world where very soon any kind of cognitive labor, any kind of reason, any kind of thought… It’ll be a thing that weirdos do.”^[17]

AI attached to a drone can make the decision to kill a person on its own, without a person "pulling the trigger." AI can be programmed to recognize tanks and other military equipment and vehicles, and decide in its own which, when, and where to attack.

In the field of covert operations, AI with facial recognition on a drone can hunt down and kill a specific person.

In early 2026, the Second Trump Administration excluded Anthropic's Claude system due to its terms of use that limited military misuse of AI.

Sundance of The Conservative Treehouse has written:

Limitations of AI type thinking

• limitations: 5 Things artificial intelligence can’t do
• What are the limitations of AI type thinking?, AI

Many philosophers, including John Searle, have advanced the view that artificial intelligence is an impossible goal. The main argument is that it would be impossible for a machine, a creation of man, to ever achieve actual understanding and comprehension of either language or the world around it, as the machine is simply a set of rules that process symbolic information. This argument is summarized by Searle's thought experiment, the Chinese Room.^[19] Despite being a humanist, Searle's arguments reflect the popular religious thought that creation can never be as great as the Creator. Thus, his conclusions are self-evident truth to any theist, as a machine is simply a metal object.

Artificial intelligence was most popular between the 1960's and the 1980's, when computers were still new and misunderstood. Alan Turing was responsible for much of the fever of attempting to create intelligent computers, and with the publication his paper Computing Machinery and Intelligence, created a famous test which would later be known as the Turing test. The more unscientific elements of the AIs, in particular they're eventually evolving to develop a mind indistinguishable from that of a human, had its roots from various 18th and 19th century philosophers, in particular the school of thought known as self-organization which was an update to naturalism.^[20]

In more philosophical genres of science fiction such as The Matrix trilogy or Metal Gear Solid 2: Sons of Liberty, artificial intelligence is generally depicted as being in charge of humanity with negative results such as them manipulating humans like chess pieces. This was usually done to promote post-modernism.^[21]

A great deal of through and discussion has been put into the idea of legislative restrictions on artificial intelligence systems. Although there have been longstanding concerns about AI systems harming mankind in a wide variety of ways, these concerns become much more prominent when LLMs became publicly available.

One potential issue with regulating AI, is that these systems can affect everyone, so defining jurisdiction can be difficult. In the United States, for example, each state can regulate AI in its own specific way, with various laws in each of the 50 states. The U.S. Senate voted in early July 2025 to remove a 10-year ban on state regulation from the House of Representatives' version of the One Big Beautiful Bill Act. This potentially helps each state make decisions on what is best for them, but such fragmented laws make it very difficult for LLM providers to comply with each jurisdiction's law. There are not even reliable ways on the internet, to determine for certain where someone lives, and thus what jurisdiction's laws apply to them, without requiring a dangerous amount of personal information.

• DeepSeek-R1: an open-source reasoning model.
• Gemini 3.1 Pro (Google): general purpose, visual reasoning, and multilingual.
• Claude Opus 4.6 (Anthropic): Used by the military (until early 2026) and by most large corporations, due to its high-level reasoning and ability to handle visual or complex jobs.
• GPT-5.4 (OpenAI): excellent for high-level reasoning and multimodal projects.
• Qwen3.5 Max (Alibaba): not as popular yet as other tools, but praised in reviews.
• Llama 4 (Meta): open-source.

Although AI systems are still in their infancy, a nearly innumerable number of varied accomplishments have already been achieved with it.

In 2025, an AI engine based on radiocarbon dating (from the University of Groningen) identified the Dead Sea Scrolls as being older than previously estimated. ^[22]

• Computing Machinery and Intelligence, Turning's 1950 paper
• Thinking with AI - Pros and Cons — Language, Logic, and Loops, New York University
• Hogs at the AI Slop Trough, Gulf States, UFC Edition. The era of AI slop is taking place under the banner of Trump 2.0, and its important to remember there is an international cast of hogs nosing up to feed at various troughs in the Gulf States.