Faults & Failures of A.I.

Wrote a minizine about A.I. because people outside of tech often ask me what the deal is and why I’m so anti-passionate about it. Printable version on Itch!

1. A.I. Revolution

The rise of artificial intelligence or A.I. has been compared to the invention of the calculator as well as the start of the industrial revolution.

These social and technological shifts, A.I. supporters say, had their doubters at the beginning, but have since made our lives easier by letting us spend our time at tasks more worth our while.

That’s the selling point of A.I. which all the major finance and tech companies have been peddling. But is it any true? Over $350 billion has been invested in building new A.I. data centers in 2025, driving 20% global (or 50% in the U.S.) increasing demand for electric power. The world economy put all its financial eggs in the A.I. basket. Why?

2. LLMs

Before asking why A.I. is being pushed by the world economy, we need to ask what A.I. does and how it works. Many technological processes (old and new) are being described as A.I. to increase companies’ stock value.

But the sort of A.I. on everyone’s mind are large language models (or LLMs): statistical models designed to predict and generate data, which are trained from analyzing existing data.

LLMs are a useful technique in science because they enable deeper statistical analyses of data than humans can do by themselves. One team used an LLM to analyze natural protein structures, which will help us better treat diseases by engineering custom proteins!

3. Generative A.I.

LLMs are useful to predict patterns in complex datasets. However, LLMs can generate new data based on the data originally used to train them, which is how generative A.I. apps like ChatGPT and Midjourney work.

Every bot message, generated image, and so on is an approximation of what a human would say or illustrate based on a description. A.I. doesn’t think how humans do, and it can’t tell truth from falsehood because it operates on the level of language alone.

There’s some controversy about how the data used to train generative A.I. is scraped from online media without regard for ownership of that media—but that’s not our business.

4. White-Collar A.I.

What started as a curiosity for some and a helpful statistical framework for others became a business opportunity for tech firms, always chasing the bag. A.I. seemed to promise to automate intellectual labor in the same way that machines did manual labor.

This has led to annoying situations for users, like having to talk to a dumbass chatbot to receive ‘customer support’, but also for workers.

For instance, software firms are under the impression that A.I. can speed up coding by 24% or more. A recent study found that coders think they code 20% faster, but are really 20% slower with A.I. than without! The best it can do is write awkward office emails.

5. Financial Costs

Whether A.I. in the workplace actually helps anyone or not, it should behoove one to ask what are the financial and social costs of A.I.? The reason why manual labor could be automated was because the machines cut down costs relative to wages.

But A.I. is expensive, costing hundreds of millions of dollars to train on data, plus hundreds of billions of dollars to build infrastructure. This funding was raised by venture capitalist firms and government subsidiaries, and it flows into tech and power firms.

The tech business model is to operate at a loss until enough market share is captured to hike up prices. Consumers will soon feel the real cost of A.I.

6. Social Costs

Billions of dollars are on the table for a thing that promises a technological revolution but delivers almost nothing of real value. Yet the tech firms have swallowed up the world economy and are laying off thousands of software developers to demonstrate confidence in their product (as well as to recover costs of A.I. development).

That means increasing unemployment as well as exploitation of workers, who are expected to do more with shoddy A.I.-powered tools.

That also means a massive increase in power usage to support AI training as well as generation, powered by cheap fossil fuels whose resurgence in turn will offset environmental gains.

7. Artificial Implications

Tech firms have spent the last decade trying to find new ways to make profit which didn’t really play out, from the blockchain to the so-called Metaverse. Capitalism always needs new markets, new blood to expand itself.

A.I. is an attempt to put a cart before a dead horse: to enjoy the benefits of automation without true automation. What’s disguised as a way to cut costs is really a collusion of tech and power firms to aggressively expand markets and drive up their stock prices.

This is perhaps the stupidest move for the world economy, shooting itself in the foot for baseless short-term profit. The market is certainly going to crash, and it'll take us with it.