There are many negative articles about “AI” (which is not about actual Artificial Intelligence also known as “AGI”). Which I think are mostly overblown and often ridiculous.
Table of Contents
Resource Usage
Complaints about resource usage are common, training Llama 3.1 could apparently produce as much pollution as “10,000 round trips by car between Los Angeles and New York City”. That’s not great but when you compare to the actual number of people doing such drives in the US and the number of people taking commercial flights on that route it doesn’t seem like such a big deal. Apparently commercial passenger jets cause CO2 emissions per passenger about equal to a car with 2 people. Why is it relevant whether pollution comes from running servers, driving cars, or steel mills? Why not just tax polluters for the damage they do and let the market sort it out? People in the US make a big deal about not being communist, so why not have a capitalist solution, make it more expensive to do undesirable things and let the market sort it out?
ML systems are a less bad use of compute resources than Bitcoin, at least ML systems give some useful results while Bitcoin has nothing good going for it.
The Dot-Com Comparison
People often complain about the apparent impossibility of “AI” companies doing what investors think they will do. But this isn’t anything new, that all happened before with the “dot com boom”. I’m not the first person to make this comparison, The Daily WTF (a high quality site about IT mistakes) has an interesting article making this comparison [1]. But my conclusions are quite different.
The result of that was a lot of Internet companies going bankrupt, the investors in those companies losing money, and other companies then bought up their assets and made profitable companies. The cheap Internet we now have was built on the hardware from bankrupt companies which was sold for far less than the manufacture price. That allowed it to scale up from modem speeds to ADSL without the users paying enough to cover the purchase of the infrastructure. In the early 2000s I worked for two major Dutch ISPs that went bankrupt (not my fault) and one of them continued operations in the identical manner after having the stock price go to zero (I didn’t get to witness what happened with the other one). As far as I’m aware random Dutch citizens and residents didn’t suffer from this and employees just got jobs elsewhere.
There are good things being done with ML systems and when companies like OpenAI go bankrupt other companies will buy the hardware and do good things.
NVidia isn’t ever going to have the future sales that would justify a market capitalisation of almost 4 Trillion US dollars. This market cap can support paying for new research and purchasing rights to patented technology in a similar way to the high stock price of Google supported buying YouTube, DoubleClick, and Motorola Mobility which are the keys to Google’s profits now.
The Real Upsides of ML
Until recently I worked for a company that used ML systems to analyse drivers for signs of fatigue, distraction, or other inappropriate things (smoking which is illegal in China, using a mobile phone, etc). That work was directly aimed at saving human lives with a significant secondary aim of saving wear on vehicles (in the mining industry drowsy drivers damage truck tires and that’s a huge business expense).
There are many applications of ML in medical research such as recognising cancer cells in tissue samples.
There are many less important uses for ML systems, such as recognising different types of pastries to correctly bill bakery customers – technology that was apparently repurposed for recognising cancer cells.
The ability to recognise objects in photos is useful. It can be used for people who want to learn about random objects they see and could be used for helping young children learn about their environment. It also has some potential for assistance for visually impaired people, it wouldn’t be good for safety critical systems (don’t cross a road because a ML system says there are no cars coming) but could be useful for identifying objects (is this a lemon or a lime). The Humane AI pin had some real potential to do good things but there wasn’t a suitable business model [2], I think that someone will develop similar technology in a useful way eventually.
Even without trying to do what the Humane AI Pin attempted, there are many ways for ML based systems to assist phone and PC use.
ML systems allow analysing large quantities of data and giving information that may be correct. When used by a human who knows how to recognise good answers this can be an efficient way of solving problems. I personally have solved many computer problems with the help of LLM systems while skipping over many results that were obviously wrong to me. I believe that any expert in any field that is covered in the LLM input data could find some benefits from getting suggestions from an LLM. It won’t necessarily allow them to solve problems that they couldn’t solve without it but it can provide them with a set of obviously wrong answers mixed in with some useful tips about where to look for the right answers.
Jobs and Politics
I don’t think it’s reasonable to expect ML systems to make as much impact on society as the industrial revolution, and the agricultural revolutions which took society from more than 90% farm workers to less than 5%. That doesn’t mean everything will be fine but it is something that can seem OK after the changes have happened. I’m not saying “apart from the death and destruction everything will be good”, the death and destruction are optional. Improvements in manufacturing and farming didn’t have to involve poverty and death for many people, improvements to agriculture didn’t have to involve overcrowding and death from disease. This was an issue of political decisions that were made.
The Real Problems of ML
Political decisions that are being made now have the aim of making the rich even richer and leaving more people in poverty and in many cases dying due to being unable to afford healthcare. The ML systems that aim to facilitate such things haven’t been as successful as evil people have hoped but it will happen and we need appropriate legislation if we aren’t going to have revolutions.
There are documented cases of suicide being inspired by Chat GPT systems [4]. There have been people inspired towards murder by ChatGPT systems but AFAIK no-one has actually succeeded in such a crime yet. There are serious issues that need to be addressed with the technology and with legal constraints about how people may use it. It’s interesting to consider the possible uses of ChatGPT systems for providing suggestions to a psychologist, maybe ChatGPT systems could be used to alleviate mental health problems.
The cases of LLM systems being used for cheating on assignments etc isn’t a real issue. People have been cheating on assignments since organised education was invented.
There is a real problem of ML systems based on biased input data that issue decisions that are the average of the bigotry of the people who provided input. That isn’t going to be worse than the current situation of bigoted humans making decisions based on hate and preconceptions but it will be more insidious. It is possible to search for that so for example a bank could test it’s mortgage approval ML system by changing one factor at a time (name, gender, age, address, etc) and see if it changes the answer. If it turns out that the ML system is biased on names then the input data could have names removed. If it turns out to be biased about address then there could be weights put in to oppose that.
For a long time there has been excessive trust in computers. Computers aren’t magic they just do maths really fast and implement choices based on the work of programmers – who have all the failings of other humans. Excessive trust in a rule based system is less risky than excessive trust in a ML system where no-one really knows why it makes the decisions it makes.
Self driving cars kill people, this is the truth that Tesla stock holders don’t want people to know.
Companies that try to automate everything with “AI” are going to be in for some nasty surprises. Getting computers to do everything that humans do in any job is going to be a large portion of an actual intelligent computer which if it is achieved will raise an entirely different set of problems.
I’ve previously blogged about ML Security [5]. I don’t think this will be any worse than all the other computer security problems in the long term, although it will be more insidious.
How Will It Go?
Companies spending billions of dollars without firm plans for how to make money are going to go bankrupt no matter what business they are in. Companies like Google and Microsoft can waste some billions of dollars on AI Chat systems and still keep going as successful businesses. Companies like OpenAI that do nothing other than such chat systems won’t go well. But their assets can be used by new companies when sold at less than 10% the purchase price.
Companies like NVidia that have high stock prices based on the supposed ongoing growth in use of their hardware will have their stock prices crash. But the new technology they develop will be used by other people for other purposes. If hospitals can get cheap diagnostic ML systems because of unreasonable investment into “AI” then that could be a win for humanity.
Companies that bet their entire business on AI even when it’s not necessarily their core business (as Tesla has done with self driving) will have their stock price crash dramatically at a minimum and have the possibility of bankruptcy. Having Tesla go bankrupt is definitely better than having people try to use them as self driving cars.
- [1] https://thedailywtf.com/articles/ai-the-bad-the-worse-and-the-ugly
- [2] https://etbe.coker.com.au/2024/04/26/humane-ai-pin/
- [3] https://www.noemamag.com/how-ai-could-help-rebuild-the-middle-class/
- [4] https://tinyurl.com/2b95xdzs
- [5] https://etbe.coker.com.au/2025/05/30/machine-learning-security/
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