Artificial Intelligence – Argument to Authority

A deeply misleading industry practice.

• Open AI’s latest model has been demonstrated falling short once again, highlighting that the practice of dressing up product launch press releases as scientific papers is deeply misleading and leads the public to think that these models are far more capable than they actually are.
• In the Artificial Intelligence industry, the days of normal press releases are long gone, and instead, a document full of complex graphs and technical terms that look just like a scientific paper is released.
• Scientific papers are held to a high standard, as when they are properly produced, they are peer reviewed and written to a standard such that their findings are reproducible.
• This is why a properly produced scientific paper that has been published in a scientific journal has a high degree of credibility and should be treated with respect.
• Scientific papers also follow a specific format of an abstract, an introduction, materials and methods, results and a discussion.
• Loosely translated, this results in: a summary, why we did the experiment, how and what we did, what we found and what it means.
• This is the fundamental method of verifying and communicating scientific progress, and when executed properly, it has been highly effective for decades.
• Consequently, a scientific paper immediately attracts a higher level of credibility, which the artificial intelligence industry has hijacked and is in the process of corrupting.
• 10 years ago, artificial intelligence research was very open and largely academic, peer-reviewed and properly produced scientific papers were a viable method of communication, but ChatGPT’s viral success changed everything.
• We are currently in an arms race to produce the best foundation model, and with billions or trillions of dollars at stake, no one has time for peer review or, in many cases, even doing the experiments properly.
• Well-known AGI sceptic, Gary Marcus highlights the latest example of this in his most recent blog (see here) where claims made by Open AI about its latest model were found to be not reproducible (a grave scientific sin).
• When OpenAI launched o3, it claimed that it could score 75% on a difficult benchmark called ARC, but others have been unable to repeat this finding, with the best score they could find being 56%.
• This is not unique to OpenAI, but other model makers are less than forthcoming with how they trained their models and disclose only the data that paints their models in the most favourable light.
• The problem here is that these findings are then dressed up as a scientific paper, where most people will assume that this means that they have been subjected to the same scrutiny and are therefore an accurate representation of reality.
• Google, Anthropic, Mistral, Meta, DeepSeek, Alibaba and so on are all guilty of this practice, meaning that anything that comes out of these companies should be treated as a marketing press release and nothing more.
• It is only when they appear in a peer-reviewed scientific journal should they be given a higher level of credibility, but even there, there are signs of standards slipping.
• Hence, when these companies announce new models, they should be treated with the same level of scepticism that Apple is subjected to every time it mentions the phrase “Apple Intelligence”.
• This practice is unfortunate because, combined with the hyperbolic commentary that accompanies a model launch, it is leading the public and the markets to believe that super-intelligent machines are just around the corner.
• If this is true, then the entire industry is greatly undervalued, but unfortunately, all of the empirical evidence points to precisely the opposite.
• This means that while there are still very large opportunities for AI to generate revenues and profits, the time when machines take over 90% of human-related tasks and become more intelligent than humans is as far away today as it ever was.
• While this is bad news for the valuations of the AI companies, it is good news for the human race, as the machines remain way too stupid to decide that humans are a threat and eliminate us all.
• A loss for science but a win for humans.