diogenes_atx

It's just basic economic theory applied to current IT market conditions.

There is intense competition in the AI industry, especially with AI firms in China driving down prices, so it seems likely that LLM's and software products may become fungible commodities that trade close to marginal cost = marginal revenue = minimum average total cost. In other words, most of the profits from AI and software development will be competed away, eventually.

Data center hardware, by contrast, is a scarce commodity with rapidly growing demand, and therefore likely to capture most of the profits from AI and software development, if current market conditions persist.

Today's IT business trends represent a remarkable change in the industry compared to the situation 50 years ago, when startups like Microsoft and Oracle, whose business models were based exclusively on software development, challenged and overthrew the dominant player, IBM, whose primary source of revenue was from hardware.

Your question implies that anyone who has not seen this latest film about Holmes is unqualified to form an opinion about her guilt or innocence. And that is what really matters in this debate: whether Holmes is in fact guilty or innocent. So it's worth asking what knowledge you have acquired about her case.

Have you read John Carreyrou's award-winning book Bad Blood? Have you seen the HBO documentary directed by Alex Gibney that chronicles the rise and fall of Theranos and its blood-testing claims, The Inventor: Out for Blood in Silicon Valley. For my part, I have indeed read Carreyrou's book, and I watched Gibney's documentary, and I also followed the press coverage of Theranos and the criminal trial of Elizabeth Holmes. It is on the basis of all this evidence that I am speaking knowledgeably about the facts concerning Elizabeth Holmes, including the likelihood that she is a almost certainly guilty of the crimes for which she was convicted.

And what about you? Do you know anything about Holmes beyond the pseudo-documentary you just saw? The reason why I have replied to your question with a question is because you also seem to be talking in a "Holmesian" manner, taking everything she does and says at face value. So it seems like a valid question to inquire about your level of knowledge about her criminal record. Another interesting question is: Do you really believe, against all the evidence, that Elizabeth Holmes could be innocent?

I was merely citing the known facts in this case. The facts have been well-established in the criminal trial and in the press coverage about Holmes. The facts are voluminous, and of all this mountain of evidence, very little of it is featured in this so-called documentary.

> If you believe that she's guilty of everything she's accused of doing, she's incredibly self-aware

There was a mountain of evidence against Holmes, and she was convicted in a criminal trial. So it seems like a safe assumption that she was guilty.

Does it follow then that, because she is almost certainly guilty, that she must be "incredibly self-aware"? Of course not. It's just as likely that she is totally lacking in self-awareness, and that she simply does not know when to shut her mouth because she has no idea how stupid she sounds.

There are a number of good books to explain the main concepts of statistics without getting bogged down in the technical details. As a grad student at a public university in the USA, I worked as a teaching assistant for the undergraduate course Introduction to Statistics. The course used SPSS software to enable the students to perform statistical analysis on data sets. Although we offered a good textbook (listed below), many of the students struggled with the main concepts, so my colleagues and I added a optional supplemental reading list to the syllabus. The following books are the ones we recommended to the students. I have read all these books, and they are pretty good at explaining statistics to anyone interested in learning the material at a conceptual level; anyone can read and understand these books, regardless of their educational background.

Ian Ayres (2007) Super Crunchers: Why Thinking by Numbers is the New Way to Be Smart.

Joseph Healey (2020) Statistics: A Tool for Social Research [This is the textbook used in the class, which I highly recommend].

John Allen Paulos (1988) Innumeracy: Mathematical Illiteracy and Its Consequences.

Nate Silver (2012) The Signal and the Noise: Why So Many Predictions Fail, but Some Don't.

Nassim Nicholas Taleb (2005) Fooled by Randomness, second edition.

Nassim Nicholas Taleb (2010) The Black Swan: The Impact of the Highly Improbable, second edition.

As a grad student at a public university in the USA, I worked as a teaching assistant for the sociology class "Introduction to Statistics." The course used SPSS software to enable the students to perform statistical analysis on data sets. Although we offered an excellent textbook (listed below), most of the undergraduate students struggled with the material, so my colleagues and I added a supplemental reading list to the syllabus for those students interested in studying the main concepts. The following books are the ones we recommended to the students. I have read all these texts, and they are pretty good at explaining statistics to anyone interested in learning the material at a conceptual level; anyone can read and understand these books, regardless of their educational background.

Ian Ayres (2007) Super Crunchers: Why Thinking by Numbers is the New Way to Be Smart

Joseph Healey (2020) Statistics: A Tool for Social Research [This is the textbook used in the class, which I highly recommend]

Catherine Marsh and Jane Elliott (2008) Exploring Data: An Introduction to Data Analysis for Social Scientists, second edition

John Allen Paulos (1988) Innumeracy: Mathematical Illiteracy and Its Consequences

Nate Silver (2012) The Signal and the Noise: Why So Many Predictions Fail, but Some Don't

Nassim Nicholas Taleb (2005) Fooled by Randomness, second edition;

_______ (2010) The Black Swan: The Impact of the Highly Improbable, second edition

> Few people doubt that this technology is going to change everything. What’s less clear is when the profits are going to start rolling in and how big they are going to be... "We are making trillions of dollars in investments on the back of tens of billions of dollars in revenues."

It seems possible that AI will eventually become an abundant commodity, with cheap alternatives available from China and elsewhere. And if that happens, profits will be competed away, just like any other commodity in a competitive market.

One would imagine that a Silicon Valley legend like Larry Ellison - who built his business empire on the basis of proprietary software products and services - would understand this better than anyone.