Distilling The Moat

Whisky Still

The original function of a Web server was to respond to queries by revealing the appropriate part of their internal data. This necessarily meant that repeated queries, for example from a search engine's or an internet archive's web crawler, could extract the server's entire internal data. Since the extracted data had been published on the Web, it was not trade secret. It was protected by the publisher's copyright. This has led to many lawsuits, for example against the Internet Archive, Google and others. It is the reason search engines only display "snippets" of the content they collect.

AI companies' intellectual property is their models. They spend vast sums funding the technical and human resources to "train" these models, the racks of GPUs in the data centers, and the hordes of workers labeling images, and having "genuine human conversations" with the nascent model. These expenditures are thought to create a "moat" around the value thus generated, because it would be equally expensive for a competitor to create an equivalent model. It is this moat that supports their extraordinary valuations, despite their lack of earnings.

Below the fold I explain why their moat is very shallow.

Portents Of Doom

Elon Musk is the world champion of totally implausible projections, and Kim Khan reported on a personal best in SpaceX sees total addressable market rivaling size of the U.S. economy:

The $28.5T forecast compares to U.S. Q1 2026 nominal GDP of nearly $32T, with the estimate for the market of AI enterprise applications of $22.7T about 70% of total U.S. economic output.

Sam Altman and Dario Amodei just aren't this good, but their projections of their Total Available Market (TAM) are still turning out to be vastly optimistic. In AI's Affordability Crisis I showed evidence that the AI platforms could no longer afford the massive subsidies they were using to artifically inflate demand for their product, and that reducing the subsidies had made their enterprise customers reconsider their enthusiasm for deploying them. This is leading to investors belatedly realizing that AI platforms' projections of their TAM and thus their valuations are totally implausible.

This re-calibration is just one of the many signs that the AI bubble is about to deflate. Below the fold I present a necessarily incomplete list of them, which I will try to update as more appear.

The Kelvin Limit

I have been a small part of the chorus of voices critiquing the AI bubble that I described in Portents Of Doom. But what if we're wrong? What if the overwhelming demand for AI sin't the result of the AI platforms massively subsidizing their products, but because the world needs more and more non-consensual sex images, slop web pages, agentic ransomware attacks, students cheating on exams, hallucinated lawsuits, and all the other benefits of this transformative technology?

Please suspend disbelief and follow me below the fold as I look into a fascinating examination of the implications of the exponential growth in the data centers needed to provide these benefits.

My Introduction To Computer Graphics

Boeing's PDP-7/340

Most of my career has been involved in various ways with computer graphics. Below the fold I recount the story of how I got started in the field just as it was getting started. To give you some idea of just how early my introduction was the Mother of all Demos had been the year before. The displays I got to work with drew lines in monochrome, not rasters in color. You created the image by writing a loop of instructions in the "display processor" instruction set. These told it the lines to draw at each refresh cycle. There was no mouse.