2026 Optical Media Durability Update

Eight years ago I posted Optical Media Durability and discovered:

Surprisingly, I'm getting good data from CD-Rs more than 14 years old, and from DVD-Rs nearly 12 years old. Your mileage may vary.

Here are the subsequent annual updates:

• 2019
• 2020
• 2021
• 2022
• 2023
• 2024
• 2025

It is time once again for the mind-numbing process of feeding 45 disks through the readers to verify their checksums, and yet again this year every single MD5 was successfully verified. Below the fold, the details.

More On Robotaxis

Cybercab

Liam Denning reports on Tesla's quarterly results flop in Someone Call Tesla a Robotaxi, or SpaceX, Quick. He notes that:

The curated list of investor questions that gets teed up ahead of these affairs had indicated some brewing discomfort about the big issue: Tesla’s lack of progress on its long-promised mass rollout of robotaxis. Musk duly tried to finesse this by pointing out that the “constraint” is safety, with Tesla trying to scale as fast as possible “while trying to ensure that we do not harm anyone.”

It is good that Tesla is "trying to ensure that we do not harm anyone" but they need to try much harder. In last February's Tesla's Not-A-Robotaxi Service I quoted Fred Lambert:

By the company’s own numbers, its “Robotaxi” fleet crashes nearly 4 times more often than a normal driver, and every single one of those miles had a safety monitor who could hit the kill switch. That is not a rounding error or an early-program hiccup. It is a fundamental performance gap.

But I went on to point out that Lambert was making the wrong comparison:

However badly, Tesla is trying to operate a taxi service. So it is misleading to compare the crash rate with "normal drivers". The correct comparison is with taxi drivers. The New York Times reported that:

In a city where almost everyone has a story about zigzagging through traffic in a hair-raising, white-knuckled cab ride, a new traffic safety study may come as a surprise: It finds that taxis are pretty safe.

So are livery cars, according to the study, which is based on state motor vehicle records of accidents and injuries across the city. It concludes that taxi and livery-cab drivers have crash rates one-third lower than drivers of other vehicles.

A law firm has a persuasive list of reasons why this is so. So Tesla's "robotaxi" is actually 6 times less safe than a taxi.

In any rational jurisdiction, six times worse than the competition even with a safety driver would get the regulators to force Tesla back to the drawing board.

The New York Times article was the best I could find at the time but it was from 2006. We now have much better and more recent data. Follow me below the fold for one of the reasons why Tesla's numbers are bad, very probably even worse than six, and even some criticims of Waymo's numbers.

Microsoft's Project Silica

598

$68.6B

$0.115

1418

$28.0B

$0.020

59.2

$0.51B

$0.003

I summed up my big picture view of archival media eight years ago in Archival Media: Not a Good Business. Whatever your choice of technology, the economics are brutal. This is a version of the table in that post, updated to 2021 and again based upon IBM data. Note that archival media shiped 4% as many bytes as hard disk and generated 1.8% or the revenue. It remains a tiny market.

I have written several times about Microsoft Research's Project Silica, most recently in last year's Archival Storage:

I'm skeptical of "commoditizing the technology". Archival systems are a niche in the IT market, and one on which companies are loath to spend money. Realistically, there aren't going to be a vast number of Silica write heads. The only customers for systems like Silica are the large cloud providers, who will be reluctant to commit their archives to technology owned by a competitor. Unless a mass-market application for femtosecond lasers emerges, the scope for cost reduction is limited.

But the more I think about this technology, which is still in the lab, the more I think it probably has the best chance of impacting the market among all the rival archival storage technologies. Not great, but better than its competitors:

I followed this with a list of eight major reasons for my opinion.

Below the fold an update on the project and an assessment.

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.