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.

Coprophagia Is Bad For You

Divine, Pink Flamingos

Wikipedia defines Coprophagia as "the consumption of feces".

Since brevity is the soul of wit", my favorite science fiction includes the 254 words of Fredric Brown's Answer from 1954. It describes a galactic civilization holding a ceremony to mark the final connection of all their computers. What happened was:

Dwar Ev threw the switch. There was a mighty hum, the surge of power from ninety-six billion planets. Lights flashed and quieted along the miles-long panel.

Dwar Ev stepped back and drew a deep breath. “The honor of asking the first question is yours, Dwar Reyn.”

“Thank you,” said Dwar Reyn. “It shall be a question that no single cybernetics machine has been able to answer.”

He turned to face the machine. “Is there a God?”

The mighty voice answered without hesitation, without the clicking of single relay.

“Yes, now there is a God.”

Sudden fear flashed on the face of Dwar Ev. He leaped to grab the switch.

A bolt of lightning from the cloudless sky struck him down and fused the switch shut.

This may have inspired Douglas Adams' similar but much longer scenario in which the answer turned out to be 42.

Below the fold I trace the connection between these two ideas.

AI's Affordability Crisis

A year ago in The Back Of The AI Envelope I pointed out that the AI platforms were running the drug-dealer's algorithm, "the first one's free". By massively subsidizing the use of their products, they were generating overwhelming demand for them. They used this demand to justify massive investments, in the hope that, by the time they had to show a return on these invetment, the users would be so addicted that they would pay the vastly higher prices needed to generate a return.

David Cahn, Sept '23

I have to confess that I was late to the party. The earliest skepticism I've been able to find was from Sequoia Capital's David Cahn in September 2023, entitled AI’s $200B Question. Only nine months later Cahn re-ran the same analysis in AI’s $600B Question. His estimate of the revenue gap had tripled. Cahn wasn't alone. Independent journalists such as Ed Zitron were flagging this problem long before I was.

I started to write this post a couple of months ago when the maiinstream business press began to notice companies complaining about the cost of the tokens their employees were burning. Since then the trickle has turned into a flood, which made finishing the post hard. Below the fold I throw up my hands and dump out a small sample from the flood.