Small Is Beautiful

Estimates are that, to justify the AI platforms' enormous capex plans, by 2030 they need to be generating around $2T/year in revenue. If every adult resident of the US spent $20/month on AI, it would generate $68.5B/year. Clearly, only the enterprise market stands even a remote possibility of generating the bulk of the $2T.

There are three major threats to the prospect of AI platforms extracting 6% of current US GDP from the enterprise market, and thus to OpenAI's and Anthropic's ambitions to IPO in the near future. First, faced with AI's Affordability Crisis, companies have been placing strict limits on employees' spending on AI tokens.

Source

Second, the gap in performance between expensive, closed-weight US models, such as OpenAI's and Anthropic's, and much cheaper, open-weight Chinese models has been rapidly closing, with the result that the US models are losing enterprise market share. Luz Ding, Spe Chen and Hayley Warren analyze this in US Lead in the AI Race With China Is Rapidly Narrowing:

Bloomberg in partnership with researchers at Vals AI, an independent AI evaluation and benchmarking platform, tested seven models from frontier Chinese and US companies to see how they performed in a real-world task. They were asked to create a fictional coffee e-commerce site called Brewberg using the same prompts. Most of the models scored 100% functional accuracy despite occasional design misses, but with very different price tags. The experiment employed the top performing models in July from Anthropic and all the Chinese firms, as well as more affordable models from OpenAI and Google.

They all did reasonably well, but the two best were Claude Fable 5 at $48.99 and Kimi K3 at $11.99. Chinese models charging much less for almost the same performance are grabbing market share:

the use of Chinese models overtook US platforms globally for the first time in June, and accounted for more than 60% of market share last month, on OpenRouter, a tech platform that offers software developers access to hundreds of AI models. It is a widely watched gauge of model usage despite tracking just a fraction of global AI consumption. The US, parts of Europe and Asia now favor Chinese labs, according to the same data.

On Hugging Face, Chinese AI models account for 41.4% of generative model downloads among developers, 5 percentage points higher than US models.

Third, it isn't just that the Chinese models are cheaper to run remotely, but also that because they are open-weight they can be run on affordable in-house systems, which means that:

• They don't give Donald Trump a kill-switch for your busines.
• They don't give Sam Altman or Dario Amodei a kill-switch for your busines.
• They don't require giving the Chinese, Sam Altman or Dario Amodei all your business' critical data.
• They provide visibility into and control over AI costs.
• They are even cheaper.

The question is "compared to the closed-weight US models, what do you lose by running open-weight models in-house?" Below the fold I discuss a major study from Stanford that answers the question.

Not A Bug But A Feature

Source

Datafinnovation are out with more impressive research described in a blog post entitled Losing The Blacklisting Race and a paper entitled Enforcement Speeds In DeFi: Limits to a Race Against Time by Ben Charoenwong et al.

The blog post is more accessible, so that's what I will quote from. But for the details you need to read the paper. The blog post's TL;DR is:

First, we explore the empirical reality of OFAC’s blacklisting efforts. The results there are stark: nearly all addresses are completely empty by the time OFAC manages to blacklist them. This result is robust across time, attacker and type of attack. Second, we construct a simple model to explain why this outcome is inevitable with competent attackers. Evasion strategies which work in our model match those we find employed in the wild.

Below the fold, I start from Datafinnovation's work and explore its context.

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.