ata.

Today, about half of Americans are daily users of AI, mostly via free products
from Facebook's parent company Meta and a handful of other familiar Big Tech
giants and venture-backed tech firms such as Google, Microsoft, OpenAI, and
Anthropic -- with every incentive to follow the same path as the social
platforms.

But now, as then, there are alternatives. Some nonprofit initiatives are
building open-source AI tools that have transparent foundations and can be run
locally and under users' control, like AllenAI and EleutherAI. Some governments,
like Singapore, Indonesia, and Switzerland, are building public alternatives to
corporate AI that don't suffer from the perverse incentives introduced by the
profit motive of private entities.

Just as social media users have faced platform choices with a range of value
propositions and ideological valences -- as diverse as X, Bluesky, and Mastodon
-- the same will increasingly be true of AI. Those of us who use AI products in
our everyday lives as people, workers, and citizens may not have the same power
as judges, lawmakers, and state officials. But we can play a small role in
influencing the broader AI ecosystem by demonstrating interest in and usage of
these alternatives to Big AI. If you're a regular user of commercial AI apps,
consider trying the free-to-use service for Switzerland's public Apertus model.

None of these choices are really new. They were all present almost 20 years ago,
as social media moved from niche to mainstream. They were all policy debates we
did not have, choosing instead to view these technologies through rose-colored
glasses. Today, though, we can choose a different path and realize a different
future. It is critical that we intentionally navigate a path to a positive
future for societal use of AI -- before the consolidation of power renders it
too late to do so.

This post was written with Nathan E. Sanders, and originally appeared in
Lawfare.

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New Anonymous Phone Service

[2025.12.05] A new anonymous phone service allows you to sign up with just a zip
code.

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Substitution Cipher Based on The Voynich Manuscript

[2025.12.08] Here's a fun paper: "The Naibbe cipher: a substitution cipher that
encrypts Latin and Italian as Voynich Manuscript-like ciphertext":

Abstract: In this article, I investigate the hypothesis that the Voynich
Manuscript (MS 408, Yale University Beinecke Library) is compatible with being a
ciphertext by attempting to develop a historically plausible cipher that can
replicate the manuscript's unusual properties. The resulting ciphera verbose
homophonic substitution cipher I call the Naibbe ciphercan be done entirely by
hand with 15th-century materials, and when it encrypts a wide range of Latin and
Italian plaintexts, the resulting ciphertexts remain fully decipherable and also
reliably reproduce many key statistical properties of the Voynich Manuscript at
once. My results suggest that the so-called "ciphertext hypothesis" for the
Voynich Manuscript remains viable, while also placing constraints on plausible
substitution cipher structures.

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AI vs. Human Drivers

[2025.12.09] Two competing arguments are making the rounds. The first is by a
neurosurgeon in the New York Times. In an op-ed that honestly sounds like it was
paid for by Waymo, the author calls driverless cars a "public health
breakthrough":

In medical research, there's a practice of ending a study early when the results
are too striking to ignore. We stop when there is unexpected harm. We also stop
for overwhelming benefit, when a treatment is working so well that it would be
unethical to continue giving anyone a placebo. When an intervention works this
clearly, you change what you do.

There's a public health imperative to quickly expand the adoption of autonomous
vehicles. More than 39,000 Americans died in motor vehicle crashes last year,
more than homicide, plane crashes and natural disasters combined. Crashes are
the No. 2 cause of death for children and young adults. But death is only part
of the story. These crashes are also the leading cause of spinal cord injury. We
surgeons see the aftermath of the 10,000 crash victims who come to emergency
rooms every day.

The other is a soon-to-be-published book: Driving Intelligence: The Green Book.
The authors, a computer scientist and a management consultant with experience in
the industry, make the opposite argument. Here's one of the authors:

There is something very disturbing going on around trials with autonomous
vehicles worldwide, where, sadly, there have now been many deaths and injuries
both to other road users and pedestrians. Although I am well aware that there is
not, senso stricto, a legal and functional parallel between a "drug trial" and
"AV testing," it seems odd to me that if a trial of a new drug had resulted in
so many deaths, it would surely have been halted and major forensic
investigations carried out and yet, AV manufacturers continue to test their
products on public roads unabated.

I am not convinced that it is good enough to argue from statistics that, to a
greater or lesser degree, fatalities and injuries would have occurred anyway had
the AVs had been replaced by human-driven cars: a pharmaceutical company,
following death or injury, cannot simply sidestep regulations around the trial
of, say, a new cancer drug, by arguing that, whilst the trial is underway,
people would die from cancer anyway....

Both arguments are compelling, and it's going to be hard to figure out what
public policy should be.

This paper, from 2016, argues that we're going to need other metrics than
side-by-side comparisons: Driving to safety: How many miles of driving would it
take to demonstrate autonomous vehicle reliability?":

Abstract: How safe are autonomous vehicles? The answer is critical for
determining how autonomous vehicles may shape motor vehicle safety and public
health, and for developing sound policies to govern their deployment. One
proposed way to assess safety is to test drive autonomous vehicles in real
traffic, observe their performance, and make statistical comparisons to human
driver performance. This approach is logical, but it is practical? In this
paper, we calculate the number of miles of driving that would be needed to
provide clear statistical evidence of autonomous vehicle safety. Given that
current traffic fatalities and injuries are rare events compared to vehicle
miles traveled, we show that fully autonomous vehicles would have to be driven
hundreds of millions of miles and sometimes hundreds of billions of miles to
demonstrate their reliability in terms of fatalities and injuries. Under even
aggressive testing assumptions, existing fleets would take tens and sometimes
hundreds of years to drive these miles -- an impossible proposition if the aim
is to demonstrate their performance prior to releasing them on the roads for
consumer use. These findings demonstrate that developers of this technology and
third-party testers cannot simply drive their way to safe--- FMail-lnx 2.3.1.0
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