Crypto-Gram
July 15, 2025

by Bruce Schneier
Fellow and Lecturer, Harvard Kennedy School schneier@schneier.com
https://www.schneier.com

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In this issue:

If these links don't work in your email client, try reading this issue of
Crypto-Gram on the web.

    Where AI Provides Value
    Ghostwriting Scam
    Self-Driving Car Video Footage
    Surveillance in the US
    Largest DDoS Attack to Date
    Here's a Subliminal Channel You Haven't Considered Before
    What LLMs Know About Their Users
    House of Representatives Bans WhatsApp
    The Age of Integrity
    How Cybersecurity Fears Affect Confidence in Voting Systems
    Iranian Blackout Affected Misinformation Campaigns
    Ubuntu Disables Spectre/Meltdown Protections
    Surveillance Used by a Drug Cartel
    Hiding Prompt Injections in Academic Papers
    Yet Another Strava Privacy Leak
    Using Signal Groups for Activism
    Tradecraft in the Information Age

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Where AI Provides Value

[2025.06.17] If you've worried that AI might take your job, deprive you of your
livelihood, or maybe even replace your role in society, it probably feels good
to see the latest AI tools fail spectacularly. If AI recommends glue as a pizza
topping, then you're safe for another day.

But the fact remains that AI already has definite advantages over even the most
skilled humans, and knowing where these advantages arise -- and where they don't
-- will be key to adapting to the AI-infused workforce.

AI will often not be as effective as a human doing the same job. It won't always
know more or be more accurate. And it definitely won't always be fairer or more
reliable. But it may still be used whenever it has an advantage over humans in
one of four dimensions: speed, scale, scope and sophistication. Understanding
these dimensions is the key to understanding AI-human replacement.
Speed

First, speed. There are tasks that humans are perfectly good at but are not
nearly as fast as AI. One example is restoring or upscaling images: taking
pixelated, noisy or blurry images and making a crisper and higher-resolution
version. Humans are good at this; given the right digital tools and enough time,
they can fill in fine details. But they are too slow to efficiently process
large images or videos.

AI models can do the job blazingly fast, a capability with important industrial
applications. AI-based software is used to enhance satellite and remote sensing
data, to compress video files, to make video games run better with cheaper
hardware and less energy, to help robots make the right movements, and to model
turbulence to help build better internal combustion engines.

Real-time performance matters in these cases, and the speed of AI is necessary
to enable them.
Scale

The second dimension of AI's advantage over humans is scale. AI will
increasingly be used in tasks that humans can do well in one place at a time,
but that AI can do in millions of places simultaneously. A familiar example is
ad targeting and personalization. Human marketers can collect data and predict
what types of people will respond to certain advertisements. This capability is
important commercially; advertising is a trillion-dollar market globally.

AI models can do this for every single product, TV show, website and internet
user. This is how the modern ad-tech industry works. Real-time bidding markets
price the display ads that appear alongside the websites you visit, and
advertisers use AI models to decide when they want to pay that price --
thousands of times per second.
Scope

Next, scope. AI can be advantageous when it does more things than any one person
could, even when a human might do better at any one of those tasks. Generative
AI systems such as ChatGPT can engage in conversation on any topic, write an
essay espousing any position, create poetry in any style and language, write
computer code in any programming language, and more. These models may not be
superior to skilled humans at any one of these things, but no single human could
outperform top-tier generative models across them all.

It's the combination of these competencies that generates value. Employers often
struggle to find people with talents in disciplines such as software development
and data science who also have strong prior knowledge of the employer's domain.
Organizations are likely to continue to rely on human specialists to write the
best code and the best persuasive text, but they will increasingly be satisfied
with AI when they just need a passable version of either.
Sophistication

Finally, sophistication. AIs can consider more factors in their decisions than
humans can, and this can endow them with superhuman performance on specialized
tasks. Computers have long been used to keep track of a multiplicity of factors
that compound and interact in ways more complex than a human could trace. The
1990s chess-playing computer systems such as Deep Blue succeeded by thinking a
dozen or more moves ahead.

Modern AI systems use a radically different approach: Deep learning systems
built from many-layered neural networks take account of complex interactions --
often many billions -- among many factors. Neural networks now power the best
chess-playing models and most other AI systems.

Chess is not the only domain where eschewing conventional rules and formal logic
in favor of highly sophisticated and inscrutable systems has generated progress.
The stunning advance of AlphaFold2, the AI model of structural biology whose
creators Demis Hassabis and John Jumper were recognized with the Nobel Prize in
chemistry in 2024, is another example.

This breakthrough replaced traditional physics-based systems for predicting how
sequences of amino acids would fold into three-dimensional shapes with a 93
million-parameter model, even though it doesn't account for physical laws. That
lack of real-world grounding is not desirable: No one likes the enigmatic nature
of these AI systems, and scientists are eager to understand better how they
work.

But the sophistication of AI is providing value to scientists, and its use
across scientific fields has grown exponentially in recent years. Context
matters

Those are the four dimensions where AI can excel over humans. Accuracy still
matters. You wouldn't want to use an AI that makes graphics look glitchy or
targets ads randomly -- yet accuracy isn't the differentiator. The AI doesn't
need superhuman accuracy. It's enough for AI to be merely good and fast, or
adequate and scalable. Increasing scope often comes with an accuracy penalty,
because AI can generalize poorly to truly novel tasks. The 4 S's are sometimes
at odds. With a given amount of computing power, you generally have to trade o

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