Everybody's Lost Their Minds

Signs of Triviality
Opinions, mostly my own, on the importance of being and other things.

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Everybody's Lost Their Minds

September 16th, 2026

Men. Some would rather vomit up a rambling blog post
wall of text that nobody's going to read than go to
therapy. So here we are.

I've seen my share of stupid over the years, but now
people with no engineering background have started
pitching "industry changing" solutions they cooked up
in their agent infested homelab; people's emails
read like bozotic LinkedIn-fluencer posts with punchy
"it's not this, it's that" single-sentence
paragraphs; online articles suffer a similar fate in
their own convergence on Meh; and half of the people
you interact with have turned into meat proxies.

Spending upwards of 75% of my time directly or
indirectly dealing with AI every day has absolutely
robbed me of most of my enjoyment of my work. Most
days feel like that Twilight Zone where you wake up
and you're the same, but everyone else is different.
(They were all
like that.)

"Ethics aside..."

The cyber hype train has been going "choo choo" for a
while, with the main AI companies trying to one-up
each other committing
crimes and somehow we let them; the conscious
choice of anthropomorphic language by the companies is
adapted unquestioned by the media, thereby absolving
AI companies of their incompetence to secure their
programs.

Built on unapologetic exploitation of intellectual
property and concentrating power in the hands of a
very small number of US companies and oligarchs, these
AI models not only lend themselves to generation
of Child Sexual Abuse Material—a product
feature for logged-in
users—1but our continued use of
them also directly supports their role in, e.g.,
military target selection, such as elementary
schools.

Meanwhile, every single company is happy to "ethics
aside..." all of that and spend unimaginable amounts
of "tokens"—a made-up currency following the
casino model2—while staring at you
blankly when you ask whether anybody has bothered to
check if that support chatbot you vibe coded and which
you fed all of your very mediocre at best
"documentation" has any ROI.3

Project Sisyphus

"Frontier Models" and AI-assisted vulnerability
research is another topic with questionable results.
Anthropic and OpenAI tried to one-up each other with
how dangerous their models are and everybody who
considers themselves an industry leader is now part of
some mysteriously named "project" (like Glasswing
and Daybreak, or
Athena
and Akrites) or
co-signed various open letters (like this
or this)
to signal just how much they're totally not left out.

Every participant in these project has thrown
absolutely incredible amounts of engineering resources
at the FOMO-induced, time-limited, "the first one's
free" offer from Anthropic and OpenAI. Dozens of
highly-paid security engineers had all of their
priorities shifted and spent all of their
time on this; the cost of the engineering hours
spent on developing and adjusting AI vulnerability
discovery harnesses, building new processes and
pipelines to shoehorn thousands of findings into their
vulnerability management processes, and of course
working with the product owners on assessing and
fixing the findings... all that must run in the many,
many millions of dollars for each organization.

And yet, despite having found literally
thousands of new vulnerabilities (only a
fraction of which were reported to Open Source
projects, by the way), I don't think that we're
any safer than before. That's because
finding vulnerabilities has never been the
bottleneck in information security. The bottleneck
isn't even verifying a vulnerability report
and validating its severity, as time consuming as that
is. The bottleneck isn't determining the fix,
creating the patch, or publishing a new release. The
bottleneck is still, as ever before, getting the
goddamn packages updated. Patching is still hard.

Now imagine that we had spent all these resources on
doing the basics: ensuring your organization has an
up-to-date and comprehensive asset inventory with
fine-grained package listings; building infrastructure
that supports regular, frequent, and automated OS and
applіcation updates; automatically rebooting systems
when they hit, say, 30 days of uptime to ensure these
updates are picked up; establishing comprehensive
attack surface enumeration across all your IP space
as well as all your cloud providers (what a
concept!); the list of basic, fundamental defenses
that nobody seems to actually do well goes on. Having
a few dozen senior engineers dedicated for 6 months to
overhauling all that, focusing on making
patching easier, would, in my book, have been
a much better investment, but that's just not very
cyber at all.

No matter what AI promises, human resources are still
a zero-sum game, and every individual feeling super
busy in their agentic silo doing a thousand things at
once does not, in the end, help solve the kinds of
projects that require cross-functional collaboration
and team work.

A strange game

At the same time, AI companies are falling over
themselves once again facetiously calling for their
own regulation because, you know, they could
accidentally end all mankind.

If you actually thought your product will kill all
humans, then you could, you know, like, just stop
building the torment nexus. All by yourself, no
government regulations required. Nobody's forcing you
to play "Theaterwide Biotoxic and Chemical Warfare" or
"Global Thermonuclear War". I mean, except your
future shareholders and your greed. Alas, that
wouldn't cockblock your competition...

But you don't need to imagine AI destroying all
humankind within the next few years via some sort of
Skynet or Paperclip Maximizer scenario when in reality
AI has of course already been hard
at work here. The environmental impact of these
companies is staggering.
The AI race demands more and more water wasting, air
polluting, fossil fuel powered data centers that
absolutely nobody wants to live close to, and
governments lift any and all environmental regulations
for these companies who not too long ago at least
pretended to have even the feeblest
greenwashing commitments to carbon neutrality or
renewable energy sources.

But "the
world looks different now", to which I can only say
"No fucking shit, Sherlock. IT'S
ON FUCKING FIRE. Because of you."

Superhuman Intelligence

You know there are two ways for AI to achieve
superhuman intelligence, right? One (theoretical) way
is the mystical "recursive self-improvement" by AI.
The other one is the path we're very clearly on: The
agentic brain worms have been spreading, and it looks
increasingly like everybody's lost their goddamn minds
already. AI is the tool that dulls its users; it
incrementally replaces understanding with a
new dependency and addiction as you actively de-skill
yourself.

AI helps people find more vulnerabilities in existing
code. To address those vulnerabilities, people use AI
to generate patches. The resulting pull requests are
then "reviewed" by AI. That is, the more AI is in the
loop, the less we understand the code base. The
mystical "human in the loop" often is nothing more
than a rubber
stamp.

So what happens when things go bump? Complex systems
fail in complex ways, and debugging code is an order
of magnitude harder
than writing code. Debugging somebody else's code
is harder still. Trying to debug large, complex,
distributed systems consisting of components that are
effectively opaque to your entire organization is
going to be impossible.

So no, I'm not going to set ethics aside and then
actively offer myself up as tribute to self-amputate
my brain. I'm sorry if everybody else can't get rid
of the brain slugs, but at this point I'm just looking
to get off this ride.

September 16th, 2026

P.S.: And no, Claude is not
conscious. J-Space my ass.

Footnotes:

[1] Damn straight I use an emdash. Fuck
you for devaluing it.
↩

[2] "Results showed that participants
gambled significantly more with chips than with real
cash." [citation provided]
↩

[3] It doesnt: LLMs are Garbage-In/Garbage-Out—if
you have shitty docs, the AI can at best polish that
turd and still only spit out nothing of use.
↩

Links:

Patching is hard. Knowing what to patch is harder still
Discussion on HackerNews (dupe)
Discussion on Lobsters

← [Sites using PQC (September 2026)]

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The author critiques the current state of public discourse and professional focus, observing a trend where individuals, especially those without engineering backgrounds, are pursuing "industry changing" solutions based on their own assessments, often resulting in superficial and unverified content. This observation extends to the pervasive influence of artificial intelligence, noting that spending upwards of seventy-five percent of time interacting with AI has diminished enjoyment in professional work, leading to a sense of stagnation where individuals feel unchanged while others diverge.

The author raises serious concerns regarding the ethical landscape surrounding AI development. Despite calls for ethics, the competitive race among major AI companies allows them to disregard moral considerations while pursuing the use of these models, citing examples such as generating child sexual abuse material and supporting military target selection. The focus on corporate profit, measured in abstract units like tokens, seems to overshadow accountability, as companies ignore whether their foundational tools provide genuine return on investment or adhere to basic ethical scrutiny.

A significant portion of the text analyzes the AI safety research landscape, termed the "Project Sisyphus," where companies like Anthropic and OpenAI engage in competitive demonstrations of model dangers. This competition has driven engineers to expend massive resources on developing AI vulnerability discovery harnesses. However, the author argues that this effort has not increased overall security. The core bottleneck in information security remains the arduous task of updating software and systems, rather than discovering vulnerabilities. The author posits that a far more beneficial investment would have been dedicating resources to foundational security practices, such as developing comprehensive asset inventories, building infrastructure for automated and frequent system updates, and establishing exhaustive attack surface enumeration across all domains, rather than focusing solely on the discovery phase.

Furthermore, the author contends that the current struggle over AI implementation does not ultimately resolve systemic issues. Human resources remain a zero-sum game, and the tendency for individuals to operate in isolated, agentic silos hinders the necessary cross-functional collaboration required to tackle large, complex projects.

The discussion pivots to existential concerns, challenging the premise that AI companies are sufficiently motivated to regulate themselves to prevent global catastrophe. The author points out the staggering environmental toll of the AI race, demanding immense amounts of energy and water from data centers, which contrasts sharply with the superficial greenwashing commitments made by these entities. The author concludes that the reality of the situation is an environmental disaster caused by this development, regardless of regulatory posturing.

Regarding the nature of superhuman intelligence, the author suggests that the path is less about theoretical recursive self-improvement and more about the spread of agentic brain worms, indicating that the tool of AI serves to dull the user's understanding, leading to a dependency that de-skills the user. This reliance on AI for code analysis and patching, where AI reviews pull requests, reduces the human role to a mere rubber stamp, thereby increasing the difficulty of debugging complex, distributed systems. The ultimate conclusion is a resignation to the difficulty of grappling with these complex systems, implying a desire to disengage from the dependency cycle.