the solution might be cancelling my AI subscription

the solution might be cancelling my AI subscription

I am trying to think of a list of all the wonderful things I've built with AI:

a speech recognition system in rust
an email archive rendering + quote collapsing tool
a jellyfin desktop clone with gstreamer and qt quick
an invidious clone in python + yt-dlp
a faithful Windows 95 notepad.exe clone in fltk ported from the Wine
sources
a machine vision thing to count traffic flows from public street cameras in
opencv
a claude ui clone in python or rust i think, i don't even remember
a regional news site i never meant to build that is actually getting traffic, python/flask
a 3d car game built on the protocol for an existing multiplayer game in
three.js
an investment backtester in python
a html clone of the lightroom ui, marvelled at the result then never made
the backend
a markdown viewer in qt or gtk or something else i can't even remember
a replacement world clock widget for my laptop desktop environment in gtk
and C
a javascript network synchronised audio playback thing
a rust client for a chinese IP camera reversed from its Android app
a sizeable SaaS in rust
maybe 50 other projects i've already deleted

Except for the SaaS, almost none of this is useful and I don't want to maintain
any of it. I accidentally run a news outlet which is surely a liability. Sure,
it has helped me "learn AI tooling" and I use many of these tools, but I didn't
need them. I can't afford to maintain any of them, not in terms of time,
commitment, belief, attention or willingness to spend on tokens.

I didn't mean to build most of these things. Usually the Claude session started
with something like write a quick script for X, and one hour later the
result is not a quick script for X, nor in the usual case is my
problem solved, whatever the original itch happened to be.

attention is all you need

On that last point, this technology is horrific for attention.
It's a thermonuclear ADHD amplifier and I have seen the same effect in every
single one of my adult friends. Folk running 3 screens simultaneously working
on totally unrelated "projects" they have little hope of maintaining, and such
little commitment to the outcome that the time is obviously wasted.

In recent times, at least once per month someone sends a screenshot for an
awesome tool they are working on. I'm like whoa, that's really something
and the sender is obviously proud and enthusiastic. I try not to ask, but am
always thinking and where will you market it?, because when the question
is asked of an engineer, the answer is unchanged since before LLMs existed.

I recently interviewed and when the topic of AI usage came up, the host
answered something like oh we're quite light on it, everyone has up to 5
rooms where they manage their agents and I immediately felt a tightness in
my stomach.

I had a vague sense of the effect a few months into using Claude. Later I
reduced my subscription to Pro in the belief a quota restriction would mitigate
excessive use. Then Claude went through a bad service period and I moved to
Codex. Codex's CLI is much nicer than Claude's and noticeably faster. And usage
started creeping back up.

The technology, when honed, is genuinely amazing. Ask it to zero shot a parser
for an esoteric grammar implemented in an esoteric language with full tests and
it's done. The tooling as it exists today promotes absolutely nothing like the
focus required to apply it judiciously.

Almost every vendor and every tool intends to do exactly the opposite: more
usage, more tokens, more output. Ask a simple yes/no question of ChatGPT and
you can clearly see that it is hard-wired to include a relevant follow-up
question to promote excessive interaction.

Slopping out a 10,000 LOC untested Python/JS mess in 5 minutes helps nobody.
The thought of this happening in every commercial environment simultaneously is
horrifying.

friction = focus, focus = product

One of my early AI experiments, exploring AI as a lens in Marshall
McLuhan-like thinking, was to connect speech recognition to a pipeline that
generated blog posts on the other side, in the belief it would encourage me to
capture my thoughts. All I needed was to press the voice note button in a
Telegram channel, and out pops an Opus-formatted post.

The output was unbridled garbage. Because the effort was removed, so was the
commitment, and with the commitment the focus, and with the focus any
meaningful product at all. Quality writing is not conversational English simply
cast through a lens: conversational English is low-bit rate noise, quality
writing attempts to capture high bit rate information with better formed
concepts, and this should have been obvious before I began.

I looked at repurposing the pipeline to capture private notes, but I have no
need for private notes. It subverts the natural process of noise being
forgotten. It is just more excess tool use.

Following from this, for as long as quality matters, I believe handwriting
can never be obsolete.

It feels like we're heading towards crisis, and I doubt the answer is "better
models" or "better tooling". Cal Newport relates
this to pseudo-productivity:

The speaker argues that digital productivity tools, including AI and email, often create a “digital productivity paradox”: they make individual tasks faster or easier, but they can leave knowledge workers busier, more distracted, and less productive overall. He cites research showing that AI users spent much more time in email, messaging, chat, and business-management tools, while spending less time in focused, uninterrupted work. His central claim is that tools designed to reduce friction often increase the volume of shallow tasks and context switching, which weakens deep work and high-value output.

He explains that this happens because knowledge work often relies on “pseudo productivity,” where visible busyness is treated as a proxy for real value. Digital tools reinforce this by making people look active: sending more messages, producing more drafts, attending more meetings, and generating more work artifacts. To avoid the trap, he recommends measuring real outcomes, identifying the true bottlenecks in one’s work, and separating deep work from shallow work so that digital tools support meaningful progress instead of consuming attention.

-- 🤖

These experiences have opened a new perception of all tool use, because beneath
it all this is not about faster development = more apps or faster email = more
communication being a desirable goal. Generically, it's about a unit time of
life and how it is spent meaningfully.

I have no idea how to manage AI at present except by curtailing use, because a
tool producing a cheap reward with minimal input and no friction can only be a
liability, and achieving that realisation is probably the only real
contribution of AI to date.

David, Sun 31 May 14:31:04 2026

The author reflects on a large body of personal projects created using artificial intelligence, ranging from speech recognition systems and application clones to machine vision tools and investment backtesters. However, the author notes that almost none of these constructed artifacts are useful and are too burdensome to maintain, leading to a realization that the utility of AI tools must be reevaluated. This reflection leads to a critique of the overall experience of using these technologies, particularly concerning attention and focus.

The author posits that the prevailing trend in AI tooling seems to incentivize excessive usage, where vendors and tools are designed to promote more output and token consumption. This is viewed as antithetical to the concentration required for meaningful work. The central philosophical argument developed is that friction directly relates to focus, and focus is necessary for creating valuable products. The author contends that by removing friction, tools can increase the volume of shallow tasks and context switching, thereby weakening deep work and high-value output.

An early experiment, involving connecting speech recognition to a pipeline for generating blog posts, demonstrated that removing effort leads to reduced commitment, which consequently erodes focus, resulting in poor quality output. This suggests that the value of information creation is tied to capturing high bit rate information with well-formed concepts, rather than simply generating conversational English, implying that quality is prioritized over sheer volume.

This observation leads to a broader critique of the impact of technology on human attention, which the author describes as a "thermonuclear ADHD amplifier." The author notes that the current landscape of AI tools seems structurally biased toward promoting interaction rather than focused contemplation. The author argues that the goal of these tools often seems to be more traffic, more messages, or more artifacts, rather than genuine productivity.

Drawing on the work of Cal Newport, the author relates this experience to the "digital productivity paradox." Digital tools, including AI, make individual tasks easier but paradoxically leave knowledge workers more distracted and busier by increasing the volume of shallow work. The author suggests that this happens because visible busyness is often mistaken for real value, and digital tools reinforce this by making users appear active through increased communication and artifact generation. To counteract this, the author recommends measuring actual outcomes, identifying true bottlenecks, and separating deep work from shallow work so that technology supports meaningful progress instead of consuming attention.

Ultimately, the author concludes that the most practical management strategy for AI is curtailing its use. Since a tool that provides cheap rewards with minimal input and no friction can become a liability, the true contribution of AI may lie in achieving this realization—that attention is all that is needed.