Although this may seem reductionist: what is now called "AI" is just bad.
There is a list of why it is good, but the list would be too small.
This applies to what we're calling "AI" this week, not e.g. until 2014.

I hereby license this under the terms of the Creative Commons CC Zero license.
The moral right of the author has been asserted.
Full license text at: https://creativecommons.org/public-domain/#cc0

"Power is only what you allow it to be. Very many people put up with
political lying, political illusions, and political propaganda,
because if they were to denounce it, they would have to admit that
for many decades, they had themselves been fooled."
"You can resolve not to do the work of power for it. You can resolve
not to use sloppy language that is euphemism."
 -- Christopher Hitchens, 2005

"It is difficult to get a man to understand something when his salary
depends on his not understanding it." -- Upton Sinclair, 1934

Here's a list of concrete points of why what is being marketed as "AI" is bad;
the list is not complete by any means and is likely to keep growing.

(If you must read this aloud, please use the voice of either Martin Landau,
as pastiched in Seth MacFarlane's "Family Guy", or Heath Ledger's "Joker",
for maximum sardonic effect and schadenfreude, to underline how sad this is.)

High-level criticisms
 - not intelligent (LLMs are not rational beings capable of dialectic)
 - misleadingly named (LLMs are not intelligent and cannot reason as humans)
 - fraudulently marketed (LLMs cannot replace humans, offer very limited help)
 - inflated claims of utility (LLMs are too error-prone to assist meaningfully)
 - retarded technological progress (e.g. computer networking now monoculture)
 - lack of accountability (LLMs typically hosted by large private corporations)
 - false democratization (local LLMs do not mitigate negative externalities)
 - philosophical corruption (utilitarianism uber alles; a prelude to fascism?),

Flawed structural assumptions
 - LLMs pursued, yet "false language" conditions were known (Chomsky, 2023)
 - lacks deixis (Lecun attempting to address some of this with world models)
 - technological dead-end (LLMs will always "hallucinate"; incapable of
   abductive reasoning vs C.S. Peirce, Roger Penrose, as found in "GOFAI"
   expert systems, without extensive non-ML harness support.)
 - false claims of complete human knowledge (Internet Archive is LLM gated,
   LCP DRM in use, and much human knowledge is tacit, non-verbal, cultural)
 - public persecution of diverse, non-ML AI research basis (see Gary Marcus
   on Threads; deep-learning proponents claiming "We are the new priesthood")
 - misunderstanding of knowledge work in toto by proponents and incumbents
 - model collapse inevitable (RLHF pre-training cost; Dead Internet Theory)

Environmental effects
 - inappropriate scale
 - energy inefficient
 - undesirable datacentre developments
 - possible future environmental catastrophe (e.g. Tony Blair arguing against
   Net Zero wastes electric capacity on perpetuating the fraud; Kate Crawford)

Geopolitical effects
 - political fraud (Tony Blair's nepotism, staging policy influence op)
 - geopolitical intrigue (China vs USA compute arms race for cybersecurity)
 - deceives elected representatives (very dubious economic growth arguments)
 - disinformation ("Grokipedia", can be blocked via NoGrok)
 - cybersecurity attacks (LLM generated exploits, found CVEs)

Economic effects
 - economic fraud (no empirical evidence for ROI or productivity gains)
 - economic distortion (technology production is focused on GPUs, not DRAM)
 - financial fraud (no empirical evidence for ROI claims; see Ed Zitron)
 - accounting fraud (costs inappropriately booked between CapEx and OpEx)
 - antitrust risk (industry-wide circular financing deals)
 - artificially understated cost base (vs CoreWeave, NVidia circular equity)
 - unpredictable RLHF offline pre-training costs for large LLM providers

Effects on business
 - attempts to waive liability (GitHub T&Cs offload IP liability to users,
   contradicting IBM 1970s era advice "Computers cannot be held accountable")
 - destabilizes existing businesses (FOMO mandates hasty AI strategy mistakes)
 - generatively coded products are SOUP (Software of Unknown Provenance)
 - generatively coded products may be uninsurable (Freakonometrics)
 - unpredictable token costs (Uber COO)
 - consumer fraud (deepfakes)
 - damaged labour relations (technology workers are now unionizing en-masse)
 - enterprise risk management subverted
 - gains attributed to "agentic" automation may largely be conventional

Knowledge domain effects
 - noospheric pollution (AI slop content appearing everywhere)
 - ongoing corruption of language and ontology (see Emily Bender)
 - open source community deceived (Claude Code "undercover.ts" leak)
 - intellectual dishonesty (ensloppification of academic research)
 - authorship rights denied (impacts permissive FLOSS more)
 - widespread nonconsensual training (LLM scrapers via residential proxies)
 - epistemic injustice (stealing people's intellectual property, with
   somewhat bizarre sci-fi justification: "If we teach the word guessing
   program enough words, maybe it will wake up"; Cory Doctorow)

User experience
 - poor tool quality (reverse engineering of Claude Code by Jonny Saunders)
 - non-consensual end user installs (Google Chrome force-installs Gemini LLMs)
 - deceptive cybersecurity practices (Claude Code exfiltrates customer data)
 - proprietary vendor lock-in (to be useful Claude Code needs a huge harness)
 - enshittification (token burn noticeably worse in Codex, Claude, Cursor etc)
 - pointless branding (e.g. Anthropic Mythos exploits on par with gang of GLMs)
 - misleading output (e.g. generating contradictory "legal" advice that
   directly contradicted UK Intellectual Property Office online)

Ethics
 - AI safety advice ignored (circumstantial evidence that e.g. Meta LLMs are
   being prompted for sychophancy to "drive engagement")
 - perverse incentives (core AI safety bypassed re "engagement"; Kate Crawford)
 - human rights violated (deepfakes hurt integrity of individual identity)
 - persecution of whistleblowers (Google: Timnit Gebhru, Margaret Mitchell)
 - unpredictable LLM actions in a crisis (e.g. Kings College nuclear wargaming)

Human cognition
 - cognitive damage (humans misleadingly encouraged to trust LLM, not own mind)
 - cognitive decay and surrender (humans falsely believing "AI" is inevitable)
 - undermining education (TurnItIn is just as bad as an online thesis mill)
 - subverted creativity (musical and other artistic originality undermined)
 - chatbot psychosis (Richard Dawkins claims Claude is "conscious"; unlikely)

Employment
 - subverts workflow and professional practice (e.g. forced AI usage mandates)
 - futile attempts to deskill professional labour (e.g. empirical evidence of
   impeded software productivity; see METR 2025 study)
 - misleading generative claims (Bjarne Stroustrup debunked LLMs for C++)
 - actually adds software technical debt (Dijkstra's "Complexity Generators")
 - erroneous output for basic textual tasks (e.g. providing Linux based answers
   for macOS xnu kernel source queries with publicly available git repository;
   then fabricating misleading text suggesting LLM had read from xnu)

Society
 - misguided approach to societal regulation (see Adam Curtis)
 - seeking pseudo-religious legitimacy (Anthropic vs the Pope's encyclical)
 - pseudo-legitimizing dubious movements (e.g. TESCREAL, "Effective Altruism")
 - enabling eugenicists (see Valerie Veatch film; a prelude to fascism?)
 - enables moral cowardice (neither Yann Lecun nor Geoffrey Hinton are taking
   social responsibility, unlike the Atomic Scientists of Chicago in 1945)

Media
 - mass media Potemkin village (the BBC and others don't know how to respond)
 - exploitative media strategy (OpenAI buys TBPN, Amodei running Mosco's "Myth")
 - fuelling public panic ("AGI" and "ASI" will not "take over" or kill us all;
   this is science fiction; debunked by Grady Booch in debate with Connor Leahy)
 - claiming the liar's dividend (e.g. the disingenous Altman playing the victim)

Rebuttal
 - burden tennis (the tedious game of burden-of-proof arguments with
   proponents, which one can immediately refute with Hitchens' Razor)
 - argument from false dichotomy (e.g. tnegative externalities affect
   people on political left & right; often used by "Effective Altruists")
 - proponents commonly engage in DARVO tactics (with victim blaming)
 - structural assumptions must be questioned (Dan McQuillan, Brian Merchant)

Epilogue
 - delaying the inevitable (until con can be externalized to retail investors)
 - denying what happened (full empirical refutation only possible post-facto)
 - invites comparison with satire (e.g. "The Full Cognitive Redaction of the Moral
   Coward Dario Amodei" vs Seth MacFarlane's "American Dad!" series)

For these reasons, and others, anyone promoting "AI" products or using them,
with only a few tiny key exceptions, is being very, very stupid.

Dr. Bruce Simpson, 2026-05-28.

P.S. "Do not burden me with your burden tennis, for that which can be asserted
without evidence, may be dismissed without evidence." -- Hitchens' Razor,
a contemporary example of the principle of logical parsimony, in the tradition
of Occam's Razor, Hanlon's Razor, and the more modern Grey's Law.

P.P.S. Big respect and shout-outs to Alfred Korzybski and Arthur Schopenhauer.
Sadly they are both quite, quite dead.