In one sentence
The target is not AI itself but the corporate hype machine around it: organizations that cannot execute basic software and operational work are being pushed toward poorly understood AI projects by executives, vendors, and “thought leaders.” Most should fix their fundamentals, adopt AI only for clear use cases, and recognize that superficial experimentation will not prepare them for either modest or extreme technological change.
Overview
Writing from a data-science background, Ludicity describes leaving the field after seeing inflated promises, weak leadership, and a shortage of real use cases. ChatGPT revives the same cycle at greater scale: companies add chatbots, announce “AI strategies,” and claim benefits that the author finds implausible. The essay’s repeated structure answers familiar pro-AI arguments—efficiency, competitiveness, demonstrated gains, future preparedness, and widespread adoption—with operational skepticism and anecdotes about failed demos, insecure systems, dubious vendors, and institutional self-deception.
The author allows three broad futures: an intelligence explosion; a plateau in which AI incrementally disrupts selected industries; or a much more capable system that changes programming and society. None makes generic chatbot deployment a sensible preparation strategy. In the moderate case, organizations should know their specific use case. In the extreme cases, ordinary “AI readiness” is either irrelevant or inadequate. The practical alternative is unglamorous competence: reliable systems, tested backups, good documentation, stable teams, sound requirements, and a culture capable of solving problems together.
Core ideas
AI hype is often organizational theater
The number of AI initiatives can greatly exceed the number of viable problems being solved. Executives may use fashionable technology to gain status, funding, headcount, or conference visibility rather than to produce measurable value.
Fix basic execution before adding complexity
Organizations that cannot consistently deliver CRUD applications, protect sensitive credentials, maintain legacy systems, or test backups are poor candidates for sophisticated machine-learning programs. AI adds infrastructure, data, evaluation, security, and governance burdens; it does not compensate for weak fundamentals.
AI may already be present as a commodity capability
Many firms can benefit indirectly from algorithms embedded in products they already buy—such as security or productivity software—without creating an internal AI program. “Using AI” is not automatically a strategic project.
A capability is not a use case
Broad language-model abilities become valuable only when attached to a specific workflow and a meaningful advantage. The author’s positive example is natural-language conversion into Todoist’s filter syntax: narrow, useful, and clearly better than learning an obscure notation for occasional use.
Survey claims and demos require hostile scrutiny
Reported success rates may reflect hype, vague definitions, or respondents’ incentives rather than reliable outcomes. Impressive demonstrations can conceal manual intervention or unsolved core problems; sales language can relabel ordinary outsourced labor as AI.
“Preparation” should match the scale of the future
If current methods improve gradually, a concrete use case will reveal itself. If AI becomes radically more capable, adding a chatbot or teaching employees prompt tricks will not protect the business. Generic middle-ground preparation may deliver little in either scenario.
Internal knowledge is an infrastructure prerequisite
Techniques such as retrieval-augmented generation presume that an organization has accurate, accessible, maintained documentation. Improving documentation and institutional memory is valuable independently and makes later AI adoption more feasible.
The author’s anger is aimed at incentives, not research
Ludicity acknowledges that generative AI could transform society and that some existential-risk arguments are plausible. The objection is to opportunists who turn uncertainty into confident corporate prescriptions, crowding out competent engineering and responsible technology use.
Practical takeaways
- Ask what precise workflow, user, cost, and measurable outcome justify an AI project; reject “we need an AI strategy” as a sufficient rationale.
- Before experimentation, audit delivery reliability, requirements, security practices, backup restoration, data quality, documentation, and staff retention.
- Treat vendor demos, executive surveys, and claimed productivity gains as hypotheses requiring independent evaluation—not as evidence.
- Prefer embedded, narrow tools that solve a recurring problem over bespoke AI initiatives created for signaling value.
- Separate three questions: whether AI is technically possible, whether it works reliably for this task, and whether the organization can operate it safely.
- If the proposed preparation is merely chatbot deployment, prompt training, or AI-branded infrastructure, ask how it would help under both a gradual-improvement scenario and a genuinely transformative one.
- Do not use AI to avoid teaching engineering discipline, clarifying requirements, or improving organizational communication.
Caveats and counterpoints
- The essay is deliberately hyperbolic and satirical; its violence is rhetorical exaggeration, not a literal recommendation.
- Its strongest claims are based largely on the author’s professional experience and anecdotes rather than systematic evidence. They are useful warning signs, not prevalence estimates.
- The argument risks underweighting organizations where AI capabilities are already materially changing workflows, or where early experimentation creates valuable learning despite imperfect fundamentals.
- “Fix the fundamentals first” can become an excuse for indefinite delay. Some firms may need controlled pilots precisely to learn what data, evaluation, governance, and skills they lack.
- The essay focuses on corporate hype and operational competence, not on consumer benefits, labor-market effects, copyright, or the broader social policy questions surrounding AI.
Questions worth revisiting
- Which of the author’s three possible AI futures seems most plausible, and what evidence would distinguish them?
- What would count as a genuinely successful AI project: cost reduction, quality improvement, speed, revenue, safety, or something else?
- Which organizational fundamentals are prerequisites, and which could reasonably be improved during a tightly scoped pilot?
- How should a company test whether a claimed AI benefit exceeds the value of a simpler rule, search system, workflow change, or purchased feature?
- Where does the essay’s contempt for executives and vendors clarify incentives, and where might it obscure legitimate strategic concerns?
Return to this when…
Return to this essay when an organization proposes an AI initiative in vague strategic language, cites impressive adoption statistics without definitions, or wants to deploy a chatbot before addressing basic reliability, security, documentation, and management problems.