Emotional Impact of AI Work

The psychological states AI adoption induces in practitioners and leaders — alienation, dulling, rollercoaster highs and lows, anxious FOMO and delusions of productivity.

Also known as: følelser, fremmedgjøring, alienation, sløv, berg- og dalbane, AI psychosis, AI Psychosis, excited anxiety, itchy brain

How authors use it

Oldest first (extraction date when no publication date is known).

AuthorTheir termTheir meaning
Skill Issue: Andrej Karpathy on Code Agents, AutoResearch, and the Loopy Era of AI
No Priors: AI, Machine Learning, Tech, & Startups · 2026-03-20		AI psychosisA persistent, anxious state of trying to keep up with the rapidly expanding frontier of what is possible with AI agents, feeling that any idle moment is wasted potential
Why Enterprise AI Has a Leadership Problem
AI Daily Brief · 2026-04-10		excited anxietyThe simultaneous genuine enthusiasm and genuine fear among enterprise leaders who recognize AI's potential but feel structurally unprepared to realize it.
SWLW #702: How I created OpenClaw, Your CEO is suffering from AI Psychosis, and more.
Oren Ellenbogen · 2026-04-20		AI PsychosisA specific pathology in executive decision-making where leaders mistake token-burning AI dashboards and agent hierarchies for genuine productivity, getting the dopamine of delegation without measuring output
Itchy Brain (Still Burning: Michael Grinich & Kent Beck on AI adoption, enterprise developer tools)
Kent Beck — Still Burning Podcast · 2026-05-20		itchy brainBeck's name for the felt phenomenon: an idea or thought lodges in your head and the discomfort does not abate until you have made the thing in the world. Beck frames it as 'a bit more selfish' than Grinich's connect-with-people version; Grinich calls it 'a compulsion,' shared by long-distance founders as 'the same kind of mental disorder.' The durable version of the maker drive — opposed to external-validation chasing, which burns out.
KI, utvikling og følelser
Vidar Moe · 2026-05-21		følelserFeelings, in a load-bearing sense — Moe uses this term to name the proper object of analysis when assessing AI's impact on developers. Not 'sentiment' or 'morale' as HR categories, but the developer's lived inner experience.
KI, utvikling og følelser
Vidar Moe · 2026-05-21		fremmedgjøringAlienation — used in a quasi-Marxian sense: the developer no longer recognizes the product of their labor because an agent produced it. Moe specifically names the transition 'fra å bygge systemet til å spesifisere systemet' (from building the system to specifying the system) as the alienating shift.
KI, utvikling og følelser
Vidar Moe · 2026-05-21		sløvDulled, dim, mentally slack — the cognitive atrophy state Moe fears developers will fall into if they simply rubber-stamp agent output. Pairs with fremmedgjøring as the second psychological hazard.
KI, utvikling og følelser
Vidar Moe · 2026-05-21		berg- og dalbaneRollercoaster — the emotional shape of working with AI agents, complete with both the highs (now we can build things we couldn't) and the lows (frustration, alienation, fatigue).

Documents (12)

Propositions touching this concept (4)

By publication month, oldest first (extraction month when no publication date is known).

2026-05

Tensions

What kind of problem is AI adoption in software development, and who is the responsible actor for fixing it?
from: Is skepticism/rejection of AI for software development still rational in 2026 — and where exactly do practitioner "adopt fully" and "abstain entirely" positions actually diverge: on the facts (does AI work), the values (what counts as harm), or the frame (individual choice vs. systemic problem)?
Assessment: These aren't competing answers to the same question — they're answers to three different questions about where causal and moral leverage lies (org-technical, individual-ethical, political-collective), and the corpus never stages a direct confrontation between them. The weakest of the three on its own terms is Brett's: his abstention argument depends on an untested demand-signaling causal claim, and Marcus's own regulatory framing implicitly undercuts it — if the effective lever is political, one person's abstention is symbolic rather than causally load-bearing, however psychologically necessary it may be for him. Majors's org-engineering frame is the most operationally developed but explicitly scopes itself to teams that already have the discipline to execute it — it has nothing to offer someone in Brett's actual position (an individual contributor under a leadership mandate, with no control over org-level tooling investment).
Does verification/validation substitute for human care and authorship as the basis of software quality?
from: Is skepticism/rejection of AI for software development still rational in 2026 — and where exactly do practitioner "adopt fully" and "abstain entirely" positions actually diverge: on the facts (does AI work), the values (what counts as harm), or the frame (individual choice vs. systemic problem)?
Assessment: A genuine values disagreement, not a disguised factual one, and under-argued on both sides. Majors asserts a substitution (trust moves from person to process) as self-evidently sufficient without ever specifying what validation would need to guarantee to make 'comfortable shipping unread code' true in practice — a gap her own extraction already flags. Brett asserts the opposite from pure introspection, without considering that his felt alienation might be a contingent response to a personal transition rather than a fact about what quality requires in principle — plenty of trusted professional domains (structural engineering, aviation, pharma) run on process-based rather than personal-craft-based trust and aren't usually accused of producing 'uncared for' outputs. Neither side defends its definition of quality against the other's; each asserts its own as obvious.
Does sustained, earnest, disciplined AI use degrade or improve a competent practitioner's skill and output?
from: Is skepticism/rejection of AI for software development still rational in 2026 — and where exactly do practitioner "adopt fully" and "abstain entirely" positions actually diverge: on the facts (does AI work), the values (what counts as harm), or the frame (individual choice vs. systemic problem)?
Assessment: Both are single-case evidence dressed as general claims, and the corpus offers a plausible reconciling variable neither author controls for: Parker's joy-gap data shows the split tracks codebase type (greenfield/small-product vs. legacy/enterprise) more cleanly than it tracks practitioner discipline or virtue. Fin is not described as legacy-constrained, and Brett's own most damning anecdote — the Linear/Claude Code project built 'without editing a line of code' — is closer to greenfield vibe-coding than to his day-to-day legacy work. The Majors/Brett disagreement may therefore be less a disagreement about AI's general effect on skilled practitioners and more two data points from different ends of Parker's spectrum, each over-generalized past its actual scope.
Is a moderate, bounded middle path between full adoption and full abstention psychologically and practically stable?
from: Is skepticism/rejection of AI for software development still rational in 2026 — and where exactly do practitioner "adopt fully" and "abstain entirely" positions actually diverge: on the facts (does AI work), the values (what counts as harm), or the frame (individual choice vs. systemic problem)?
Assessment: The sharpest values/psychology divergence in the corpus, and arguably what the central question is actually asking about, since 'is skepticism/rejection rational' presupposes a real choice-menu with a stable middle rung. Majors's evidence for stability (a team ritual everyone follows together) is a much weaker test than Brett's (a year of earnest solo attempt at bounded use against colleagues' and management's full-throttle adoption) — a shared team policy is a different psychological ask than one individual holding a line alone, which is closer to Brett's actual situation. On the specific question of whether bounded use holds under social/organizational pressure to go further, Brett's case is more probative than Majors's, even though his conclusion ('so it must be all-or-nothing') over-generalizes a personal finding into a universal claim.
Is 'skepticism' (doubting AI's capability) the same stance as 'rejection/abstention' (refusing to use AI), and does 'still rational in 2026' apply equally to both?
from: Is skepticism/rejection of AI for software development still rational in 2026 — and where exactly do practitioner "adopt fully" and "abstain entirely" positions actually diverge: on the facts (does AI work), the values (what counts as harm), or the frame (individual choice vs. systemic problem)?
Assessment: This is the central question's own hidden ambiguity, and the corpus resolves it by demonstration rather than argument. Brett is a live counterexample to the premise, embedded in Majors's own title, that 'not being skeptical' (updating on capability evidence) and 'using AI' are the same move. Once separated, Majors's essay is only an argument against doubting-that-it-works, and has nothing to say to someone who concedes capability and objects on different grounds. So 'is skepticism still rational in 2026' and 'is abstention still rational in 2026' are different questions the corpus never poses side by side — and the practitioner discourse largely collapses them into one fight when they aren't the same fight. On the narrower question Majors actually argues (is doubting AI's capability still rational), the corpus evidence favors her: nobody, including Brett, disputes that current tools work. On the broader question the central question is really asking (is abstention still rational), the corpus offers no resolution at all — only two incompatible frames (organizational-engineering vs. individual-ethical) that never engage each other directly.

Open gaps (27)