A VibeAxis autopsy of hype, hubris, and products that should’ve stayed in Figma.
The Pattern (a.k.a. why this keeps happening)
- We didn’t get Skynet. We got awkward gadgets, ethics debt, and decision engines with no appeal button.
- Most “AI flops” weren’t about compute—they were about bad fits, bad data, and worse incentives.
- If you can’t explain the value without a sizzle reel, it’s a future recall.
Hype outruns human reality. Teams ship a demo; users ship norms and lawsuits.
Data ≠ domain expertise. Medicine, hiring, credit—complex systems don’t bend to vibes.
Incentives pick the outcome. ad-clicks > consent, growth > guardrails, fast > right.
No off-ramp. When the model misfires, there’s no recourse—just “our team is investigating.”
For receipts on how this manifests in the wild: Ethical AI, Without the Halo and AI Can’t Read the Room.
Exhibit A — Google Glass: “Cool” is not a privacy policy
Face computers tried to colonize public space without consent.
The tech wasn’t the fatal flaw—the social contract was. Battery died; nicknames didn’t (“Glasshole”).
Failure mode: perfect storm of surveillance vibes + zero upside for bystanders.
Lesson: if the interface makes strangers your beta testers, you’ve already lost.
Exhibit B — IBM Watson for Oncology: PR versus patients
“AI doctor” looked great on stage and shaky in clinic.
Sparse, messy, high-stakes data plus overconfidence equals expensive decision support that doctors couldn’t trust.
Failure mode: domain complexity + synthetic certainty.
Lesson: in medicine, explainability and liability precede accuracy. No appeal path? No adoption.
Exhibit C — Tay (and Zo): The internet will teach your bot to swear
Microsoft’s chatbots learned from public text streams and went feral in record time.
Models don’t have values; they have gradients.
Failure mode: uncurated data + adversarial users + no constraint hygiene.
Lesson: “learn from everyone” is just scale without taste. Put guardrails on day zero or enjoy the bonfire.
Exhibit D — Echo Look: AI taste is a contradiction
A camera in your bedroom judging outfits—what could go wrong? Style is context, culture, and body politics; the model saw pixels and engagement.
Failure mode: low-stakes “fun” feature with high-stakes privacy.
Lesson: if the upside is novelty and the downside is a dossier, the market will pass.
Exhibit E — Voice Assistants: Plateau by design
Siri/Cortana/Assistant promised “AI butler,” delivered command grammars with search stapled on. Great for timers; brittle for anything adult.
Failure mode: shallow integrations, brittle memory, and business models that punish openness.
Lesson: without deep context + tools + trust, “assistant” becomes hands-free UI, not intelligence.
Exhibit F — Risk Scoring & Hiring AI: math laundering
From résumé parsers to “behavioral” loan models, we automated opaque judgment and called it efficiency.
Failure mode: proxies (ZIP, device, time-of-day) stand in for character; appeals are vibes.
Lesson: if you can’t explain a denial to a human in plain language, you built governance theater.
See: The Algorithm Thinks You’re Poor, Dangerous, or Lying.
What we (collectively) missed
Trust is UX. If people don’t feel in control, the accuracy graph doesn’t matter.
Context beats generality. Narrow tools with clear boundaries beat “AI for everything” that solves nothing.
Receipts > rhetoric. Demos lie; incident reports don’t. Publish them or expect backlash.
Power flows matter. When benefits accrue up and harms flow down, the public notices—and opts out.
How to spot the next AI flop (before your team ships it)
The pitch needs a montage to make sense.
Training data is “proprietary,” provenance is foggy, and consent is “assumed.”
There’s no kill switch or pre-committed pause criteria.
“Improve our models” is checked by default.
The product replaces judgment instead of augmenting it.
Support can’t tell you how to appeal a wrong outcome—because there isn’t one.
If you checked two boxes, congratulations: you’re pre-ordering regret. Read: Why We Keep Building AI Tools Nobody Asked For.
What actually works (unsexy wins)
On-device transcription, translation, autocomplete. Fast, private, useful.
Narrow copilots for drafting, summarizing, templating—with humans on decisions.
Fraud & spam filters with clear false-positive recovery paths.
Focused recommenders that you can steer (no autoplay by default, ever).
For a keep/cage/kill rundown: AI Tools for Everyday Life.
Salvage ops: turning a wobble into a win
Shrink the problem. Do one thing deeply well; draw hard boundaries around everything else.
Show your work. Data provenance, known blind spots, failure modes—publish the map.
Add recourse. Human override with authority; days-not-quarters SLAs.
Price the harm. Attach dollars/time to bad outcomes. Align bonuses with reducing them.
Commit the kill switch. Thresholds, not adjectives. If X, pause. Then actually pause.
The road ahead (without the poster)
AI isn’t failing because it’s weak. It’s failing where we make it carry things only culture can: taste, consent, accountability, meaning. The fix isn’t bigger models; it’s smaller claims with sharper guardrails. Build for use, not legend; optimize for recourse, not reach; and stop pretending ethics is a slide.
Want the receipts on how we test this stuff? Start with Ethical AI, Without the Halo, then sanity-check your pitch against Hallucination Nation.
