What Killed Innovation?

Perceived decline vs. natural maturation

• Many argue “innovation” hasn’t died so much as the field has matured and converged on a small set of patterns (bars, lines, basic maps, violin plots, etc.) that reliably work.
• Early “Cambrian explosion” of flashy experiments was expected: lots of ideas, most not very useful; the good ones become conventions and the rest fade.
• Some lament the loss of variety and experimentation, comparing it to older eras of camera or architecture design; others say some domains become “solved” and need little further innovation.

Utility of complex / interactive visuals

• Repeated criticism that the article’s showcase examples are visually impressive but confusing: unclear what’s being quantified or how values relate.
• “Scrollytelling fatigue” is common: readers don’t want to labor through long animated pieces; they want key points surfaced.
• Flashy or novel views can distract from the data; for many audiences a straightforward bar or line chart is best, especially when time and attention are limited.
• Some see complex visuals as “data porn” or essentially marketing/branding, not information tools.

Standard charts, literacy, and trust

• Growing data literacy makes people more skeptical of elaborate charts, given how easily visuals can mislead (axes, colors, aggregation).
• Others stress that real data literacy needs to cover the full pipeline (collection, analysis, interpretation), not just visualization tricks.
• Debate over nonzero axes: some call them “borderline fraud,” others note many variables (e.g., temperatures) can’t meaningfully start at zero.

Economic and organizational incentives

• High‑end, bespoke visualizations are expensive and typically yield only short‑lived engagement; there are cheaper ways to drive clicks (e.g., rhetoric).
• In many real workflows (boards, enterprises) only static PDFs/PowerPoints are acceptable, limiting experimentation.
• Broader complaints: MBAs/MVP culture, quarterly-profit focus, tax and regulatory structures, and large incumbents (via lawsuits, acquisitions, lobbying) all dampen long‑term, risky innovation.

Web vs. native platforms

• Long subthread debates whether the web as an “OS in a document reader” constrains visualization innovation.
• Some blame the web’s model and performance; others counter that browsers now approach native capability, offer sandboxing and painless cross‑platform deployment, and are “here to stay.”
• Cross‑platform native toolchains (Qt, JavaFX, etc.) exist but are seen as niche or leaky abstractions; the web won largely because everyone agreed on one stack.

Election “paths to victory” example

• The article’s US election “paths to the White House” graphic is widely criticized as confusing and overdesigned.
• Several argue simpler displays (“X more seats needed,” cumulative maps and bars) better convey the situation; the metaphor of “paths” is seen as a media narrative device rather than an analytic necessity.

Innovation cycles and AI

• Some say craving constant novelty in a mature medium is misguided—like asking for new wheel shapes.
• Others caution that declaring problems “solved” is premature; occasional paradigm shifts (example given: recent hash table work) still happen and justify fundamental research.
• A few see current “innovation” shifting from bespoke viz to AI/LLM‑driven tools: models auto‑generate charts, dashboards, and even explanations; the “best viz” may often be no viz at all, just an answer you can query.