AI Hype, Accessibility, and a Blind Skeptic’s Warning

A blind author challenges the community’s embrace of AI, arguing it often swaps accuracy for a sense of independence. He predicts that as hype wanes, blind users will have to fight for accessible AI platforms and contend with worsening web accessibility from unchecked AI-generated code. While he sometimes uses AI for rough descriptions, he rejects the hype and opts for the indie web and human solutions.

Key Points

AI and LLMs provide blind users with information they often can’t get from people, but the results are frequently inaccurate and the underlying models are often mismatched to tasks like image description.
The blind community’s enthusiasm is driven by a desire for independence and perceived objectivity, even at the cost of accuracy, amid persistent human and systemic failures to provide accessibility.
As AI hype fades, the author expects new advocacy battles to make LLM platforms, outputs, and developer workflows accessible—while AI-generated code may further degrade web accessibility.
Past tech promises (OCR, self-driving cars) didn’t deliver as hoped, and the author sees current AI trends repeating the same hype cycle and fragility, including risks of service shutdowns.
The author occasionally uses AI for a starting point but rejects its hype, points to industry turmoil and ethical issues, and chooses to invest attention in the indie web over platform-driven AI solutions.

Sentiment

Mixed: many applaud tangible AI benefits for blind users and argue capabilities have advanced beyond the article’s 2023 assumptions, while a substantial contingent underscores ongoing accessibility gaps, hallucination risks, and the danger of substituting real accessibility with AI shortcuts.

In Agreement

LLM platforms and their UIs often aren’t accessible; blind users must advocate for proper ARIA/live region announcements, focus management, and streamed-response handling.
AI-generated code can worsen accessibility because developers may not test or fix what models produce.
VLM-based OCR can hallucinate and lacks calibrated confidence, making it risky for critical use.
Dependence on centralized AI services risks brittleness (servers go down, products get killed); local models improve reliability.
Companies may use AI to check compliance boxes (e.g., overlays, low-quality captions) rather than invest in real accessibility.
Creative labor concerns are valid: replacing human voice actors and translators with lower-quality AI harms quality and livelihoods.

Opposed

The article’s 2023 skepticism has aged poorly: multimodal LLMs and OCR have improved rapidly (e.g., Gemini 2.5 for vision, Mistral OCR).
LLMs are well-suited to image description now; capability growth contradicts the claim that ChatGPT was the wrong tool.
For many blind users AI is a strong net positive—imperfect but highly useful information beats no information.
With proper prompting and human-in-the-loop workflows, AI translation can handle nuance (puns, footnotes) better than claimed.
Accessibility advocacy can feel preachy and alienate developers; emphasizing practical wins (e.g., auto alt text) drives more progress.
Some accessibility outcomes may improve as dev effort decreases with AI assistance; results will be a mixed bag rather than uniformly worse.