The slow collapse of critical thinking in OSINT due to AI

Scope Beyond OSINT

• Many see the described failures (outsourcing hypotheses, source checks, and perspective-taking to AI) as happening across domains, not just OSINT.
• Commenters tie this to older worries about calculators, search engines, smartphones, and social media reducing cognitive effort.

Overreliance and Automation Bias

• Strong concern that GenAI’s speed, fluency, and confidence make people treat it as an oracle.
• People skip basic tradecraft: verifying locations, cross-checking sources, and seeking disconfirming evidence.
• AI’s confident tone is repeatedly highlighted as a key risk; confidence is mistaken for accuracy, just as with charismatic humans.

Is the Problem AI or People?

• One camp blames human laziness/gullibility: the same people who outsourced thinking to podcasts or TikTok now outsource it to LLMs.
• Another camp emphasizes structural pressures: too much data, time pressure, and expectations of speed push analysts to accept AI shortcuts.
• Some compare this to blaming Facebook or alcohol: tools amplify preexisting tendencies, but don’t create them.

Usefulness and Limitations in OSINT

• Practitioners stress that OSINT tradecraft predates modern AI and requires rigorous methods and multiple passes.
• AI can help with triage and search—narrowing huge datasets, suggesting leads, summarizing—but is poor at fresh, time-sensitive, or niche facts.
• Several anecdotes show LLMs confidently wrong on geolocation, domain ownership, military inventories, or clinical/technical details.

Impact on Learning and Creativity

• Some users feel AI slows deep learning (e.g., programming languages) by doing too much of the thinking.
• Others report success using LLMs as tutors—asking “why” and “when to use” rather than “do this for me.”
• Concerns are raised about declining ability to handle figurative language, satire, and artistic standards; others see this as recurring generational panic.

Trust, Confidence, and Verification

• Repeated stories of bogus but polished code, “tested” claims that fail immediately, and apologetic AI responses.
• Suggested best practice: treat AI as an unreliable but useful witness—generate hypotheses, then verify manually.

Skepticism About the Article’s Claims

• Some view the piece as moral panic or self-interested (a trainer warning about AI replacing careful analysts).
• Others note it doesn’t really measure whether overall analytic quality has worsened, only that workflows are changing.