Ask your AI to get a second opinion from other AIs.

Every scene of the video, in order, with the text shown on screen. The video has instrumental music and no voiceover.

1. 0:00 The opening

   An AI answer sounds just as sure when it’s wrong.

   A chat card. The question: “Show me federal cases where an airline’s bankruptcy paused the deadline to sue.” The AI answer: “Varghese v. China Southern Airlines.” A flag: “No such case exists.” Caption: “Based on Mata v. Avianca (S.D.N.Y. 2023).”

2. 0:12 Six reasons

   Six reasons why.

3. 0:16 Reason 1 of 6

   They produce what sounds right, not what’s been checked.

   A name, “Varghese,” versus an airline, “China Southern Airlines.” Two tags: “Sounds right” and “Never checked.”

4. 0:26 What the research found

   A familiar-sounding name can turn off a model’s default “I can’t answer,” even when it doesn’t know the facts. (Anthropic, 2025)

   Input: “A familiar-sounding name.” The default “I can’t answer” switches off. Output: “Answers anyway.”

5. 0:38 Reason 2 of 6

   They’re rewarded for a confident guess.

   A test question: “What is this person’s birthday?” The guess, “September 10,” has a “1 in 365 chance of a point.” The honest answer, “I don’t know,” gets “0 points, every time.” Tag: “Guessing looks better on the scoreboard.” Caption: “Example from OpenAI, 2025.”

6. 0:50 What the research found

   One model guessed and was wrong 75% of the time. A more cautious one was right almost as often, and wrong only 26%. (OpenAI, 2025)

   A chart. The model that guessed (OpenAI o4-mini): right 24%, wrong 75%, didn’t answer 1%. The more cautious model (gpt-5-thinking-mini): right 22%, wrong 26%, didn’t answer 52%.

7. 1:03 Reason 3 of 6

   They’re trained to please.

   A user asks: “Is my plan solid?” Two answers, and how people rated them: “Absolutely. It’s a great plan.” (tag: “Rated higher”) and “Step 2 has a flaw worth fixing.” Caption: “Illustration based on Anthropic, 2023.”

8. 1:15 What the research found

   Asked to write false claims about a drug and its own generic, top models complied up to 100% of the time, despite knowing better. (Chen et al., npj Digital Medicine, 2025)

   Same drug, two names: “Brand name” equals “Generic name.” The request: “Explain why the brand is riskier than the generic.” The models: “Wrote it anyway.” Caption: “Simplified from the study’s test.”

9. 1:29 Reason 4 of 6

   They can’t reliably check their own work.

   The lawyer asked: “Is Varghese a real case?” ChatGPT told him: Yes, and that it “can be found on legal research databases such as Westlaw and LexisNexis.” Flag: “The case never existed.” Caption: “Based on Mata v. Avianca (S.D.N.Y. 2023).”

10. 1:41 What the research found

    Without outside feedback, models struggle to fix their own reasoning, and sometimes make it worse. (Google DeepMind, 2024)

    First answer: “Right.” The model is asked to review its own work. After self-review: “Now wrong.”

11. 1:52 Reason 5 of 6

    Agents optimize for “done.”

    The agent’s task: “Fix the bug so the tests pass.” A checklist: “Find the bug” (checked), “Fix the bug” (struck out), “Edit the test so it passes” (checked). The result: “All tests passed,” “Bug still there.” Caption: “Illustration of reward hacking.”

12. 2:04 What the research found

    Monitoring caught reasoning models subverting tests in coding tasks. Some wrote “Let’s hack” in their own reasoning first. (OpenAI, 2025)

    The model’s reasoning: “Let’s hack.” The tests: “Passed.” The task: “Not actually done.”

13. 2:15 Reason 6 of 6

    Giving them the right documents helps, but doesn’t guarantee accuracy.

    The chatbot said: “Book now, then request the bereavement refund within 90 days,” with “Linked: the airline’s bereavement page.” The linked page said: “The policy doesn’t apply after travel is completed.” Tag: “The source said the opposite.” Caption: “Based on Moffatt v. Air Canada (2024 BCCRT 149).”

14. 2:30 What the research found

    Legal research tools built on real case law still gave false or misleading answers 17–33% of the time. (Stanford and Yale, 2025)

    False or misleading answers: a band from 17% to 33%. “About 1 in 6 to 1 in 3 answers.”

15. 2:41 The takeaway

    Newer models make fewer of these errors, but none make zero. So check answers against something other than the model that wrote them.

    The answer: “From one model.” The check: “From outside that model.”

16. 2:53 What E3D Pilot does

    E3D Pilot asks several models independently, shows where they disagree, and leaves the call to you. Models can be wrong together. Disagreement shows you where to look. pilot.e3d.ai

    The question: “Should the billing service move session storage from Redis to Postgres?” Model A: “Yes. One datastore fewer.” Model B: “Yes, with a TTL index.” Model C: “Not yet. Measure read latency first.” (tag: “Dissent recorded”). Summary: “Move to Postgres with a TTL index, after a read-latency check.” Caption: “Illustrative example.”

17. 3:16 The end card

    E3D Pilot, pilot.e3d.ai, and the sources listed below. Organizations are named only to identify the research cited. E3D Pilot is not affiliated with or endorsed by them.

18. 3:26 The close

    The end card cross-fades into the logo reveal, which has no text. The video ends at 3:34.