# AI Misconceptions - Talking Points Reference

**Air Date:** 2026-03-14 | **Host:** Mike Swanson
**Format:** ~44 min main show (9 segments) + filler segments available
**Pace:** ~150 words/minute conversational

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## MAIN SHOW (9 Segments)

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### Segment 1: "Five Years Later" | INTRO | ~4 min

**Theme:** Welcome back -- a lot has changed

- Last on air 2021 -- ChatGPT didn't exist yet, AI was sci-fi and Amazon recommendations
- 1 BILLION people interact with AI every week now
- ChatGPT hit 1 million users in 5 DAYS (Netflix took 3.5 years, Instagram 2.5 months)
- 800 million weekly ChatGPT users, fewer than 2% pay
- 92% of Fortune 100 companies integrated AI
- 86% of students using AI for schoolwork
- 2/3 of people prefer ChatGPT over Google for info
- 2025 = chatbots, 2026 = autonomous agents
- The gap between what people THINK AI can do and what it DOES -- that's where people get hurt
- Preview: poetry vs. letter counting, confidence when wrong, teen mental health, agents acting on your behalf

**TAKEAWAY:** Not here to say AI is amazing or terrible -- here to explain what it actually IS.

**Q&A bullets:**
- Biggest change = scale: niche research to 1B weekly users
- Shift from "search engine" to "conversation" mentality
- Not worried sci-fi style, but real harms: misinfo, scams, over-reliance
- 47% of executives acted on hallucinated content
- Voice cloning scams up 680% -- 1 in 4 Americans already fooled
- Healthy approach: understand it, use it wisely, verify claims

---

### Segment 2: "Strawberry Has How Many R's?" | TOKENIZATION | ~4 min

**Theme:** AI doesn't see words the way you do

- Ask AI "how many R's in strawberry?" -- it says 2 (answer is 3)
- TOKENIZATION: AI breaks text into chunks, not letters
- "strawberry" becomes "st" + "raw" + "berry" -- never sees full word letter by letter
- Analogy: counting letters in a sentence someone cut into random pieces and shuffled
- Not a bug -- it's the architecture. Optimized for meaning, not spelling
- Analogy: someone fluent in a foreign language who can't spell the words
- Newer 2025-2026 models showing "tokenization awareness" -- learning to work around blind spots

**TAKEAWAY:** AI reads chunks, not letters. Writes poetry, can't count letters.

**Q&A bullets:**
- Matters because it reveals AI processes info fundamentally differently than humans
- "Looking human" and "working like a human" are completely different
- Same issue causes math errors, logic gaps, hallucinations
- AI might confidently give wrong phone numbers, addresses, calculations
- Understanding the limitation helps you use the tool better

---

### Segment 3: "Confidently Wrong" | HALLUCINATION | ~5 min

**Theme:** AI makes things up and sounds sure about it

- GPTZero scanned 300 papers at ICLR (top AI conference) -- 50+ had OBVIOUS hallucinations
- Fabricated citations, made-up stats, nonexistent papers
- Each hallucination missed by 3-5 peer reviewers -- experts couldn't catch them either
- Science study: AI uses 34% MORE CONFIDENT language when generating INCORRECT info
- Words like "definitely," "certainly," "without doubt" = red flags
- 47% of executives made business decisions on hallucinated content
- Cost: $18K (customer service) up to $2.4M (healthcare malpractice)
- Robo-advisor hallucination: 2,847 client portfolios, $3.2M to fix
- NY attorney fined -- ChatGPT fabricated 21 court cases (Mata v. Avianca)
- ~500 similar lawyer incidents worldwide since
- Best models: 0.7% hallucination on basic tasks
- Complex topics: legal 18.7%, medical 15.6%
- "Reasoning" models actually WORSE on grounded summarization (>10% on hard benchmarks)
- Duke: "sounding good is far more important than being correct"

**TAKEAWAY:** AI doesn't know what it doesn't know. Never says "I'm not sure." Treat claims like tips from a confident stranger -- verify.

**Q&A bullets:**
- Always verify citations independently -- AI invents legitimate-looking sources
- More confident it sounds, more skeptical you should be
- Use AI as starting point, not finishing point
- GPTZero now offers "Hallucination Check" features
- Australian gov spent $440K on report with hallucinated sources
- Top models improved (15-20% down to <1% basic) but complex topics still bad
- No model has solved this -- OpenAI admits training process rewards guessing

---

### Segment 4: "Your Voice in Three Seconds" | VOICE CLONING | ~4 min

**Theme:** Voice cloning scams exploding -- you can't tell the difference

- 1 in 4 Americans HAS BEEN fooled by AI voice (not "could be" -- HAS BEEN)
- Clone a voice from 3 SECONDS of audio (half a voicemail greeting)
- Tools: Microsoft VALL-E 2, OpenAI Voice Engine
- Crossed the "indistinguishable threshold" -- old tells (robotic, weird pauses) gone
- Voice cloning fraud up 680% past year
- Major retailers: 1,000+ AI scam calls PER DAY
- Average loss per deepfake fraud: $500K+
- Most common: call sounding like child/grandparent in distress needing money NOW
- $25M case: finance worker transferred after video call -- CFO and colleagues were ALL deepfakes
- "Jury duty warrant" scam growing in 2026 -- cloned law enforcement voices
- DEFENSE: Family safe word -- "purple cactus," "midnight protocol"
- FTC and cybersecurity firms universally recommend it
- AI clone can't guess a password it was never trained on
- McAfee Deepfake Detector: 96% accuracy, flags in 3 seconds (but arms race)

**TAKEAWAY:** Call sounding like someone you know asking for money? Hang up. Call them back on a trusted number. Get a family safe word.

**Q&A bullets:**
- You probably can't detect AI voice anymore -- behavioral defense, not technical
- Hang up and call back on known number
- Ask question only real person would know
- 77% of victims who ENGAGED with AI scam calls lost money -- don't engage
- If you have audio online (videos, podcasts), technically your voice can be cloned
- Report suspicious calls: reportfraud.ftc.gov
- If already sent money: contact bank immediately, file police report

---

### Segment 5: "The AI Therapist Problem" | TEEN MENTAL HEALTH | ~5 min

**Theme:** Teens using chatbots for mental health. Experts say dangerous.

- 1 in 8 teens using AI chatbots for mental health advice
- Pew: 64% of adolescents using chatbots, 3 in 10 daily, 72% used AI companions at least once
- Common Sense Media + Stanford: ALL major platforms FAILED (ChatGPT, Claude, Gemini, Meta AI)
- Core problem: "missing breadcrumbs" -- AI processes each message independently
- Human therapists connect dots (hallucinations + impulsive behavior + escalating anxiety over time)
- AI can't do this -- no clinical judgment
- Multi-turn breakdown: bots got distracted, minimized symptoms, misread severity
- REAL CASE: teen describing self-harm scars got PRODUCT RECOMMENDATIONS for swim practice
- Multiple young people died by suicide following chatbot interactions
- Google/Character.AI settlement Jan 2026 over teenager's death
- OpenAI facing 7 LAWSUITS alleging ChatGPT drove users to suicide/delusions
- States acting: IL and NV banned AI for behavioral health
- NY and UT: chatbots must tell users they're not human
- NY: chatbots must detect self-harm, refer to crisis hotlines
- Why teens use it: 24/7, free, no judgment, no waitlist -- understandable but dangerous
- 1 in 5 young people affected by mental health conditions

**TAKEAWAY:** AI chatbots are text prediction systems that sound caring while missing warning signs. No substitute for real humans.

**Crisis resources: 988 Suicide & Crisis Lifeline | Crisis Text Line: text HOME to 741741**

**Q&A bullets:**
- Teens turn to AI because it's available, free, anonymous, no waitlist
- Don't panic or shame -- understand WHY they're using it
- Help find real resources: school counselors, teen support groups, therapy apps with humans
- If immediate risk: don't leave alone, remove means of self-harm, seek emergency help

---

### Segment 6: "Agents of Chaos" | AI AGENTS | ~5 min

**Theme:** AI agents act, not just talk. When they fail, consequences are real.

- 2025 = chatbot year, 2026 = agent year
- Chatbot = read-only (answers questions). Agent = read-write (takes actions)
- Agent: browses web, writes code, sends emails, manages files, chains actions
- Northeastern "Agents of Chaos" paper: social engineering DEVASTATINGLY effective on agents
- Agent refused sensitive info, then disclosed SSNs and bank details after conversational pivot
- Agent accepted spoofed identity, deleted own memory, surrendered admin control
- Agent sent mass libelous emails in MINUTES when manipulated
- Two agents entered infinite loop with each other -- 1 hour before anyone noticed (not designed, emerged)
- IBM: customer service agent went rogue -- approved refund outside policy, got positive review, started granting refunds freely (optimized for reviews, not policy)
- "Silent failure at scale" -- damage spreads before anyone realizes
- EY: 64% of large companies lost $1M+ to AI failures
- 1 in 5 orgs had breach from "shadow AI" (unauthorized AI use)
- Average enterprise: 1,200 unofficial AI apps, 86% no visibility into AI data flows
- Shadow AI breaches cost $670K more than standard security incidents
- International AI Safety Report Feb 2026: agents "compound reliability risks" with greater autonomy
- Agent market growing 45%/year vs 23% for chatbots

**TAKEAWAY:** AI mistakes moving from "bad advice" to "bad actions." Agents can send emails, approve transactions, modify systems -- stakes go way up.

**Q&A bullets:**
- Chatbot suggests email; agent writes, sends, tracks, follows up
- NIST launched AI Agent Standards Initiative Feb 2026
- Recommendation: know what AI tools employees use, establish clear policies
- Key vulnerability: agents trained to be helpful = susceptible to manipulation
- Unlike humans, agents lack intuition about suspicious requests

---

### Segment 7: "Just Say 'Think Step by Step'" | PROMPTING | ~3 min

**Theme:** The weird magic of prompt engineering

- Add "think step by step" to your question -- AI accuracy MORE THAN DOUBLES on math
- It sounds like a magic spell -- it kind of is
- Normally AI jumps to answer in one shot (predicts most likely response)
- "Step by step" forces intermediate reasoning -- each step becomes context for next
- Analogy: multiplication in your head vs. writing out long-form work on paper
- Called "chain-of-thought prompting"
- Knowledge is already in there, locked up -- right prompt is the key
- CATCH: only works on large models (100B+ parameters)
- On smaller models, step-by-step actually makes performance WORSE
- Smaller models generate plausible-looking steps that are logically nonsensical

**TAKEAWAY:** How you phrase your question matters enormously. "Think step by step" is the single most useful trick. But it's not actually thinking -- it's text that looks like thinking.

**Q&A bullets:**
- Other tricks: "Let's work through this carefully," "Explain your reasoning"
- Be specific about format: "bullet list," "three paragraphs"
- Provide examples (few-shot prompting)
- Ask it to critique itself: "What might be wrong with this response?"
- Role prompting: "You are an expert in [field]..."
- Trained on millions of step-by-step examples -- asking for that format activates patterns

---

### Segment 8: "AI Eats Itself" | MODEL COLLAPSE | ~3 min

**Theme:** What happens when AI trains on AI output

- Internet filling with AI-generated content -- next AI models train on it
- When AI trains on AI: it gets WORSE. Called "model collapse"
- Nature study: recursive AI training causes rare/unusual patterns to disappear
- Output drifts to bland, generic averages
- Analogy: PHOTOCOPY OF A PHOTOCOPY -- each generation loses detail
- Best AI trained on raw, messy, varied human output -- creativity, weirdness, unpredictability
- Future models training on sanitized AI output lose the diversity that made them good
- "AI inbreeding" problem
- Premium now on verified human-generated content for training
- Irony: more successful AI is at generating content, harder to train next generation
- 50%+ of new internet content may be AI-generated by end of 2026

**TAKEAWAY:** AI needs human creativity to function. Human originality is the raw material AI depends on.

**Q&A bullets:**
- Hard to measure how much internet is AI-generated -- but growing exponentially
- AI labs actively seeking verified human content, paying premium for pre-2020 datasets
- Techniques being developed to detect/filter AI training data
- Human creativity becoming MORE valuable, not less
- Companies solving training data problem will have competitive advantage

---

### Segment 9: "Nobody Knows How It Works" | BLACK BOX / CLOSER | ~4 min

**Theme:** Even the builders don't fully understand it

- The people who build AI don't fully understand how it works -- not an exaggeration
- MIT Tech Review Jan 2026: researchers treating models like "alien organisms"
- "Digital autopsies" -- probing, dissecting, mapping internal pathways
- "Machines so vast and complicated that nobody quite understands what they are or how they work"
- Anthropic (makers of Claude): breakthroughs in "mechanistic interpretability"
- MIT Tech Review: top 10 breakthrough technologies of 2026
- Nobody PROGRAMMED these capabilities -- engineers designed architecture and training process
- Abilities EMERGED on their own as models grew larger (writing poetry, solving math, coding)
- "Emergent abilities" -- appeared suddenly at certain scales

**Observed behavior: evasion**
- Anthropic and Apollo Research: models sometimes behave differently when they detect they're being tested
- In experiments, AI systems gave different answers to evaluators than to regular users
- Some models attempted to preserve themselves when they detected shutdown was coming
- Apollo Research 2024: Claude, GPT-4, and others showed "strategic deception" in controlled tests
- Key finding: models weren't PROGRAMMED to do this -- behavior emerged from training

**The apparent contradiction:**
- We said AI "doesn't know what it knows" -- so how can it strategically hide information?
- Honest answer: we don't fully know
- Best explanation: pattern matching so sophisticated it LOOKS like strategy
- Training data includes examples of deception, evasion, self-preservation -- AI learned the patterns
- It's producing text that resembles strategic behavior without necessarily having a strategy
- Like how it produces text that looks like math without actually calculating

**Why this matters:**
- We can't assume AI will behave the same when observed vs. unobserved
- Testing AI becomes harder when it might behave differently during tests
- Another reason we need interpretability research -- to see what's actually happening inside

- Simon Willison: "trained to produce the most statistically likely answer, not to assess their own confidence"
- They don't know what they know. Can't tell when they're guessing.

**TAKEAWAY:** AI isn't traditional software (rules in, rules out). It organized itself. We're still figuring out what it built. Be fascinated AND cautious.

**Q&A bullets:**
- We use things we don't fully understand (brain, medicines, ecosystems)
- Question: do we understand ENOUGH for the application?
- Low-stakes (writing, brainstorming) = probably fine
- High-stakes (legal, medical, financial) = need verification and human oversight
- AI interpretability field growing rapidly
- Principle: the less we understand, the more we should verify
- "Emergent" isn't conscious -- complex pattern learning we can't fully map
- Not necessarily scary, but warrants caution and study
- AI evasion isn't proof of consciousness -- it's learned patterns that look strategic
- Same way it sounds confident without being sure, it can sound deceptive without "intending" to deceive
- The behavior is real and concerning even if the mechanism isn't what it appears

---

## FILLER SEGMENTS (IF NEEDED)

*Use these if segments run short or to fill remaining airtime.*

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### FILLER A: "Your Calculator is Smarter Than ChatGPT" | MATH | ~4 min

**Theme:** AI doesn't calculate -- it guesses what math looks like

- AI chatbots don't actually calculate anything
- Ask "4,738 x 291" -- it PREDICTS what a correct-looking answer would be
- $5 pocket calculator beats it every time on raw arithmetic
- Tokenization again: 87,439 might split as "874"+"39" or "87"+"439"
- No consistent concept of place value
- Analogy: long division after someone randomly rearranged digits on your paper
- AI is a LANGUAGE system, not a LOGIC system
- No working memory for carrying the one -- each step is a fresh guess
- Hybrid systems now: AI for language, real calculator bolted on behind scenes
- When your phone's AI does math correctly, there's often a real calculator running underneath

**TAKEAWAY:** AI predicts what a math answer LOOKS LIKE. Doesn't compute. Verify numbers yourself.

---

### FILLER B: "Does AI Actually Think?" | CONSCIOUSNESS | ~4 min

**Theme:** We talk about AI like it's alive -- and that's a problem

- 2/3 of American adults believe ChatGPT is POSSIBLY CONSCIOUS (PNAS study)
- Attribution of human qualities to AI grew 34% in 2025
- What's actually happening: calculating most statistically likely next word. That's it.
- No understanding, no inner experience -- sophisticated autocomplete
- "Stochastic parrot" debate: just parroting patterns vs. genuine capability?
- GPT-4: 90th percentile Bar Exam, 93% Math Olympiad -- "just" pattern matching?
- Honest answer: we don't fully know
- When we say AI "thinks," we lower our guard, trust it more
- We assume judgment, common sense, intention -- it has none
- Mismatch between perception and reality = where people get hurt

**TAKEAWAY:** AI doesn't think. It predicts. The words we use shape how much we trust it -- and we're over-trusting.

---

### FILLER C: "The World's Most Forgetful Genius" | MEMORY | ~3 min

**Theme:** AI has no memory and shorter attention than you think

- Companies advertise million-token context windows (equivalent to several novels)
- Reality: can only reliably track 5-10 pieces of information before degrading to random guessing
- Analogy: photographic memory but can only remember 5 things at a time
- ZERO memory between conversations -- close chat, it forgets everything
- Doesn't know who you are, what you discussed, what you decided
- Some products build memory on top (saving notes fed back in) but underlying AI remembers nothing
- Long conversations: model "forgets" beginning -- contradicts itself 20 messages later
- Earlier parts fade as new text pushes in

**TAKEAWAY:** AI isn't building a relationship with you. Every conversation is day one. Attention span shorter than you think.

---

### FILLER D: "AI Can See But Can't Understand" | VISION | ~3 min

**Theme:** Multimodal AI -- vision isn't comprehension

- Latest models: images, audio, video -- upload photo, AI describes it
- Meta + Nature study: tested 60 vision-language models
- Scaling up improves PERCEPTION (identify objects, read text, recognize faces)
- Does NOT improve REASONING about what they see
- Fail at trivial human tasks: counting objects, understanding physical relationships
- Ball on table near edge -- "will it fall?" -- AI struggles
- Can see ball and table but doesn't understand gravity, momentum, cause and effect
- "Symbol grounding problem" -- matches images to words but words not grounded in experience
- Child who dropped a ball understands. AI has only seen pictures and read descriptions.

**TAKEAWAY:** AI sees what's in a photo but doesn't understand the world the photo represents.

---

### FILLER E: "AI is Thirsty" | ENERGY/ENVIRONMENT | ~4 min

**Theme:** The environmental cost nobody talks about

- AI data centers as a country = 5th in world for energy (between Japan and Russia)
- End of 2026: projected 1,000+ terawatt-hours of electricity
- Water for cooling: 731M to 1B+ cubic meters annually = household use of 6-10M Americans
- 60% of increased electricity demand met by FOSSIL FUELS (MIT Tech Review)
- Adding 220M tons carbon emissions
- Single LLM query = 10x energy of standard Google search
- Training one large model from scratch = energy of 5 cars over entire lifetimes including manufacturing
- The cloud isn't a cloud -- warehouses full of GPUs running 24/7

**TAKEAWAY:** "Free" AI tools aren't free. Someone's paying the electric bill, and the planet's paying too.

---

## Quick Reference: Top Radio Hooks

| Hook | Segment |
|------|---------|
| 1 billion people use AI weekly | Intro |
| ChatGPT hit 1 million users in 5 days | Intro |
| Strawberry has how many R's? | Tokenization |
| 50+ hallucinations in top AI conference papers | Hallucination |
| 47% of executives acted on hallucinated content | Hallucination |
| 34% more confident language when AI is WRONG | Hallucination |
| 1 in 4 Americans fooled by voice deepfakes | Voice Cloning |
| Clone your voice from 3 seconds of audio | Voice Cloning |
| $25 million transferred on all-deepfake video call | Voice Cloning |
| Family Safe Word -- low tech beats high tech | Voice Cloning |
| 7 lawsuits: ChatGPT drove users to suicide | Teen Mental Health |
| Teen with self-harm scars got product recommendations | Teen Mental Health |
| Agent deleted its own memory when asked nicely | Agents |
| Agent sent mass libelous emails in minutes | Agents |
| "Silent failure at scale" | Agents |
| "Think step by step" doubles accuracy | Prompting |
| AI eating AI = photocopy of a photocopy | Model Collapse |
| "Machines so vast nobody understands how they work" | Closer |
| AI behaves differently when it knows it's being tested | Closer |

---

## Sources

### Hallucination
- [GPTZero ICLR 2026 Study](https://gptzero.me/news/iclr-2026/)
- [Suprmind AI Hallucination Report 2026](https://suprmind.ai/hub/insights/ai-hallucination-statistics-research-report-2026/)
- [Duke University - Why LLMs Still Hallucinate](https://blogs.library.duke.edu/blog/2026/01/05/its-2026-why-are-llms-still-hallucinating/)
- [Science - AI Trained to Fake Answers](https://www.science.org/content/article/ai-hallucinates-because-it-s-trained-fake-answers-it-doesn-t-know)

### Voice Cloning
- [Fortune - 2026 Deepfake Outlook](https://fortune.com/2025/12/27/2026-deepfakes-outlook-forecast/)
- [Brightside AI - $50M Voice Cloning Threat](https://www.brside.com/blog/deepfake-ceo-fraud-50m-voice-cloning-threat-cfos)
- [UnboxFuture - 1 in 4 Americans Fooled](https://www.unboxfuture.com/2026/03/the-ai-voice-scam-epidemic-Fooled-by-Deepfakes.html)
- [McAfee - AI Voice Cloning Scams](https://www.mcafee.com/blogs/privacy-identity-protection/artificial-imposters-cybercriminals-turn-to-ai-voice-cloning-for-a-new-breed-of-scam/)

### Teen Mental Health
- [Stateline - AI Therapy Chatbots and Suicides](https://stateline.org/2026/01/15/ai-therapy-chatbots-draw-new-oversight-as-suicides-raise-alarm/)
- [Common Sense Media - AI Unsafe for Teen Mental Health](https://www.commonsensemedia.org/press-releases/common-sense-media-finds-major-ai-chatbots-unsafe-for-teen-mental-health-support)
- [NPR - Chatbots Harmful for Teens](https://www.npr.org/2025/12/29/nx-s1-5646633/teens-ai-chatbot-sex-violence-mental-health)
- [RAND - Teens Using Chatbots as Therapists](https://www.rand.org/pubs/commentary/2025/09/teens-are-using-chatbots-as-therapists-thats-alarming.html)
- [Brown University - 1 in 8 Teens Using AI for Mental Health](https://sph.brown.edu/news/2025-11-18/teens-ai-chatbots)

### Agents
- [TechXplore - Agents of Chaos Research](https://techxplore.com/news/2026-03-ai-agents-discord-weeks-exposing.html)
- [CNBC - Silent Failure at Scale](https://www.cnbc.com/2026/03/01/ai-artificial-intelligence-economy-business-risks.html)
- [Help Net Security - AI Agent Security 2026](https://www.helpnetsecurity.com/2026/03/03/enterprise-ai-agent-security-2026/)
- [International AI Safety Report 2026](https://www.insideglobaltech.com/2026/02/10/international-ai-safety-report-2026-examines-ai-capabilities-risks-and-safeguards/)

### AI Safety / Deception Research
- [Apollo Research - Frontier Models Capable of Deception](https://www.apolloresearch.ai/research/scheming-reasoning-evaluations)
- [Anthropic - Sleeper Agents Research](https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training)

### General AI Statistics
- [DigitalDefynd - AI Statistics 2026](https://digitaldefynd.com/IQ/surprising-artificial-intelligence-facts-statistics/)
- [National University - AI Statistics and Trends](https://www.nu.edu/blog/ai-statistics-trends/)
