# AI Misconceptions - Talking Points Reference

# AI Misconceptions - Talking Points

Air Date: 2026-03-14 | Host: Mike Swanson
Format: ~44 min main show (9 segments) + filler segments available | Pace: ~150 words/min conversational
Main Show -- 9 Segments
1 "Five Years Later" Intro | ~4 min
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

Key Takeaway Not here to say AI is amazing or terrible -- here to explain what it actually IS.
Q&A Bullets (if needed)

- 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

2 "Strawberry Has How Many R's?" Tokenization | ~4 min
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

Key Takeaway AI reads chunks, not letters. Writes poetry, can't count letters.
Q&A Bullets (if needed)

- 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

3 "Confidently Wrong" Hallucination | ~5 min
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"

Key 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 (if needed)

- 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

4 "Your Voice in Three Seconds" Voice Cloning | ~4 min
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)

Key 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 (if needed)

- 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

5 "The AI Therapist Problem" Teen Mental Health | ~5 min
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

Key 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 (if needed)

- 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

6 "Agents of Chaos" AI Agents | ~5 min
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
- 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

Key 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 (if needed)

- 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

7 "Just Say 'Think Step by Step'" Prompting | ~3 min
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

Key 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 (if needed)

- 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

8 "AI Eats Itself" Model Collapse | ~3 min
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

Key Takeaway AI needs human creativity to function. Human originality is the raw material AI depends on.
Q&A Bullets (if needed)

- 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

9 "Nobody Knows How It Works" Black Box / Closer | ~4 min
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.

Key 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 (if needed)

- 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 -- Use If Needed
A "Your Calculator is Smarter Than ChatGPT" Math | ~4 min
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

Key Takeaway AI predicts what a math answer LOOKS LIKE. Doesn't compute. Verify numbers yourself.
B "Does AI Actually Think?" Consciousness | ~4 min
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

Key Takeaway AI doesn't think. It predicts. The words we use shape how much we trust it -- and we're over-trusting.
C "The World's Most Forgetful Genius" Memory | ~3 min
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

Key Takeaway AI isn't building a relationship with you. Every conversation is day one. Attention span shorter than you think.
D "AI Can See But Can't Understand" Vision | ~3 min
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.

Key Takeaway AI sees what's in a photo but doesn't understand the world the photo represents.
E "AI is Thirsty" Energy / Environment | ~4 min
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

Key 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
- Suprmind AI Hallucination Report 2026
- Duke University - Why LLMs Still Hallucinate
- Science - AI Trained to Fake Answers

### Voice Cloning

- Fortune - 2026 Deepfake Outlook
- Brightside AI - $50M Voice Cloning Threat
- UnboxFuture - 1 in 4 Americans Fooled
- McAfee - AI Voice Cloning Scams

### Teen Mental Health

- Stateline - AI Therapy Chatbots and Suicides
- Common Sense Media - AI Unsafe for Teen Mental Health
- NPR - Chatbots Harmful for Teens
- RAND - Teens Using Chatbots as Therapists
- Brown University - 1 in 8 Teens Using AI for Mental Health

### Agents

- TechXplore - Agents of Chaos Research
- CNBC - Silent Failure at Scale
- Help Net Security - AI Agent Security 2026
- International AI Safety Report 2026

### AI Safety / Deception Research

- Apollo Research - Frontier Models Capable of Deception
- Anthropic - Sleeper Agents Research

### General AI Statistics

- DigitalDefynd - AI Statistics 2026
- National University - AI Statistics and Trends