The Modern Therapist's Survival Guide with Curt Widhalm and Katie Vernoy

Are We Really Better at Empathy Than the Bots? What the Research Says About AI in Therapy

AI in therapy, therapy chatbots, and AI empathy: what the latest research actually says, and what it means for your clinical work.

Curt Widhalm, LMFT, and Katie Vernoy, LMFT close out AI month by setting the hot takes aside and looking at what the research actually says about AI in mental health. They walk through the Dartmouth Therabot randomized controlled trial, where an expert-built, safety-monitored therapy chatbot outperformed a waitlist control, and a Communications Psychology study in which third-party raters judged AI-generated responses as more compassionate than those of expert human crisis responders.

The findings are uncomfortable, and that's the point. In short in-the-moment exchanges, AI empathy can read as warmer than a human's, which is exactly the kind of result that makes therapists defensive. Curt and Katie talk through what these studies do and do not show, why a quick hit of empathy is not the same as a real therapeutic relationship, and the safety risks of consumer-grade chatbots that can validate delusions or miss a crisis.

This is a conversation about staying credible instead of fearful: getting literate about AI therapy tools, talking with clients honestly about where these tools help and where they are dangerous, and leaning into the clinical depth, presence, and human connection that AI cannot fake.

In this episode, we discuss:

- What the Therabot trial really shows about AI therapy chatbots: better than a waitlist, not better than a human therapist

- Why third-party raters judged AI as more compassionate than expert crisis responders

- The difference between in-the-moment empathy and the long-term therapeutic relationship

- How sycophancy and "context pollution" can make AI empathy feel good while keeping clients stuck

- The safety risks of consumer-grade chatbots, and the regulation starting to address them

- How to talk with clients about AI in a way that makes you more credible, not less

Timestamps:

00:15 - Welcome and the two traps therapists fall into with AI

06:24 - The Therabot trial: AI therapy chatbots vs. a waitlist control

09:14 - Do people rate AI as more empathetic than human responders?

12:56 - Sycophancy, context pollution, and deceptive empathy

19:40 - Safety, crisis risk, and the regulation that is coming

26:27 - What people actually want in a crisis moment

35:45 - Be credible, not fearful: talking with clients about AI

37:40 - Deliver what AI can't fake: presence and clinical depth

41:36 - Closing: be aggressively human

Full show notes and transcript: mtsgpodcast.com

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How AI Tools for Therapists Are Built: Trust, Data Privacy, and Choosing What to Adopt - An Interview with Ian Knox and Megan Toomey

Ian Knox and Megan Toomey of SimplePractice take therapists behind the scenes of how AI tools for mental health are actually built, trained, and kept secure.

We're in the middle of AI month, and Curt and Katie wanted to move past the buzzwords and talk with the people who actually engineer this technology. As part of the show's partnership with SimplePractice, they sit down with Ian Knox, Chief Product Officer, and Megan Toomey, Sr. Director of Clinical Support AI Product Management, to pull back the curtain on how AI tools for therapists get designed, what "training the model" really means, and how client data is handled.

They get specific about what to evaluate before adopting any AI tool, from vendor trust and HIPAA compliance to data practices, and why note taking is the most mature use case while insurance, scheduling, intake, and referral matching are still emerging. Ian and Megan also take on the fear that AI-first companies want to replace therapists, and explain why clinicians have to stay at the center of care and review anything they put their name on.

This is a grounded, practical conversation for any therapist trying to decide what AI belongs in their practice, and what to be cautious about, without panic or hype.

In this episode, we discuss:

- How an EHR decides which clinician tasks AI is mature enough to help with

- What to evaluate before trusting an AI vendor with client data

- Why "HIPAA compliant" is a floor, not proof of strong security

- What "training the model" does and does not mean, and why SimplePractice says it is not training an LLM on your data

- How transcripts are retained, and the new opt-in for de-identified data

- Why you remain responsible for every AI-assisted note you sign

- How to talk with clients about AI and get meaningful consent

Timestamps:

00:16 - AI month, and why we wanted to talk to people who build AI

01:33 - Who Ian and Megan are

03:21 - SimplePractice's history with AI tools

05:38 - What therapists should know when adding AI to a practice

08:44 - Where clinicians can streamline with AI

12:23 - How to choose which AI features to adopt

16:10 - Why you can't fully trust AI, and best practices

19:29 - What clinicians should know about their data

21:13 - The Trust Center, and "are you here to replace therapists?"

23:55 - How AI systems are trained (the puppy analogy)

27:00 - White-label LLMs and the "golden data set"

29:11 - De-identified transcripts and the opt-in

31:40 - Current tools and the product roadmap

Guest bios:

Ian Knox is Chief Product Officer at SimplePractice, with prior product leadership at Expedia and Microsoft.

Megan Toomey is Sr. Director of Clinical Support AI Product Management at SimplePractice, leading the team building AI tools for behavioral health clinicians, with prior experience at Microsoft and Amazon.

Full show notes and transcript: mtsgpodcast.com

Join the Modern Therapist Community

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Voice Over by DW McCann: https://www.facebook.com/McCannDW/

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When Clients Bring AI to Therapy: Working with ChatGPT, Sycophancy, and the New Third Wheel in the Room

Clients are bringing AI into therapy, from ChatGPT chat logs handed over as homework to AI companions that never disagree, and therapists need a clinical response.

Curt Widhalm, LMFT, and Katie Vernoy, LMFT dig into what happens when clients bring their own AI into the therapy room. The profession built ethics guidance for when therapists send clients toward AI, but it largely sidestepped the reality already showing up in sessions: clients processing the same issues with ChatGPT between appointments, using chatbots to interpret and respond to relationships, and increasingly offloading their social and dating lives onto AI companions.

Drawing on emerging research about chatbot use, loneliness, emotional dependence, and sycophancy, Curt and Katie make the case that concern is more useful than alarm. Clients are already using these tools, so the clinical task is to assess how and why they are using them, provide psychoeducation on sycophancy, confidentiality, and safety, and separate the healthy, adaptive uses from the problematic ones.

They also share concrete in-session moves: entering the opposite position to expose how a chatbot validates any side, reframing how clients use AI for medical and diagnostic questions, and recognizing when AI has become a client's primary support system, along with how to begin weaning a client back toward real-world connection.

In this episode, we discuss:

- The three main ways clients are bringing AI into the therapy room

- Why a client's AI use can be a clinical opportunity, not only a risk

- How to assess the function and pattern of a client's AI use

- What the research suggests about chatbot use, loneliness, and sycophancy

- How to teach critical thinking and protect client confidentiality

- What to do when a client has become dependent on AI

Timestamps:

00:15 - AI as the new "third wheel" in the therapy room

04:15 - Three ways AI is showing up in clients' lives

05:55 - What the research suggests: loneliness, dependence, and sycophancy

10:24 - Why client AI use can be a clinical opportunity

13:24 - Assessing use and treating chatbots as public spaces

18:17 - Explaining AI's mechanics: it is not an objective third party

20:14 - A critical-thinking exercise: enter the opposite position

24:47 - Using AI for medical and diagnostic questions

30:53 - When a client is already dependent on AI

Full show notes and resources: mtsgpodcast.com

Join the Modern Therapist Community

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Voice Over by DW McCann: https://www.facebook.com/McCannDW/

Music by Crystal Grooms Mangano: https://groomsymusic.com/

Why AI Mental Health Chatbots Fail When It Matters Most: The Hidden Vulnerabilities Stress-Testing Reveals - An Interview with Shirali and Arul Nigam of Circuit Breaker Labs

Shirali and Arul Nigam of Circuit Breaker Labs on why AI mental health chatbots fail, how stress-testing exposes their hidden vulnerabilities, and what therapists need to know.

Curt and Katie talk with Shirali and Arul Nigam, the sibling co-founders of Circuit Breaker Labs, about what therapists tend to get wrong about AI, why the safety infrastructure behind many mental health chatbots is weaker than it looks, and how their team stress-tests these tools to find dangerous failures before real users ever encounter them.

Generative AI is probabilistic, so the same prompt can return a safe answer one moment and a harmful one the next. Shirali and Arul explain how guardrails get bypassed by a misspelled word, a teenager's slang, or the hundredth message in a long conversation, why mental health chatbots tend to fail in the moments that matter most, and what stress-testing hundreds of thousands of simulated conversations actually reveals about model safety.

The conversation closes on what clinicians can do now, why clinical insight is the missing ingredient in AI safety, and why third-party validation is becoming the standard regulators and developers expect. Used well, AI can be a supplement to care or a gateway to a human therapist, but it is not a replacement, and getting there safely starts with building clinical insight in from the foundation.



In this episode, we discuss:

- Why people usually turn to AI in place of no care, not in place of a therapist

- Why generative AI's unpredictability, not a single bad answer, is the real safety problem

- How a misspelling, slang, or a long conversation can slip past chatbot guardrails

- Why AI mental health chatbots tend to fail in the highest-risk moments

- What stress-testing hundreds of thousands of conversations reveals about model safety

- Why clinical insight is the missing ingredient, and what clinicians can do now



Timestamps:

0:00 - Introduction

1:38 - Meet Shirali and Arul Nigam and Circuit Breaker Labs

3:37 - What therapists get wrong about AI

5:28 - Deterministic versus generative AI, and why the risk scales

8:26 - The safety problem in AI mental health: trust, training data, and agreeableness

11:29 - Guardrails, lifeguard models, and the 988 problem

17:30 - Deploying clinical insight at scale and building safety in from the start

21:09 - How stress-testing works: context pollution and adversarial simulation

27:11 - What the stress tests reveal: variance, typos, and bypassed guardrails

30:59 - Regulation, credential hallucination, and third-party validation

36:58 - What clinicians can do, and the missing clinical insight

40:20 - Where to find Circuit Breaker Labs



Guest Bios:

Shirali and Arul Nigam are siblings and the co-founders of Circuit Breaker Labs, which autonomously pressure-tests the AI systems that interact with people to find hidden mental health vulnerabilities before they reach real users. Shirali brings expertise in neuroscience, translational research, and clinical work, with experience at the Howard Hughes Medical Institute's Janelia Research Campus, NIH NINDS, Harvard's Wyss Institute, Johns Hopkins, and Children's National. She holds a BS in Biomedical Engineering from The George Washington University and an MBA from The Wharton School, University of Pennsylvania. Arul has conducted technical and policy research on ethical AI, with a focus on bias and fairness, at Georgetown University and Thomas Jefferson High School for Science and Technology, and holds a BSBA in Operations and Analytics from Georgetown University. Learn more at circuitbreakerlabs.ai.



Full show notes and transcript: mtsgpodcast.com



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Voice Over by DW McCann: https://www.facebook.com/McCannDW/

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AI Ethics Codes for Therapists: What CAMFT, APA, NASW, and AAMFT Now Require

How the CAMFT, APA, NASW, and AAMFT ethics codes now regulate AI in therapy: a CE podcourse on competence, informed consent, data privacy, bias, and human accountability.

Curt Widhalm, LMFT, and Katie Vernoy, LMFT, walk through the recent artificial intelligence updates to the major mental health ethics codes. Centering on the new CAMFT updates and cross-referencing the APA, NASW, ACA, and AAMFT codes, they turn the new language into a practical roadmap for everyday clinical work.

The conversation covers what competence with AI actually requires (closer to safely driving a car than building one), the three CAMFT obligations that travel together (disclosure, informed consent, and informed decision making), and a client's right to opt out. They get specific on data privacy, including business associate agreements and why "HIPAA compliant" is a marketing claim rather than a certification, on spotting demographic and medical-model bias in AI output, and on why the human in the loop still owns and is accountable for anything signed under their name.

This continuing education podcourse is for therapists, supervisors, and clinical educators who want to use AI tools ethically and stay accountable in an automated world.

In this episode, we discuss:

- Why the codes aim for broad, durable AI principles instead of tool-specific rules
- What competence with AI actually requires of a clinician
- The three CAMFT obligations: disclosure, informed consent, and informed decision making
- How to vet data privacy, including BAAs and why "HIPAA compliant" is only marketing
- How to catch demographic and medical-model bias in AI output
- Why the clinician remains responsible for anything produced under their name

Timestamps:

- 0:55 - Why the ethics codes are updating for AI
- 3:30 - Defining artificial intelligence (California AB 2885)
- 9:49 - The four Beauchamp and Childress principles
- 12:28 - Timeline: CAMFT, APA, NASW, and AAMFT updates
- 16:11 - Competence and the "driving a car" metaphor
- 21:09 - Disclosure, informed consent, and the right to opt out
- 33:18 - Data privacy, BAAs, and "HIPAA compliant" as marketing
- 41:20 - Bias, social justice, and reading the output
- 44:52 - The human in the loop and accountability
- 53:12 - Is AI right for this client?
- 1:00:59 - Supervision, education, and the AI vetting guide

1 CE unit is available through the Modern Therapist Learning Community: moderntherapistcommunity.com

Full show notes and transcript: mtsgpodcast.com

Join the Modern Therapist Community
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Modern Therapist's Survival Guide Creative Credits
Voice Over by DW McCann: https://www.facebook.com/McCannDW/
Music by Crystal Grooms Mangano: https://groomsymusic.com/