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Hey everyone, Bob here.
Today we have a special episode spotlighting the relationship between AI and mental health.
Recently, OpenAI has faced several lawsuits alleging that its chatbot contributed to suicides or mental health breakdowns.
We're sharing a conversation that I had a few weeks before the suits hit, with two on-the-ground experts Brown University's Ellie Pavlik, director of a new institute dedicated to exploring AI and mental health, and Soraya Darabi, a VC firm TMV, an early investor in mental health AI startups.
Ellie and Soraya talk candidly about the pros and cons of applying AI to emotional well-being from chatbot therapy to AI friendships and romance.
Recorded live at the Innovation at Brown Showcase in Providence, Rhode Island.
The conversation includes themes that are controversial and, for some listeners, potentially distressing, so please take care.
I'm Bob Safian, and this is Rapid Response.
I'm Bob Safian, live at Innovation at Brown Showcase, and I'm here with Brown University's Ellie Pavlik, director of a new institute on AI and mental health based at Brown, and Soraya Darabi, lead partner in VC firm TMV, which has 200 million under management and is a backer of mental health well-being startups, among many other things.
Ellie, Soraya, thanks for joining us.
Good to be here.
Nice to see you, Bob.
So, among the many fields that have been disrupted by generative AI, mental health and the personal relationships we build with AI agents are among the most fraught.
A recent study showed that one of the major uses of chat GPT for users is mental health, which makes a lot of people uneasy.
Ellie, I want to start with you.
The, The new institute that you direct, known as ARIA, which stands for I have to read this to get this right.
I always have to read it too.
The AI Research Institute on Interaction for AI Assistance.
It's a consortium of experts from a bunch of universities, backed by 20 million in National Science Foundation funding.
So what is the goal of ARIA?
What are you hoping it delivers?
Why is it here?
You know, mental health is something that is very, I would say, I don't even know if it's polarizing.
I think many people's first reaction is negative, the concept of AI mental health.
So, as you can tell from the name, we didn't actually start as a group that was trying to work on mental health.
We were a group of researchers who were interested in the biggest, hardest problems with current AI technologies.
What are the hardest things that people are trying to apply AI?
To that we don't think the current technology is quite up for.
And mental health came up and actually was originally kind of taken off our list of things that we wanted to work on, because it is so scary to think about if you get it wrong, like how big the risks are.
And then we came back to it exactly because of this.
We basically realized that, you know, this is happening, like people are already using it.
There's companies that are like startups, some of them probably doing a great job, some of them not.
The truth is we actually have a hard time even being able to differentiate those right now.
And then there are, you know, a ton of people just going to chatbots and using them as therapists.
And so we're like the worst thing that could happen is we don't actually have good scientific leadership around this.
How do we decide what the technology can and can't do?
How do we evaluate these kinds of things?
How do we build it safely in a way that we can trust?
There's questions like this.
There's a demand for answers and the reality is most of them we just can't answer right now, like they depend on an understanding of the ai that we don't yet have, an understanding of humans and mental health, that we don't yet have a kind of a level of um discourse that society isn't up for.
We don't have the vocabulary, we don't have the terms, like there's just a lot that we can't do yet um, to make this happen the right way.
So that's what aria is kind of trying to provide this.
You know uh, public sector, academic kind of voice in to help lead this discussion.
Soraya, like you, you're not waiting for this data to come out or for the final.
You know, whatever academia might say this consortium might say about, you're already investing in companies that do this.
I know you're an early stage investor in Slingshot AI, which delivers mental health support via the app Ash.
Is Ash the kind of service that, you know, Ellie and her group should be wary about?
What were you thinking about when you decided to make this investment?
Well, actually, I'm not hearing that Ellie's weary.
I think she's being really pragmatic and realistic.
In broad brushstrokes, just zooming back and talking about the sobering facts and the scale of this problem.
1 billion out of 8 billion people struggle with some sort of mental health issue.
Fewer than 50 of people seek out treatment.
And then the people who do find the cost to be prohibitive.
That recent study that you cited.
It's probably the one from the Harvard Business Review, which came out in March of this year, which studied use cases of chat GPT.
And their analysis showed that the number one, four and seven out of 10 use cases for foundational models broadly are therapy or mental health related.
I mean-
We're talking about something that touches half of the planet.
If you're looking at investing with an ethical lens.
There's no greater TAM than people who have a mental health disorder of some sort.
We've known the Slingshot AI team, which is the largest foundational model for psychology, for over a decade.
We've followed their careers.
We think exceptionally highly of the advisory board and panel they put together.
But You know, I think what really led us down the rabbit hole of caring deeply enough about mental health and AI to frankly start a fund dedicated to it and we did that in December of last year was really kind of going back to the fact that AI therapy is so stigmatized and people hear it and they immediately jump to the wrong conclusions.
They jump to the hyperbolic examples of suicide.
And yes, it's terrible.
There have been incidents of deep codependence upon suicide chat, GPT or else otherwise whereby young people in particular are susceptible to very scary things.
And yet those salacious headlines don't represent the vast number of folks whom we think will be well-serviced by these technologies.
I mean, Ellie, these tools are moving and changing so fast.
Like, how do you think about what your research can do and sort of what the impact can be?
Or...
Or is it as much about sort of creating guidelines that will help folks like Saroy and the folks who work at Slingshot to navigate?
So there's kind of a few, I would say like lanes in which we're thinking about things that will kind of converge and inform each other, but you can think about separately.
So one is things like guidelines, how to design systems and how to evaluate them.
We're really thinking about like the development process for AI right now, like what is the current process and what about that is not well suited for mental health.
Like I would say, I was initially fundamentally skeptical and one of the thing that's made me more optimistic is the most optimistic in our team about the use of AI mental health are the people who work most closely on mental health, so the, the physicians, the um, people who have spent time working on mental health technology and like there's real potential here right,
The people who are most scared are the researchers, who do basic science and have never thought about mental health.
I also hear from students all the time.
I got an email earlier today with someone who's like I was so excited you work on this because me and my friends have all been talking about what a great like guide these chatbots have been right.
So there is real potential here.
But part of the problem with this development is it's not being developed for that use case, right?
We're building these huge generalist systems, and the same system that's going to be churning out new chemistry compounds is also going to be doing mental health support and is also going to be helping you cheat on your homework, and is also going to be like formatting Excel formulas.
Like what, right?
Like maybe maybe, like we're in an innovative like, maybe that's true, but like we shouldn't take for granted that that's what this looks like.
So I think one thing is just not getting too much in the exploit phase too soon, of being like look, we have a recipe for building big AI systems and it's exciting, and therefore that's what we're doing and we're just going to run with this.
But actually opening the discussion and seeing what do we want AI to do?
What about our system might produce that technology and what won't?
And from that perspective, we're really thinking about these more participatory design kinds of processes, thinking about yeah, what do we want the technology to look like?
Let's not assume it's a chatbot.
Maybe it is.
Maybe it's not.
Like let's actually let's think about this because um, the stakes are too high to just be like let's run with the first thing.
I think having companies trying to do stuff is part of that, right?
Like we can't just sit in a room and like think about it forever.
Like we need to prototype stuff.
There is a basic science development part, right?
So there's, like I said, these big jammerless systems.
The reality is we know so little about them.
Like my own lab's work and a lot of our work is kind of on understanding how language models work, like what's happening inside, because we kind of stumbled upon this.
So when people talk about making them safe or placing guardrails, what we're really doing is guessing and checking right.
We won't really know what's happening.
And we do need scientific leadership in conjunction with stuff being deployed like that needs to happen in real time now.
And then we also need to be like, we're not like, this is not the end of it.
This is not the AI.
And now we basically have this and we either have to make it work or scrap the whole thing, right?
Like We're kind of at the start of stuff, so there needs to be a discussion about what else might AI look like.
What other learning algorithms?
What other architectures?
What other models?
What other interfaces?
There's so many options there.
You said this phrase, we kind of stumbled on this, one, four, and seven uses for ChatGPT.
It's not what it was created for, and yet people love it for that.
It makes me think about 20 years ago, when everybody was freaking out about the fact that kids were on video games all day, and now, because of that, we have Khan Academy and Duolingo.
Fear-mongering is good actually, because it creates a precedence for the guardrails that I think are absolutely necessary for us to safeguard our children from anything that could be disastrous.
But at the same time, if we run in fear, we're just repeating history, and it's probably time to just embrace the snowball, which will become an avalanche in mere seconds.
AI is going to be omnipresent everywhere.
Everything that we see and touch will be in some way supercharged by AI.
So if we're not understanding it to our deepest capabilities, then we're actually doing ourselves a great disservice.
To this point of, yeah, people are drawn to AI for this particular use case.
So on our team in ARIA, like we have a lot of computer scientists who build AI systems but actually a lot of our teams do kind of developmental psychology, core cognitive science, neuroscience.
There are questions to say like, what Why?
Right.
The whys and the hows.
Like what what are people getting out of this?
What need is it filling?
I think this is a really important question to be asking soon.
I think you're completely right.
Fear mongering has a positive role to play.
You don't want to get too caught on it.
And you can point historically to examples of like people freaked out and it turned out OK.
There's also cases like social media, like maybe people didn't freak out enough.
Right.
Like and I would not say it turned out OK.
People can agree to disagree.
And there's you know, there's plus and minuses.
But the point is, these aren't questions that really...
Like we are in a position that we can start asking questions.
You can't do things perfectly, but you can start to, you know, you can run studies.
You can say, what is the process that's happening?
What is it like when someone's talking to a chatbot?
Is it similar to talking to a human?
What is missing there?
Is this going to be okay long-term?
What about young people who are doing this in core developmental stages?
What about somebody who's in a state of acute psychological distress, as opposed to as a general maintenance thing?
What about somebody who's struggling with substance abuse?
Like these are all different questions.
They're going to have different answers.
Again, I think, just I feel very strongly that the one LLM that just is one interface for everything is.
I think a lot is unknown, but I would bet that that's not going to be the final thing that we're going to want, right.
And I think we can do these things carefully if we're having conversations in the open.
And if we're having like healthy disagreement, like you have, companies trying to move forward and people pushing back and regulators and a lot of people in part of the conversation we can make this happen the right way at the right pace.
We don't want fear of everything to break into what I think AI commonly is now, which is like the hypers and the naysayers.
And this prevents any kind of reasonable middle ground progress, because you're basically like with us or you're against us.
And then that's not productive.
But I think we're at a point right now where we can have like a really sober conversation and say we have an opportunity here, but let's not move too fast.
But let's not let fear prevent us from doing anything.
You mentioned, Soraya, that you mentioned kids.
I mean, adults also. are using these tools, right?
I mean, it's not just kids.
Well, in fact, it's not just adults, but it's predominantly men.
And this is super interesting.
So, in addition to Slingshot, we're also investors in a company called Daylight Health, which allows nurses and folks who assist doctors the ability to offer mental health services, which PCPs primary care physicians normally don't have time to do.
But we learn in our research and diligence for that particular investment that the vast majority of men feel too nervous to admit to anyone other than their primary care physician that they're dealing with symptoms of depression and anxiety.
And the most beautiful part about AI therapy if you use it well or technologies like Daylight Health, is that it provides a certain amount of accessibility and acceptance.
I'm curious what you think about AI support in other forms.
I'm thinking about sort of the friendships and even romantic relationships that people have started to develop with AI agents.
You know, when OpenAI released GPT-5 like there was this outcry because thousands of people lost their boyfriends and girlfriends.
Or you know their Reddit groups with thousands of people on them about virtual romantic partners.
Like, is that progress?
Like, is that opportunity for... allowing people to have connection that they crave and need?
Or is it just scary?
Again, I know I'm going to the extremes again, but... I mean, maybe.
I heard someone in Silicon Valley someone high profile say that gun violence is completely out of control in America and maybe what we need to do is get AI romantic companions.
And I mean, that's an extreme example, right, of a particular solve for a terrible epidemic.
But...
You know we can't immediately write off the fact that AI relationships are bad, because it's not what we're normally used to.
I know that I mean I love the movie Lars and the Real Girl because I think that movie in particular addresses the thought of a fake companion so beautifully and so eloquently, in that you know we're all just out there seeking community and companionship, and whatever form it comes in should be socially acceptable.
I agree.
I think you know I'm first and foremost a scientist, so it's like I don't.
There's not a huge value to us speculating on this.
Like, is it good?
Is it bad?
I think there is an instinctive like judgment that comes to it.
Like, oh, that's weird.
But like, like this is, you know, again, this kind of classic, like something is new and different.
The default, because so much of the discourse around AI is kind of like a human.
So we think of it as replacing human companionship.
But a pretty powerful analogy that I think it's also Maybe more like right now is journaling.
And that's actually something that would be great if more people did this personal reflection like actually externalizing your thought process.
And I know some of the people who work on mental health within ARIA have been looking on these kind of AI-assisted journals.
You know a good therapist will do this kind of just get you to think through your own life experiences, to reflect practice gratitude, think about your goals and ambitions, work through something that you might be having trouble voicing to other people.
It's a really valuable process to go through.
So we shouldn't just right away say the process of somebody playing out a fantasy, working through an imaginary scenario, talking while being by themselves in a room is a bad thing.
It could be a positive thing.
If it's replacing other things, it could be a negative thing.
So I think there's again just questions to ask and research that needs to happen to say What is happening.
What are the risks that we should be aware of and start tracking?
And hopefully we can course correct if it's going the wrong direction.
I wasn't quite sure how Ellie and Soraya were going to respond to my questions about AI-based relationships.
The idea makes so many people uncomfortable.
But as they both say, if folks are using it, then we shouldn't just dismiss it.
Still, we do need to understand it.
So how do we judge whether AI is helping or hurting our cognitive state?
We'll talk about that after the break.
Stay with us.
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Before the break we heard Brown's Ellie Pavlik and TMV's Soroya Darabi talk about the rise of AI-based mental health treatment.
Now we explore how AI might change us as humans, the challenges in effectively evaluating AI, mental health tools and more.
Let's jump back in.
Yesterday I was at an event where Siddhartha Mukherjee, the author of the Emperor of All Maladies, was talking.
He's got an AI company called Manus AI.
And he was saying how customer service representatives that are now like becoming AI agents that like, once you're done with your customer service, that agent may say to you so what else is going on?
You know, how else are you doing?
And it's like he's saying it's going to be like this your customer service agent and your therapist are like going to come together.
And I don't know whether, when you hear that, like Ellie's laughing, I don't know whether Saroy is thinking like hmm, is that a market I can like, but are we going to need to train all of the agents that are everywhere to be more understanding about, you know, the mental health impacts, because it's so alluring for us to have that conversation.
Oftentimes, when people talk about AI therapy, some of the big headlines for what one might be concerned about include AI can't be empathetic because it doesn't have real lived experiences.
No, I'm not interested in these add-on experiences and making everyone a bot or everything a bot.
And also...
AI will figure out empathy soon enough, and some argue it already has, because the data of speaking to millions of people about millions of problems will ultimately result in some version of a lived experience.
And through that we can create this inference layer of hopefully, ethics and empathy that may be applied to other things.
So maybe not the customer experience guy, but maybe when you go to the grocery store, at the grocers will be trained on how to speak to you more cordially.
And that might be rooted in data.
And yeah, perhaps that's really cool.
Is there a way you research differently when you're I mean you're looking at sort of the cognitive reactions we're having right?
Is there a way you research that differently because it's an AI or is it sort of, we're humans, AI is not changing the way our brains work.
So like, it's the same?
First, I want to touch on this, this concept of empathy, because I think this is a really good example of what I mean about there being like, questions we want to answer right now and just the levels at which we don't have the scientific understanding to answer them.
We take for granted humans have empathy and lived experience and models don't.
The truth is we don't have really good definitions of these things, right?
What do we really mean by empathy? a word like understand or empathize.
Like when we talk about, like it's important for humans to get empathy from another, like be more specific.
What do they actually need?
Because some of these things we can imagine approximating with AI, some of these things we can't, which is the one that has the positive effect on that person's life.
This is a really good opportunity to not pretend we know more than we do.
You think about something like CBT as a therapeutic device.
There have been successful studies, you know, trials of AI-assisted bots that are basically just reminding you to think through things in a certain way, kind of reprogram your brain.
This doesn't depend on like a deep psychoanalytic like oh yeah no, your mom was totally the one in the wrong there.
Like that's not an important part and can be counterproductive, right?
In other cases, we actually really feel like you can tell a human interaction you have where the person's faking it, versus not and you sometimes filter out the friendships where you're like oh this person's saying things but they don't mean them right and so there's some we need to figure out how important these different things are how much of this is part of how we're raised and programmed and and kind of innate and how much is something that's societally programmed and will drift over time as ai starts to fill a role and we start looking for other types of things so much is on the table so much is unknown so i think it's just impossible to anticipate um anticipate what direction this is going to go.
On your last question, like AI will not change how, I think that's just definitely wrong.
AI is absolutely going to change how we think.
If nothing else, that just knowing that AI is out there as one of the things we might be interacting with will change our expectations.
That's one of the most fascinating whether good or bad from now, looking forward is like how are we, as individuals and as a society, going to change as a result of having this in the mix?
What I'm also hearing Ellie describe is something that we're seeing play out within 911 call centers.
So AI has already basically infiltrated call centers.
So when somebody dials 911, you might not know this, but initially you're being screened.
Are you experiencing a true emergency or a non-emergency?
And now, with the advent of AI, including companies I know well, like Hyper AI, you're able to immediately, within seconds, figure that out, so that the true emergency calls are rooted to real humans and the non-emergency calls are redirected.
My cat's stuck in a tree and a local firefighter might be able to go and help the cat fall out of the tree safely, so that the person who is held up at gunpoint may actually find the kind of emergency response they need.
I think there are perfect parallels within SMEs serious mental illness and, for instance, your example of CBT cognitive behavioral therapy whereby people who just need on repeat the training to work through their day-to-day anxiety can get that access at scale so that we're redirecting the SMEs to people who actually can provide that high touch, empathetic and bespoke concierge service.
There's so much emotion around this topic.
How do we know whether we're making progress?
By far the hardest problem in AI is evaluation.
There might be a small number of things that are easy to evaluate, but all of the things that are really getting people excited about AI is going to disrupt.
These sectors really depend on having good evaluations of, on things that have thus far not even necessarily been quantitative sciences.
Mental health is a really good example.
Education is another one that's a really good example.
Any of this other kind of like having AI help with things like managerial work.
Any of this kind of classic white collar stuff leading teams, coming up with ideas, doing science.
And, like anyone, can pick their field and think about how they're evaluated in their own job or how their children are evaluated in school.
And no one is satisfied with these evaluations.
The truth is, you'll have good quantitative metrics for most things.
We have a lot of proxies and correlates and we know that they make mistakes.
AI really depends on very specific success metrics, which is why we're seeing like progress in code.
AI is very good at writing code because it's pretty easy to evaluate if the code was correct.
And in other cases we're just assuming that because it worked for code and because people who write code think that that's like the top of the hierarchy.
Like if it can do code, surely it can do everything else.
Jokes on them.
I actually think that That was the easiest thing.
Like, by definition, this other stuff is, by definition, harder.
That's why we haven't figured out how to codify it yet or how to quantify it yet.
So, yeah, so for mental health, we're going to have to define success.
It's not going to be.
We labeled a data set on like this was the correct response.
This was the incorrect response.
And have a leaderboard and see who gets to the top and do reinforcement learning against it.
It's not going to be that.
ARIA, a big focus is on this participatory AI approach.
So it's definitely not computer scientists and AI researchers and companies who get to define this and then crank on the metric.
It's going to be a large, messy conversation.
It needs to include people who think AI is absolutely the wrong way to approach mental health, people who are super gung-ho, everyone in between.
Everyone needs to be a little bit involved in this so that we come up with, like what does success look like?
And we really don't know.
Like I think until we've had this really large conversation of what do we want to look like, we just can't really even speculate on what it's going to be, except to say that I will speculate which is it's not going to look like, what current AI evaluation looks like, which is a leaderboard?
When you think about AI's impact on mental health and its potential impact excitement optimism, potential on one side risk fear, you know, sort of destructiveness potentially on the other side.
Where in that spectrum do you feel like we fall?
I'm always a cautious optimist when it comes to technology.
I don't think you could do what we do for a living and not be.
So I'm somewhere just above the middle zone.
And also the coming wave has crashed onto shore.
And if we look at everything through this dystopian lens, which is quite easy to do, we're missing the plot, I feel.
And the plot is it's here, it's happening now.
In this instance, people are using foundation models, Cloud AI, ChatGPT to solve everything from I'm nervous to go to work today to I'm a doctor who needs an AI scribe to help me keep up with my patient load.
And industry is capitalizing on it.
Okay.
And society should also work with folks like Ellie who have the nuanced academic and research perspective on how we can tread water as carefully and safely as possible.
I would say that right now I am optimistic and it's because I think we're still in this narrow window where we get to have a choice to make this happen the right way.
I can imagine myself quickly losing that optimism if I feel like that window closes.
And like the main narrative I hear about AI is how fast things are moving.
So I think right now, you know, it depends on the technology.
It depends on people being sober minded, thinking about it, not being histrionic in positive or negative direction, that we're thinking about this as a whole.
Society like this is all of our problems.
I think we have this window still where we can like get it right.
I want to thank both of you, Ellie and Soraya, for being here and doing this.
Thanks so much.
I went into my conversation with Ellie and Soraya thinking that they might be at odds about AI's role in mental health.
What I find telling is how aligned they are.
We can't ignore that people are using AI for emotional support, yet we also can't ignore that most general AI tools were not built for this specific use.
As Ellie notes, just because foundation models work well for coding, that doesn't mean they're ideal for everything else.
Here's hoping we take the time as a society to examine our assumptions about this new technology, to take it beyond the realm of engineers and entrepreneurs, but also to use engineers and entrepreneurs in that exploration.
These are wide open times, and it's only healthy to rely on each other to make it through.
I'm Bob Safian.
Thanks for listening.
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Every company that scales has that moment.
The one that could have gone either way.
We all remember the choices that shaped the course of our lives.
And in business, world-renowned venture capital firm Sequoia Capital calls them crucible moments.
Their podcast brings you inside the pivotal decisions that have defined some of today's most influential companies.
Tune into Sequoia's new season of Crucible Moments to discover how some of the most transformational companies of the modern era were built.
Crucible Moments is available everywhere you get your podcasts and at cruciblemoments.com.
Rapid Response is a Wait What original.
I'm Bob Safian.
Our executive producer is Eve Troh.
Our producer is Alex Morris.
Associate producer is Mashumaku Tonina.
Mixing and mastering by Aaron Bastinelli.
Our theme music is by Ryan Holiday.
Our head of podcasts is Lital Malad.
For more, visit RapidResponseShow.com.
