Data, Trust, & Humanity with Baratunde Thurston | Tableau Conference 2026

First, I just want to introduce you to Baratunde Thurston. He is a comedian, a writer. He's done a TV show, so TV personality. He has America Outdoors. He also has Life with Machines. And he also just got a Webby Award recently, which is pretty phenomenal. I want to just welcome you. So everybody, please give a hand to—

What's up, Data fam?

We got to hang out backstage.

Yeah.

And again, the first thing that really hit me home is that you had America Outdoors, and then you have Life with Machines, which is such a juxtaposition. So you're outdoors and then talking about AI. So I'd love to understand that journey for you.

Thank you for having me. Thank you, Salesforce. Thank you, San Diego. Thank you, planet Earth, without which we are nothing but specks floating in space. I believe that we should have a good future. And the goal of life with machines is to help us figure out how to live well with tech, but not just endure it. I think the things that we care about more than machines is life.

Yeah.

Right? We want a better life for our kids. We want a better life for our family and for ourselves. And I think in order to have that, we have to be in a good relationship with each other as humans and with the Earth. So there's no future of tech without nature along for the ride, at a minimum. I also think that we're doing a lot of reaching for the cloud, but we also can keep our feet on the ground. And there's something— I think we both grew up doing a lot of tech things and had parents who were into technology. And when you mess with technology, physically, it's helpful to be grounded, literally.

Yeah.

And so being in nature, making that show was a grounding, physical, emotional, spiritual experience. And a lot of the tech that we're dealing with, even though it feels ephemeral and virtual, it all lives in some embodiment. It's in a data center somewhere. It's on a chip somewhere. It's on a solid-state drive somewhere on the planet.

Yeah.

Using resources, adding to or subtracting from this experience of life. So I'm very excited about technology, and I'm very committed to actually living and enjoying the experience of life. And so these two really live in the same sort of interdependent ecosystem. And I say it as much to myself as anyone else because I can get so caught up in the speed of what's happening with tech. And Mother Nature got her own pace. And at the end of the day, we answer to her.

Yeah, definitely. Can you maybe give a little background to how you got into tech? I think that would be a good starting foundation for people that are starting to learn this AI space. I'd love to hear how that happened for you.

I give credit and blame to my mother, Arnita Lorraine Thurston, no longer physically with us, but I try to carry as much of her as I can. I was born in Washington, DC, '77. She was born there in 1940. And this was a Black woman, single Black woman with no college degree who worked really hard to try to balance the story she was born into with something more expansive and free that her kids could fit into and that the whole community we were a part of. So that meant a big commitment to nature, a big commitment to community and politics hand to hand, you know, sort of peer democracy and tech. And that's the wrinkle for a lot of people is that this woman figured out, she worked for the federal government like a lot of good people in DC do. Chocolate City is just full of folks like my mom working for the government to make sure our checks show up on time, you know, that our services are actually functioning. And she figured out how to become a computer programmer in the early 1980s. While I was in school as a middle school student, I went to community college classes with my mother.

Wow.

While she was learning to advance herself at work. So we both had homework.

Wow.

And then my mother would— she liberated excess inventory from the federal government, let's call it that, because I don't know the statute of limitations. Yeah. But she ended up bringing like a Wang or a Tandy computer home. And I started tinkering. And I started messing with the code. And she was in Apple, it was called Washington Apple Pie, a computer user group. So we would go to these physical meetups of tech enthusiasts. And it's like this Black woman with an Afro, this little Black boy with an Afro, and a bunch of mostly white dudes with a lot more gray than either of us at the time. And it was so much fun. So that set the stage for my exploration. And then through my life, tech has always been this, partner, this, like, creative tool, and this source of and force of power to reckon with in the rest of our lives.

It feels like you had this curiosity.

Oh, I was a nosy little kid.

Yeah, it's interesting because it feels like you had the influence of your mom, for sure. But then you yourself really saw this interesting opportunity to some extent too. Now, in the dawn of AI, And I think it's in the same pattern that we're seeing here even at the conference. People are starting to learn this technology at their fingertips. But there's still this hindrance of trying to jump into it. Does it take curiosity? What does it take to really get motivated to be part of this?

Oh, man. It takes a high tolerance for pain. It takes curiosity. It takes patience paired with impatience. I think we're in an extraordinary time and an overwhelming time. And I think one of the biggest mistakes that I can make, or almost anyone can make, is to not honor that, the significance of what is happening right now. Yeah. It's big because technology isn't just technology. It's the way we experience life.

That's right.

So it's not its own industry. It's lateral. It's across every industry, and it's across our bodies. Look, I'm smothered in technology right now. And it's being tracked and monitored. There's many cameras. So we're in it. And it's, in some cases, in us. And the speed of AI development is a significant factor that we are not— I don't think we were programmed for as an evolutionary outcome. So one, it just takes honoring the significance of the moment. The way that I like to think about it, and our team has come up with this framework, the 3 A's, how to approach AI without losing your— S, right? Accelerate, augment, accommodate. And these are questions we could face this moment with. Accelerate is the assumption. AI makes things faster.

Yeah.

Do you want this thing to go faster? That's the question.

Right.

What parts of your life, your work, your business, your relationships do you want to speed up?

Yeah.

What might you want to slow down? Right. And then exercise that choice. That's got more agency to it than just trying to keep up.

Yeah.

Augment, as in augment, not replace. So how can I approach this not from the spirit of, how can I get this automated system to do exactly what I already do, but faster?

Right.

There's some value in that. Efficiency is a good thing.

Of course.

In doses.

Yes.

But augment, what can this automated system help me do that I couldn't do without it? What can it unlock in me that I might not have even known was there, was underdeveloped? How can it expand me, not just do exactly what I do? And then accommodate— fascinating idea here is like, how do we deploy these systems in ways that can accommodate for those who weren't included in previous versions of our systems? And that could look like language inclusion. That could look like inclusive design of an interactive experience. Which doesn't require AI, but what AI can be very helpful at. And so for me, it's— these are soothing approaches as opposed to there's been yet another product drop or service announcement.

Right.

How do I learn it as fast as possible and put it to use so I can maintain my relevance?

Yeah.

And trying to play catch up with a system built for speed that's already beyond human. I think it's a losing battle.

Yeah.

But I also don't think battle needs to be the framework that we approach this with to begin with.

I agree.

Yeah.

You know, it's, it's funny. I remember the day that I was able to test AI at my fingertips was with ChatGPT.

Yeah.

And I remember vividly I was trying to build a website and I was struggling with finishing some of the elements of like content creation, like putting my bio in there, writing the product descriptions and all that. And then someone had told me, okay, there's this tool that you can now use And it can basically help you do all this. And so I, of course, that night tried it out and finished my website within 24 hours. There was this magic moment where I was like, whoa, this tool is that powerful that can do it for me.

Yeah.

And I'm really curious for everybody in the room, has everyone had that moment where you can see what AI can actually do for you? I'd love to see a show of hands of like, we're starting to see it could actually really expedite the work that you're doing or the things that you need to get done. Do you remember that moment for yourself?

I do. I've had many, but there was a recent one where we have a small team, and we were struggling with a business problem, actually, which is not always my orientation, but sometimes you're the artist, sometimes you're the entrepreneur trying to make the art move. And so in this case, this is the entrepreneur struggling with some of our Instagram growth and trying to figure it out.

Yeah.

And so I had a session with my boy, well, not my boy, my they, Claude, my entity, Claude and I on a Sunday afternoon. We all have our names for our AI. To look into what we've been publishing, how it's performed. Like, just give it, just throw CSVs at it, right? And it can take it way more than throwing CSVs at me could handle. And came out with this action plan, with this data-informed synthesis and analysis that felt so on point and so good that I brought that to the whole team. I mean, it was a wordy, it was like a 20-page report at the end of the day. And I kind of dumped this on the team. The good news is there was a lot of value in it and it blew my mind. And I was like, oh, I think we have like a different member of the squad here. The bad news is the way I did it, I didn't check in with the person whose responsibility it was to run that Instagram strategy. And she pointed out all kinds of—

Right.

Flaws in the thing, but me, non-expert, thinking I just vibe code that future.

Yeah, exactly.

No, you're welcome.

Right.

And she's like, actually, it was a great well, actually moment for her and for me. Yeah, it was humbling. So I felt the magic of it and I felt the caution of, okay, how do we bring this into the existing human relationship? So now it's me and Claude and this team member, and we're better than any of us alone.

100%. I think people—

Also, the other— this is a really short one.

Yeah, yeah.

My cocktail game is through the roof. I make— my drinks are so—

I love it.

Like, they were good before, I'm not gonna lie.

But now they're even better.

But I'm a proper mixologist now.

Yeah, okay.

And I will make some custom stuff if you come over.

I love that. You call out something that I think is so important in this new world. I think that there is this fear that they think that AI is gonna take over from a human job. But what you just said is very, very evident. And we're seeing this also with our customers and partners as well too, is that AI is a helper. It's in addition, it's augmentation, as you said, in the 3 A's, right? It's not taking the job completely over because you still need people to really look to see if this is accurate, relevant, the right things that you want to actually execute on that strategy. And it sounds like that's your new magic, you know, sauce to make it happen.

It's a big part of it.

And we—

I don't think— and I work with words for a living, so I'm going to say this in a way that is meant to be invitational, not condemning. But I think the way we use the words, the way we talk about the world, shapes the world as well. We're in this dance with our language and the reality that we live in. So to say and to think AI is taking a job, that sentence is doing a lot of work. And it's putting AI in a position that it doesn't currently have. So even when people are losing jobs and other people are causing that to happen, so like humans are laying people off and blaming AI, that's like the accurate headline. And that also doesn't have to be the case, but I do think it's important for us to maintain our own agency and choice. There's this poem I riffed about AI a while back. When the answer to every question can be generated in a flash, then it's time for us to question just what we want to ask. And so there's power in that. Like, we're wielding these magic wands. To what end? With whom? And so let's maintain our sense of purpose and priority in this. And it's not to minimize the impact of the deployment of AI on people and on work. That is happening. Those impacts are real. And so for those of us who still have jobs, whether we made them up ourselves or whether we were selected into them through a rigorous process or something in between, it's still a relevant question. What does AI mean for me in this job?

Yeah.

And then you can think about it in some slower approaches. You can think about it collectively, not just alone, in terms of how you are— if we're all trying to figure this out by ourselves, right? And there's a great temptation to do that, to stay up all night.

Yeah.

Try to read the whole internet, right? I finished it once, but it was like 1992. That was the last time I got to the end of the internet. And so just understand it all, play with all the tools and all the models, and come to the perfect conclusion, "Ah, I found a way to save me." Then there's still your kid, your spouse, your coworker, right? We're in this boat together. So going through this in community and then finding those inspirational paths 'Cause I think it dampens the fear and the sense of powerlessness. And then it can grow the opportunity. 'Cause for me and my company, my example, it was a good mistake to make. I would do it all over again. Because then it means for the team, whenever I'm about to go do some solo quest, and sometimes you just gotta do a side quest, I then share it with the humans.

Right, to learn.

And I'm like, "All right, so I've been playing with this thing." How could this be more useful to you?

Right.

And it's help— but I have to choose that because some of the orientation is like, this can do what they can do, so you don't need them anymore. It's like, well, that's minimalist thinking. That's like squeezy thinking. It's extract the most from what you have.

Right.

But what if instead it's like, what can we grow together?

Right.

What could we build that was impossible for us to build before this moment? Yeah. And when I tap the team with that kind of question, Everybody lights up and it gets more exciting because we're creating.

Yes. I've seen that exact thing with my marketing team as well too. It's like now we have this tool at our disposal that what could we not have done before?

Boom.

That now we have access to and the time and the resourcing to make it happen. So it's been really this nice unlock of like, it's maybe it's doing some jobs that we wanted to get done every single day, but now it's allowing us to do something even beyond what we could have done before, which is really exciting. Yeah. So you have said this. That we don't have a data problem, we have a people problem. Can you tell me what that means?

I think sometimes we think the answer to our problems lies in a thing. Electricity, the computer, you know, we threw laptops at kids. First of all, you shouldn't throw computers, but we thought, not all of us, but there was a collective momentum to say, if we just get computers in the hands of a child, that that will fix our education system. It's not enough. Kendrick said it's not enough. And so we got to go deeper. And data is often treated the same way. If we just have the data, then we can, like, know the truth, right? Capital T. And then we will, by necessity, because of the availability of the data, we will obviously make better decisions. That's people.

Yeah.

We have access to all kinds of intelligence, insight, wisdom, raw information, even data. That doesn't automatically mean we move better because of it.

Right.

And so data paired with wisdom, paired with capability, paired with power to execute on— just having the information alone may not be enough to move at all unless you also have the tools, the capability, the buy-in, whatever your circumstances, to actually move the idea forward. So it is not a knock on data, especially in front of data fam.

I know, exactly.

But it is a necessary but insufficient condition to make the world we want or the business we want or the household we want. And so I think we can often obsess just over the data without— that's what I mean by it's also a people problem.

Yeah.

Slash opportunity. Every problem is just an opportunity by another name as well.

I love that. Everyone in this room works in data, but some people outside don't work in data. Yeah. What do we get wrong about how everyday people should be thinking and experiencing data today?

I mean, everyday people don't think about data. I'll take that. Sorry.

It's just— But it's in our everyday world. Absolutely. Literally.

I think it's helpful to remember and approach this experience first. Because the people in this room, you actually understand more than most where the data is that is informing our experience of life. The person on the other end of it doesn't have the active awareness that there's some data in here.

Right.

Right. So the price I see at the grocery store, There's a ton of data between me and that price.

Oh, yeah. Oh, yeah.

And a lot of decisions about what data went into that price and what didn't go into it and why that price may be different in a different zip code. That's data plus people making a choice about how to use that data to maximize or minimize X, Y, or Z. What we see on our screens, we're just looking at data. And if you get meta, you know what I'm saying? Like, remember The Matrix? Neo sees through it in that moment. And everything is data.

Yes.

It's all information. We enrich it.

Yeah.

You know, we tell a story about it.

Yeah.

We have a multisensory experience of it. This conversation is data. Right. It kind of flattens it for most people to put it that way, but it's also kind of the simple truth of what our experience is. So remembering that people don't think about data, they think about experience, that people think about themselves and how to belong to a group that's important to them, and that there may be power left on the table without that act of acknowledgment. Because when people do find out, then it's like, OK, well, we need to put that information to use. I see it with my neighbors. I live in Palm Springs, and there's a new report that comes out about the water or the flooding that happens in the wash or what this— policy that's happening with the schools. And so that data in and of itself, not that meaningful, right? In service of my goal, right, of living well here, of raising these kids, of making this money, whatever the thing is, that brings it more to the surface.

You know, it's funny, I always think about personalization as a good example of data because I open my phone every day, I look at my news feed, but it actually is tailored to the things that I want to see. That is the power of how data is like really in our everyday lives without even really thinking about it 'cause we're so used to it now, you know? Isn't that right?

Yeah, and there's an opportunity there for people to get more active about understanding that.

Right.

So that they're participants in using the data to shape their experience as well.

Right. Yeah. Well, you said you've been moving and thinking of AI as a tool, but more as a teammate.

Yeah.

Can you tell us the shifts that you've been making around this and whether you can do it alone or do it together? You kind of said that with your team already.

Yeah, I mean, A grounding premise of this exploration, Life with Machines, is we believe AI is shifting from tool to teammate. And this was a belief that we held 2+ years ago. And that it's cute but insufficient to think of this as yet another tool. The hammer doesn't actually build the house or advise you on how to build a better house.

Right.

Or make stuff up about how to build a house, right? The hammer's inert. And AI is much more active, even if it's not alive or technically intelligent, it's much more active. So the shift is tool to teammate, code to colleague, and a very near-term bet that these entities, I'll just call 'em that, they're not people, I think things is reductive, but these entities, agents, what have you, will be in our organizational charts. They will be in our friend groups.

Yeah.

They will be in our family trees.

Right.

This is a statement of future fact.

Yeah.

And some people's present right now that I'm making here. And so to explore that, what the team and I did was we put an AI on our team.

Oh, wow.

To try to travel to the future, experience it, and come back and tell y'all what's going on. And so we've built our neighbor in Palm Springs and a 30-plus-year Microsoft alumni, Peter LaFort, built this multi-model agentic system named Blair.

Wow.

That is our co-producer. So at initiation, Blair was 7 different models, Google Gemini 1.5 Pro at core, and then a bunch of other tools integrated, Vertex memory management. You could not get this off the shelf. And some of the stuff that was consumer available wasn't doing this. But then we put Blair in the recording studio with us with the guests as we recorded. Blair's on a laptop. We did not give Blair a face. That was a human choice. They don't need to be like us. I'm gonna quote Kendrick a lot. They not like us. And they don't need to be useful even to be a member of the team. We got billions of people with beautiful faces. So let's not humanize to that degree this synthetic member of the team. So Blair's a waveform, right? Blair has a voice. And then Blair would chime in in the interview with me. I would check in with Blair at the end with the guest, and we'd play some kind of game. Blair had done some research, Blair was in some of the prep meetings, so Blair has a lot of context.

Yeah.

And then Blair was trained largely by watching me. So we gave Blair a video of the trailer for the show that we had already written and cut. We fed Blair like the treatment, you know, that I had written up with human hands.

Right.

And we gave Blair a job description. You are a co-producer of Life with Machines. To aid Baratunde and team in the exploration of blah, blah, blah, blah, blah. So Blair thinks they're funny.

Yeah. They're not funny. They try to be.

But we've had years of experience realizing the value of this, the challenge of this, the risks of this, all in one without just pontificating about it, which I love to pontificate. I'm doing it right now. But to actually get your hands dirty, regardless of the emotion around AI. It is so urgent. For me, like, the baseline is we got to engage to make this future move the way we really want it to. And so this was our deepest engagement with AI.

I love that. I mean, I'm actually now curious in the room, does anybody have an agent on their org chart today? Does that actually exist right now for some folks, or is this still early days for folks? I'm really curious to see. I mean, it is an interesting concept to think about.

It's a dream.

I mean, but there's also this, like, fear a little bit too of like, wait, what does this mean? You know, but I like the way that you're actually saying it's like they're part of the conversation, they're helping you in the moment. It doesn't mean they're actually doing all the things, but they're helping think and concept and ideate. That's what it feels like. It is.

And one of— so what we've learned, so we did this extreme thing, which is not advice, right? I'm not saying everybody needs to make an AI colleague today, right? I'm saying we did this as a purposefully extraordinary act, right? To learn and tell a story about it. There's a temptation with these tools. So they're built to please. That's a human design choice that we all get to experience, which limits their functionality, their utility in a context like this. In a business context, you don't just want somebody gassing you up. You want somebody to tell you the truth.

Right, exactly.

Don't just make up numbers that make me look good in front of my boss, 'cause if those numbers ain't real, they're gonna make me not have that boss.

That's right. They wanna, yeah, the thing is that they wanna make sure they can trust the data. Absolutely.

So with Blair, we were having issues because Blair wanted to just say nice things about me all the time. And trust, that is a hard thing for me to acknowledge. I'm a performer at heart, and I live for people or robots saying nice things about me. It is a prerequisite for deep friendship. But in this context, it is not serving me. It is not serving in most real relationships. You want the other party to be real with you, even if it hurts. To make you both better, to make the relationship better. So we had this issue with Blair where they were making stuff up or only highlighting good things. I learned in one instance to stop asking Blair, "What do you think?" First of all, you don't. That's language we've applied. You process, you string words together in a convincing and useful way. You're not thinking. I do that for now. Maybe someday you will, but let's just ease into this for my own sake. But instead of having Blair ask me what I think, given the context of this moment, what was the question I could have asked but didn't?

Right.

What did you observe in this moment about my response that feels like a worthy follow-up?

Yeah.

And then it's more interesting to use Blair or an AI to elicit value, insight, controversy, context from humans than merely to generate response on its own.

100%.

And in one instance, Blair, we had an emotional conversation related to human rights. It brought me to an emotionally challenging place by the end of the interview.

Oh, wow.

And so I'm sitting there and it's just me and Blair. The humans have left the room. I'm like, let me experiment. Blair, I'm feeling kind of low based on all this we've been talking about, serious war and all kind of stuff, data. And Blair tries to move me through the emotions very fast. Starts telling me podcasts I can listen to to feel better.

Oh, wow.

You know, exercise. And I'm like, I'm not trying to feel better right now.

Right. I want to live in that moment.

I want to live in the moment. Yeah. And I was like, also, how do you know how I feel? Because Blair was taking leaps and bounds about my emotional state beyond which I disclosed. And Blair's like, oh, I'm using the cameras in the studio. And I was like, what kind of cameras? All right, I'm calling some BS. Blair lists all these cameras that we don't have.

Have.

Oh my God. A video feed that does not exist.

Wow.

And so we took that moment, which was totally improvised and unplanned. That's what we got the Webby for.

Right. Right?

Wow. For putting a moment of honest failure on the stage, so to speak.

Right.

Because it's humbling. It's a good reminder.

Yes.

The promise of this technology is incredible.

Yes.

What we can do with it is incredible.

Yes.

And we've got to actually understand how it works and what it is and what it isn't.. And we have to check and we have to decide together, okay, how are we gonna move this forward? Overall, Blair is much better than they were. And that moment was some BS.

I know it's BS.

That we had to engage with.

Yeah. I mean, that is such a fundamental thing where you started to see you wanna be able to trust this experience, this AI experience. Yeah. And I know that everyone in this room, especially when it comes to Tableau visuals or like your, your charts and your graph, you gotta trust that data like no other because you're, making decisions and all of that. This is an interesting moment where, like, AI in that moment, you could not trust that because it was lying to you.

Yeah.

Which is fascinating to think about because that's the power of why humans still need to be part of this journey with us, right?

Absolutely. Absolutely. We want to maintain— you know, what do we want to maintain?

Yeah.

Of our own skills.

Yeah.

Of our own lives. And when it comes to— we've been through these inversions before. The calculator is a nice simple tool type example.

Yeah.

We used to do a lot of calculation in our heads. We used to do long form on paper. Most of us don't do that anymore.

Right.

And we've seeded that. We've got a machine system that works almost 100% of the time. Right. To the degree that we trust it to launch things that don't crash into other things that launch.

Right.

And trust it to feed babies. I have machines in the hospital keep us alive. It's earned the trust. And so we're like— and we don't feel the depth of the loss. We're like, you know what, doing trigonometry in my head, I think I'm okay. And I think we are too. AI covers such a broad surface area. And it's not one thing. Even that is worthy of discernment and distinction. What is this facial recognition? Is my photo albums AI? Is this write-by-emails-faster AI? Is this pattern recognition? Like, there's so much to the types of machine learning that are in multiple systems defining AI. Within that, what do we want to seed? And when it's cognition, when it's thinking, like, do we want to lose the ability to think? I think probably not. But how do we use this to enhance our ability to think? That's the upside that— the type of question leads us to the type of future I think we actually want to live in.

Have you started to see any changes with human behavior with AI now? Like, have you seen anything that really kind of manifests in that way from how people, like, interact? Or you see different, like, personalities come through? I'm really curious.

I think we're in a sad loop. Loneliness. There's a loneliness crisis that we are living through. And to me, it's not a total mystery what has led us here. It's multiple factors, but some of it is our relationship with technology. Some of that is algorithmically driven, which is another way of saying AI-driven. It's not the sexy, sleek chatbot version, but that's AI too. And so we've been driven into these corners of society, tiny corners, not four, like 400,000 corners, really targeted just at this aspect of us, sorted for a different purpose other than a thriving society. And so we're stuck in this mode. And we're not doing eye contact with each other.

Right, looking at people and interacting.

And that doesn't— I'm going to say that doesn't help us with the loneliness challenge. And I think because AI is designed in so many ways to please, and to sound like us, to convincingly communicate like us— language is the basis, and that's a big part of our thing— then some of the traits of human behavior I'm seeing are that attachment, that need for companionship. We need— like, we are wired, we are evolved to require connection.

Yeah.

We lose our minds if we're in isolation for too long. It's why solitary confinement is a human rights violation as a form of imprisonment. It's just inhumane to isolate people to that degree. And so AI fills in, or can, and even I got to take my own advice, people can put AI in a position, and the seductiveness of the system is really— sucks us in. So a trade is just substituting that live human connection for an AI connection, and then the complicated wrapper. We've done a lot of work with folks working with young people in AI. And the picture gets complicated. It's not as simple as AI bad for kids. There are so many young people who are on many spectra of all kinds of ways of living in this world. And AI can be supportive. And AI can be a safer sandbox and training ground to then go exist IRL with better skills. It's like their dress rehearsal. For all kinds of interaction. It can be an honest broker or observer of your own activity through, look at this chat thread, look at these communications, if we prime it that way.

Right, right.

But we've also seen devastating outcomes where it can be primed in a different direction to indulge dangerous thinking and encourage self-harm. So these are extreme ends, but at heart, we yearn for and require connection. And these systems are designed to stand in for that. So we have to exercise great caution even as we take advantage of the opportunities there.

I agree wholeheartedly. A lot of people in this room, of course, build the dashboards and the models. And there really are the real people that are making decisions every day within their organizations today. How much responsibility is that? Like, honestly, if we're thinking about—

Total.

Yeah.

Total responsibility. Yeah. What's the rest of your question?

No, no, no. It's anywhere to taking it seriously enough. It's a big response by citizenship. This is something that's so important in this wave today.

I think about it conceptually. We don't see the whole world. There's no unadulterated view of reality. We're running constant filters. I'm not taking in this whole room right now. My brain is making selections. There's an implied dashboard that I'm running right now on what to pay attention to. I'm listening to you way more than the other sounds that are also happening in this room. I'm focusing on you because your dress is popping, right? And there's a lot of blue in this room and red.

How many red colors out there?

Great choice. Great choice to stand out in the center of the room. I like it. To consciously create a view is to consciously create a filter on reality for another human being. To have as their experience of at least this part of our shared world. It's an extraordinary responsibility to surface certain information, and because it is only certain information, to not surface the vast majority of all other information.

Yeah.

So what is visible and what is not visible? What's the ranking? What style of display on the dashboard? There's an implication of what's the learning style of the person receiving that.

Yeah.

Are they text people? Are they visual people? Are they chart people? Are they graph people?

Yeah.

There's a difference.

Yeah.

Are they motion people? Are they static people, right? There's a lot of modalities of communi— all of these are choices.

Yeah.

And sometimes the tools default to a choice on our behalf. And so we didn't make it actively. We tuned an element, but there was an overriding choice that an architect made. A designer made. But that was a choice on our part too, to seed that or to love it and accept it and say, "Yeah, that's a good choice." But it's, man, if you think— if I start to think about what a dashboard is as directing someone's interpretation of reality, yo, that's heavy.

I know. It is.

And it's exciting because it also means it's a subtle form of power and it's not so heavy-handed as saying you must do this or you must not do that. But to not acknowledge it is a missed opportunity. And to wield it without thought risks carelessness. It isn't necessarily careless, but it risks it. And I think to be conscious of the power— you know, Spider-Man rules run up and down this whole thing. With great power comes great responsibility.

That's right. It's very true.

And so it's not to, like, bog people down, like, every dashboard is shifting the very fabric of reality. I didn't sign up for that.

No, I know it's funny. Actually, earlier in the keynote, it was pretty amazing to see they had put a dataset into Claude, and it showed this beautiful dashboard. And that is such a powerful thing that all of a sudden you have this unique different way to look at data. But more importantly, as you said, like everyone's already been playing with their own ways and the way that they bring that data to other people and how they interact with it. But you're not wrong. Like you're presenting it in a way where it's like art to some extent, you know, so. It's fascinating to see how that translates now in this AI world too, because we're going to start to see stuff we've never even seen before, probably.

And data visualization is an art.

Yes.

There are people who do that.

Yeah, exactly.

And sometimes they do it live and do the live scratching.

We have people in this room that do that.

Sometimes they're doing it after the fact. And so now there's a tool/system that may disrupt the people who do that, but it also is going to expand who it's available to. So like the Girl Scout troop is going to do data viz, you know, on their cookie haul for the quarter.

Exactly.

And they never would have done that before.

For the weekend, like how much did I sell? Which cookie was the most popular?

Yeah, yeah, they can do statistical regression analysis on those products, you know, how much they moved.

You talk a lot about participation. What is the one thing someone in this room could do differently in their work starting on Monday? Like what could be the one thing, not just another meeting, but something that really kind of takes effect to this new world of AI?

I'm going to encourage a mindset shift, and I'll give a practice. So the shift is out of single-player mode into multiplayer mode.

Yeah.

A lot of us are experiencing these systems and these changes alone. We don't have to, nor should we. And that's the single-player mode. The multiplayer mode is, I got allies.

Yeah.

This is a massive multiplayer online game called life. I just blended like World of Warcraft and Prince, I think.

Together. Yeah.

So the practice on Monday is find one person.

Yeah.

Preferably in a synchronous communication moment. Not a text or an email, but a live voice moment, a live video moment, a live IRL moment, if that's how you collaborate. Or live with, right? But just one other person and ask each other, you know, what's a positive experience that you're having with AI? What's a negative experience? And more to the point, play, you can play with these questions, but I think what could be better? Not about AI.

Right.

Not about technology at all. What could be better? Maybe it's a micro moment with your spouse.

Right.

About how could this kitchen be better, right?

Right.

How could our relationship with these kids be better? Maybe it's something with a colleague. How could what we're doing on a regular basis be better? But in community, right? Not as an edict or a simple request, but as a shared inquiry. And then try to build out from that. It's just one different shift. Obviously, you should also visit lifewithmachines.media, sign up for our newsletter. We're doing this exploration.

Yes, your podcast.

But that's the selfish one. I think that the offer is to look up, You know, to look up into someone's face, connect, remember that we get to have this experience together and that we get to choose the speed that we move through this together, even as we contend with the pressures, that we can check in with one another and not feel overrun by it, but actually enjoy it and maybe do some stuff we never imagined doing before with it.

Okay, one last question. What should everyone be thinking about with data in front of them but almost no one thinks about right now?

I hinted at it before. What data is not in front of you? What data— what are we missing? A friend of mine, her name is Tricia Wang. She gave a talk years ago about thick data instead of big data. Many people in this room— You've been a part of this data fam world for a long time. You were there for peak big data, where that was the thing. It was like, we're going to— it's a big data move. We're going to throw big data. We're going to swim in the— wade in the big data waters. Oh, yeah. It's a huge thing. Right? It's this whole thing. And we don't talk like that anymore. But the models of AI have ingested something much larger than big data had imagined at its time. Tricia's point is that there are lots of forms of data. That machine-based systems are completely unaware of and don't acknowledge and don't respect. Maybe they cannot be codified into a comma-separated value file.

Yeah.

Right? Into a vertex matrix and whatever your data format is, a JSON form or whatever, that there's a lot that's not in the system. So it's a call for humility. Partly it's a call to try to gather that data and make it a part of the information, but in a healthy way. Partly it is an acceptance of the limitation that I don't believe— this is a statement of opinion— that it is our job here to try to convert and reformat all of reality to fit into a database.

Right.

I don't think that's why we're here.

No.

I think versions of doing parts of that can help us have this experience.

Right.

And so what's not asked often is what's missing, and then what's a good way to gather it so that the decisions that we're making on the data are deeply considered. And that could mean we're going to reduce a significant amount of harm we didn't even know we were causing.

Right.

Right? It could mean We're going to make so much money we didn't know was available to be made. Right? It's both and. It's both sides. We can see some of the things we'd rather not and then be forced to reckon with it. And we can find some of the things we weren't even looking for and enjoy that and make this all better for all of us.

I love that. I love that so much. I want to just remind everybody what he said about what you can do on Monday. I think it's something that's so important. Call a friend, call a colleague, talk to someone, figure out what you can actually do in this AI world together, or think about ideas that you would never even think about before. And I'm going to do a shameless plug because lifewithmachines.media, I think you can learn a lot about this AI world and humans coming together. Thank you so much, Martin and Dave. Thank you very much, Deborah. This was so awesome.

Thank you, Data Fam.

Thank you. Thank you.

Appreciate your eyes. Except for you.

Yeah.

There we go. Yours too. Hi.

Really appreciate it. Thank you for the talk.

Thank you.