#90 How Microsoft's AI Innovation Officer Actually Uses AI | Dr. Michael J. Jabbour on Thinking, Not Just Tools

Summary

Podcast Summary: TranscendingX #90 — How Microsoft's AI Innovation Officer Actually Uses AI | Dr. Michael J. Jabbour on Thinking, Not Just Tools

Main Theme and Purpose

This episode, hosted by speech therapist and communication coach Uri Schneider, features a deep-dive conversation with Dr. Michael J. Jabbour, AI Innovation Officer at Microsoft. Instead of focusing on the technical mechanics of AI, the discussion explores the nuanced, practical, and philosophical ways that AI intersects with human creativity, learning, work, and personal growth. The conversation addresses how AI tools can be used for more than productivity—encouraging listeners to leverage AI for responsibility, impact, and self-actualization.

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Key Discussion Points & Insights

1. Reframing the AI Revolution

• Beyond the Industrial Revolution: Dr. Jabbour argues that the AI shift is more profound than the Industrial Revolution, comparing it instead to when "humans met fire," triggering fundamental biological changes ([04:50]).
• Biological and Cognitive Effects: The introduction of technology, such as GPS, has already physically altered brain structures, and similar changes are anticipated with AI ([04:50], [05:41]).
• Adoption Speed: Unlike electricity, which took decades to become ubiquitous, AI services like ChatGPT scaled to hundreds of millions of users in months ([07:09]).

"The Industrial Revolution is probably not the best metaphor. I would go back to something closer to when humans met fire..."
— Michael Jabbour ([04:50])

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2. AI as a Tool for Individual Empowerment

• AI is democratizing skills: everyone can be a 'photographer' or 'musician' with the right tech ([08:14]).
• Creative empowerment: AI tools like Suno and Songify allow individuals to transform, augment, and create music or art without traditional expertise ([08:14], [09:33]).

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3. Transforming Education and Learning

• Education vs. Training: The current system prioritizes predictable training over education that fosters ambiguity tolerance, adaptability, and potential extraction ([06:09]).
• The Value of Copilots: Microsoft’s study in Nigeria found that six weeks of AI copilot instruction was equal to two years of traditional human teaching in educational outcomes ([12:51]).
• Prompt design and critical thinking: The manner of engaging with AI (e.g., asking "teach me" instead of "tell me") significantly impacts outcomes and learning depth ([16:22], [17:06]).

"Six weeks of copilot use was equivalent to two years of human instruction as far as gained educational outcomes."
— Michael Jabbour ([12:51])

• Ambiguity in Learning: Modern learners often lack ambiguity tolerance—a skill now more vital than ever ([10:43]).
• Critical Bot Prompts: How you prompt matters; teaching children to shift cognitive and emotional load to the AI changes the nature and value of their learning ([15:06]).

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4. Three Modes of AI Engagement (The Substack Model)

a) Assistant Mode

• AI as task-doer: simple, transactional queries for information or routine tasks ([20:02]).
• Most users operate here (e.g., summarize an article, find a restaurant, correct grammar) ([41:41]).

b) Partner Mode

• Collaborative, interactive problem-solving and creativity: AI helps plan, brainstorm, or critique, almost like a human collaborator ([20:54], [22:05]).
• Beyond completion—AI as a thought partner or even an "editor" pushing for depth, accuracy, and self-challenge ([24:14]).

"I want an AI that is going to do more of that with me, for me, to me. Right. To ask me those questions, to be able to, to partner with me in that mode..."
— Michael Jabbour ([46:22])

c) Explorer Mode

• Open-ended, unstructured exploration of unknowns, ideas, or philosophical questions; "riffing" with the AI to discover new perspectives ([26:50]).
• Jabbour estimates ~80% of his AI interaction is here ([27:00]), using multiple models to challenge and expand his thinking.
• "It might be irresponsible for me not to" use AI due to its transformative potential ([27:19]).

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5. Comparing AIs: Copilot, ChatGPT, and Claude

• Microsoft Copilot: Safe for enterprise and educational use; shines at integrating with Office tools and team workflows ([40:15]).
• ChatGPT: Dominates market share, great for 80–85% of tasks, excellent at assistant tasks, but can perform deeper work with specific prompts ([41:41]).
• Claude (Anthropic): More holistic and robust thinker, especially good with visual prompts and nuanced problem-solving; seen as providing deeper, less "averaged" perspectives ([42:09], [43:07]).

"I do think that Claude is a deeper thinker... approaches a problem very holistic or robust and it like will catch things that I will catch..."
— Michael Jabbour ([42:27])

• Using different AIs as separate "opinions" or "editors" is standard practice for deeper insight and to counteract bias ([23:24], [23:35]).

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6. Human Creativity, Authenticity, and AI

• Creativity Unleashed: AI can be a co-creator when others aren't available or to push boundaries in art and music ([37:46]).
• AI-generated Media: Most people (kids and adults) can’t distinguish between human and AI-generated works, though the emotional impact may differ ([34:56]-[35:41]).
• Role Shift for Creatives: AI isn't the end, but an evolution—creatives move from doers to orchestrators, explorers, or directors ([37:46], [37:54], [38:53]).

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7. Risks, Limitations, and the Call to Responsibility

• FUD (Fear, Uncertainty, and Doubt): Widespread negative narratives about AI are often baseless and can hinder creative and productive use ([36:26], [36:43]).
• Greatest Risk = Inaccessibility: The biggest dystopian danger is uneven or absent access, not AI itself ([54:07]).
• Getting Stuck in 'Minimal Use': Most users limit themselves to shallow tasks, missing the deeper gains by not fully engaging AI as a true partner or explorer ([54:07]).
• Critical Self-Acceptance: AI as a mirror for self-improvement—people avoid the vulnerability (and growth) that comes from deep self-reflection, a key to progress ([55:13], [56:05]).

"A person can basically only grow to the proportion that they can accept truth about themselves without running away."
— Michael Jabbour ([55:13])

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8. Societal Impact and Future Outlook

• AI gives the possibility for every person to have "a Stanford doctor, an educator in their pocket"—shifting healthcare, education, and work more dramatically than the internet did ([50:29], [50:38]).
• The positive potential includes global access to high-quality medical and educational services, with information and opportunity expanding far beyond today's internet ([51:17]).

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9. Final Reflections and Practical Takeaways

• Push AI Beyond the Basics: Don't just ask for information—use AI to challenge, question, and expand your thinking.
• Leverage Multiple Models: Treat AI models as different perspectives; consult more than one for richer results ([23:24], [43:38]).
• Human-Led Growth: Use AI to facilitate critical self-examination and personal development, not just efficiency.
• Aim for Positive Impact: Jabbour's closing message: "Do good and be good." ([56:52])

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Notable Quotes & Moments (with Timestamps)

• [04:50] “I would go back to something closer to when humans met fire, that kind of thing, where we experienced biological change as a result of some external stimulus.” — Michael Jabbour
• [12:51] “Six weeks of copilot use was equivalent to two years of human instruction as far as gained educational outcomes.” — Michael Jabbour
• [22:07] “Help me think about an idea that doesn't exist. Yes, help me think about something that's very difficult for me.” — Michael Jabbour
• [27:19] “Let's assume that I consult with like 60 to 70% of things within AI if I can. Why? Not because it's helpful, because it might be irresponsible for me not to.” — Michael Jabbour
• [42:27] “Claude is a deeper thinker...it approaches a problem very holistically or robustly...” — Michael Jabbour
• [46:22] “I want an AI that is going to do more of that with me, for me, to me. Right. To ask me those questions, to partner with me in that mode...” — Michael Jabbour
• [51:17] “The information that was distributed in the Internet will be small in comparison to what you're seeing right now, right now, not in three years from now.” — Michael Jabbour
• [55:13] “A person can basically only grow to the proportion that they can accept truth about themselves without running away.” — Michael Jabbour
• [56:52] “Do good and be good.” — Michael Jabbour

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Important Segment Timestamps

• AI paradigm shift — [04:50]–[05:41]
• Democratization through AI — [08:14]–[09:33]
• Microsoft’s Nigeria copilot study — [12:51]–[13:59]
• Prompting & critical thinking in education — [15:06]–[17:06]
• Assistant, Partner, Explorer modes — [20:02]–[28:05]
• Comparing Copilot, ChatGPT, Claude — [40:15]–[44:40]
• Music/Art, Authenticity, Emotion — [34:56]–[37:46]
• Risks: FUD, inaccessibility — [36:26]–[36:43], [54:07]
• Societal impact: work, medicine, education — [50:29]–[53:07]
• Personal growth and vulnerability — [55:13]–[56:42]
• Final message: Do good and be good — [56:52]

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Conclusion

Rather than focusing solely on efficiency or productivity, Dr. Jabbour advocates harnessing AI for creativity, self-awareness, and collective good. The future is not about fearing AI, but about embracing it as a tool for authentic human flourishing—provided we push its boundaries, ask better questions, and ensure equitable access.

Billboard Message:
“Do good and be good.”
— Michael Jabbour ([56:52])

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For Listeners:
To get more out of AI, move beyond using it as a simple assistant. Use it to challenge yourself, explore deeply, and create connections between ideas—partnering with multiple AIs as you would with a team of trusted advisors. Most importantly, let it sharpen your own human qualities—curiosity, vulnerability, and the drive to “do good and be good.”

Transcript

[Michael Jabbor]

Not because it's helpful, because it might be irresponsible for me not to. The Industrial Revolution is probably not the best metaphor. I would go back to something closer to when humans met fire, where we experienced biological change as a result of some external stimulus. Famous example of cabbies in London where they saw a shrinkage of certain parts of their brain physically while when they were using GPS versus ones that were not using gps.

[Uri Schneider]

Michael Jabbor is a friend of mine for more than 20 years. But I got to say, when I met him 20 years ago, I had no idea what he would be doing today. He's a thought leader. He's an AI Innovation officer at Microsoft. In the office of the cto, we have a conversation that's not a technical conversation about AI, but it brings the soul of humanity to the intersection with AI and the cusp of all the technology that's unfolding all around us. This conversation sheds light on creativity, work, education, and how all of us can use not just one model, but multiple models to make ourselves not only more productive, but more creative. One of the things that comes out of this conversation is that he uses AI for about 70% of his work during the day, not because he's trying to get it done more efficiently, but because he feels it would be irresponsible not to use AI in the work that he does on a day to day basis. Listen to this conversation and see how you can use AI not just to be more productive, but to be more responsible and make a bigger impact in the world.

Welcome to Transcending X. Whether it's stuttering public speaking or crucial conversations, all of us have something that holds us back.

What if there was a way through it?

I'm Uri Schneider from Schneider Speech, where we help people talk more and fear less. And I'm the host of the TranscendingX community. Join me as we talk to high performers, researchers and everyday heroes to discover how they transform their challenges into breakthroughs and most of all, find ways for each of us to transcend X in our own lives.

All right, Michael.

[Michael Jabbor]

Mj, Good to be with you. Been a long, long time.

[Uri Schneider]

This was on short notice. The opportunity struck and in true fashion, we show up and we make it happen. For the guy who's the AI Innovation Officer, the this is just another example. Like, sure, I could be in Soho on 24 hours notice, no problem. But that speaks to the human connection that we share and that I think I want to really highlight. I think at the beginning of this year, the beginning of 2025. Everyone was terrified that AI was taken over. There was a feeling of being overwhelmed, of being swept up of scarcity, of fear. And I think here we are at the end of 2025, and I think the vibe is very, very different. So I hope we can get into that and talk about what's happening, but most importantly, what's something that doesn't show up on your resume that you'd want people to know about?

[Michael Jabbor]

M.J. i mean, I love humans of all ages. I am a person that just loves the experience of life, both its challenges as well as its benefits. And being able to lean into that wherever I can is a huge opportunity for me. So it doesn't matter whether it's AI or it's trying to figure out how to repair some part of someone's body or mind, but it's important and it's something that has been a thread throughout my entire life.

[Uri Schneider]

What's your job behind all these words? Innovation Officer at the office of the CTO at Microsoft. What's your role?

[Michael Jabbor]

My job is the same job as anyone else at Microsoft, all several hundred thousand employees, which is to ensure that we are successful as humans, to empower every person and organization on the planet to achieve more. That's our mission. And so making Microsoft successful is really making the world successful. I love that about where I'm working now.

[Uri Schneider]

So if we think about AI and the time that we're living in and kind of framing it in other revolutions that the world has gone through, the Industrial Revolution and others, can you give us a little bit of perspective, set the stage of kind of like what we're going through, what's already kind of come through the world, where we stand today, and kind of what to expect is coming next.

[Michael Jabbor]

Sure. The Industrial Revolution is probably a. Probably not the best metaphor, I would say, for where we are right now. I would go back to something closer to when humans met fire, that kind of thing, where we experienced biological change as a result of some external stimulus. And that's very much where we are right now, where you're going to see changes. So at the very basic introduction of AIs and technology to humans, you can go back to a very famous example of, of cabbies that they tested in London, where they saw a shrinkage of certain parts of their brain physically when they were using GPS versus ones that were not using gps.

[Uri Schneider]

So the brain and the neural networks in the brain actually changed as a result of using GPS technology.

[Michael Jabbor]

Well, parts of the brain shrunk. They atrophied, actually so the neural networks is somewhat of a more complex, non deterministic beasts. But. But yes, physical change as a result of technology influence. And we're in a place right now where that is scaling exponentially in.

[Uri Schneider]

I think when we were, when we met each other, we still remembered people's phone numbers. Yeah, be like another example.

[Michael Jabbor]

Yes, that's right. Well, you remember those string, the string of digits, you still remember their names now, but now you're dialed by name instead of dial by number. That's right. So that transition was also interesting, but it was an easier one than the one that we're going through right now. During the Industrial Revolution, since you brought it up, you did see pretty big changes in how manufacturing was occurring, how precision was achieved, what different types of labor could do, different types of work. And a lot of our system today is heavily oriented around exactly what that was versus where we are today. Right. So even school is less like education, which prepares us for ambiguity and situations that we can't prepare for versus training, which is situations that we can prepare for. Right. So educare, the Latin etymology for education, means to pull out, to extract a person's highest potential versus pushing in. And so the idea was to push in this knowledge, skills and abilities to enable someone to reliably do something in that line of achieving scale and precision at scale. That's Industrial revolution. Right. Keep in mind it took 50 years for electricity to be widely distributed across the world. Here you had chat GPT going to hundreds of millions of users within months. And now you've got a capability like diagnosis, for example, for our physician that might actually scale and become available to every person on the planet, regardless of their location, their means, or their, even their technology access.

[Uri Schneider]

So framing it when humans come in contact with fire, that it actually changes the human biology. So what, where are we at? What's come into play in the past couple of years? Where do we stand and what's next in this process? And then we can talk about in what ways it's affecting us as humans. I really enjoyed our conversation about the way we think and the way we operate has to grow and shift.

[Michael Jabbor]

Yeah. What changes occurred is still being studied. So I don't want to necessarily lean into the current state of science because it's evolving so fast right now. And even our ability to detect what's changing is still, we're still entry level at that point. But what's changing in terms of work has already made pretty drastic moves. So in where it comes down to let's just take a Simple example of the phone, the iPhone, right? So the iPhone basically turned everyone into a photographer. It didn't say, okay, yes, you had to have a DSLR and you had to have X thousands of hours of training and you had this much exposure to these types of subjects. And so now everyone is a photographer right now. When you think about something like making music, for example, like I took some music that I recorded from 20 years ago, which you've heard, and dropped it into Suno, for example, and it just did amazing things with it. Like I had never been able to hear my music with like actual real, you know, drums and an UD and like all different types of instruments that I've always wanted to layer in there.

[Uri Schneider]

Not to mention extracting the sheet music with an app like Songify.

[Michael Jabbor]

Exactly right, that's right. You can now extract sheet music. You can bring that sheet music to life. You could iterate with an AI on it. And so that idea of enabling every person to be successful in these really incredible ways is absolutely. Part one of that. Part two is that jobs will change, right? Doesn't mean that jobs necessarily go away, but they're definitely changing. And for example, you're not going to have like a string of typewriters necessarily that are available to you for, for typing in some sort of editing studio. You're going to have AIs that are even typing the typewriters for you, so to speak. And so you're. When we look at each of these disciplines, whether it's creative or tactical technical work, you're going to see AIs be able to learn it. Why? Because AIs will excel at training, they're going to excel less at educating education related topics.

[Uri Schneider]

So talk to us about the education. How do people have to think differently? What skills become more of a premium, what things are being offloaded and what things need to be flexed so that we can succeed.

[Michael Jabbor]

Well, I mean, I'll give a paraphrase or semi quote of one of the universities I spoke to, spoke at a great university recently. I was talking to the grad students and the student dean was there. And after my keynote she was like, that was crazy. And I was like, sorry if I over provoked the students. And she was like, no, no, your presentation was great. She was like, I'm talking about the students. She was like, it was the first time in a long time that I felt sad. She tells me, because I watched the level of ambiguity that you were throwing at them and their ability to tolerate it was so low. And I created that. Wow. And it clicked for me. A lot of things clicked when I heard her say that because she was able to see a system in flux and, and be able to pivot where most people actually will not be able to see it until it's sort of already passed that moment. Right. But you're in this school, you're in this system of learning. It doesn't matter like necessarily like the age range. But what are we really spending our time with and teaching these students? Like how are we helping them? Are we helping them to self learn? So the time that we spend with them is going to be about mentoring and coaching and guiding and extracting their highest potential. So a lot of open questions around like what education is, what it should be. There's some fascinating examples also of schools that have taken this very seriously. Some of the research that Microsoft, just as a. To give you some lead in there that we produced, we did a study in Nigeria with World bank on education methods. And we're looking at controlled trial where we were looking at a virtual teacher copilot use and educational outcomes. And what we found was that six weeks of copilot use was equivalent to two years of human instruction as far as gained educational outcomes.

[Uri Schneider]

Explain that in simple English. What did that look like?

[Michael Jabbor]

So competency basically just what was the co piloting?

[Uri Schneider]

What does co piloting look like?

[Michael Jabbor]

So co pilot, a copilot would be working with an AI assistant of some sort that is helping you and guide you. Right.

[Uri Schneider]

So if you're as a learner, as the instructor.

[Michael Jabbor]

Both, both. Right. But let's just you, let's focus on the learner for now. That was really what the focus of the study was on. So the learner can ask a simple question and just say hey, teach me linear algebra. It's very broad topic. So the AI has come back and ask you like, okay, like what area of linear algebra do you want to learn? Or something specific like angular momentum and physics, looking at the moment of inertia and angular velocity and being able to understand rotational force in that way. And so when you're engaging with this AI, you're really asking it for, for it to assist you with a very specific topic. In some cases very specific, like help me push this button on my computer. In other cases it's teach me a topic. In other cases it's guiding me through this epic of topics.

[Uri Schneider]

Taking this study that you talked about that you did in Nigeria, was it Nigeria?

[Michael Jabbor]

Yeah.

[Uri Schneider]

What, what degree of instruction or training or experimentation was there with the prompting? Meaning how much did the learner have to know how to prompt to get the best out of the AI versus how much was it kind of relied upon the AI to drive the show.

[Michael Jabbor]

So it's a great question. I'd have to go back and look at the study to confirm. But even theoretically, but I believe that it wasn't based on the student prompting at all. I don't think that we were running trainings in order to guide their behaviors. Now, my kids, I do teach them at least some basics that they can remember all the time. You know, sometimes like you spend time like exercising and then you're like, I can't remember what I'm supposed to do right now. So you need like that one or two exercises that you like, carry with you. So for my older kids, I tell them that they should tell the AIs to put the cognitive and emotional load on me before their prompts. And funny story, I will not mention which child, but I can tell which children are using that prompt and which ones are not. And when.

[Uri Schneider]

What does it look like? What's the difference? What does the prompt look like and then what does the output look like?

[Michael Jabbor]

Well, I didn't look at their prompts, I looked at their outputs. So you can see in the outputs that their ability to think critically about a topic because I would be able to challenge them. Like, is this what you really agree with? Like you wrote something? Is this what you agree with? Because this doesn't sound like the person that I know right now. The writing was on par because the students can now get the writing to be in their language. They can get it to be at

[Uri Schneider]

their Persona, their style.

[Michael Jabbor]

Right? So to be able to detect like this child's writing over and over, the child's regular writing, their AI writing versus regular, very difficult. And that, that's only gonna, that's in terms of form, style, form, style, but not substance.

[Uri Schneider]

What you're saying is, in terms of substance, is this what you agree with? Are these the ideas?

[Michael Jabbor]

I know my children's minds and what's in their hearts to some degree, right? So like, I can, I can sense when something is off. Now a teacher, maybe not so much anymore. It's going to be more and more difficult for them to teach, like in that way. My five year old, though, I have to give her a little bit different type of instruction because she was born about a week before pandemic shutdowns here in New York City. That was totally fun.

[Uri Schneider]

That was sarcastic. There's the laugh.

[Michael Jabbor]

Fun, right? And she grew up talking to Alexa, Sierra's Remote controls. She. She demands that those machines respond to her in a meaningful way. And, you know, if Moana doesn't show up when she calls it, like, all hell will break out in the house because her expectations are so high. But when I see her engaging with the AI, I tell her to tell the AI to teach me and not tell me. And so even if it says something to her, I have to help make sure that she is continuously going through that process of saying, you know, not just what is the answer to this question, but teach me, right? Give me, guide me, grow me.

[Uri Schneider]

So many people I find will talk about this, this great article that you wrote on your substack, which is amazing. All the articles are amazing. Very thought provoking and very, very helpful for framing, like, how to approach and think about this whole time and this whole technology that's evolving, the different ways people engage. Like, so even like Google, right? Google search. I grew into the time where Google search became a thing and I watched how different people were able to find more good content that they were looking for, and other people would find content that it wasn't what they were looking for. And a lot of it had to do with understanding. Like, how do you type in a search query? So when you talk about your five year old saying, like, instead of tell me, teach me. So, like, what is the prompt? What's the difference in the prompt? If you could like, create that contrast in the way that you see young adults, whether they're students, professionals, how are they using, let's say even open source, you know, ChatGPT and Claude and Microsoft.

[Michael Jabbor]

So let's talk about the search engine for my older kids for a moment because I think that their transformation, their change was very noticeable. With my daughter, it was basically second nature. And I have so many funny stories about her interactions. But my older kids, they've totally transitioned out of the search for information to the search for answers. So even for a lot of us, we might naturally go into like a Bing or a Google or some search engine and, or even ChatGPT, and we would search in that limited way for information. Right.

[Uri Schneider]

What's a good kids movie for the weekend?

[Michael Jabbor]

Basic information, right? Can you give me information that you have on this type of skin condition? Right, Sure. I can get you information on that. Right? So these searches.

[Uri Schneider]

Information.

[Michael Jabbor]

Search for information versus the search for answers. So when you're asking a question like, how do I solve this problem? That's a very different type of search.

[Uri Schneider]

I need to be in this city for this presentation at this time. And I have this other constraint. What's the best, you know, what would be some solutions or ways that I could go that would save time and

[Michael Jabbor]

effort and that is a search for an answer, right? So you want someone to go in and do that work for you. So normally you would ask like your, your friend, your spouse, your assistant, your child to, to help you to match the constraints. Because it's constraints like who wants to do, you know, constraint satisfaction like that is like the worst type of exercise. And I know a lot of people who are really good at that. But. But it's a really difficult exercise. But if you ask an AI agent to do that for you, you're already in world. So you mentioned the substack. There are three different types of modes that I commonly find people falling into. One is assistant mode.

[Uri Schneider]

Assistant.

[Michael Jabbor]

So assistant mode is that search for answers. I need someone to do a task or tasks for me on my behalf in order to make my life easier. So hey, you assistant, go do those things for me.

[Uri Schneider]

What are three good coffee places for MJ and me to get a coffee after this?

[Michael Jabbor]

Right? Like as an example or you know, re research these three articles on the Microsoft study and tell me, summarize their focus was.

[Uri Schneider]

Summarize this article for me.

[Michael Jabbor]

Exactly, Summarize this article. Like those are, those are tasks that your assistant can do for you. It's kind of blind and a little bit dumb sometimes, depending on how you go about your work with an AI and uh, but that's an assistant, a basic task doer on your behalf. Then there's partner mode.

[Uri Schneider]

Partner mode.

[Michael Jabbor]

Partner mode, Right. So you can think about it just like a partner in business, partner in life, partner in marriage. Right? You work together to achieve something.

[Uri Schneider]

I remember the first time my brother told me, yeah, you know, Chat and I, we worked on this plan together. And it was. I can tell you exactly what it was. I think it was Rosh Hashanah. And we were sitting there talking and I thought it was the craziest thing in the world. And then I found myself on a flight on the way to a conference and I need to finish the slides. And not only did I need to finish the slides, but I said, you know, I want to stop working to perfect everything. I just want to get this done in 20 minutes. Help me frame the following concepts into 30 slides. Give me some suggestions. And we were literally working together and asking it to prompt me with questions along the way, to check and see. And what I ended up delivering was the best presentation I ever gave. And it was the most efficient preparation because I got out of my own way and I had a partner. And then I turned back to my brother and I'm like, thank you. I laughed in your face when you were talking about it with, like, this anthropomorphic kind of attribution. But it totally, totally, totally was like a great partner for me.

[Michael Jabbor]

Yes.

[Uri Schneider]

Would that be an application of what you're talking about?

[Michael Jabbor]

Perfect application. Excellent way to think about it. And. But I want to extend also your idea around partner. Help me think about an idea that doesn't exist. Yes, Help me. Help me think about something that's very difficult for me.

[Uri Schneider]

Yeah.

[Michael Jabbor]

Right. So now you're. You're almost in, like, this therapeutic relationship.

[Uri Schneider]

Yes, Done that as well. I would say, like, I know I have a tendency to get really theoretical, challenge all the places where I'm getting really abstract, and challenge me to come up with practical applications for the audience, make sure I don't drift.

[Michael Jabbor]

It's. It's a great. That's another great example.

[Uri Schneider]

We're here. Take this piece of writing and show me all the places that I need to challenge where my ego is getting into this.

[Michael Jabbor]

Yeah. Here, I'll give you a specific prompt that you can use, please. So some of my articles, I wander and I. I have thoughts that are seemingly disconnected or extremely disconnected that I know belong together, but they're hard for me to pull it together. And so that's where I spend a lot of time. But I will tell after I've written down some of my thoughts, I will ask a separate AI, not the AI that maybe assisted me with research or something, and say, take a look at this article.

[Uri Schneider]

Just to break that down for someone who doesn't follow what you just said. A separate AI. So just give a practical example.

[Michael Jabbor]

So let's say I use Copilot, Microsoft Copilot, to draft an article, and then I use ChatGPT to check an article. And then I might use Claude as a third opinion.

[Uri Schneider]

Mm.

[Michael Jabbor]

Right. So one second or third opinion AI, you could put it. So it doesn't have any context. Now, sometimes you can easily do that just by opening up a new session. Right. But with people, a lot of people have memories turned on now, so it gets a little bit poisoned. The context gets poisoned. But if you use a third party,

[Uri Schneider]

say a confirmation bias starts to be formed.

[Michael Jabbor]

Something like that. Yes. So let's say you go to another AI and you say, here's my article. Check if it is deductively and inductively sound. And then after that's Gone through. Ask it what the first principles are of that article and check if I've really delivered on communicating those first principles.

[Uri Schneider]

Wow.

[Michael Jabbor]

I mean, you'll get feedback that you

[Uri Schneider]

don't love, but then at least you're aware.

[Michael Jabbor]

Yes. At least you're aware and at least make a decision.

[Uri Schneider]

You have some critical thinking to do.

[Michael Jabbor]

That's correct.

[Uri Schneider]

Do I want to just deliver what I just did, or do I want to level it up?

[Michael Jabbor]

And sometimes I will ignore the. The. The suggestions.

[Uri Schneider]

I know I've done that.

[Michael Jabbor]

Yeah. But. But I think if it's a pure logical fallacy that I've communicated something that doesn't make sense or doesn't connect in any way. Like. Yeah, there's a. It's. It's as if you're visually looking at text and there's a huge gaping hole.

[Uri Schneider]

Yeah.

[Michael Jabbor]

So I can't see that hole. Sometimes we're blind, but the AI see them very easily. Yeah, that's right. They partner with you. Okay, so.

[Uri Schneider]

So those applications cover the range of what you're thinking of in partner mode?

[Michael Jabbor]

I mean, I think so, for the most part. I just. I would want to make sure that you would include some level of pushing the models in ways that might seem like you could only push a human in that way. So partners will satisfy that. So an example would be, I totally don't agree and don't understand what it is that you're communicating with me. And I need you to work with me. Like, teach me, explain to me, guide me down the path of your thinking. Show me your sources.

[Uri Schneider]

Yes.

[Michael Jabbor]

You're not gonna do that to a search engine. Right.

[Uri Schneider]

Right.

[Michael Jabbor]

So you're not even.

[Uri Schneider]

It's fact checking. It's checking hallucinations or skipped steps or skipped resources.

[Michael Jabbor]

That's right. And it also might be the difference between having one assistant versus multiple assistants. Right. So it's the equivalent of having multiple minds. Multiple assistants in that loop. Right. Like, I don't want one type of editor to look at my sound. I want four different types of editors.

[Uri Schneider]

Because now I find it fascinating. We'll come back to it. But, like, it's all based on mathematical frameworks and logic, and yet you would think one plus one equals two. But when you give instructions and you say, use these three sources to provide such and such information, and then you'll read it. And I challenge and I say, well, it doesn't seem like you really used source number two at all. You're right. You're right. I didn't.

[Michael Jabbor]

Yeah. So we might have come back. That's a big topic. That's, you know, hallucinations, but the challenging. Right.

[Uri Schneider]

There's hallucination and then there's omission.

[Michael Jabbor]

I mean, that's Right. Well, yes, And I, I think that there's a. That's a particular type of filtering bias that occurs.

[Uri Schneider]

Yeah.

[Michael Jabbor]

Right.

[Uri Schneider]

So, okay, so we got. We got assistant mode.

[Michael Jabbor]

Yes.

[Uri Schneider]

Partner mode. Yep. And then Explorer mode. Oh, that sounds good. Let's explore that.

[Michael Jabbor]

So explore mode is. I feel like I live probably like 80% of my. My current AI existence inside of Explorer mode.

[Uri Schneider]

How much of your life do you live in your AI existence?

[Michael Jabbor]

I mean, I want. I honest would love to say it's very little, but it's very integrated into the way I think and solving problems.

[Uri Schneider]

Very Jewish answer.

[Michael Jabbor]

Yeah.

[Uri Schneider]

What percentage of your waking hours or sleeping hours are AI? Yeah. Experiences.

[Michael Jabbor]

Let's assume that I consult with like 60 to 70% of things within AI if I can. Why? This is the key. The key is why would I do that? Not because it's helpful, because it might be irresponsible for me not to. Right. So I think people have yet to hit that place in their AI use in the larger public domain where they are using it in a way that, that it is reliable enough. Where they're like, there's consequences for me not using this.

[Uri Schneider]

Let's come back to that. Because I distracted us from Explorer Mode. So this 70% of your. Whatever you run by AI because it feels irresponsible not to correct. But what's this explorer mode? Does that look like?

[Michael Jabbor]

So the explorer. The explorer mode is the unknown. A lot of the topics that I'm. I'm exploring in my substacks are the unknown. Like, like what. What is the, the meaning for us from a human perspective? Like, is it a right. You know, is it correct for us to transition a particular type of work in. In a. At a particular speed? Even my, My most recent article just past week, about the. Something in. In. In mysticism that they. They talk about the. The short, long path and the long, short path. Right. So one of the things that stimulated me to start writing about that. One of several. One of them was my wife, because she's just. She's an incredible person and she inspires me. And. And that's also a longer story, but I. When I was thinking about this, the difficulty associated with growth, because through friction comes growth, and without friction you have atrophy. Yeah. And so I was wondering to myself, okay, just for background, for those who don't understand you don't know that teaching, right? It's. You have a short path, let's say person has two paths to choose from. One of them is just filled with poisonous thorn bushes and it's very difficult to get through it smells terrible. But it's short. It's like 10ft long. Right. Versus a long path which might be a little bit windy, but smells great. And how you're having more than enough space to walk along it. And in the end, by the time you go down this long path, you get there before the short path. Right. So you.

[Uri Schneider]

And in better shape.

[Michael Jabbor]

And in better shape, you're alive. Right. So there's a competing idea with this also from some mystical backgrounds around friction. Right. So. But to bring it down to earth, if I had to break all my patient base down, I could break them easily down into two categories. Those that can tolerate increasing doses of friction and those that cannot. And typically those that can will be successful in some form of therapy.

[Uri Schneider]

Yeah, right.

[Michael Jabbor]

So I was thinking to myself, okay, so friction is good, but the path, the short path seemed bad. I was like, like, what's the role? And I'm, and I'm always struggling with our, our cognitive ideas around friction. And so I was, I wanted to explore that. Here's a very clear paradox, a very clear conflict of ideas and conflict of even the emotions associated with those ideas. Like, is it possible that AI could help me understand where I sit in those areas, what the research says about those areas, and to write about it, like to actually explore a somewhat unexplored or maybe fully unexplored topic. So that's more in the philosophical domain.

[Uri Schneider]

But what did that look like? Give us like a little window into your AI experience there.

[Michael Jabbor]

So for me it looked like it

[Uri Schneider]

sense that you could tolerate friction. I mean, that's my read, but I'm

[Michael Jabbor]

not co pilot, for better or worse. Like, yes, I think life set me up for being able to tolerate some level of friction. And I've had interesting and also challenging experiences that have enable that for better or worse. But I would say for better. I think ultimately I became a different person, a better person, more alive, more connected to what life actually is.

[Uri Schneider]

Comes back to what you shared at the beginning. You love humans.

[Michael Jabbor]

I love humans. But also loving humans means that you love the mess, right? Like if you're looking for a cold existence where a child just doesn't break anything, doesn't, doesn't mess anything up, then

[Uri Schneider]

kind of locked into assistant mode.

[Michael Jabbor]

You're kind of locked. Thank you. That's exactly what it is.

[Uri Schneider]

Gotta be ready for the abyss.

[Michael Jabbor]

You gotta be ready. Right. For the unexpected, the most unexpected areas.

[Uri Schneider]

The unexpected. Totally.

[Michael Jabbor]

Exactly.

[Uri Schneider]

If you could do hard thing, hard things become easy.

[Michael Jabbor]

Exactly. Right, Easier. And so yeah, so this topic was difficult. This, this, the topic was difficult for me and I was just trying to struggle with it. And um, you know, I. Because I appreciate both. So I appreciate what looks like the short, long path and I appreciate what looks like the long, short path. And I was thinking about like things that are abstracting it to other areas. Right. Like education. How long do you stay in education? Is that the long, short path? So I had a lot of open questions about my own ability to wander and my own relationship with friction. And what is good friction? What is bad friction? Is it definable? Is it researchable? You know what, what has been researched already? Like what do we know? What's the state of science, state of knowledge.

[Uri Schneider]

So this was the back and forth you're having with your.

[Michael Jabbor]

Yes, yes. Because, because there wasn't an article that I could read and that would answer this question.

[Uri Schneider]

So it was like the conversation we're having now. Yeah, kind of like a spontaneous, unstructured developing.

[Michael Jabbor]

Well, we totally did not plan this conversation. So it is by definition.

[Uri Schneider]

No, but I'm saying it's different than as you said, assistant mode might be like search looking for information or for answers. Partner would be, be a thought partner with me, help me see what I can't see. But this would be like a spontaneous conversation of exploring and just riffing on where things go.

[Michael Jabbor]

That's right. Yeah. And at a certain point that will also happen with movies and music and other areas.

[Uri Schneider]

Explain that. So we talked about this. You were talking about how your kids may not know the difference between human generated music and AI generated music.

[Michael Jabbor]

Yeah, they can't tell. But. But also what I figured out is that I, I showed some adults the AI generated versions of my music and they also couldn't tell.

[Uri Schneider]

Yeah.

[Michael Jabbor]

Which I thought was totally fascinating. I was like, so what's the value of me playing?

[Uri Schneider]

You could ask what's the value? And you could ask like, what's the loss?

[Michael Jabbor]

All those things. Yeah, right. So yesterday I was with my daughter and we were just playing and she was like, can you put music? And I was like, sure. And sometimes we'll dance and you know, break out. Five year old dance parties are always fun. And she was like, but I want your, your music. Can you put your music on? And my kids don't usually ask for my music from like 20.

[Uri Schneider]

They don't ask years ago.

[Michael Jabbor]

I know they don't. They're like. It's like, can I really? Okay, sure. Can I read the article you wrote, Papa? No. But she asked my music and I was like, okay. But then she. That she followed up on her own. She. She's just about six now. In a few weeks, she's the COVID baby. She's the coed baby. And she was like, no, I don't want the computer music. I want your music.

[Uri Schneider]

Wow.

[Michael Jabbor]

This was just last night.

[Uri Schneider]

So is it that she couldn't. She could or could not tell the difference or she. She was requesting the real thing and asking you to give her the real thing?

[Michael Jabbor]

I think that she couldn't tell the difference at a basic level, cognitive, emotional level, but I think she could feel the impact differently.

[Uri Schneider]

So she wouldn't be able to tell you this was generated by AI or this was generated by my father. But.

[Michael Jabbor]

But something about it.

[Uri Schneider]

She was seeking the impact.

[Michael Jabbor]

Yeah.

[Uri Schneider]

The transmission of this is the music my father made.

[Michael Jabbor]

Yes.

[Uri Schneider]

And could she tell the difference in her experience if not for asking you to provide it and trusting. Trusting your provision?

[Michael Jabbor]

I didn't prompt her and also didn't ask her what music she wanted. Right. So she just asked for that out of the blue, which was. It was very sweet and I felt very moved by it. But I also.

[Uri Schneider]

Unless your father might have delivered the AI Music.

[Michael Jabbor]

Right. Experiment, totally. Yes. There's a lot of experimentation here. And I do think that you can get deeply impacted by media that's been crafted in a certain way. You can get deeply impacted by an audio that is generated by music. I believe that. Right.

[Uri Schneider]

It will.

[Michael Jabbor]

It can bring you to tears. It can move you in emotionally way. It can. And I can tell you some interesting stories there too, about. About music.

[Uri Schneider]

I feel like that's a big. That's a big inflection point that we passed in the past couple months.

[Michael Jabbor]

Yes.

[Uri Schneider]

Like creative. The ability to apply AI into creating art.

[Michael Jabbor]

Yes.

[Uri Schneider]

Music, film, visual arts.

[Michael Jabbor]

And I think there's a lot of fear in the community out there about it. And by the way, there's a whole industry out there that's just fudge. Fear, uncertainty and doubt around AI. And anyone who's listening to this, I would advise that you stay far away from that industry.

[Uri Schneider]

Where do they park themselves?

[Michael Jabbor]

Pretty much everywhere. I mean, Stanford, I think, evaluated that it had a 30% impact on financing and other types of support areas that people give fear, uncertainty and doubt based on nothing, even just. Just based on no Real evidence.

[Uri Schneider]

Interesting to look at the overlap of the people with FUD around AI compared to the general FUD people.

[Michael Jabbor]

Yeah, well, the general fud, the most like known, well known FUD is like the old, old IBM fud. I wouldn't say it's today's IBM, but maybe, I don't know. But that, that idea of you'll never get fired for making this big purchase decision. Right. We're the best in our class and so like just, just programming someone like that is a very particular type of marketing.

[Uri Schneider]

Yes.

[Michael Jabbor]

And so like I, I think that creatives have an. A lot to look for, a lot to look forward to of being more creative, having more to someone to explore with.

[Uri Schneider]

Like, I don't know, most people listening are like thinking this is very threatening to creatives. So explain that.

[Michael Jabbor]

Yeah, so like, but, but how you're

[Uri Schneider]

thinking about the creative is playing a somewhat different role.

[Michael Jabbor]

Yeah. So when I'm thinking, when I'm playing guitar, for example, like I'm, I'm, I, I feel my music like, I feel it like, like vibrating against my chest. I can like connect with whatever it is that's being produced right now. Do I always have someone that can play the darbuka or that can, can somehow like just jam with me? No, I don't always have people that can jam with me. And yes, I do want people to jam with me all the time. And if I could be playing music all the time, I might, but that's not something I can afford to do right now.

[Uri Schneider]

Is that partner mode or that's explorer mode?

[Michael Jabbor]

I think there's a combination of partner and explorer mode. Right. Because when at the edges of the partner mode you start to explore with your partner.

[Uri Schneider]

Co exploring. Right.

[Michael Jabbor]

And so I think it's the edge of partner and you're now moving into something where you're exploring, where you can see. Like I had an idea for some art that I wanted to put on a particular wall and I'm not necessarily a fantastic artist. I can do stick figures like anyone's business. But like beyond the stick figure, already

[Uri Schneider]

light years beyond me, beyond the stick

[Michael Jabbor]

figure, I don't know. But I had in my mind like a visual, I had in my mind an art piece that was unique that I had never seen before, that I wanted to create. And yes, I did go into ChatGPT to create it. And you know what was interesting? I didn't get it the way that I wanted to get to, but what I ended up doing was going to Claude, which is a highly visual AI. I described to it what I wanted and what I was dreaming and visual. Visualizing. It created the AI prompt for ChatGPT.

[Uri Schneider]

I do that all the time.

[Michael Jabbor]

And then it created this thing that was in my mind and it's pretty

[Uri Schneider]

wild for people that don't realize this. How about, could you do this? Could you like there's a style of, there's a style of basketball that's east coast and there's a style that's west coast and you could describe it as like, faster pace, more fast breaks, less half court sets. That's west coast. East coast is much more slowed down, you know, half court sets and so on. Just a broad stroke for people out there. Could you like describe the qualities or characteristics for good and for good, for good and for bad of chatgpt which is one leader, which is OpenAI and then you've got Claude, which is anthropic, and then you've got Copilot, which is Microsoft and any others. But could you give like a little bit of a character? Character profile, sure.

[Michael Jabbor]

So Microsoft Copilot uses both firstly and I would say that that is designed for the safest type of use, both for kids and for adults and in an enterprise setting. So as far as the safe for work, good for work AIs, I think copilot stacks the highest. It also understands constructs very more natively, like what a team's transcript versus like a Word document and how to navigate those things. Like I find Copilot in teams to be fantastic. I think it's excellent at recording meeting notes in context and can answer great questions. So that's where I'll. That's my go to for that work. Right. When it comes down to chat GPT vs Claude, both very interesting. Chat GBT has got, you know, over 80% of the market share. So like they're, you could. The easiest way to think about it is that they're great at 80 to 85% of the tasks. So like, you know, you go in there, you're like, hey, look this thing up, do this thing, fix this assistant mode, fix the grammar. It's great at assistant mode. There's also pro models that can do very, very deep vertical thinking. That's like GPT5 Pro, for example, if

[Uri Schneider]

you took all the chat GPT users, I think there's a very, very small portion that are using it that way. Generally speaking, like you said, it has the most market share and a lot of people would be fair to say most people are using it for, for Assistant tasks.

[Michael Jabbor]

Yeah. Yeah, assistant task, searching even.

[Uri Schneider]

Yeah, right.

[Michael Jabbor]

So searching, helping assistant, feedback, all that stuff. All the partner kind of mechanisms that does really, really well. I feel like Claude is a. Claude

[Uri Schneider]

is my people, just for the record. So I do have a bias. I'm super interested in hearing how you typify characterize Claude.

[Michael Jabbor]

Yeah, my people, your people.

[Uri Schneider]

You know, look, even in AI, we're going to have like tribes.

[Michael Jabbor]

Yeah, we totally, I totally get it. I totally understand and, and, and agree. Like we're going to have a lot.

[Uri Schneider]

It's an open tent. Like you could eat, you could eat kosher and halal under one tent. We can do co pilot and Claude and chat GPT all under one tent.

[Michael Jabbor]

Yeah, that's my tent. My tent's pretty large. I include other models too. But I, but I do think that Claude is a, a deeper thinker. Not in the sense, sense of like just vertical depth, but I feel like the way that it approaches a problem is very holistic or robust and it like will catch things that I will catch or it will emphasize something I will emphasize versus like just what is the, what is the most appropriate thing for this setting, which to me feels like a great average.

[Uri Schneider]

Right.

[Michael Jabbor]

This will go to the edge a little bit more and say these are, these are ideas that I'm thinking seeing. I also find Claude to be a much more visual model.

[Uri Schneider]

So not in producing visuals.

[Michael Jabbor]

Producing visuals. Also front ends for front ends.

[Uri Schneider]

Yeah.

[Michael Jabbor]

So front ends for applications. I think most people are thinking of

[Uri Schneider]

making like cartoons and memes.

[Michael Jabbor]

No. So it doesn't do that. It doesn't generate images like that.

[Uri Schneider]

Right. Like code for front end.

[Michael Jabbor]

Yeah, but like I said for substacks, my substacks, I generally like to include some sort of AI generated image.

[Uri Schneider]

Like I thought you drew those.

[Michael Jabbor]

No, I definitely not. I will, I will ask Claude to think through the problem and generate the prompt in order for AI. And I actually connected them together. So Claude can now use OpenAI to generate the image that it thought of.

[Uri Schneider]

Yep.

[Michael Jabbor]

Right. So I do that regularly and so I find that Claude is, has that, that level of depth, but it also doesn't do a great job at like managing its tokens and managing large documents. Some, some of the, the surrounding ways that it manages artifacts I feel like will grow and we'll get there, but are not there yet. Yeah, but even in the development area, like Claude code is excellent at developments. On the coding side, I think of

[Uri Schneider]

it as like, when would you use a journal? Or when would you use an iPad? Or when would you use A laptop. They each have a time and a place. It's not one is good or one is bad.

[Michael Jabbor]

That's exactly right. And we're not even in the era of like, flavored AIs yet, but we'll get there.

[Uri Schneider]

What would your flavor be?

[Michael Jabbor]

So I think my flavor would be very eclectic. Kind of like me.

[Uri Schneider]

No surprise, I think.

[Michael Jabbor]

Right. Like, I, I, I would want it to like, really push hard on the edges, the things that were like, less average, that were less common, and to be able to report back to me on something and say, hey, yeah, I was just thinking about this odd thing. Like I, I, I want to hear those, like, when I'm with students, I don't, I don't need them to, to give me the, the answer that I'm expecting.

[Uri Schneider]

Right, right.

[Michael Jabbor]

By the way, a great, great teaching tip that one of my friends at Microsoft picked up because she's a teacher. I'm not in that respect, like a traditionally trained teacher.

[Uri Schneider]

Well, I'm your student, so that makes you a teacher.

[Michael Jabbor]

Okay, fine. So I'm a teacher for this minute. And I, you know, she said to me a long time ago that when I was giving keynotes and lectures, at the end I would ask, you know, does anyone have any questions for me? And I would get like minimal questions. I would get some who would be courageous enough to jump in and be like, I have no idea what I'm doing, but here's what it is. She just made like this one or two word correction to say, what are your questions?

[Uri Schneider]

Does anyone have any questions? Nope, I just got an out. What are your questions? Means? I believe you have a question.

[Michael Jabbor]

I know that you have a question. I have faith, I trust that you have a question. What is it? Right. And being able to extract that kind of thinking and you know, even a lot of my keynote, it's are all questions or more questions than they are answers.

[Uri Schneider]

Yeah, right. So the art of the good question.

[Michael Jabbor]

Yes. And I want an AI that is going to do more of that with me, for me, to me. Right. To ask me those questions, to be able to, to partner with me in that mode, to assist me in that mode. And so you're going to have those types of flavored AIs that are unique to a person's context, to their, to their life story, to what they're trying to achieve, how they're trying to achieve it. And we're seeing just the opening act of whatever that might be.

[Uri Schneider]

A good analogy, I think I was like, you get a car, we're here In New York City you get a lot of different rain, snow, heat. We don't have a lot of sand dunes. But you know, you've got different sports mode, power mode, economy mode. So these could be thought of as like different ways you could take the same machine and flavor it or, you know, dial it up or dial it down, change the settings for different purposes, different terrain.

[Michael Jabbor]

Yeah.

[Uri Schneider]

That's what you're talking about.

[Michael Jabbor]

Yeah, definitely. And we do have things that will allow you to have it think more deeply.

[Uri Schneider]

Right.

[Michael Jabbor]

You know, like hard thinking versus soft thinking, but like. Or fast thinking versus slow thinking. Better way to put it.

[Uri Schneider]

It's very interesting because it's counterintuitive. Generally we keep buying and upgrading devices with more RAM and quicker responses and more snappy. But one of the things I like about Claude, it thinks there's a lag between the prompt and the response. And when I go to ChatGPT, it's like I'm like, don't be so snappy. Don't be so snappy. Even though like I'm used to everything else being like a microwave, you know? Yeah. Let's go. 30 seconds. Cooked. Yeah. But it's fascinating that that's like baked in and it feels really good.

[Michael Jabbor]

Yeah. And you can get Chat GPT to do that other totally model you could

[Uri Schneider]

do it and it's like that it's organic and natural in Claude in a. You know, I'm saying that in a

[Michael Jabbor]

provocative way because I totally agree with you. Yeah.

[Uri Schneider]

I like that's the default and it sets the tone and the experience.

[Michael Jabbor]

Yes. And I think you have to look at also the who it's serving.

[Uri Schneider]

That's right.

[Michael Jabbor]

Right. Like what is the most tasks that are done and what do people mostly want in ChatGPT versus mostly what do they want from Claude? Right. So like for me, I don't view them as competitive as at all like I. I'm using them totally in partnership. I have them partner with each other totally continuously.

[Uri Schneider]

It's like consulting with people on both sides of the aisle.

[Michael Jabbor]

Yeah, that's right.

[Uri Schneider]

What would you say as we come to the end, what would you say is like the most exciting, promising application that you either are currently using it for or look forward to seeing how it can solve problems in the world and then contrasting that with some of the greatest concerns or risks of what can go wrong?

[Michael Jabbor]

Sure. So tiny topic to end on.

[Uri Schneider]

Small topic. Yeah. Just like one anecdote was top of mind. We won't go too deep.

[Michael Jabbor]

Right. So three areas.

[Uri Schneider]

One thing I would Say, to preface, we might have even sat in the same lecture where it was a discussion about. It was a discussion about human beings. And if we look at, let's say, the Holocaust. So Hitler demonstrates the full extreme of evil and destruction and the worst of what human beings can do to other human beings. And this person, I think it was Usher Wade, was talking about the fact that he also teaches us and inspires us that if he could take it this far to the side of evil, then we could take it this far to the side of good. And I think about that with the Internet, and I think about that with technology. And I once heard someone else say, it's like kitchen knives. Everybody has kitchen knives in the kitchen, but you better watch how the kids are using them or something dangerous could happen. But no one would cook without kitchen knives. So anything that has a power for the good also has the risk of doing harm. So without being a futter, without being a fudder, seeing the full potential of recognizing we have to be intentional.

[Michael Jabbor]

I'll never call you a fudder.

[Uri Schneider]

Don't worry. Thank you. I'm worried about people that are listening, that are futters. Some of the people that listen to this are futters. Don't be a fudder.

[Michael Jabbor]

Don't be a fudder.

[Uri Schneider]

What's the opposite of a fudder?

[Michael Jabbor]

I'm not sure, but an optimist.

[Uri Schneider]

Fear, uncertainty, doubt, and doubt. So we'll think of the opposite. You know, comment on this. Let us know what you think.

[Michael Jabbor]

Yeah, I. Okay. Three areas.

[Uri Schneider]

Yeah.

[Michael Jabbor]

In society, the areas at least I pay a lot of attention to. Work, medicine, education.

[Uri Schneider]

Yeah.

[Michael Jabbor]

So as our CEO Satya said several times, this is the first time in history where it's very possible that every person could have a Stanford doctor, an educator in their pocket. We're at that point in history. Right. Smartphones are ubiquitous pretty much everywhere in the world. And access to the Internet is becoming more ubiquitous everywhere. You're going to be able to have access, access to knowledge and information in a way that has never been seen before and will make the Internet look very small.

[Uri Schneider]

Say that last part again.

[Michael Jabbor]

So when it comes down to the access for information, the access to. To getting educated, to growing in that in all those ways, you're going to see the information that was distributed in the Internet will be small in comparison. It will be microscopic in comparison to what you're seeing right now, right now, not in three years from now. Just the more that you access it. I think at this moment, when we're accessing all these AIs remember that the aperture is this big. And so it's like trying to extract the world's knowledge through the mouth of a five year old and the mind of a five year old. That's the current status. So in a world where everyone can have a top doctor and a top educator, that just changes everything about what we're trying to do in terms of having a better society. Right. A, a positive outcome where people aren't trying to take themselves, take themselves out. Instead they're building, building each other. You know, there's a great this, it's not a, it's not a story. It's like real life. But crabs, when crabs are in a bucket, they never have to cover a crab bucket because they're all holding themselves down. They never actually escape the bucket. So I view like our society to some degree, many parts of our society, like a bucket of crabs that doesn't require covering. And so here we're in a place where we could actually build each other in ways that we can't even yet start to predict. And that's going to be on, I would say, the medicine and education side, which by the way are very related to each other like health outcomes, your educated society, et cetera. When we're looking at work, I do think the work is going to change. I think it is going to. We're going to be able to lean into work in a very meaningful way. I do think that certain jobs will go away and many jobs will come also in terms of the way that we can now leverage human potential. And you're going to see compounding and precision at scale happen in a way that even Ford and that type of manufacturing could never have ever envisioned. Right. So you're going to see change in those three dimensions and potentially for the better.

[Uri Schneider]

You know what? I'm going to modify my other side of the question instead of the. It's not talking about dystopian fear, but more like what are the ways that today people are engaging with AI which is just minimalistic and limited and kind of capping the value they can get out of it.

[Michael Jabbor]

So I like that you reframe that and thank you. Because I think dystopian ideas are only helpful in very specific scenarios. Right. I do think that where people can lean in. Well, first of all, the worst case here is that people don't get access. So that's the most dystopian future. Inaccessibility, inaccessibility, uneven accessibility, just not having access to the models.

[Uri Schneider]

Right.

[Michael Jabbor]

Unevenness will eventually even itself out, I think, but, but no access at all is a problem. And as far as where people need to lean in and they're probably not leaning in is just how far can I push this to help me? Right. So ask the AIs to ask you 10 questions in the, in, in, in amidst the prompt or in this conversation, in this dialogue.

[Uri Schneider]

I think the example you gave that I've come to is also asking Claude to help create a prompt to prompt chatgpt. But you could do it within a model. Like you're saying, like, help me flesh this out. What 10 questions should I be asking?

[Michael Jabbor]

That's right. Yeah. Yeah. And I, I, I think another area that people can, can lean into is looking deeper into their own insecurities around things. So the, there's a great Leland quote that I talk about in some of my, my lectures around that a person can basically only grow to the proportion that they can accept truth about themselves without running away. And one of the things that I've seen with teaching so many people how to use these AIs in person is that these tools are great communicators, generally speaking, and humans are mediocre communicators, generally speaking. Some are terrible.

[Uri Schneider]

And so speaking and listening both, both,

[Michael Jabbor]

both those things, we don't like to hear everything that we have to hear correct, myself included. And I, I think that if, if we can lean into those areas of vulnerability, that human messiness, to like, understand, like, why am I thinking about something? Yeah, I'm probably being stubborn. I'm probably like, like leaning in in a way that might be too heavy or inappropriate or I'm not leaning in enough. And so just being able to come face to face with who you are and how you operate and what that means to those around you is going to be an incredible unlock for a lot of people.

[Uri Schneider]

Imagine you had a billboard. You could take a billboard in that in Times Square. What would be your slogan? What would be your message?

[Michael Jabbor]

Do good and be good.

[Uri Schneider]

Do good and be good. This has been awesome. This was really good. Thanks for coming.

[Michael Jabbor]

Thank you. Thank you for having me. It's so good to see you.

[Uri Schneider]

Thanks for listening to TranscendingX. If you enjoyed this episode, share it with someone who could benefit from it. If you want free tips to help you talk more and fear less, sign [e-mail] email until next time. Remember, tomorrow's breakthroughs start with what we do today. Let's keep talking.