Introduction

Hi, how are we feeling today? Good? Excited? Okay.

Why This Talk: The Human–Technical Intersection

So, this is an interesting viewpoint that I'm taking in this talk because I actually come from the technical background. And as I dove into AI, I've built models since 2016, worked in product engineering, the business side, with startups and all that.

I also happen to be a coach for people, for the people side, not just the technical side and that intersection between both happens to be very interesting and one that we should care about today more than ever so in

short I'm an AI advisor and coach I work with leaders organizations on AI adoption and governance they also work with professionals and founders on the coaching side of things as we're navigating the complex shifts that we're going through today.

The Gap Ahead: “Who Are You Becoming?”

So I'll start with a question and bear with me. I want to see a hand raised for anyone who has in the last year delegated a good part of their work to AI. Something that you used to spend a lot of time on and I want you to keep your hands raised.

Nobody does emails and and all that I really mean things that you used to do a lot of time on yeah okay keep your hand raised if someone your organization your industry someone told you clearly what your role is going to be about in five years you yeah interesting right in five years we used to know exactly like like what degree we should be doing in five years that's going to be changing the world. And now we don't even know what are we going to be doing.

And I don't mean to ask that to make you uncomfortable because I sat in multiple rooms with some of the most brilliant people in AI in that silence, and nobody can provide that question yet. And so it comes in a very easy question.

I made it a little bit complicated in two parts, but the essence is who are you becoming really and what I want to talk about today is the gap the gap between what we already know and where we're heading and what we don't really see yet that gap has a shape and when you see that shape then you start making different decisions about how you build, how you lead, and the choices that you make about how you live and your work.

Where AI Is Today (and What’s Next After LLMs)

Very quickly, where we actually are. We are in a very interesting transition.

We've seen a lot of it, call it bubble, call it hype, call it whatever it is. And we talk about LLMs.

They're extremely excellent at pattern recognition, right? A lot of things that we describe in writing and it's tokenized in different ways, could be voice, could be video, etc. Those things it's really excellent at.

But the next frontier is actually world models. I don't know how many of you have heard of world models. And so we're trying to transition from pattern recognition to an understanding of the world. And we're very far from getting there. We're early.

That's why we don't all have robots who are doing our laundry and doing all that obviously so when you think about that

this is just llms i don't know if any of you saw the report anthropic generated last uh last week like this shows you that theoretically where llms are right and the adoption is in red so we're very far from even the capability of what LLMs are able to provide to even get to that transition of the human side and how that future will be like.

The Human Edge: Judgment, Intuition, and Relationships

And what I want to talk here is not in that lived space in between. It's not soft skills. It's not personality traits.

We hear a lot about the skills in 2030, and they're very important. I really want you to think about

yourself and I want to talk about something very specific judgment intuition relationships pattern recognition in the on the intuition side the gut like your experiences your conditioning your culture and all that that cannot get tokenized it can't get fine -tuned in models

it's compounded outcomes that are built through consequence, through errors, through something that cost you something, right? And so when we think about that, that's the uncertainty that we can't necessarily develop in models.

When you sit with the discomfort of making decisions without all the data that are are there when you walk into a room and you feel something is off we use this in cyber security

when you open an email that's like first practice you feel that something is off you don't respond you start digging into the data and then you do the facts and all that right and i want to give

A Responsible AI Example: When Instinct Disagrees with the System

an example when i was at microsoft i worked a couple of years at microsoft and i led responsible ai work we had the systems we automated everything the compliance checks uh the the workflows and all all that.

But sometimes when we have reports, some of the outcomes and the instincts that I have is different than the system. And it does not mean that system doesn't work. It's just that

we ask questions that are outside of the checklists. And that sits in that intersection of your experience. When I was between engineering and product and customer insights and business

business decisions, that instinct that gets developed, that does not come from frameworks. What are frameworks? It's the experience, the successes, the patterns that then make it into the checklist and the documentation and frameworks and all that. It's not the opposite.

So that will never go away. Even as you automate, there's always things that you ask as a human with that that experience that go beyond what the algorithm says, right?

So if we move, yeah, okay.

Automation and Skill Atrophy: Can You Still Do the Work Without AI?

So quick question in that regards. When you think about one task that you have done in the past, say, month for AI, and now it's, you know, every time you think about it, you're just going to open your CharGPT, your Cloud, your Gemini, et cetera.

and you're going to do it you're not going to give it a chance let's say electricity goes off and you have to do that for a week are you still good at it yeah you stopped right yeah so and that's like the the transition that's interesting to navigate in that moment and you

have to ask yourself very sincerely what it means for you to stop developing those things that the AI doesn't do and won't do anymore.

And before I move to the next one, I'm sure you've read it in different posts and different, it's like, AI won't replace your job, human judgment elevate it. But what is judgment?

Judgment doesn't exist. It gets built. It gets built in the discomfort.

It gets built in those moments when you are wrong, when you learn from your experience, when you sit with the work to feel it.

And so when we think about what AI is automating, and that's where when I asked you if it's a task you can do it without AI right now and do it as good as you were, AI actually removes those conditions and that context, which judgments get built on.

And that's where you pay attention to what are you actually developing as you're making use of AI and automating all those tasks and everything that you think is easier and faster to do.

Capability vs. Dependency (Individually and on Teams)

And the deeper question that I want us to talk about now in the question of who are you becoming? Are you becoming more capable? Are you deepening your judgment? Are you building through consequence? And are you getting better at making the hard calls?

Instead, are you becoming dependent? Yes, you have faster outputs and you are atrophying that that judgment. And you're getting very good at prompting. And I'm talking about this for you individually, but also when you're leading teams. Are your people getting more capable or are they getting more dependent?

And it's not philosophical. It's actually very strategic. Because when you can pound the judgment and build in the skills that AI won't do, you get people and a team that becomes extraordinary. And that is irreplaceable. A faster team is replaceable. That's the risk that we're running with becoming more dependent than capable.

And there is nothing wrong with the dependent, and that's where I do make the most difference in when I say I'm an advisor and I'm a coach. The advisory is where the dependency goes. Yes, we need to automate and we need to do all these things. The coaching comes in the capability because if you don't know what you're doing in five years, we don't know in 10 years and things are only getting faster and they are going to change.

The speed at which these things change is only getting even faster that's why it's very hard today to see how fast we can automate all the tasks that AI can do but we're very slow at figuring out where is the human role in the question because we're not used to the speed it's something new that we're adapting to so I want to

go back to a raise of hands I think that it's not we don't know what the future is the future is getting built today with the choices that you're making what gets built what gets optimized what gets left out who are the people in those rooms who are making those default settings and we'll talk about that in the panel the

diversity and women and all that all these capabilities are within the same equation so when you think about what you're developing and the skills that you're developing and who you're becoming.

I don't want you to think about a model. The models are becoming cheaper, they're becoming faster, they're becoming more capable.

The competition is crazy so you can even imagine in one month, two months how they're gonna become. But what are the skills that are you developing yourself and how are you compounding that building of the judgment?

And those are skills that compound forever so the instinct of automating everything right and reducing the frictions in your team in how you do your tasks and all that

that is right but that is exactly that's making you not only automate your workflows and change your workflows you're also changing yourself it's invisible but it's happening very quietly and so in that decision when you

give when you're trying to automate a lot of thing you think about have you actually sit with the work enough to feel it have you say of what is the ripple effect of that work in the future how is it going to impact you how is it going to impact your team how is it going to impact your organization where Where are the skills that are actually leading the innovation are developed?

Because AI was not developed by the technology that was before that and was a revolutionary. It was developed by human. So what are those skills that you're investing in to lead to the next innovation?

A Practical Framework: “AI What You Can, Human What You Must”

And this is where I end with, this is a framework that I work with a lot, which is AI what you can and human what you must.

Every time you hand something off to AI you need to think about what is changing and who are you becoming.

It can be a career question, it can be a business question, it can be a productivity question but at the end of the day what are you compounding in that bigger picture of what you're trying to AI?

Compounding What AI Can’t: Embodiment, Intuition, and Wisdom

Are you deepening in the embodiment experiences what i mean by that is the develop your intuition intuitive sense your gut those things that come with wisdom that come with experience are you deepening your

relationships genuinely that's what brought us to this this room right shaton was just saying how we all met this is really how we met and this is how we develop how we collaborate and we bring new new ideas together.

Why Genuine Relationships Matter More as Knowledge Gets “AI’d”

And that's the right moment to do that because the knowledge will be AI'd, the network won't. Those genuine relationships, who is picking up the call when you need something? Who are opening rooms to possibilities that were not described in a model that are new,

that people are thinking about? And ask the questions that are outside of the checklist. list.

Innovation Comes from Questions Outside the Checklist

I feel like this is one that might be intuitive, but not really. Because when you think about innovation, again, you think about those questions that your model did not give you. Your model, as you ask it, and as you get really good at prompting, and you add those layers of break this down, break this down, give me the reports, give me the questions that were not asked,

try to go outside of the box all right so the reason why I'm going to keep this talk at this level it's very easy you're all I'm sure are from different industries you have different roles you have different conditioning it's very easy to list a couple options of what you could think about as you think about your next five years and I am explicitly in this talk taking completely

Coaching Lens and Next Steps

off my hat of the advisor and really tapping into the coach.

The first principle that I apply as a coach is we people are naturally creative, whole, and resourceful.

And the opportunities that we have now are not opportunities that are on lists. They are getting shaped. They're getting developed.

We have an opportunity to go inside and to figure out for ourselves what that future might look look like, what that gap of what we're seeing happening very quickly and what's yet to happen is about. And we have that ability to do it.

So I invite you in this moment to start asking yourself the question, it's not scary, it's not philosophical, and it can be very practical. It can be very experimental.

Make the Future Concrete with Micro-Experiments

I run a course like this where we really get into the micro experiments of what that future could look like, portfolio careers, some spark of, you know, dreams that you had for so long that you didn't necessarily tap into and that could be individually

Closing Invitation and Q&A

but also for your organization that's pretty much it for me if there are any questions if this conversation sparked some ideas um find me after a lot of ideas uh firstly round of applause for omaima