[Automated Transcript]

Deep: Hello, everyone. I'm your host, Deep Dhillon. Today on this special episode of your AI Injection, we'll be taking a look into the future of AI. Over the years, we've had the privilege of hosting incredible experts who've shared their predictions, insights, and a healthy dose of skepticism about where AI is headed in the near future and what can go wrong.

Today, we're bringing it all together to tackle the question of If all goes as planned, or maybe not as planned, how will I transform our world in the next 5 to 10 years?

Brent: First of all, I think tutoring is going to become a mainstream thing, a thing that is required in education for students to continue to move forward. I think the pandemic exposed that a little bit and now they're getting the taste of it.

They're seeing what it does. I think it's going to be hard to let go of that. So I think tutoring is here to stay. I think it's going to grow across the board. So now the question is, how do we do it effectively? How do we do it so that they can afford it? And I think that's where AI is going to be really cool.

I think a future five to 10 years from now, AI is going to be running behind the scenes, throwing up red flags on. The student over here long before anybody else realizes saying, Hey, they're falling behind. Let's catch them now before they're a year behind. Let's get them while they're just starting to fall behind.

I think that's one area. I think tutors and teachers are going to become more effective as they can get direct feedback on how they're doing in the classroom and virtually. I think both are going to be there. I don't think we're going to see a future where AI is the tutor or AI is the teacher. Until AI can actually look at a student and know what's going on in their lives and understand them emotionally and otherwise taking the human interaction out of that is not a good thing at all.

Teachers, you're not in jeopardy being replaced by AI anytime soon. Not to mention that education, especially K 12 is very slow to adapt. And so I think it's going to be a while before we're there, but I love the idea that it's a tool to help that teacher be better or help that tutor be better.

Yuval: We are transforming how the entire industry is using AI and automation. to make it easy for everyone to file, explore, analyze and leverage regulatory data. I hope that we'll be able to get to a point where these sorts of generic understandings are put in front of everyone, not just the experts like the one working at the regulatory office in a utility or the regulators themselves.

When I think about an ecosystem, one of my consumers should be you and me, going and asking, what Tell me why did, , Georgia Power increase the rates last year versus, uh, what it was, uh, two years ago? And the system might generate something, right? I'll be able to read it and understand, okay, we had a bunch of storms, there was a fire, maybe a nuclear power plant came online, and it was only a decade behind schedule.

Now I understand, okay, I may not like it, but I understand why. Things of that nature, I think, will go a long way. Visibility usually helps maintain reasonable prices. This today is the job of the regulators, but the more we distribute this power to average Joe's like me and you, I think the better we're all off.

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Sarah: I heard an interesting quote. A few months ago, it was actually someone complaining about AI saying, I thought that AI would be doing the dishes and folding my laundry so that I could write poetry and paint paintings. But instead, you know, it's the other way around.

It's writing all the poetry and I'm doing all the dishes. So I don't know, will that trend persist into the future? I hope not. I hope that humanoid robots. Continue to progress the way that they have been and that AI can just continue to do more and more of the kind of work that we want to delegate to AI so that we can do work that feels less like grunt work and more like the work that matters.

That was the kind of ethos that I've always had starting Seek. Like our initial website before we bought the Seek. ai domain. It was seek what matters. com focus on the things that matter. And it was always about you being able to delegate to seek all the just manual work you don't want to do. And you're, if you're a data scientist or a data analyst, you're just wondering like, wow, I got a master's in physics and went into the industry so that I could be like answering these random questions.

Like, this is my job. That's where it all started is I really liked the idea of allowing those types of people to be able to delegate that kind of work so they could do the work that they set out to do. And I hope that we'll see that persist and grow as AI gets better and better. Just because someone's not writing the code, it doesn't mean they can't play a role shaping the role that AI is going to have in the world.

Gary: This is the dream that keeps us going all the time. I think, I mean, it's.

I see having permanent presence on the lunar surface, the beginnings of an actual economy up there where you have not just materials being made to build stuff and maybe ship back to Earth eventually or to other things we're building on orbit, but, but actually business to business transactions happening there to support the humans that are on the space.

We see multiple commercial space stations emerging potentially by the end of the decade. And if this works out, like, we would like to see that happening, like in 10 years, definitely there are plans for multiples of these. I want to see also large structures being manufactured on orbit for various applications.

People are starting to test out, you know, solar power in space, large antenna arrays that can look into deep space or can assess larger areas of, you know, the environment where we're managing traffic. I mean, I think the activity is going to go way up. In the optimistic plan. Talk about 100, 000 satellites on orbit by the end of the decade.

If that's happening, we need maneuver for all those satellites. And it's sort of a vibrant industrial economy that's starting to emerge within 10 years. It will still be the beginning. But 

Deep: and with respect to CisLunar, are your forges are on the moon on the space station in asteroids? 

Gary: Yep. Um, 

Deep: yeah, 

Gary: maybe asteroids by then.

I don't know. But for sure, I would expect us to be on the lunar surface with an initial, capability there. I would expect us to be, in space stations to process their metal waste and also on a platform to start processing, satellites at end of life.

Probably that before debris, but and eventually space debris. I also see our power systems being all over the place too. So that's the other piece of the business that we're working on.

Adam: I think the first thing for us, like the journey I think we're on is.

How does our AI become a true companion, a coach, something that knows when I care, why I should care, and helps me actually learn and develop into being better at what I'm trying to do? And in our world, of course, that's very focused on a manager. How do I make managers better managers? Not a ton of science out there.

for how to be a better manager, how to lead people, how to develop people. And if you suck at it, you'll stop you being a manager in a, in a year or two. Right. After you've probably lost half your team. And so I think that's kind of my dream is like, how do we get that? And then how do we get a lot of science around that?

Right. So that we actually know the right things to do and recognizing that that's a hyper personalized reality, because what works to manage one employee doesn't work to manage the next employee. So it's never as simple as you just everyone do it the same way stuff. . I think that's the dream we're driving towards how to make people more effective as managers and then obviously indirectly obviously make their teams more successful, more competent, more high performing.

I think one of the challenges we're going to see in the near future, though, just to look back on the other way is how do we create learning environments? You look at AIs today, we're very clear. If you want up to journeyman level, um, writing the AI can do that for you.

Mastery today, most people in those jobs have mastery. So they're using these things to optimize their performance, which is great for them because, you know, just take away all the mundane work I don't want to do. But if you're a new hire coming into the workforce in 10 years time, when. The first 10 years of your job can be done by an AI.

How do you get to the mastery of the 11th year, right? How do you learn your way up and how is somebody going to pay you for 10 years when the AI can do it better? It's a massive 

Deep: societal question, right? I mean, I mean, we can only hope that we can leverage these tools into being great trainers. Yeah. And that we all just get to a better place.

That today's top performers are not 10 years from now's top performers. They'll be much better. Yeah. Because it'll be so much easier, quicker and more efficient to learn and maybe learn what we need to because we can all like lean on these reasoning engines. Now 

Adam: there's an old quote that I always use a lot, which is Bill Gates quote, right?

People always overestimate how much can get changed in one year and underestimate how much will change in 10.

Deep: Let's fast forward 10 years out, give us two scenarios. One where we're all screwed and everything's hell and the other one where something actual positive happens and it's not as bad, how can the AI and the other technologies, like if they play out really well, help us out. And how can they not matter and us fail as humans to like do the right thing and how bad does that world look?

Chris: My biggest concerns around AI, and I really don't have a lot of concerns around AI, I, again, I view AI as another tool in the toolbox. And I, I think it all depends on how we as humans decide to use that tool. And hopefully we decide to use it for the better. Some of my concerns around AI specifically in the context of conservation are perpetuating these sort of entrenched biases that we have.

Both social biases and the data that we're using to train these models. A lot of the biodiversity data that we have is from North America and Europe and China. Which is paradoxical because most of Earth's biodiversity is housed in the global South. And so we need. More data collection efforts in the global south and tropical countries, and we need to make sure that our use of A.

I. Is not perpetuating these sorts of biases and that the potential benefits of a are being shared among all these diverse communities. I would say my real dystopian future view. Which I don't believe in, because I think we will get our act together, is that, as E. O. Wilson said, the collapse of insects will be the collapse of humanity.

We cannot survive on this planet without insects. If we don't do something to curb their decline, we're gonna lose out in the end as well. My optimistic future, on the other side of that, A very common perspective in ecology is the idea that the whole is greater than the sum of its parts. It's a really important idea in ecosystem sciences where we know that each individual species is playing an essential role and together we including through the interactions that we all have with other species on the planet we form.

What is the biosphere, all of life on the planet, a self sustaining system. We need to perpetuate that model, including how we deal with AI. So I really see that if we use AI intelligently, it will be another essential entity in Those parts that form a greater whole. And I really think that the combination of human ingenuity, the capabilities of AI and the intrinsic value that biodiversity has on this planet, as well as all of the services that biodiversity supplies can lead to a very, very exciting future where we can all thrive.

Paul: I think they're going to be far more models, far more augmented uses of sort of day to day life. I think there is a little bit of implementation of, let's say, productivity tool, generative AI.

I think that'll be far more prevalent in five years from now. I think once the costs get down, then that we get over the hump. When it's 30 per user per month, cheap at 50, expensive at 100, 000 people. Once that gets down to pennies or dollars, then you're going to see much wider use. Now it's not just a couple people in your organization, it's all people in your organization.

Emails will be drafted. Documents get drafted. Slideware gets drafted. Lots of things get drafted. And I always talk about AIs being less about a volume problem, but a velocity problem. So, the goal isn't to draft more documents. The goal is to draft the document you plan to draft. I think that'll be far more prevalent because of the amount of embedded automation that will exist.

That'll just be out of the box, right? I will start with draft this slide based on the strategy document and the slide will be drafted. And then I would just make modifications. That will be the norm. You won't open up a blank PowerPoint anymore. It just won't happen. 

Austin: I have this talk I've given on this a few times and where I think we're going with LLMs or AI kind of, especially in the developer space. So this is very specific to the developer spaces.

Like all of the best practices we have in software development, solid principles, abstraction, layers, programming languages, having they're nice to use because of this reason or that reason are to make it easier for people to reason about like infinitely complex systems. And I think especially within a decade, we will have an entirely new set of best practices to interface with a computer and program them.

Solid principles will be a thing of the past. And part of me thinks even now, like, Five years from now, we'll be writing, I'll be writing Python, remembering how great Python was, but like in my job, there will be no language that looks like Python anymore. 

Deep: And what do you think it will be replaced with?

Just like some like natural language or. 

Austin: I don't know if it's natural language or a, like a more logic based language that doesn't have the esoteric syntax and if statements and all the other stuff that has to exist in a programming language, because it's so in malleable. 

Deep: It's pseudocode oriented, right?

Like. 

Austin: Exactly. Or even like a no code editor where you're dragging and dropping stuff together, where it can generate much, much more powerful machine code than we can now. Like all the no code solutions now are pretty bad, but I think they could get substantially better.

Avi: I think there's two dimensions of how we will improve the world. There's improving the upside, right?

Understanding all of the things that aren't measured now in policy or that are underinvested in now life, liberty, happiness as three hypotheticals. It's getting the public to understand how valuable those things are and the environment and mental health and starting to allocate resources there that are commensurate with the value, right?

So it's really building policy around humans, number one. Number two, it's limiting harm. You were talking about the addictive negative outcomes of social media because it's unethical to run a randomized controlled blinded experiment to see how much social media you need to expose a 14 year old to before they start getting depressed.

Yeah, that's a good example. Mark Zuckerberg can stand in front of Congress and he can say social media does not cause Mental health, but you could use subconscious to prove that. Yes, indeed it does. That's intriguing. And so on the harm reduction side, we would like to build an FDA for mental health, where the things that might be harmful that the children are exposed to either in the real world or online are regulated and protected.

So it's, in short, protecting children.​

Oded: So I'll give you a personal story. My father was diagnosed with cancer a few years ago. It re emerged again, and then he was on the monitoring.

Yeah. And so every six months he gets a PET CT scan. Recently it re emerged again, and it was relatively big, we found out that six months ago when he did the test. It was already there, and it wasn't detected because humans, right? It happens. I mean, that's reality, right? Now, the challenge is, even after you get detected, you need to, they need to give you all sorts of, scans and tests and all those things.

And from what I understand is, like, it can take weeks. It can take sometimes months. Right? Like the accessibility of that is, and the reason is, it's usually a lot of the time, it's people. You just don't have the enough technicians to, to do this. And so my hope is, is that once one 10 years from now, again, we have less of those cases that are being missed.

And then the ability to handle more patients. So you don't have to wait for so long to actually get the treatment has improved significantly. And then even when you're in within the process, Again, the entire, it's not necessarily AI, but the workflow, everything is more efficient. So you basically more likely to get detected and not missed.

If you are detected, you're going to get treated. You get the right treatment. There's always new treatment coming in and like we can help surface, Hey, there's a new treatment that might be relevant. 

Deep: Yeah. 

Oded: Right. 

Deep: So we can, or even like even servicing clinical trials that somebody, you know, actually do that.

Yeah. 

Oded: That's great. Like we identify today patients and surfacing them as potentially for like a candidate for a clinical trial. And instead of having to go to a big deal 

Deep: because it's so hard for so many people are sitting. I mean, it's kind of heartbreaking. It's particularly in cancer because there's such amazing treatments that are just right on the horizon.

And there's people who are looking at a terminal case. Somebody has got to get better at matching them up with clinical trials. It's 

Oded: twofold. One, for the people that are getting into the clinical trial, it might be a life saving. Yeah. Like, which is important. But it's furthermore, the clinical trial itself takes so long because you have to Yeah, you can shrink that time.

So if you could Right. So instead of three years, it's two years or whatever. You get a new drug to market faster. So our mission is, you know, to get more patients treated, to get to the right treatment, to life saving treatment, right? And so do I think it's going to be perfect in 10 years? It's probably not, but hopefully it's going to be a lot better, 

Artem: I would say that with years, the whole problem of like doctor scarcity, especially in developed countries becomes more evident because of the aging population, like less doctor in the industry.

And just doctors have to serve more people, more and more people every year. That's one of the trends. And essentially, I would say that all of these like remote patient monitoring tool. That would help to overcome this trend and make the healthcare more available than we have today. So we have better care, just sitting at your home, probably at a cheaper price.

I hope so. I really hope so for that as well. I believe that it would become just the thing that People don't even realize it's there. It's like convenient air travel like we have like right now and people 50 years ago Didn't have that because it was like super expensive only for some high class guys and right now I can buy tickets like to the other side of the world and Just travel there by airplane.

I don't even think that it's kind of something special. It's just, it's just the usual. And I'm hopeful that in 10 years, uh, all of these tools that I'm building, that all other companies are building, just going to make, going to become a commodity, essentially, for patients. It just, the stuff, it's, it's there and it's helpful.

And people don't even think that it's something that they cannot have.