Technology, Connected - What If AI Teaches AI To Use AI?

Episode Date: June 11, 2026

The Vij brothers join Thinking on Paper to discuss Neo, an autonomous machine learning engineer designed to automate parts of the AI development process.As demand for AI systems grows, companies and governments are competing for a limited pool of experienced machine learning engineers. The challenge isn’t only access to data or computing power. Many organisations also lack the technical expertise required to build, test and deploy effective models.Neo uses a multi-agent system to perform tasks normally handled by machine learning engineers, including analysing datasets, selecting modelling approaches, running experiments and evaluating results. The aim is to automate repetitive technical work while allowing human engineers to concentrate on higher-level decisions and more creative problems.In this episode, we discuss:What an autonomous machine learning engineer isHow Neo’s multi-agent AI system worksWhy skilled machine learning engineers are in such high demandWhich parts of AI development can be automatedHow autonomous agents compare with traditional machine learning workflowsWhy Kaggle Grandmasters are considered leading practitioners in applied machine learningWhether AI agents can match expert human performanceHow automation could affect machine learning jobs and salariesThe evolution of GPUs from graphics hardware to AI infrastructureWhat the Vij brothers learned from working at CERNHow autonomous AI systems could change business, creativity and technical workNeo is intended to expand access to machine learning expertise rather than simply generate code. Its development raises a wider question: what happens when AI systems can perform the specialised work required to build other AI systems?This conversation examines the technical capabilities of autonomous machine learning agents, the shortage of experienced AI talent and how automation could reshape the role of engineers--Timestamps(00:00) Why Are There So Few Machine Learning Engineers?(01:54) Meet Gaurav Vij and Saurabh Vij(02:57) Lessons Learned from Working at CERN(04:45) How to Explain The Importance Of A.I. to Your Parents(07:24) The World’s First Autonomous Machine Learning Engineer: What AI Problem Does NEO Solve?(08:17) AI Competitions and Kaggle Grandmasters(11:06) How Many A.I./ML Engineers Do We Need?(17:30) Fixing The A.I. Hallucination Problem(18:09) Hot Buttons: 5 AI Questions In 30 Seconds(18:46) Hollywood: Doomed by A.I, or Reborn?(20:26) AI News: Nvidia Digits Explained(21:51) Moore's Law And Could AI Models Be Motivated by Rewards?(25:42) AI And Quantum Computing(29:45) The Thinking on Paper Carry-Over Question(30:16) After Hours: Backstage Extra--Check out NEO: https://heyneo.so/Learn more about the show: www.thinkingonpaper.xyzFollow Thinking On Paper On Instagram: https://www.instagram.com/thinkingonpaperpodcast/

Disruptors and Curious Minds. Welcome to another episode of Thinking on Paper, your personal vehicle to understand the intersection of merging technologies and culture, business, relationships, family, what it means to be human, what this technology is doing to affect us. My name's Jeremy.

This is Mark. We're writers. We're curious minds. We're trying to figure all this stuff out. Here we are today. Mark, it's a new year. We've got some great segments coming up. We've got three new segments to the show.

We're trying to be a little more organized. a little more intentional. So stay tuned for these new segments. It's going to be a blast. I'm excited. Mark, what are we learning about today? Artificial intelligence, AI. So every AI product you use, every service you use, it begins with machine learning engineers.

But there's a problem. There are only about 300,000 of them worldwide. And maybe only a fraction of those are actually good, like 600 who can actually change engineer meaningful products. Why is that? That's what we're asking today. Why are there so few engineers when all eyes are on AI?

Is it because it's beyond most? Is it because it's too cumbersome, dare I say it, unenjoyable? Is there too much up front grunt work? What is the problem? And today's guests are going to give us a solution to the challenge.

So, yeah. That sounds like a good focus. We'll take good notes. And stay tuned for the very end of this show. We're actually going to unpack our immediate insights for what we learned today, because we're here to learn as well. So, Mark, who do we have on today? And where are they from and where are we headed?

Well, this might be a first. Myself, I have two brothers. I'm a middle child. And today's guests are brothers, Suraf and Garav Vige. And I'll let them explain where they are. I think they're in different continents. Maybe that's a brother thing to keep working together. I don't know.

But find out. But welcome to the show. Yes, welcome. Why don't we start with you, Garav. Tell us how you got into all of this. The cost of building an ML model is very high due to the complexities involved, such as the cost of computing on cloud platforms and also hiring a good ML talent, scars and costly. So these were the two road blockers that turned out to be the biggest challenge that I ultimately wanted to solve for me and later on realize that the entire machine learning community is dealing with that. So that's what inspired me to

go on this path of solving these challenges. And you get to build with your brother, which has got to be the best part. Sara, what about you? I was a particle physicist. Fortunately, I got a chance to work at CERN where they found the Higgs Bousson. Media called it with the weirdest name like God Particle and many other things like that. So quick, quick footnote, CERN is one of the places where they figure out the magic of the universe. and the Higgs particle is proof of a theory of the Higgs field

that literally kind of created everything or potentially created everything. Back to our show. But that gave me exposure to high-performance computing systems and neural networks. And it was fascinating how using AI machine learning at a very basic level, even 10, 15 years back, had a very significant impact on particle physics.

And that really, you know, inspired me to work, work, this field. Then I collaborated with Gaurav, you know, over a project which became our first startup. This is our second startup now. So the first startup, you know, was inspired by an idea that I got from my research days at CERN, which was about distributed computing. To give you a brief about that, what happens, generally what happens is when you want to run your AI models, you run this on public clouds like AWS or GCP. Our core idea was, what if you can run your AI models on thousands of crypto mining rigs because the same crypto ricks also have GPUs which are incredibly powerful but they were not designed for AI they were

designed for gaming or crypto so we repurposed them it kind of became an Airbnb for GPUs but specifically for AI so we had built the entire stack solved for the computing layer we ran that for around four years out more than one million computing arts this led to 80% lower cost and that became the foundation for whatever you know we are doing right now where you know you let's say you're sleeping and your laptop is computing for, let's say, a data scientist in other part of the world while you get paid for that and he gets to leverage a much lower cost, you can say, computing system.

What do you tell your friends and your family about machine learning, about AI, to kind of pull them in to the inner circle, to give them a bit of a leg up, to help them understand it faster? At this age, you know, most of the conversations, you know, would always converge around health. and when it comes to health, you should never go to Google to get an answer because it will directly lead to the most dangerous things because there is no brain. There is just enormous amounts of data. Now, what if there is a human being which can synthesize all of that data and give you a summary

and also filter out bad information? Now, that human being is an AI where you can talk to that. Instead of just talking to a doctor, you can talk to this guy for the early symptoms and it will give you, you know, a very synthesized but very high quality answer to some of your questions. And the same goes for, you know, little kids who are working on maths problem or someone who is trying to understand physics. So just imagine having a PhD level human being all a time with you to help you with any of your problems. So that's what, you know, AI is and that's what we, that's how we explain this to, you know, our friends and some. I used to tell them in a way

that this is like your personal, Ginny. Like, I'll give you a quick. example, like we have all of our parents every morning, they send good morning text messages to their family members and friends. I asked that, like, you can create a message with chat GPT. People may often talk about education and educating people about emerging technologies. How important is it for me to explain to my family about AI? I think if you, let's say if you are the expert among us, among them, you should definitely, you know, kind of give them a warning because I can bet on this

in the next few years there will be a lot of misinformation in every single form in text, audio, video, it's like getting closer to reality. You would have seen the latest video models by some of these companies. They're getting close to reality. So it's very

becoming easier to fool people. So misinformation is going to first, there will be volume of, more volume of information and then it will also spread much faster. So we need to warn people here. We need to help your family members understand to some extent how to differentiate between real information and AI generated information. Quick shout out too. So we have a book club as well as every week interviewing the leaders in tech.

We have a book club. And at the moment we're reading the nexus. And it's all about information networks, disinformation, misinformation, misinformation, thinking on paper XYZ. What is Neo and what problem are you trying to solve with it? So Neo is the, the world's first autonomous machine learning engineer. The demand for machine learning is increasing exponentially. Every company wants to build their models or wants to, you know, fine-tune on existing models. But the problem is it's getting complex day by day. There are hundreds of new research papers, hundreds of new architectures and new optimization frameworks. Now, to really integrate that and leverage them and use them, you need highly skilled machine learning engineers

with years of experience. The demand has completely outstripped the supply. There are only, you can say, 2 million in total data scientists and machine learning engineers, but out of that, only 300,000 highly skilled machine learning engineers that can really do the job and help you build very high-quality models and the entire pipeline.

And out of those 300,000 ML engineers, there are only 5,000 that ever became Caggle masters and 600 Gaggle Grandmasters. Cagle, K-G-G-L-E is the largest. just AI and ML community with over 21 million machine learners where they stress test, share and stay up to date on the latest technologies. They do a lot of contests figuring out who can solve the biggest challenges in the area. Our vision with NEEO is to provide an AI Kaggle Grandmaster in every company on the planet.

Neo automates the entire machine learning workflow from building your ETL pipelines, cleaning data, to building models from scratch. you can interact with Neo just through a chat and share your problem. It could be that I want to build a credit risk model and here is my data sets. So it will just take that information and take over the entire workflow and build the model for you. But we have designed it in a way that you have humans in the loop. So you can always change its behavior, direct its behavior. And when you integrate that within your company, it's like inducting a new employee.

just like a new employee would learn your playbook, your processes, where you store your data, how you clean your data. It will learn all of that and that will make it more and more human-like. Kaggle is like a competitive platform where ML engineers participate in various different competitions. The competition could state that you have to build. The task is to build a model for sentiment analysis. Sentiment classification. Here is the dataset. Sentiment analysis identifies the emotional tone behind a body of text in a large

dataset. So you would have to come up with a unique approach to solve that problem and there is a evaluation that happens along with the validation data set that is provided in the competition. Now with that validation comes a score and that score is identified on the leader board that suggests that where do you stand with your approach? One definition of casual grandmaster is you have to win five gold medals in five life competitions. Once you are able to To do that, you become a Kaggle Grandmaster, and that's why they are so few. It is purely mathematical, and it comes from the basis of your approach. You have trained a model.

There is an accuracy of that model. Now, the higher the accuracy, the higher will be your score. And based on that, you can win a medal. Ah, so push, push the subjectivity aside, purely objective, purely mathematical. Wow. Okay. All right, so listeners, this is super important, right? So if you're looking for resources, right?

Correct me if I'm wrong, gentlemen. If you're looking for resources that machine learning engineers, people in this, that would be a pretty good spot to go and say, hey, where could I find a good one? Where could I validate a good one? Not by Mark's opinion, but by math's opinion. I think that's pretty cool. Just a quick Google search as well. And there are about 2,000 chess grandmasters.

So there are, in fact, my masses. For all times as many chess grandmasters are there are machine learning. Gaggle Grandmasters, which is very surprising in this world. How many do you think we need? I know it's a bit of a stupid question, but in the next five years, we have 600. How many do we need to keep up with the demand for AI? Do you think, ish? By 2030, there will be almost a million AI native companies.

When I say AI native companies, what I mean is you would need a three to four machine learning. engineer kind of team in your company, building your own models. Because now the trend is, a lot of these companies have so much data that they can't leverage unless they, you know, fine-tune these models or build their own models. You can't just use open AI for all the different, you know, use cases. So there will be one million AI native companies, but there are only 600 carrier grandmasters today. Well, let's hit on that again. 600 experts today with a projection of a million AI native companies by 2030. Pretty big opportunity for those wanting to learn some new skills. So the vision, you know, with NEEO is can we provide a Tadal Grandmaster for all of these

companies? So imagine you have not even a PhD, beyond a PhD level expert in your team that works for you 24 by 7 for a fraction of the cost. Open AI, you know, recently what they did was they created a benchmark for agentic systems like NEO. This benchmark is called MLE bench and can be found at openAI.com. Newder, multi-agent system. So they created a benchmark for these kind of system, and the benchmark was again on cattle competitions. They, what they did was they ran their model along with different agents

on 75 cattle competitions. These are like some of the toughest problems. It can take almost a month sometimes to solve one problem. They are so complex. Now, Open AI, with the best model they have. At that time, it was in October 24, just three months back, with the best model O-1, they got a score of 16.9%. Neo got a score of 26%. So Neo was able to outperform Open AI at a benchmark that they created.

If we use an analogy like, I'm an airplane builder, right? And instead of me bending the metal to make the wings and riveting these things in place and screwing on bolts to make the jet engine work, I can put automated entities in place to help me with that while I figure out how to fly the plane faster, better, smoother, cheaper. Exactly, exactly. You can go for 10 times bigger goals in life because now you don't have to deal with bowls and screws or, you know, taking care of engine oil and all of that.

So all that grunt work, which you would, you know, today give to interns or you can say a lot of junior level engineers can now be automated. It's a lot of dirty work. It's a lot of grunt work that they are forced to do right. now and that waste a lot of time. And generally what happens is it's not a one-time thing. You have to keep on doing that. You have to scale up your systems and these things, you know, tend to come in your way when you're really, when you really just want to focus on building better models and you want to be closer to your customers. Let's stay with our airplane analogy. How are you building

trust with the people that are going to lean on your systems? Are we back to objective, demonstrable math or are we trust us subjective? Exactly. No, that's, that's a very good question and a perfect analogy here. So to trust an autopilot, you would look at its history. Was it able to first, did they test it out you, let's say with one million simulation runs or they were able to test it out for, let's say, 10 years in real life scenarios? So what Cabell has done for us is these are some of the toughest problems given by these big companies like Tesla, Open AI, some of the hedge funds in financial markets.

they have given these thrown out these problems. And it's because of this open-ey-eyes benchmark, it has become really objective. And that's why this code, this score of 26% really helped us in getting our initial traction. It really resonated with a lot of, you can say, machine learning engineers and heads of AI and data science heads. They, like some of the biggest brands, you know, on the planet,

they signed up on our wait list. Purely, I would say, because of this validation, this objective validation of the score. As a pilot myself, I might have 10,000 hours of flying experience. When I have this much experience, I learn on each hour of flying the plane, how to land, when to launch the gears. So these are small things which as a human I learn. The same way, Neo, being an autonomous heavy engineer,

is capable of learning from the experiments that it performs. And with each experiment, it is learning. It is storing that in its memory, and that ultimately leads to a more powerful system as it progresses through these experiments and Kaggle challenges which we made it participate in. When it's learning from previous behaviours, how does it deal with mistakes that it is made? There are many steps in the ML engineering pipeline at which it can fall apart. Here we have a system which is presented an unseen problem.

Now it might have to work on a dataset that it has never seen before. it might have to work with an model architecture which it has never even thought about, like how many parameters are there, how many dense layers are present in this neural network architecture. It may not have that information prior to getting started with the task. So much experimentation and the learnings that it has gained, it is able to generalize across different scenarios. And that is what we call as multi-step reasoning that enables the system to perform exceptionally

well at such complex challenges that we presented to it. We created an agent which was trying to mimic the behavior of an ML engineer. And agents tend to hallucinate because LLMs behind them hallucinate. So one of the ways we solved, you know, this problem was instead of a singular agent, we created multi-agent system. With that, we are trying to mimic an entire team. So what happens in reality is whenever there is a problem, a machine learning problem, a single engineer is not solving that problem.

It's a team of data scientists, data engineer, ML engineers, ML researchers, all working together with a strong feedback loop among themselves to solve a problem. And that's what we are building with multi-agent system. And when we switch from single agent to multi-agent, you can say error rates reduced drastically, hallucinations reduced drastically, and we were able to get much more accurate results. Welcome to New Thinking on Paper segment, five questions in 30 seconds from Mark Fielding. One, what is the best AI assistant for the general non-coding population? Non-coding population.

General AI assistant, I would say, right now in my experience, it is Claude. Number two, what is the best book on AI fiction or non-fiction? Doesn't matter. Nick, Nick, Bossram's superintelligence. Okay, question number three, what will AI agents use as currency? Dodge coin, if Elon Musk would promote it? Hollywood. Doomed by AI or reborn?

Okay, so we're going to pause the timer here because this answer was too good to rush through. But my view is entirely driven by history. We're Michelangelo. They were, you know, artists, sculptors before Michael Angelo. And if you were to build an AI, you know, based on all the training set, all the data that you would have collected from the previous generation of artists, you would have built something, something really beautiful. But the thing with Michael Angelo was he went far. He crossed boundaries. He went after cadavers, you know, at night.

to really understand deeper levels of anatomy. And that gave him so much insight that when he built his first, you know, at the age of 19 when he built Hercules and later when he built his David, you could actually see the expressions, the nerves, you know, coming out of those marble pieces. You can only do that when you go beyond and cross these boundaries. So the problem with the AI systems would always be about two things. One, it will always follow the rules and the bound within the boundaries.

of the entire training set that has been given. Second, it's about taste. So, you know, the greatest artist of our time and in the history, they had a very nuanced and a very different type of taste. And I don't know how to create a mathematical equation to describe taste. So based on these two things, I would say people who are really great at their craft, now they will become superhuman. Instead of, you know, like Christopher Nolan, instead of releasing one movie in three years,

probably he can now do more. And you would love to see that. And so it will increase volume. But people who are mediocre, their jobs can be taken away. What a great wrap to our hot button section. Next up, the TOP News. All right. So it's CES week.

Specific to Invidia, we want to kind of focus on some announcements that Nvidia has put out there. What is digits and is it going to do anything to influence the AI revolution? There is no excuse now. For $3,000, you get a fucking supercomputer. there are three bottlenecks, you know, always. Data, compute, and talent. We are solving for talent with Neo.

Now, you know, if you are a good Python engineer, a regular software engineer, you don't have to, you know, go through a complexity of machine learning engineering. Using Neo, you can bridge that tab. Second is about compute. So digits is solving that. So with Neo and digits,

the things you can do now is going to like blow up everything. Like in the next three to five years, it's going to be the best time, you know, the entire history of our planet. There will be scientific revolution. It is a new renaissance, I would say. Let's talk about this. So in 1965, there was a guy named Gordon Moore

that decided to predict how quickly transistors would double over time, right? So Moore's Law, two years doubling of transistors. The CEO of Invidia now says Moore's law is bunk. It's, you know, but what could a common metric, a new common metric to define the progress of AI supercomputers moving forward? There has to be very high quality data, which goes hand in hand with the number of parameters. But definitely having more parameters gives the edge to a model

for generalizing to a lot bigger data set. Bigger models definitely tend to offer this type of perspective, and that could definitely be, in my personal opinion, number of parameters can definitely lead to a signal that how intelligent the model is, and that could be considered as the unique value just like we have had moves law for transistors. Pushing this, you can say, the stage of AI transformer-based architecture models is memory. Like this is a concept which came in an year back only, I believe. The ideas is like how a model can memorize concepts beyond the training data on which it was trained.

Now that leads to learnings in real world scenarios. Just like humans, as a baby, we start to crawl and then step by step, we start to walk. In case of reinforcement learning, there is a concept called us reward functions. Like let's say I get hit by a car while walking on a road. I get negative reward. If I, the idea, the concept is then,

how do I save my set? This type of system can learn in real world scenarios and definitely a very large model which already has been trained on very huge amount of generalized data, high quality data, along with the capability to memorize concepts,

while as it interacts with the world, can potentially lead to a general intelligence type of system. So a metric could be how good, that model, that system takes in a concept and applies it to becoming more efficient at what its job is. Then chat GPT came up with RLHF, reinforcement learning with human feedback. That provided a reward function which enabled the model to learn what to do and what not to do. But the ideas is models cannot be, we cannot keep training because training is costly. There has to be making the models in real world scenarios when the training data has been exhausted.

Now let's put it in real world scenario and then it can. go through certain experiments and come up with what it should not do. Could models be intrinsically motivated to get excited about being rewarded? Every human has a distinct nature where we get aligned toward. Like someone might have a sharp nature of understanding things quickly. Somebody might be a slow learner. But that's how our different beings work. Similarly, a model can be tuned to like focus on positive reward function.

There was a concept called us Chain of Thought reasoning. that was introduced a few years back and was implemented again in O-1 which enabled it to reason very well compared to previous models. So chain of thought was implemented right in the dataset itself. The data set was presented with examples to the model so that it can learn on these examples

like how to think in form of a chain of steps. So just like that, we can potentially create a data set that could make the model align towards focusing on positive reward And that could lead to potentially better or quick learning for the model. But it's almost like the relationship between coaches and athletes. I coach lacrosse players are motivated if you get in their face and get really, hey, what do you do and whatever? And then there are other people that are motivated in different ways that that might not work for them. So similarly, what you're saying is these models can be set up to take in a particular reward mechanic.

We can potentially make a model that can be tuned towards thinking. like O-1 is thinking. So it's doing chain of thought reasoning. Do either of you to have any views, opinions on quantum and AI and how you see the two paths going ahead, merging, not merging, in the imminent future and the more long-term future? The ability of a system to perform so many amounts of multiple simulations in parallel, iterations in parallel in a way to come up to a solution.

That ultimately, I believe, is the key to getting super intelligence, having so much amount of compute available. A normal computer, we can increase flops. We can keep on adding transistors. Unless we change the architecture, unless we go beyond transformer architectures, models would need, bigger models would need more compute. That would be costly. And beyond a certain limit, we would face issues with networking

because we would have multiple GPUs connected to each other over network, latencies and all those things. And this I think also is a perspective that as a human, when we are tasked with a problem, if we have a capability of thinking about multiple solutions ahead of time, then we always come up if we get enough time to think. So similarly, a quantum computer can compute multiple iterations in parallel. That gives it the opportunity of solving the solution in a very quick time. If it is given the capability of thinking multiple iterations in Parzl, then it can come up with a better solution compared to what it might have come up in the first scenario itself. So quantum computing can, in my opinion, definitely help AI go beyond the phase of where it is just as a chatbot today.

Let's say you're solving a scientific problem where you have to build, let's say, a new form of, let's say, material or new alloy. Now, generally, it's a very experimental job in nature. You have to go through so many combinations. What if you can simulate all those different combinations in like five minutes? That would, you know, this impact it would have on, you know, this scientific exploration of new materials, new research, you know, when you're solving, you can say medical problems. You can simulate all those different paths.

And this will accelerate a lot of things, not just, you know, you can say in the field of energy. in material science, but also in the medical science. But the cons, and these are all the pronouns of, I would say, pros of quantum computing. But one of the problems, I think, which world is not ready, I would say yet, is when quantum computers are able to solve encryption,

what happens to all the blockchains, you know, that we have built so far, what happens to the highest encryption standards that we have built, if quantum computers can break that. So that's one area where, I think, as researchers, everyone, should invest a lot of time in building much more robust, you can say, encryption systems. Before we launch, you know, we push the nuclear button of quantum computers onto the world.

So just a quick, quick shot back to previous episodes. We had the post quantum cryptography lead from IBM. Apparently, we have till 2032, I think, until all shit hits the fan. So what about a carryover question for our next guest? It can be on any topic, any subject. It doesn't have to be AI. It doesn't have to be quantum. It doesn't even have to be tech.

Philosophical, whatever you want. Based on the announcements by Vilo, the Google's quantum computer, one of the things that came out was it was able to compute something that would have taken billions of years for a supercomputer. It was able to do that like,

I think in five minutes or something. And one explanation, which is really weird and crazy right now, is that it was able to go through multivolts. So it's a multi-world universe. And some of the people are saying that it's an evidence that we live in a multi-parallel universe kind of scenario. Now, the question for the next, you can say folks are,

is there any other explanation that how was it able to really get the right answers within those five minutes? Is this about, you can say, superposition of different scenarios and collapsing onto a reality, or is it something really fundamentally different that is happening at the quantum level, which is actually able to deliver those results? Disruptors and Curious Minds, you are now backstage with Mark and Jeremy as we slow our heads from melting about the idea of parsing off compute, into multiple multiverses to have some compute happening over here,

some happening over here, but it's all at the same time. We spoke about that on Book Club when we were reading which was quantum supremacy and maybe the order of time we spoke about that tunneling between worlds with information. It's a great question that we can ask next week's guests. How are you feeling about Neo-AI engineering and what you've just experienced and learned, Jeremy? It's a concept that's that's been throughout time.

I think the idea of like automating and optimizing workflows that let us think about higher order things. I think is the important thing that landed with me. And Neo has apparently figured out a way to automate in a trustworthy way and an objectively provable way to engineers to offload certain tasks that they'd rather not do. and they can focus on solving bigger and more challenging problems. I'm unsure what the difference between a gaggle grandmaster and one of the other 5,000 who are good.

So I don't know what somebody who's number 5,000 could do as opposed to somebody who's a grandmaster. I mean, what the real difference is in their abilities. So when you're speaking about the greater creative endeavors, I think just as in real normal life away from AI, in any company you get people who are tasked with creative thinking and people whose job is the mundane and the repetitive. I don't understand why they're after so few engineers

and it must be to do with that gunwork and maybe it's just not much fun. And if you are a developer of any kind, there's more job satisfaction to be had elsewhere. Perhaps there's more money to be had at now, but maybe there's more job satisfaction to be done elsewhere. And Neo taking away a lot of, lot of that. I see it as almost inevitable that it brings more people in and we get more of those

developers that the world so desperately needs apparently. Like there's going to be a million AI companies. One thing about making predictions about the year 2030 that he says it was going to be one million AI companies, you could easily triple that, couldn't you? Quadruple it. I don't know. A call back to Julio Otino, right, with Nexus and Nexus thinking. And there are people that thrive in these divergent thinking spaces. And there are people that prefer more of the convergent mindset where they could be really 100% the best at one particular thing and do that one particular thing. And I think we're moving towards this polymathic tendency of society and of individuals trying to do interconnected things, I think. And people are starting to be more

open to that. But he said the world wasn't ready for quantum computers. And I wonder if the world is ready for AI. You know, we talked about it being a genie, right? And, you know, we can't stuff that thing back in the bottle, man. And, you know, there's there's a lot of stuff that freaks me out a little bit about, about AI and, you know, offloading certain things. We've talked about this a ton. Like, if we push the easy button and we're susceptible as humans to pushing the easy button, we're optimization machines. We want to try and find the shortcuts to everything and our thinking and all of that.

And if we push this AI button more than pushing this brain button, like, are we going to lose our ability to creatively think and all that? So that's always running in the back of my mind as we have these discussions. And I'm not saying AI, there won't be some benefit to it. I think there's tremendous benefit. Taking on paper. comy Z, tell us how we're doing, man. We started this show for us, but we're making it for you.

We want this to be a valuable tool for you guys to understand what's happening in the tech space and how it affects us as it's humans, our relationships, our families, our jobs, all of that fun stuff. So let us know how we're doing. We also have book clubs. Stay tuned. All the infos on thinking on paper. Dot XYZ.

Stay disruptive. Be curious. Keep thinking on paper.