Technology, Connected - How AI Compute Became the New World Currency - Kony Kwong, GAIB

Episode Date: June 20, 2025

Compute has become the new oil. Whoever controls it, controls the future of intelligence.In this episode of Thinking on Paper, Jeremy and Mark sit down with Kony, CEO of GAIB, to unpack the real shift happening in artificial intelligence: power itself is being redistributed.GAIB is turning GPUs, the hardware behind every model, every chatbot, every act of machine cognition, into financial assets. It’s a new market built on the infrastructure of thought. Tokenized compute. Real yield. Shared ownership of the AI supply chain.But beneath the economics sits a harder question: When intelligence becomes a currency, what happens to human value?This conversation moves from the architecture of data centers to the architecture of power—how control over compute defines who gets to participate in the next phase of civilization, and what remains for the rest of us.AI Power Shift: How Compute Became the New Currency.Listen. Think. Then decide who should own the machines.Please enjoy the show. And share with an AI friend.Stay curious. Stay disruptive. Keep Thinking on Paper.Thanks--Links and resourcesGAIB: ⁠https://gaib.ai/⁠ Kony: ⁠https://x.com/konyk001⁠ --Follow Thinking On Paper 🎙️PODCAST: ⁠www.thinkingonpaper.xyz⁠ INSTAGRAM: https://www.instagram.com/thinkingonpaperpodcast/⁠⁠X: https://x.com/thinkonpaperpod⁠ -- Chapters (00:00) Intro: Why Compute Is the Next Currency (01:30) From GPUs to Blockchain: GAIB’s Origin Story (04:05) The $7 Trillion Problem: Inside GAIB (06:38) Funding Data-Centres Fast: GAIB’s Capital Playbook(09:17) Cloud vs Data-Centre: Who Really Owns the GPUs? (11:07) Why Local GPU Hubs Beat Latency (13:50) Scaling Safely: Token Standards for Compute Financing (17:35) Pricing an H200: Turning GPUs into Cash-Flow Assets (21:50) AID Token Explained: The ‘Mutual Fund’ of Compute Yield (23:07) Global GPU Partners: First NVIDIA-Approved Clouds in Asia & Beyond (27:02) Will AI Kill Work or Create It? (32:49) What should humans be?

Disruptors and curious minds. Welcome to another episode of Thinking on Paper. My name's Jeremy. This is Mark. We explore technology, the future, what it means to culture, to life, to us as humans, what's going on in the soft matter of our brains. We unpack it here. We've got a great discussion today.

We're going to talk about compute and compute potentially being a new currency, a new, new valuable thing for people to invest in and out of because it's going to be what powers this AI revolution. Mark, what are you thinking? What are you excited about today? How are we teaing the show up for our listeners? I'm interested in getting my piece of the AI pie on the back of the news that META have invested $15 billion in a 49% stake in scale AI. Between 2013 and 2023, US and EU AI companies received over $560 billion in funding. We're talking trillion dollar investment by 2030. And I want to be able to invest in AI. I don't have $100 million to invest in a company. I'm not.

a VC. I don't have access to these funds. And today's guest, Connie, CEO of Gaibe, is running a platform that's going to give you and me the opportunity to invest in the AI infrastructure, which, as we all know by now, is going to be the infrastructure of the future. Not financial advice, by the way, Lister. All right. Connie, hey, more than us. Hey, Jeremy. Yeah, my pleasure. Thank you for thinking on paper with us. Yes, Gaibe, AI infrastructure, investment. Cryptocurrency, blockchain, could you just, before we get into the details, paint us a big 5,000-foot plan view of what Gaieb is and the challenge that you're solving? I would like to start with our name, Gaip.

It obviously draws inspiration from doing the movie from Lissonnau-Gai-eep, where it means the future done seen. And actually, Guy-Eb is an acronym for three key pillars that we focus on, GPU, AI, and blockchain or bond, which is the financial perspective of the whole thing that we piece them together. So what Gaiib is trying to do is number one, we're financializing and tokenizing these infrastructure assets for crypto and AI, which is GPU, to make it into a yield bank asset on-chain. And on top of that, we build a whole DFI ecosystem and Lego and Pustles to make the AI economy to thrive. That's what we do. And what problem do we solve here?

Well, there's several problems. One, we want to bring actual AI yield into the market, especially to crypto market. Because we all know in crypto, a lot of the DeFi portfolio. calls are just our borrows. You have to rely on token emission to install the people to do a lot of things. There's no actual real yield in the market. And the second thing is what we want to solve is to create new markets and new assets for people to invest into AI. Because right now, for anybody to invest into AI infrastructure, the only in that way is for you to buy and feed their stock. Done. Nothing else. But what if now today we can actually invest directly

into the AI infrastructure assets that power the whole whatever chip, chat TVD you're using, the Microsoft AI you're using, that will be great. So you're actually getting into the most important asset that everybody is relying on, which is compute. So these are the two problems that we want to solve and provide solution to. I look at three kind of blocks for you guys. One is this cloud data center provider, people that are actually building these facilities that use the GPUs that allow people to train models, build apps, run apps, all of that stuff. You have investors, like you mentioned is right now the only way to really invest in AI is to buy Nvidia stock or buy some other some other stock like that. And then you have this AI economy and what the effect is on the AI economy. So my question is, so I come from the data center space. I help people build data centers. I help people consolidate data centers. I did that for a long, long time. And I worked with VC firms to do due due diligence audits on portfolios that they were buying. So my first question is, do you guys have anyone on your team that has done data center, traditional data, center financing in the past because you seem very fluent in that in that language can you tell me

about that yeah sure like myself i comes from a traditional finance background i used to in investment banking focusing on credit research equity research and private equity so the whole structuring financing very familiar with that and also during my study i'm i'm from at the university of hongong like doing finance and finance and international business and then also pashd job out so i have like these kind of a area of expertise. And then the other hand is Michael founder Alex. He also runs a cloud company himself these days and his family runs the semi-conduble business. So we have the first line of information whenever we want in order to do the cross-check, do decisions on the other cloud companies and the customers or the assets they claim to. And even we can have a direct channel with all the OEMs

and ODMs of Nvidia so that we can do some cross-check and reference check there. And then the third is for the growth lead on Web2 Cloud company side, our team leader, the growth leader, he used to work at Biddeer, which is a Bitcoin miner and at the corporate strategy and development team where he was the one helping to do like a surgery MNA on C-Things, mining farms, and AI companies.

He also did like a successful merger for Biddeer to invest into other GPU farms and cloud companies. Team-wise, we have a group of professionals from backgrounds across finance, AI and also mining and the crypto side, of course. So that's like, we know, we share the blood, we share the DNA from that angle. Very cool. So let's stay focused on why guy matters to this cloud data center provider bucket.

And then Mark, I know you want to go into how you as an individual can invest in this kind of stuff. So where this is headed, everybody. By 2030, there's a McKinsey study that the data center funding requirements for compute, for network storage, all of that stuff will be above $7 trillion. $7 trillion to power what's going to happen in 2030. So we've got to do things a little differently, which is really why Gaieb is very interesting. So when you invest in a data center, not as an individual, but as an investment firm, you invest in building a data center. It takes a little while for returns to come. Because the revenue that data center has is either with what's called wholesale leasing, whether you get a

big chunk of space or services out of that or individual retail type arrangements. But how does your model Coney speed up this return on investment for people on the data center side. We don't speed up on the IOI for the investment side, but what we do is actually help them to get capital for whatever the expansion. So that's what we do. And then for data centers, so we work at two types of companies. One is data centers, the other is call company. So data sensors are the one to lease the show space to all this car company.

You want to put the GPUs in. And the other one is the. company who is actually buying, hosting, and putting the GPUs into the data centers. So our key focus, two of the, both of them can be our customer because sometimes data centers will vertically integrate to provide cloud services as well. But the other times, like it will be mainly the cloud companies that actually renting these like a space from the data centers to provide a cloud services. So what guy is working?

Just let me jump in real quick just for clarity purposes. So difference between data center company and cloud company, data center company, Data Center company provides this controlled power and cooling space for people to rent and put their equipment in. What cloud companies do is they actually put the equipment into that space and they lease services out of that. So it's a little bit different. I just wanted to clarify that. Correct. Correct.

Yeah. So we can work with both because sometimes they do vertically expand and they can even build their own data centers. So that's why we can work with both. And what Gaiib is helping and working with these two terms of company is that we provide a solution for them to get. capital. You as a cloud company, now you want to expand, you need to get capital to buy these machines and put it into the distances and host it up and get to get clients back. So the usual traditional way for you to get capital, there mainly three ways. One is either you having money on your balance sheet or profit. The second way is to go to VCs to say, hey, I want to raise some

money through equity financing and you invest into us. And the third way is through private lending, going to family offices, the credit firms, banks, and other traditional institutions to get money. But most of the cloud companies these days can only have access to the first two routes. They can't really do private lending or private borrowing in this case. Number one, international institutions are pretty rich efforts after the 2008 financial crisis. The second thing is, if they were to do this, they still find it hard to understand GPU as an asset class. Because it's novel. It's not so common that, okay, they understand the whole GPU economics, the cloud company economics.

So it may take some time for them to do it. And the third is, there's no standard for years. on how do you appraised the asset, how do you understand the company's cash flow. So what we want to do is we guide you as a platform. Number one, we're on the set a standard. How do we price these assets? How do we structure this asset? How do we tokenize this asset?

And second is, because we have to know how. We are inside the cloud's company space. So we understand the cloud company economics. That's why we can easily be the one to underwrite and package these deals together for our investors or for our buyers' users on the platform. And the third way is when we actually do this process, because we know the space, it's way faster than the other traditional players in the market. And let alone, there are not many traditional players.

Even if you look across Web 3, Web 2, there are very few of them. If you're talking about a billion, $2 billion company, or even a $10, $20 billion company like Kauwif, they could have whatever the capital market means they want. They can go to the bank and say, hey, this is one stock, this is my client, and get money. But those smaller cloud companies, which are a few hundred million and few billion dollars, they may find it hard to get capital. And this is exactly the segment of companies

that we want to focus on, which are number one exponentially growing. Second thing is there is this gap missing for them to get capital there. And what we're seeing in terms of the trend of the global expansion for these cloud companies is that there will be more and more regional

and localized cloud companies being spun up to meet the local demands and regional demand. Back in the days for cloud, when we talk about cloud, we all would come to our mind as a public cloud, AWS, GCP, SOR. But if we're talking about AI cloud,

these traditional top public cloud may not meet the needs. That's why we need more, like new type of regional cloud is coming up and it's ticking over the space. As you mentioned, we need $7 trillion of capital to invest in the whole tech stack of the AI supply chain.

This is exactly where we want to become a funding channel to make that happen. And along the way, anybody can invest it's not only those rich institutions can do it, I want everybody want to get a bite of the growth of AI can actually invest through the asset class, the most important asset class, which is GPU and compute there. Yeah, and to kind of break it down a little bit further, the large facilities will be out there, these cities, they're cities of compute infrastructure.

The reason why you have to have localized version of those is basically it's a network limitation, right? Connie, so if you're trying to pull a whole bunch of data from one site that's 1,000 miles away versus getting that distributed to somewhere five miles away, we're going to be able to do a lot more with the data. That's the reason why that's a great argument for why this small to midsize folks are going to be really important, which bodes well for what you're doing at Guy. Correct, correct. Yeah, perfect. Yeah. I love Jeremy, such an expert. I'm not going to say much in this show at Arkand-Feedle. You mentioned the speed of the investment and I imagine, depending on how good the idea is, going to

VCs, go to prior investment, pitching, pitching, pitching, pitching, pitching again. very time consuming, nothing moves quicker than AI. Okay, a small to mid-level AI company comes to you. What's the turnaround? How quickly could they be financed once you decide that they are the right partner for you? We should have a process of pre-leam due diligence and a thorough one. Prelim one, we can finish that within a week. So if you're in the questionnaire, we understand what is the capital you need,

how much you need, how long you need, and then what GPUs that do you have? And who is the basic information. And after that, if we see, we have few criteria. One is we want a work with company that has existing asset, GPUs there. We want to work with company there's some good transactions. Because at the end of the day, the kind of party, the risk is a key risk there. So we have to know who the money that we're giving to and what is their background. And after the pre-inem deed is done, then we'll enter into a more like thorough due decision process,

which will need to take in the third party auditor, the big four, accounting firms. to understand the capital structure, financials, the client, the payment, history, the asset, appraised, how much they're worth, etc. So that one, depending on how, like, cooperative the company is, it could be pretty short period of time, like, within a month or two, or it can be long if they are not co-bought cooperative. So it all boils down to how both sides want to move things faster. And because we have a pretty standard framework internally in terms of how we underwrite the risk, how we analyze the risk, how we structure the threshold, and the product. So it's just like plot and play. When every material's field, boxes being checked, then we're good to go. So that's the same that we want to set to the market as well.

Because ultimately, we don't, there will be more people doing this. I have no doubts. What we want to do is we want to become a platform where we can onboard other people who can bring in these cloud companies, data centers, or even capital on a platform as someone bringing the deal. Like, if you look at Lytle, Lido has a few operators. They get EVE operating a node, and then to run to get network running. On our platform, we want to be the same. So we are the initial operator. We want to have other operators bringing more views,

bring more assets and connections to the platform to tokenize these assets for people investing to. So there needs to be a standard. Otherwise, it cannot scale. So this is exactly the foundation that we're laying here. So real quick, disruptors and curious minds, don't come to Kony with an idea if I want to build a GPU farm.

You have to have GPUs work and you have to ideally have some transactions on these things, which makes sense. That was my initial question. So you answer very well. So let's talk about how you value because one of your core competencies I think you mentioned in a few minutes ago was the ability to evaluate this type of asset based on your team, based on their experience. So if I picture, you know, this is like the gold rush, right, where I bring my pile of gold to your desk and you start biting it to see if it's real gold. Talk me through how you evaluate a GPU and give it value or don't give it value.

So GPU as any other asset, there has an asset value attached to it. And the reason why we usually mention what we do is not only tokenization, but also financialization, is that we have to understand these assets value and the future's value because we want to buy something. We're buying into whatever cash flow they're generating in the future, not only the asset value itself, because these assets can depreciate. If you look at any asset, it means like there always will be the depreciation in terms of residual value there, whether it's house, a car, or even GPU that we're talking about.

So we have to not only look at the depreciation as a non-catch cost, but also look at the future cash flow. So what we do is you come to us with a GPU. Number one, we have to know what type of GPU they are, and then how long they have been used for. The third is how much they're being sold out to. For example, what is the hourly rate that you're renting it out for? And the third is how long that rental contract or even your client contract actually lasts for so that we can determine, let's say in the next year, we're going to give you money, or we don't invest into the asset here for the next three years,

how much can I get back? And if we say, okay, we can get back $100, then we can discount it back to the actual present value of how much these assets are being worth of. It's just very simple. We have done the same asset value appraisal for any asset class out there, aircraft, cars, houses, even other equipment.

So we can apply the same principle on GPU assets. How did GPU service contracts come into the valuation? of them. GPU service contract actually is the revenue part. So if you look at an asset, there has to be a few parts. One is on the revenue, the other side is cost. So these GPU service contract, they are the revenue part where they drive the return of these asset value. So they're usually two models of how these GPU is being used for. One is called a reserved model, meaning, hey, I ran your GPU for two years. Whether I use it or not, I still have to pay you. Whether I use the 80%, 90%, and 100% capacity. I still pay you how much the employer.

for. And then the second way is more on-demand, which is you're using, for someone, you're using AWS. Most of the time you're using on-demand, meaning pay as you go. Pay a use model, meaning how much you use, how much you pay. That is more short-term, you may or may not even have the contract for it. So this is a C.3 higher price. What we prefer is you have a long-term contract, so that cash flow is more predictable, even to a certain level. And then on the other hand, on the other hand of the calculation on formula is the cost. So we have to understand, like, what is the cost to the GPU. For example, the majority of the cost will be like data center cost.

If I want to host it, a visit, what is the per hour cost to the GPU character? So by understanding the cost driver, the revenue driver, then we know, like, how much these is worth today, even for the next year, for the next two years, next three years, so on and so forth. So more tangible question of that. Let's say we've got an Nvidia H-100, 30 grand each-ish, right, give or take. Yeah. So say I have an H-100 or H-200, that is the revenue that the services off of that have paid it off up to about 70%. So like there's 30% yet to be paid back on the cost. And then Mark has one that has about 80% left to pay back the cost of it. Mine is more valuable than his, right? Correct. Correct. Yeah, yeah. Got it. Okay. Let's switch gears a little bit. Let's switch gears going on to this individual. investment ability. So you have what if Mark's coming in and he he can't buy a thousand shares of

Nvidia stock, but he wants to get into this in this game, he's not going to finance a 20 million data center bill. So here's a here's a way he can get into it and do it. Is what we're looking at and I'm just trying to break this down. This might be ridiculously stupid. But is is this vehicle kind of like a mutual fund for cash flows of GPUs? Is that isish? That could be a good energy. That could be a good analogy. So what we introduce in the market right now is called AI, it's called AID, AI dollar, which is a synthetic asset. It functions as a portfolio, essentially, a basket of all the token IDU deals. So similar to mutual fund, you have different assets underneath that, according to the mix of the portfolio. The same thing here for the AID. We have different types of

token algebra assets. For example, maybe 10% go for a company, CalCompany 1, 20% Clock 20% Clock 20%, 3% clock company 3 and partial of that will be left for liquidity on the T-Bu side. So that functions the portfolio. Essentially, what you get is a blended yield of underlying GPU asset there. And also, you're pretty diversified. You're not like, okay, just being attached to one deal there. You're diversified to a portfolio of dues and the risk is solidly diversified as well. Because the cloud companies are working with right now, the global.

So you can get access to not only US-based are cloud companies, data sensors. but you can get Essex to the outside of the US, or you can give you some of the check-in balance in case of geographical tensions there, if there's any case, just like a diversified portfolio. AI is increasingly what feels like a gated world, where me and Jeremy and a lot of the listeners of thing in paper, okay, we click buttons on chat GPT,

but we have no access to the underlying structure. And that is a bit like big tech and the social media platforms, and you don't add in your content and very few companies control everything. And Gaiab's kind of pushing against that. And so for all the crypto Twitter people, I've got a few hundred dollars and I want to invest. Could you walk it through for a non-GPU,

non-data center expert? Just an interested part. He wants to knock down some of those walls. The reason why we're doing the tokenite GPU products, why are we doing the AID products? is that no matter how much you want, send out $100,000, if a million dollars, we don't differentiate you.

You can just buy the portion that you want to the level that you can afford and the risk that you can take. It's very simple. If you want to get involved, just go to a website, check out. We're running a pre-deposit campaign. So you can deposit USDT, USDC,

or any chains that we supported, which include Ethereum, arbitral day, say, going to, a couple other chains more. So just deposit there. And what you're getting is the more one. We have some incentive program coming up. We have the underlying like GPU there as well.

So it's just as simple. You essentially holding a U-Bang asset as you're buying like Ethereum token, which is not your bank. Stick Eath is another story. And getting back to the, I want to add one more thing is exactly what Adderion was saying. Like a lot of the big tech companies

trying to build their own AI Cloud, completes AI, like data centers these days. And what we want to do is to not only let big guys be the one that benefit from the underlying asset, but also anybody that want to invest in these asset class can do it. So that's exactly why we use token for. Because when you tokenize one thing, you can break you down into pieces. You can fragment it to the level where it's affordable on dollar's level, $10, $100,000, $1,000. You don't have to be deep-bocked cap with $10 million.

To play the game, what's the minimum buy-in here? To some height, like to these big tech guys, they buy in the 10-20 mil. But what we want is lower it down. Buying is $1,2. You can do it that way. You don't have to, because of your capital to be blocked out of the game. That's what we don't want. As I mentioned earlier on this podcast,

the problem that we want to solve is, number one, is bringing actual yield to crypto. The second thing is to break down barriers, introducing new markets and new channels for anybody to invest. into. So these are the key things. Why are we doing here and what crypto is here for? Because they allow people to invest and lower down barriers. So I know you're probably on the other side of this equation. You're trying to bring in people to the platform to jump in as investors, but you're also building the portfolio, right? Of, you know, GPU owners and stuff. Tell us about how that side's

going and how soon that that may be ready for prime time. Yeah. The supply side of GVU cloud companies. we're working with over 10 cloud companies globally right now, including the first NVDA cloud partner on the southern level in Thailand, first one in Taiwan, first one that privately held NCP. NCP is a term where NVDA use, meaning they give privileges to these cloud companies, their official batch, like, hey, you are my affiliates, that we want to give you the best access to GPUs, best clients in the region.

So we're working with like these three in that region. And the other cloud companies that are working with, span across Malaysia, Vietnam, Japan, Singapore, Hong Kong, in the Asia, on the Asia area, and then US, Canada, go into Mexico and the America side. And then Europe, we're talking to CloudComps in Nordic area, Iceland, and even on the Apex side, we're also venturing into Australia. So the space has expanded multiple when we started the project.

We came out last year around this time, like June, July, that time. It's kind of still a small scene where people, there are more companies coming up, but not exponentially growth yet. But after what we have seen since the launch of DeepSik R1 and V3, the whole market changed. We both thought that the demand for compute, demand for computation asset would drop with the more efficient model comes out. Well, in fact, it is not. If you go to look at Corv, which one of the urban rising cloud companies against GCP, you see the results for Q1 is amazing.

like they have a significant growth in the first quarter and there are a lot of other non-leasted count companies has experienced such growth in just one quarter and a lot of reason behind is that number one I always use this analogy. The invention of steam engine doesn't really reduce the use of coal but it actually accelerates that meaning the more efficient air model being used or being generated

they actually can allow, can drive more demand on the computer side because you can do so much more things right now with lower cause with more capabilities. There's exactly what happened after Thieck R1 comes out. Less Moore's Law, more Jevin's paradox, right? Yeah. With that. All right, so you've got 10 plus partners that you're in various states of evaluation

onboarding. Is there a target date that you have set right now that, hey, we'll have this pool of GPUs tokenized and ready for people like Mark to participate in? So the reason we're doing the pre-deposters campaign right now is to prepare for the actual main. launch where we where we have like the GPUs to be tokenized in the pipeline we have over two billion dollars to be ready to tokenized in the next one two years immediately in the next three four months of few hundred mil is just ready to be ready that when we actually launch they will come on

bought did you say a few hundred million yeah just just a just a few hundred me he said it in passing just a few hundred million right but yeah continue the tvls are already like if you go you can see the millions that's that's pouring in yeah so we yeah the dollar's coming with in and then we are tokenizing it bed by batch. So introducing it gradually on the platform. I feel like I have my head around this a lot better than when I jumped in to do my research. So thank you for helping our listeners understand what you guys are doing.

I have a hot take question mark that I want to drop into this. It's definitely tangential. But all right, so we have the news that the Anthropic CEO put out there that basically like, hey, you know, all this talk that we are hearing right now, the stuff's going to happen quicker, jobs are going to go away faster, entry-level positions are gone, and then you have the Nvidia CEO kind of clapping back and going, hey, man, you're totally wrong in this. I completely disagree. But the funny thing that I saw was that the Nvidia CEO or the Anthropic uses Amazon chips and not Nvidia chips. Talk to me about this feels very WrestleMania in a lot of ways.

Like, how do you feel about that interaction and discussion? Who do you think is right on the timing of of this real impact on human jobs? We're definitely seeing a lot of like more mundane and autonomous child being replaced. For example, those low level summary, content writing, translation to certain level are being replaced there. And then the other way is,

I'm not sure where you pay attention, but it's very funny. In China, robots are cleaning the road right now, not the cleaners, not the workers anymore. So like one machine replaced like four workers for cleaning the road in China these days. And then there are like machines, Some for gimmick, like brewing your coffee, mixing your tea there,

some for gimmick, but some for, like, actual industrial use these days. So that one... Is that across China or just in Shanghai? Is that happening in multiple cities across China? Well, for the road cleaning, it's happening on multiple cities. And then for the cooking side, it's just like sporadically across China. I mean, even outside China, we've seen some adoption there. For example, if you go New York and North Central,

you see some machine cafe being spun up, like, okay, there's the robots cooking for you there. So we're seeing some adoption in that trend. But whether that's massive, not yet. But on the other hand, the AI adoption on the autonomous driving level, RoboTexis is coming. Texas is launching Robo taxi. That Musk's big play, isn't it, really?

The robot is cheap. Cars are expensive to build. Robo tabs on. Yeah. Well, this all, and it's really interesting. There's some other news that is happening right now. It's pretty timely. like there's new models that actually allow AIs to do what we do, essentially create a model of the world

and then test how the AI could react in that model of the world, just like I look around the room and I go to navigate across. I create a model of the room in my head. And so this stuff's like actually starting to happen. The humanoid thing is real. We're still trying to get a few more guests in that regard. But our research is hot and heavy and at. I know there's a company, is it human that is deploying robots in Spartanburg, South Carolina at the BMW plant. They're doing things that they haven't done before. Amazon's. Figure doing that's what it was. Figure, figure, figure, figure. And filmmaking, Evo, flow, the Google stuff, that's getting better and better and better, isn't it? The Evo one is actually got the Evo model for filmmaking is good. So, I mean, for some of these content

interaction, they're definitely going to be a stereotype for it. I mostly replacing human, but making human smarter in other ways, like leaving the more mundane and more like time-consuming task to the machine and then you do more creative stuff there. Even if you're talking about it's a supply chain. We want jobs to go back to America, right? And, but the fact is the labor cost is just too high. Maybe we can get some machine to reduce the cost there and the human can do other things and the whole supply chain. So these, we're seeing disruption and not necessarily negative destruction in the whole different types of work variety there. Yeah, this is a very interesting era that we're living in.

And I would love to see more innovation. So I want to push back on the mundane because for good or ill, a huge proportion of the planet's population work in mundane, boring jobs. And it puts a free on the table for millions, if not billions of people. How do we counterbalance that? We can't all chuck in our mundane jobs and get creative. So how do we, or is that just what I call AI collateral damage? And that's just the cost?

That is a very spicy take because when I mention mundane, it doesn't mean like it's a boring thing. It just means it's repetitive. For example, you're repeating doing one thing. Maybe machine can do it better on a precise level. That's somehow we need to operate that. But human can still be there, do the checking and do other tasks that would be required. And unfortunately, because of the shift of a cycle, we would definitely see people need to upscale. People need to be retrained for that, like, business.

task that we're doing these days. It happened to other industries, unfortunately, for example, when a lot of these, like, factories move out of China to the SEA regions. A lot of the people in China lost a job. Like, the unemployment was real. So they need to be retrained and get back to the market. This is the whole how systematic is doing. That's why I'm not sure you know, like in Japan, all these banks, they're asking their

employees to learn how to use AI. You know, then if they don't, they don't take a lesson. they don't get a certificate, they would just lose their job. So I think the key point here is AI, can we play some part? But what we want, what we can do is to how should we leverage AI to make ourselves better, to make our skills better, so that we can learn more, new, newer tasks that we can spend more time on, more high value at a task that we can do. Tony, you hit a very important note right there, and a lot of previous shows talk about this,

is you've got companies like Shopify and a few, other companies doing this AI first mentality where I would ask for headcount. Shopify's like, hey, prove to me that AI can't do it, right? And then we had a couple other guests talk about, hey, you can learn anything, right, with AI, you know, go figure it out. So you're, you're, we're moving toward this orchestrator of generalism, right, as opposed to specialization, which I think is really good. And then lastly, friends and neighbors, if you're freaked out about AI, go listen to our episode unpacking, packing McCormack's most human wins. all we're going to do is move up another level of abstraction.

Our old outputs are going to become inputs and everybody's going to be fine. And we'll also have invested in Gaib, so we'll have lots of return on our investment. So when we get more creative, we'll have the funds to pay for our creativity. Not investment advice. Yeah, do not. No, invest in advice. Not investment advice. Okay, two important questions that we're going to finish with here.

Coni, number one from Kevin Kelly. What should humans be? And how does technology help us get there? I think humans should be creative because each one of us is a very interesting soul where creativity is something I don't think machine can replace like everybody every day we're thinking of new solutions we're thinking of new ideas that make us improve make us strive and we create AI not AI created us so I think that trend will continue to be the case if humans maintain our creativity think of new ways think of ideas

drive things for us. That's where, this is where evolution goes. And the second thing is, I think humans should still be, maintain the feeling, maintaining the emotions, maintain the touch. Like, this is what AI cannot do. We are humans made of fresh and blood. And we do have sympathies that machines may not have. Spread the love, spread care to the people. I think that's the most important thing that we should be. Before we get on the final question, I'm going to hit a soapbox just for like 45 seconds. Don't get on a soapbox. So, Connie, you said something really important that resonated with me. Maintain our creativity.

Maintain our creativity. There was a study in the 70s that tell us we get less creative as we get older. To the point of exponential dips in creativity, let's define creativity as the ability to think divergently. Instead of focusing in a very linear mode, you're kind of putting things together, being creative, mashing things together. We lose that ability if we choose to not work on it. Find a gym for your divergent thinking. Guess what? Writing does that.

Thinking on paper does that. Mark, your next question. That would be an awesome Instagram short there, Jamie Weldon. Coney also said that we are organic. We are flesh and blood. And that ties in nicely to our book club, irreducible, where we're talking about consciousness, what it means to be human and whether AI will ever be conscious if it is not organic. Second question, Connie, we're bringing in the big stuff last week our guest, asked this question for future guests. If money and time was not a barrier, what technology would you create?

I would say a time machine. Money and time is not a problem. I will create a time machine because it can allow us to see things through the way and even travel back and forth. Number one, go back to the past. Not to change anything though. Like go back to the past and see and feel what was done wrong. Learn from that. History, learning history is one way, but going there, like bringing that scene back is another thing. And the other way is going to the future. Because it's only when you go to the future. because it's only when you go to the future, you look back, you realize some of these conflicts, some of these wars may not make sense. If you look at these conflicts and wars across the long time horizon,

they're just like a tiny dust in the whole universe. Why do we harm people? Why do we kill people? Let's just stop, stop these. Stop these conflicts, stop these wars. We live on the same planet. And also, when you go to the future, you will see that, human evolution human and generations we iterate like hopefully is that one one generation better than the

others we we have done it in the past thousands of years we continue to do so and yeah that's what drive us and keep us moving forward like we have a goal we have we keep on creating innovating and hopefully the society everybody is living a better life ultimately wonderful answer so that's too now we have a time machine jeremy to add to our spaceship to explore the stars it's good question. I'm also going to, I'm going to add an empathy module into his time machine so that when people go to the past, they can actually activate empathy and really connect with everything. Connie, this has been a fantastic conversation. Thanks for following all the past that we went down. And I think this presents a pretty clear picture to our listeners, what you're trying to

do. I think it's really innovative. I think it's pretty accessible. So keep us in the loop. Give us an update, maybe pop back on and chat with us again. Thanks for, thanks for being here. Mark, closing thoughts? Yeah, gaib.a.ai to learn more about Connie and Gaib Thinking On Paper. To learn more about thinking on paper. And thank you for thinking on paper with us, Connie. We'll leave it there. We'll see you next week.

Be disruptive. Stay curious. Thinking on paper.