WEBVTT NOTE Open Interest — enzo-coglitore-gte. Draft: automatic transcript (faster-whisper small.en) of the released edit, automatic speaker assignment (resemblyzer, 2 speakers), corrections applied from the editorial glossary and OCR of the burned-in captions. Not yet reviewed line by line. 1 00:00:00.000 --> 00:00:02.540 Welcome back to the Content Suite in Singapore. 2 00:00:03.020 --> 00:00:04.980 Today, we have Enzo from GTE. 3 00:00:05.600 --> 00:00:09.860 Before we get started about GTE's new products, do you want to introduce yourself, Enzo? 4 00:00:10.280 --> 00:00:10.500 Sure. 5 00:00:10.720 --> 00:00:11.160 Happy to. 6 00:00:11.580 --> 00:00:16.800 I'm Enzo, the founder of GTE, where we built the only trading platform that gets better with you. 7 00:00:17.460 --> 00:00:20.140 It learns based off of you, your actions, and your behaviors. 8 00:00:20.520 --> 00:00:28.720 As you take trades with GTE, our shared AI co-pilot starts to suggest trades, set-ups, recommendations for you to the next trade that 9 00:00:28.720 --> 00:00:29.120 You have. 10 00:00:30.120 --> 00:00:38.540 Before we get into GTE and this expansion towards AI, maybe we can go into how you got into crypto and what led you into 11 00:00:38.540 --> 00:00:40.100 Building GTE. 12 00:00:40.460 --> 00:00:41.620 Yeah, I'm happy to. 13 00:00:42.520 --> 00:00:47.700 My background to get into crypto is a bit atypical, but very typical for where I'm from. 14 00:00:47.840 --> 00:00:50.760 I grew up in the Bay Area and I grew up in Palo Alto. 15 00:00:51.660 --> 00:00:53.860 My dad worked in tech for a long time. 16 00:00:54.340 --> 00:00:56.920 He was one of the original pioneers of the data center. 17 00:00:57.340 --> 00:01:02.400 He built a lot of the first data centers for Google, Amazon, eBay, PayPal, 18 00:01:03.660 --> 00:01:06.640 Yahoo, YouTube, Napster, you name it. 19 00:01:07.140 --> 00:01:14.800 I took that company public when I was a kid and then worked at Facebook as the senior director of hardware engineering in 2007, 2008 20 00:01:14.800 --> 00:01:15.660 To 2015. 21 00:01:16.320 --> 00:01:24.040 I grew up in this super nerdy household where the internet and everything was very native to me. 22 00:01:24.600 --> 00:01:28.120 And I was very into video games, in particular this game Minecraft. 23 00:01:29.140 --> 00:01:32.740 And I realized when I was about nine or 10 years old, like if you could inject 24 00:01:34.100 --> 00:01:40.620 JavaScript into like the Minecraft client itself, you could turn off certain blocks and you could see diamonds. 25 00:01:41.340 --> 00:01:45.460 And I realized like if I played on online servers, I could monopolize resources. 26 00:01:46.140 --> 00:01:52.200 And I became like a trillionaire by using a voice changer and like being a part of this faction in this clan in Minecraft. 27 00:01:52.520 --> 00:01:55.860 And I would sell TNT for like in-game currencies. 28 00:01:56.000 --> 00:02:00.080 And that's where I kind of got into the world of virtual marketplaces, digital marketplaces. 29 00:02:00.240 --> 00:02:01.060 And I kept doing this. 30 00:02:01.200 --> 00:02:07.780 I started expanding to World of Warcraft, Guild Wars 2, selling gold, buying gold, counter-strike, counter-strike global offensive. 31 00:02:08.340 --> 00:02:11.120 Then you really start to understand middlemen, trust, 32 00:02:12.140 --> 00:02:13.780 Online marketplaces, everything else. 33 00:02:14.180 --> 00:02:22.520 And that led me to after I graduated college, I ended up in Eastern Europe with the Ethereum Foundation where they were focused on scaling 34 00:02:22.520 --> 00:02:22.880 Ethereum. 35 00:02:23.180 --> 00:02:24.980 This is about the beginning of 2023. 36 00:02:25.200 --> 00:02:28.460 So I was living with Vitalik in Montenegro for about a month. 37 00:02:29.140 --> 00:02:32.280 And that's where I met my co-founder and the rest is kind of history for us. 38 00:02:32.700 --> 00:02:32.900 Nice. 39 00:02:33.060 --> 00:02:35.400 When did you meet your co-founder and when did GTE start? 40 00:02:35.980 --> 00:02:43.900 I met my co-founder in the middle of 2023 and GTE started right at the kind of end of 2023, going into 2024. 41 00:02:44.580 --> 00:02:50.720 And correct me if I'm wrong, but when GTE started, you wanted to work on the biggest marketplace in crypto, which I think is perps. 42 00:02:51.540 --> 00:02:56.580 Tell me about the story of like going from perps to kind of the pivot that you're going towards with AI. 43 00:02:57.400 --> 00:02:57.880 100%. 44 00:02:57.880 --> 00:03:01.600 So there's kind of two great like mega trends that are happening right now, right? 45 00:03:01.680 --> 00:03:08.960 There's like the mega trend of crypto and tokenization of everything you can trade, like quite literally anything, anywhere at any time. 46 00:03:09.320 --> 00:03:11.180 Every exchange is the everything exchange. 47 00:03:11.180 --> 00:03:14.020 Hyperliquid is at $96 at the time of this taping. 48 00:03:15.040 --> 00:03:16.780 You can trade anything you want, right? 49 00:03:16.820 --> 00:03:17.980 You can trade Bitcoin. 50 00:03:18.180 --> 00:03:22.680 You can use Bitcoin as collateral to get a loan on Hyperliquid and open up a space expert. 51 00:03:23.180 --> 00:03:25.860 And that's kind of always been like the holy grail of finance, right? 52 00:03:25.900 --> 00:03:32.400 Is the ability for people all over the world to take a sovereign kind of asset, Bitcoin or a stablecoin and use that to 53 00:03:32.400 --> 00:03:34.400 Get exposure to Western capital markets. 54 00:03:35.000 --> 00:03:37.540 When we were starting GTE, this was not the case, right? 55 00:03:37.700 --> 00:03:41.880 Like a majority of things that were traded on chain were meme coins and governance tokens. 56 00:03:42.320 --> 00:03:48.380 And we thought, okay, this is a very similar parallel and analog to the internet in which as you start to scale up a lot 57 00:03:48.380 --> 00:03:55.180 Of the base underlying infrastructure, a lot of value starts to move to applications that have real utility for the end users. 58 00:03:55.780 --> 00:04:03.340 And so if the internet's constraint was compute at the time during the first dot com bubble, and then subsequently, the excess compute enabled applications 59 00:04:03.340 --> 00:04:11.280 Like YouTube or Facebook, we thought a similar sort of phase shift will happen where the constraint during the last bubble was block space was 60 00:04:11.280 --> 00:04:13.000 Very expensive to do transaction on Ethereum. 61 00:04:13.380 --> 00:04:14.280 Solana was very unstable. 62 00:04:14.740 --> 00:04:16.340 It's hard to get price discovery on chain. 63 00:04:16.880 --> 00:04:22.100 When that gets solved, all of the value will move to applications that are able to actually bend these assets. 64 00:04:22.420 --> 00:04:31.400 And so when we started GTE, our focus was on how do we contribute scaling technology that actually enables 24 seven permissionless markets around the 65 00:04:31.400 --> 00:04:31.620 World. 66 00:04:32.060 --> 00:04:32.540 I think we're there. 67 00:04:32.960 --> 00:04:41.160 And I think in the other side of that mega trend, you know, is the AI vacation of everything AI in the past, like three 68 00:04:41.160 --> 00:04:45.920 To four years has gotten quite literally smarter like than any human that has ever existed ever. 69 00:04:46.160 --> 00:04:48.280 And the rate of change is an exponential. 70 00:04:48.820 --> 00:04:59.340 And so we were having this conversation last year, the best AI model app was like probably Claude Sonnet for like GPT five 365 days 71 00:04:59.340 --> 00:05:06.160 Later, those models look like toys like they're like, you wouldn't even like want to like use it in any capacity because what it can 72 00:05:06.160 --> 00:05:06.620 Do now. 73 00:05:06.860 --> 00:05:13.400 Right now, you open AI today put out like I think it was like 106 different like mathematical advancements that they made with their new 74 00:05:13.400 --> 00:05:14.400 Model that's unreleased. 75 00:05:15.220 --> 00:05:18.360 And that is like an example of like what exists today. 76 00:05:18.820 --> 00:05:25.580 Our belief is that by this point next year, we're going to be talking about use cases that AI enables for finance and on chain 77 00:05:25.580 --> 00:05:29.300 Activity, that our mind will things that we couldn't have even thought of. 78 00:05:29.620 --> 00:05:38.020 And so when we saw, okay, we now have very liquid exchanges, Hyperliquid, Lighter, Solana, the next kind of crash course is that AI 79 00:05:38.020 --> 00:05:45.060 Is going to see this marketplace and start to absorb that people will then start to enter the marketplace with agents, where the agents able 80 00:05:45.060 --> 00:05:48.420 To read across all the different chains and make an objective decision. 81 00:05:48.920 --> 00:05:51.200 And we're the best places to execute that trade. 82 00:05:51.580 --> 00:05:55.080 Maybe you want to trade Bitcoin perps for you, that could be Hyperliquid. 83 00:05:55.080 --> 00:06:02.380 If you have capital on chain, maybe you want to trade meme coins for some people, that's Robinhood Chain, the AI has no opinion, it 84 00:06:02.380 --> 00:06:04.420 Will just execute what the demand of the user is. 85 00:06:04.600 --> 00:06:09.520 We're very focused on making that next leg of access possible for the end consumer. 86 00:06:10.440 --> 00:06:18.600 That makes a lot of sense as perp exchanges meme coins kind of decentralized and a lot of the old pains that we had in 87 00:06:18.600 --> 00:06:21.140 Like 2021 have gotten solved by the new exchanges. 88 00:06:22.100 --> 00:06:26.140 You guys want to give the ability for everyone to utilize AI. 89 00:06:26.760 --> 00:06:31.940 I think you're correct in that the equivalent of a PhD is in our pocket now through Astra. 90 00:06:32.500 --> 00:06:39.040 And you would have to previously pay an employee or a quant, let's say a six figure salary, but you can do that now with 91 00:06:39.040 --> 00:06:40.000 Like a $10 subscription. 92 00:06:40.560 --> 00:06:42.600 Where do you see that enables GTE? 93 00:06:42.760 --> 00:06:49.600 What kind of trading or different types of strategies are possible now that weren't let's say a year ago, 100% like one of our 94 00:06:49.600 --> 00:06:56.000 Taglines is Wall Street intelligence in the palm of your hand, where you're able to quite literally have, you know, I would say it's actually 95 00:06:56.000 --> 00:06:57.540 Smarter than a PhD, right? 96 00:06:57.760 --> 00:07:01.880 Like I would say like the smartest person in the world is probably Terence Tao by raw IQ. 97 00:07:02.900 --> 00:07:06.800 And AI is doing math that like Terence Tao cannot do anymore. 98 00:07:06.980 --> 00:07:08.440 And even he's kind of conceded defeat. 99 00:07:08.880 --> 00:07:13.020 If you look at like there's like Jelani Nelson, who's like the CS professor at Harvard that went to Berkeley. 100 00:07:13.560 --> 00:07:17.040 That was actually like Jeff's like teacher for when Jeff was like a TA. 101 00:07:17.720 --> 00:07:20.480 I don't know how goes class even he's joined Anthropic, right? 102 00:07:20.620 --> 00:07:25.040 It's like, okay, like, yeah, academia has like basically conceded defeat to AI, right? 103 00:07:25.240 --> 00:07:26.700 And what does that mean in practice? 104 00:07:27.440 --> 00:07:34.380 What it means is that people who have some semblance of Edge are going to have an even bigger Edge and find the limits of 105 00:07:34.380 --> 00:07:38.040 Their Edge much faster in terms of relation to the markets and trading. 106 00:07:38.700 --> 00:07:44.660 So if you, you know, we call this kind of like a one man firm have the ability to quantify information and stack that with 107 00:07:44.660 --> 00:07:51.640 Qualitative beliefs, and then use that to make a trade, you're able to do that much faster with an agentic tool suite. 108 00:07:52.080 --> 00:07:56.360 And so the way that we think about our product is it's not a product where you can just tell the AI to make money. 109 00:07:56.820 --> 00:07:57.740 It's almost the inverse. 110 00:07:58.260 --> 00:08:04.060 The AI is waiting for you to tell it what your ideas are, to make money and validate it for you. 111 00:08:04.380 --> 00:08:12.320 Use back testing, use simulations, track flows across finance, buy-in and Hyperliquid, be able to traverse the world of on and off chain 112 00:08:12.320 --> 00:08:16.160 Finance and help you make a decision with confidence much faster. 113 00:08:16.640 --> 00:08:25.100 So what this means is that people that previously would have had to set up entire desks, quantitative developers, infrastructure teams, execution engineers can now 114 00:08:25.100 --> 00:08:31.560 Rely on a suite of agents through a singular agent that we call Edge to define and determine what their Edge is in relation to 115 00:08:31.560 --> 00:08:32.400 The rest of the market. 116 00:08:32.660 --> 00:08:37.200 As they start to trade more and more Edge starts to remember who you are and what you did. 117 00:08:37.800 --> 00:08:39.740 So let's say we made a trade where we made a lot of money. 118 00:08:39.980 --> 00:08:45.820 I like Meta Muse as an example, I bought Meta Muse or I bought Meta stock when when Muse came out. 119 00:08:46.160 --> 00:08:47.600 The AI remembers that now. 120 00:08:48.100 --> 00:08:50.560 So it's going to ask me, why did you buy Meta? 121 00:08:50.820 --> 00:08:55.960 Like what did you learn about the world that you were right when the market opened on Monday? 122 00:08:56.600 --> 00:08:59.940 If you bought it on a Saturday, you got gifted with like a God candle. 123 00:09:00.720 --> 00:09:04.600 And the next trade that you make, it's going to remember that you've made that trade. 124 00:09:05.100 --> 00:09:09.960 And then by the 10th or 11th trade, it's going to find like the next setup for you. 125 00:09:10.220 --> 00:09:14.040 Because what we've done not only in building this harness for the AI is we've also given it a computer. 126 00:09:14.520 --> 00:09:16.260 So it has the ability to be omnipresent. 127 00:09:16.500 --> 00:09:18.660 It's looking and searching for things while you're asleep. 128 00:09:19.140 --> 00:09:19.820 And it never stops. 129 00:09:20.320 --> 00:09:26.960 And so we believe that just today, those are a couple like example use cases by next year, it's going to be able to surface 130 00:09:26.960 --> 00:09:32.540 Insights better than any human ever could have and do research better than any human ever could have. 131 00:09:32.580 --> 00:09:33.140 Got it. 132 00:09:33.340 --> 00:09:43.460 So using GTE's new product Edge, you have the equivalent of having a CFO analyst, on-chain analyst, or really whoever you want on your 133 00:09:43.460 --> 00:09:46.440 Team managing your finances, or at least your portfolio. 134 00:09:47.340 --> 00:09:50.240 Tell me a little bit more about how you are using it. 135 00:09:50.540 --> 00:09:53.940 Am I using this as a new harness or am I bringing it into my harness? 136 00:09:54.460 --> 00:09:57.000 What is the first product suite for Edge look like? 137 00:09:57.520 --> 00:09:59.080 So we built our own custom harness. 138 00:09:59.600 --> 00:10:02.320 And the way it works is it's built inside of GTE. 139 00:10:03.320 --> 00:10:08.480 And what we do is obviously we're this Labs Corporation and we license that product to GTE 140 00:10:09.360 --> 00:10:09.760 Company. 141 00:10:10.300 --> 00:10:13.600 And on our front end, now we have a representation of the harness. 142 00:10:14.260 --> 00:10:17.320 And so what you're able to do now is you're able to set it up with your past. 143 00:10:17.800 --> 00:10:20.080 Give us your wallets, give us your past brokerages. 144 00:10:20.560 --> 00:10:26.280 Let us read and create a trader profile for you and create a universe where it's uniquely you and the AI. 145 00:10:26.920 --> 00:10:29.720 So the AI goes in and says, Hey, you bought Hyperliquid at $4. 146 00:10:30.040 --> 00:10:30.800 What were you thinking? 147 00:10:31.540 --> 00:10:35.960 Most people did not treat that as a consensus bet, obviously, because Hyperliquid ripped to $96. 148 00:10:36.200 --> 00:10:38.800 So you had alpha in relation to the rest of the market. 149 00:10:39.180 --> 00:10:39.440 Why? 150 00:10:39.840 --> 00:10:40.480 Tell us why. 151 00:10:40.640 --> 00:10:46.420 As we start to build this world with you and the AI, the AI starts to get better as it starts to understand what you 152 00:10:46.420 --> 00:10:49.080 Like to look for, what your alpha is, what your Edge is. 153 00:10:49.500 --> 00:10:56.540 And so the reason why we had to build our own harness is because general purpose models and general purpose harnesses do not really apply 154 00:10:56.540 --> 00:10:59.220 Well to domain specific industries like finance. 155 00:10:59.740 --> 00:11:03.860 The reason why AI is so good at math and science is because there's a verifiable reward. 156 00:11:04.480 --> 00:11:12.600 There is an outcome that's deterministic where after smashing 10,000 agents, it's able to solve a millennium problem. 157 00:11:13.280 --> 00:11:15.100 Finance is a bit more like time dated. 158 00:11:15.580 --> 00:11:22.300 You make a trade, you are either right or wrong in that trade, but there are a series of time has to happen before you 159 00:11:22.300 --> 00:11:22.840 Get rewarded. 160 00:11:23.300 --> 00:11:29.220 If, for example, you could buy hype at $4, you only get rewarded the $4 to $96 move by holding it the entire time. 161 00:11:29.340 --> 00:11:29.960 What was your conviction? 162 00:11:30.160 --> 00:11:30.760 What was your belief? 163 00:11:31.600 --> 00:11:36.640 And so, but as the AI starts to collect more and more of the data in that time series, it starts to get better operating 164 00:11:36.640 --> 00:11:37.200 Within that. 165 00:11:37.280 --> 00:11:43.820 But the only solution that we found was to create a harness and subsequently what we call an ontology, which is the relation of objects 166 00:11:43.820 --> 00:11:46.320 That the AI has the ability to understand. 167 00:11:46.940 --> 00:11:49.500 It's like a term for metaphysics essentially means, 168 00:11:50.320 --> 00:11:57.820 You know, the AI knows what a tool is, what a sandbox is, what a back testing is, what simulation is, how to search Twitter, 169 00:11:58.060 --> 00:12:01.280 How to know when to call what tool for each specific function. 170 00:12:01.840 --> 00:12:07.440 So if a user says the word backtest, it calls our custom backtesting tool, and it knows the ability to do that. 171 00:12:07.560 --> 00:12:12.760 If a user says the word perps, it determines whether or not they're saying finance or Hyperliquid. 172 00:12:13.260 --> 00:12:15.380 That is like where a lot of the value lies. 173 00:12:15.660 --> 00:12:18.480 If you ask ChatGPT or Claude, it gets you pretty close. 174 00:12:18.920 --> 00:12:21.900 But it eventually suffers under the noise. 175 00:12:22.320 --> 00:12:24.100 And this is why companies like Palantir exist. 176 00:12:24.300 --> 00:12:25.460 That's where my co-founder worked. 177 00:12:26.200 --> 00:12:35.420 You have the ability to actually surround the model with what you just called a harness and really exemplify its abilities for the specific domain 178 00:12:35.420 --> 00:12:36.260 That it exists in. 179 00:12:36.900 --> 00:12:37.820 That makes a lot of sense. 180 00:12:38.180 --> 00:12:46.980 I've trained my OpenClaw and Hermes agent now to do specific crypto things, but it still hasn't reached the level that I want. 181 00:12:47.540 --> 00:12:53.700 Another concern that I have as a potential user is really just, how do I know my funds are going to be compromised? 182 00:12:54.120 --> 00:12:55.920 If this agent has my private keys, 183 00:12:57.100 --> 00:13:03.060 I could wonder if it could get injected, prompt injected, and send my keys or like my funds to someone else. 184 00:13:03.420 --> 00:13:07.340 What kind of security measures are you guys thinking about as trading becomes more agentic? 185 00:13:07.760 --> 00:13:08.220 A hundred percent. 186 00:13:08.460 --> 00:13:12.580 So the first is, we try and keep it extremely locked down. 187 00:13:12.760 --> 00:13:17.720 We were exploring things like TEEs where we don't even see the inference that you have in relation with the AI. 188 00:13:18.020 --> 00:13:23.740 I think Muse is a perfect example of this where you're able to cross the chasm, make it so that even if it has sensitive 189 00:13:23.740 --> 00:13:24.440 Information, 190 00:13:25.300 --> 00:13:27.880 A human still has to approve all of those different actions. 191 00:13:28.480 --> 00:13:35.500 So even if somebody tries to prompt inject it in relation, that already means they have access to your device, which means that you've been 192 00:13:35.500 --> 00:13:36.500 Compromised pretty hard. 193 00:13:37.040 --> 00:13:43.280 And so for things that are API based, things that somebody, if they're able to get access into the system, we try and build as 194 00:13:43.280 --> 00:13:49.100 Many guardrails as possible that require the agent to actually have multiple levels of approval. 195 00:13:49.560 --> 00:13:51.800 This is where this ontology comes inside of. 196 00:13:52.400 --> 00:13:56.660 Eventually at some point, there's these benchmarks where the agent is supposed to make money. 197 00:13:57.240 --> 00:13:58.740 It always ends up like, boom. 198 00:13:59.360 --> 00:14:04.200 It's because the agent, in isolation, it just eventually suffers from noise. 199 00:14:05.020 --> 00:14:12.020 And what we've done is we spent a lot of time making sure the agent isn't just going to eat your balance into a perp 200 00:14:12.020 --> 00:14:15.380 And in position because it thinks it's a good idea and it just gets wrecked. 201 00:14:15.860 --> 00:14:20.500 Because the worst thing that can happen here is not just like, oh, you have the wrong response. 202 00:14:21.060 --> 00:14:23.400 The worst thing that happens is Ken Griffin takes all your money. 203 00:14:24.160 --> 00:14:26.400 And you really don't want that to happen. 204 00:14:26.560 --> 00:14:33.340 That's even worse in some capacity than having databases deleted or having the AI send a text to your ex-girlfriend. 205 00:14:33.660 --> 00:14:35.440 This is the next level of that. 206 00:14:35.880 --> 00:14:40.020 So we've spent a lot of time applying, what does AI look like in enterprise? 207 00:14:40.380 --> 00:14:42.960 What does AI look like in terms of getting traders to trust it? 208 00:14:43.420 --> 00:14:48.840 If we're able to get a trader to trust it, and these traders are what I call security aware. 209 00:14:50.620 --> 00:14:54.040 Then we're able to probably cross that chasm for everybody else. 210 00:14:54.040 --> 00:14:54.680 Very nice. 211 00:14:55.240 --> 00:14:59.410 I have so many questions because you've used this more than anyone else now. 212 00:15:01.200 --> 00:15:07.780 Whether you call it an agent or Edge and how much Edge it actually gives you, you only know. 213 00:15:08.200 --> 00:15:15.340 What are some of the surprises that you found testing Edge that maybe people should look forward to or I could try once it comes 214 00:15:15.340 --> 00:15:15.600 Live? 215 00:15:16.220 --> 00:15:20.960 Yeah, so Edge is really good at crossing strategies. 216 00:15:21.720 --> 00:15:24.440 AI is really good at copying. 217 00:15:25.580 --> 00:15:30.880 So let's say, for example, you have some sort of macro trader that you love, Stanley Druckenmiller. 218 00:15:31.140 --> 00:15:34.000 It's like, okay, Kevin Warsh worked for Stanley Druckenmiller. 219 00:15:34.340 --> 00:15:35.460 He's now the new Fed chair. 220 00:15:36.140 --> 00:15:40.260 You tell the AI, okay, go use yt-dlp. 221 00:15:40.360 --> 00:15:42.020 This is like the Python YouTube downloader. 222 00:15:42.360 --> 00:15:46.720 Download all the Druckenmiller's transcripts for everything he's talked about. 223 00:15:47.420 --> 00:15:53.680 Download all of the Kevin Warsh when he was getting selected by Trump to be Fed chair post. 224 00:15:54.600 --> 00:15:59.460 Understand, like, okay, in what world did he actually decide to raise rates? 225 00:15:59.660 --> 00:16:03.160 What world do we live in right now based off what these guys have said this surpass people? 226 00:16:03.500 --> 00:16:04.100 This is macro. 227 00:16:05.060 --> 00:16:07.980 Now, let's find assets that are correlated to this. 228 00:16:07.980 --> 00:16:14.340 So, okay, like if they're raising rates right now, but ultimately in the long term, like Druckenmiller and Warsh, they want more volatility. 229 00:16:14.740 --> 00:16:16.420 What assets are going to benefit from that? 230 00:16:16.760 --> 00:16:19.620 And then from within that basket of assets, what assets do I know? 231 00:16:20.760 --> 00:16:23.000 Crypto, stocks, tech stocks, et cetera. 232 00:16:23.260 --> 00:16:26.240 When do I know assets are mispriced relative to what other people think? 233 00:16:26.720 --> 00:16:30.660 Then how am I able to then start to apply more quantitative thinking to when's the proper time to buy? 234 00:16:31.160 --> 00:16:35.380 Do I have another trader that I know that times trades really well that are shorter term variations? 235 00:16:35.760 --> 00:16:39.160 Say I'm long term bullish on tech, but I'm looking for an entry. 236 00:16:39.400 --> 00:16:40.440 When is an entry? 237 00:16:40.540 --> 00:16:42.200 What qualifies an entry? 238 00:16:42.700 --> 00:16:43.540 Well, there's another trader. 239 00:16:43.680 --> 00:16:45.160 Let's say I like Ansem as an example. 240 00:16:45.380 --> 00:16:51.060 Ansem, by the way, has great trading knowledge from like 2021 before the meme coin shit started, like really smart. 241 00:16:51.440 --> 00:16:56.540 You can take some of those where they're talking about specific entries, then apply that to the thematic sector. 242 00:16:56.980 --> 00:17:04.280 What I've done now is I've just taken information from the Fed chair, the best macro investor of all time, and I've combined that with 243 00:17:04.280 --> 00:17:08.520 Like these crypto guys that are really good at timing flows and in and outs and momentum plays. 244 00:17:08.900 --> 00:17:12.000 That gives me the ability to now see both sides of the game. 245 00:17:12.320 --> 00:17:14.340 I don't have to just buy Nvidia and chill. 246 00:17:14.700 --> 00:17:21.560 I can actually accumulate properly based off what I think is the best entry point relative to my greater grand thesis. 247 00:17:22.040 --> 00:17:26.640 That is where I see Edge and AI really starting to like kind of [unclear] users' behaviors. 248 00:17:27.220 --> 00:17:33.020 It would have taken you a whole team of analysts to study just Druckenmiller, all team of analysts just study momentum trading, whole team 249 00:17:33.020 --> 00:17:37.100 Of analysts just study execution on top of that, break thematic sectors, et cetera. 250 00:17:37.300 --> 00:17:38.900 But like an agent can do this all in one run. 251 00:17:39.320 --> 00:17:40.960 It's able to do this and then remember it. 252 00:17:41.080 --> 00:17:45.280 Then if you make the trade and if you were right, agent knows you have alpha relative to everybody else. 253 00:17:45.620 --> 00:17:50.560 Example of this is like I did a trade where I bought Intel right after Claude Code came out because I was like, okay, CPUs 254 00:17:50.560 --> 00:17:51.060 Are going to rip. 255 00:17:52.540 --> 00:17:56.940 The agent helped me because you have a lot of emotions as a trader. 256 00:17:58.140 --> 00:18:02.920 Where you lose money is you either overtrade or you get flooded out of the position and then it rips. 257 00:18:03.100 --> 00:18:06.980 It's like the classical story of like it's like a dollar away from my liquidation price. 258 00:18:07.140 --> 00:18:12.320 I close the position and then like Zcash ripped like $1,000 and I would have made a million dollars. 259 00:18:12.900 --> 00:18:15.320 As a human, you're subject to these biases. 260 00:18:16.020 --> 00:18:18.600 AI does not suffer from these same biases. 261 00:18:19.100 --> 00:18:21.660 What it's really good at is coaching you to stay calm. 262 00:18:22.260 --> 00:18:28.960 When I bought Intel, it was at like I think $40 and then it went back down to like $25 and I was like, should 263 00:18:28.960 --> 00:18:29.860 I close this position? 264 00:18:29.960 --> 00:18:30.320 Am I wrong? 265 00:18:30.460 --> 00:18:31.740 It was like, tell me your fundamental thesis. 266 00:18:32.500 --> 00:18:33.280 What did you think here? 267 00:18:33.840 --> 00:18:35.020 Why did you think that? 268 00:18:35.660 --> 00:18:43.640 Well, if you're right, then the risk to reward calculation is greater than the drawdown another $10 if you're playing a long term game. 269 00:18:43.920 --> 00:18:45.980 It like basically soothes my emotional state. 270 00:18:46.460 --> 00:18:48.820 Three months later, it ripped to like $20. 271 00:18:49.200 --> 00:18:56.460 It's like, oh my God, I will never not use AI to structure my trades and think through my trades with me because it's going 272 00:18:56.460 --> 00:19:03.080 To catch my faults as a trader and it's going to magnify my wins and magnify the things that are good about me. 273 00:19:03.420 --> 00:19:07.200 And that creates a really good co-pilot to exist within a financial market. 274 00:19:07.520 --> 00:19:14.200 I think co-pilot is a really good way to describe this potential product, mostly because trading is a solo game. 275 00:19:14.500 --> 00:19:19.340 It's more similar to golf than it is a tennis or like game with other teammates. 276 00:19:19.760 --> 00:19:23.540 And now you have the equivalent of like talking to other agents in use. 277 00:19:24.180 --> 00:19:31.220 Do you imagine this to be some kind of like UI where I have my Druckenmiller agent and then my portfolio agent and then my 278 00:19:31.220 --> 00:19:31.840 On-chain agent? 279 00:19:32.000 --> 00:19:37.760 Or do you kind of imagine it to be one agent that kind of has context to everything? 280 00:19:38.420 --> 00:19:40.260 Yeah, it's a good point and a good question. 281 00:19:40.680 --> 00:19:43.440 I actually am not bullish on like the [unclear] setup. 282 00:19:43.760 --> 00:19:45.620 I think it's just too hard to manage. 283 00:19:46.540 --> 00:19:50.380 Like, you know, you have your chief of staff agent or like my manager agent. 284 00:19:50.700 --> 00:19:58.020 I'm much more bullish on like what we've done, create a singular agent with the ability to like nest sub-agents but hide that from 285 00:19:58.020 --> 00:20:04.840 You where you have just your Edge, you can name it, and then you can interact with it. 286 00:20:05.060 --> 00:20:13.200 But it has the ability to spawn up sub-agents and orchestrate different workflows on demand, but that gets all abstracted away to the end 287 00:20:13.200 --> 00:20:13.500 User. 288 00:20:13.980 --> 00:20:16.820 This seems to be the next frontier of agent experiences. 289 00:20:17.680 --> 00:20:20.880 You can see everybody kind of going for this, OpenAI with [unclear], Meta with Muse. 290 00:20:21.960 --> 00:20:27.020 And so our belief is that this is going to continue to increase and we want to specifically create that agent for finance. 291 00:20:27.560 --> 00:20:34.620 What we don't want is to show you, hey, like, here's like a base straight agent, or here's like a Druckenmiller agent. 292 00:20:34.780 --> 00:20:41.280 Like, that is just like noise in relation to like actually working with an agent that's doing research that only you know is real. 293 00:20:41.760 --> 00:20:45.960 That only you know is good information because you took the trade and you got that data yourself. 294 00:20:46.460 --> 00:20:52.540 What we don't want to be is like telling you like download GTE and like use our agents and like we're going to make you 295 00:20:52.540 --> 00:20:52.780 Money. 296 00:20:52.980 --> 00:20:54.580 Like it's actually the complete opposite, right? 297 00:20:54.680 --> 00:20:56.780 It's like download GTE, use our agents. 298 00:20:56.880 --> 00:21:02.660 If you make money, you're going to make way more than you would make just trading on [unclear] or the same thing on trading 299 00:21:02.660 --> 00:21:05.520 On Robinhood Chain on Solana or anything else. 300 00:21:06.440 --> 00:21:13.800 I mean, from everything that Enzo has told us, this does seem like the most comprehensive agent solution for not only on-chain trading, but 301 00:21:13.800 --> 00:21:16.000 Really portfolio diversification and management. 302 00:21:16.740 --> 00:21:19.580 When can people expect and try it themselves? 303 00:21:20.240 --> 00:21:27.620 Yeah, so probably at the time of like this recording coming out, I expect like we rolled it out to like 20 to 50 kind 304 00:21:27.620 --> 00:21:28.440 Of VIP traders. 305 00:21:29.020 --> 00:21:35.440 Think like your favorite names on CT or like people like head of trading at like major HFTs that are just like trading on their 306 00:21:35.440 --> 00:21:35.760 PA. 307 00:21:36.220 --> 00:21:37.940 We want to roll this out cohort by cohort. 308 00:21:38.080 --> 00:21:43.080 We're very interested in collecting data and see what people use for what we call financial intelligence. 309 00:21:43.640 --> 00:21:45.240 So what are they asking the AI to do? 310 00:21:45.560 --> 00:21:47.380 What questions are they asking it to actually do? 311 00:21:47.560 --> 00:21:48.020 Is it sandbox? 312 00:21:49.080 --> 00:21:49.680 Writing code? 313 00:21:49.800 --> 00:21:50.440 Is it back testing? 314 00:21:50.800 --> 00:21:51.340 Is it research? 315 00:21:51.540 --> 00:21:52.520 Is it looking at Twitter? 316 00:21:53.040 --> 00:21:54.600 Is it checking flows across exchanges? 317 00:21:55.120 --> 00:21:56.080 All these different things. 318 00:21:56.680 --> 00:21:57.980 Then we want to scale that out. 319 00:21:58.220 --> 00:22:01.480 The thing about this product is like pretty compute intensive. 320 00:22:02.340 --> 00:22:09.280 And so we don't view ourselves as going for the mass market here. 321 00:22:09.700 --> 00:22:11.620 Actually, I'll be very opinionated in saying that. 322 00:22:12.100 --> 00:22:20.440 Like if you want to just like monkey see, monkey do, there are really good social trading platforms for you to trade on. 323 00:22:20.980 --> 00:22:27.640 We are much more interested in the person that does $10 billion worth of volume that wants to $100 billion worth of volume while retaining 324 00:22:27.640 --> 00:22:31.520 Their alpha and their P&L gets magnified in exponential for that. 325 00:22:31.620 --> 00:22:34.260 If that's you, then I think you're going to love using GTE and you can find me. 326 00:22:34.820 --> 00:22:35.020 Nice. 327 00:22:35.200 --> 00:22:40.740 It does seem like you're building the product for yourself as well as like the team members that I've met from GTE for the people 328 00:22:40.740 --> 00:22:42.040 And fans of GTE. 329 00:22:42.140 --> 00:22:46.180 Thus far, is there anything else that you want to share or let know what's happening? 330 00:22:46.760 --> 00:22:50.460 Yeah, I think that what I would say is it's taken us a long time to get to market. 331 00:22:50.460 --> 00:22:51.740 And there's a good reason. 332 00:22:52.180 --> 00:22:54.860 We only wanted to put a product out there that was fundamentally 333 00:22:56.460 --> 00:22:57.700 Accessible only on GTE. 334 00:22:58.420 --> 00:23:03.920 And for the longest time, I think GTE was very focused on creating something but faster. 335 00:23:04.860 --> 00:23:05.760 Solana, but faster. 336 00:23:06.460 --> 00:23:07.940 Hyperliquid, but faster. 337 00:23:08.940 --> 00:23:15.920 And what we realized is like speed is only a single like lever that we can pull in that, you know, Hyperliquid is pretty 338 00:23:15.920 --> 00:23:16.340 Fast. 339 00:23:16.800 --> 00:23:17.880 Solana is pretty fast. 340 00:23:18.520 --> 00:23:21.620 It can definitely get faster, but fast is not enough. 341 00:23:22.080 --> 00:23:23.380 What we need now is intelligence. 342 00:23:23.800 --> 00:23:27.240 And what we've built is a product that you can only access on GTE. 343 00:23:27.600 --> 00:23:31.160 And that's why we're excited to roll this one out now because we've been using it internally. 344 00:23:31.160 --> 00:23:36.080 We've turned over a couple hundred million dollars worth of volume just on our PAs in the last couple of weeks. 345 00:23:36.520 --> 00:23:38.660 We've been making like money doing this. 346 00:23:39.320 --> 00:23:43.780 And what we think is going to happen is that this is going to be kind of like a genie out of the bottle. 347 00:23:44.220 --> 00:23:49.920 When people start doing this, it's going to exponentiate and magnify volumes where we're on top of any chain or any exchange. 348 00:23:50.140 --> 00:23:57.660 We're able to go and talk to the most sophisticated traders that have setups there and work with them to magnify their setups. 349 00:23:58.160 --> 00:24:01.660 Our main focus is to really exponentiate volume across the board. 350 00:24:02.120 --> 00:24:06.600 We're big believers in the perpification of everything, the tokenification of everything. 351 00:24:07.040 --> 00:24:10.360 We think AI is going to love crypto if it doesn't already already. 352 00:24:11.000 --> 00:24:16.000 And we think that this is going to be the next leg to turn the volumes from like one to two trill to like 10 353 00:24:16.000 --> 00:24:16.400 Trill. 354 00:24:17.000 --> 00:24:24.380 Because when that gets combined with RWAs and tokenized assets, by the end of the decade, these agents will be helping you process trillions of 355 00:24:24.380 --> 00:24:25.220 Dollars worth of volume. 356 00:24:25.220 --> 00:24:26.220 Very nice. 357 00:24:26.760 --> 00:24:28.900 Very exciting updates from GTE. 358 00:24:29.180 --> 00:24:32.160 Enzo, thank you very much for your time and we'll wrap it up. 359 00:24:32.600 --> 00:24:32.840 Thank you. 360 00:24:33.120 --> 00:24:33.320 Yeah. 361 00:24:33.880 --> 00:24:34.260 Nice, man. 362 00:24:34.380 --> 00:24:34.640 Thank you.