【WEB3 Founders Real Talk EP13 Recap】Covalent: Fueling AI & DA with a Decentralized Data Indexing Veteran

Host: Blair Zhu, Mint Ventures

Special Guest: Ganesh Swami, CEO of Covalent

Introduction of Ganesh and Covalent

Blair: Hey everyone, welcome back to Web3 Founders Real Talk, where we dive into real unfiltered conversations with the true game changers in the industry. Today we’re thrilled to have Ganesh, CEO of Covalent, joining us. Welcome aboard.

Ganesh: Blair, so happy to have us on the show, excited to chat further with the community.

Blair: Thank you so much for coming. Can you briefly introduce yourself? How did you land in the crypto industry? How did you get to start your project? And also, please tell us a little bit about Covalent as well.

Ganesh: Absolutely. I’m Ganesh Swami. I’m one of the founders of Covalent. Covalent has been around for over five years, so it’s one of those OG projects. My journey into crypto has been quite accidental. It was just a chance that I entered. I was not in crypto. I actually come from a database world, building data infrastructure. Prior to that, I used to do cancer research. I was doing physical chemistry, and building antibodies for drug design. I was on the founding team of a company that is Canada’s biggest biotech company. It’s listed on the NASDAQ, and there are a couple of drugs in clinical trials. That’s my background. I pivoted out because pharmaceuticals take 10 years to build an MVP. It just takes a while. My friends in IT were just shipping MVPs and going to market, raising capital, and going through M&A in two years. I was missing that pace. So I pivoted to data infrastructure. That is when cloud data warehouses were becoming popular like Snowflake. A lot of the on-prem workloads were moving to the cloud. The cloud is like a new infrastructure piece. I was helping a lot of these companies move to the cloud. I did that for about a decade, and I was working out of a co-working space. A mentor of mine said, hey, you should check out this decentralized database project hackathon. This was during the bull market in 2017. I was like, okay, I’m in Vancouver. It rains a lot in Vancouver, so I don’t have anything else to do on Saturday, let me go check this out. I know that in a database world, ultimately, it doesn’t matter what your database is, people still want to do the analysis in Excel. That’s the front end for all databases. It doesn’t matter it’s Oracle or SAP or Microsoft. What I built in this hackathon is a way to pull blockchain transactions directly into Excel. So that was the idea. It’s like a search engine, Google for the blockchain, whatever you want to call it. Back then in 2017, it was just ICOs, simple like ERC20s and transfers. That’s it. No DeFi, no NFT, none of the complicated stuff. We ended up winning that hackathon, and then we were saying, this is a cool idea, and this could open up a lot of things. What I got wrong was the market timing, because the next two years were just a bear market and it was brutal. So please don’t get my advice on market timing. I have the worst track record. Anyway, we started this company Covalent. Covalent comes from the chemical word covalent bond if you remember your high school chemistry. We’re binding centralized systems and decentralized systems, databases and blockchains, and things of that nature. So that’s the analogy. That was the genesis story for Covalent. So we started this company, essentially it’s like looking at blockchains like a database. You can query it, you can index it, you can do all kinds of things from that blockchain. The first couple of years were a struggle, and then DeFi Summer hit, and the right time, the right product. They say it like overnight success, but we’ve been working on this for two and a half years by then. There’s a little bit of a detour here and there, but that is generally the genesis story. If it didn’t rain that Saturday in Vancouver, there would be no Covalent. Super simple. But the core crux idea here is that it doesn’t matter what kind of infrastructure or change happens under the hood. People are not going to retrain or retool. They’re not going to throw away Excel. They’re not going to retrain your existing workflow, your existing business process has to adapt. So you need this bridging agent like middleware. That’s really what Covalent offers from day one. We’ve never really like pivoted or anything of that nature. So that’s what it is. It’s called by different names today. Some people call it an indexer, some people call it a data availability layer, and so on. But that’s fundamentally what we do. We just make blockchain data more accessible in a decentralized fashion.

Challenges & Resistance

Blair: Wow. That’s the most interesting story I’ve ever heard. It’s all about the weather and I’m so glad that Vancouver rains a lot. That’s why you get to establish Covalent. You get to establish this fantastic project with pretty substantial pain points because you identify that’s the thing, and now you’re doing a great job. I just want to know if you guys have encountered any sort of challenges or resistance during the journey. Like you said, timing, especially, due to the whole crypto industry thing, that could be a pivotal indicator. Did you guys encounter any technical challenges or any other kind of macro-environmental challenges?

Ganesh: I’m a serial entrepreneur, and this is my fourth startup. Any kind of mission journey startup that you do has risk and it comes in risk bundles. There’s like market risk, which is where we failed the first, like two, three years. There are product risks, technology risks, financing risks, and team risks. All of these are like bundles. I would say we had definitely financing risk because nobody was writing checks during the bear market. There was market risk because the market just wasn’t there. There are no applications. We built the product. We are pretty good engineers, the finding team, and my other co-founder Levi has built databases his entire life. He knows way more than I do by no stretch of the word there. There’s definitely financing risk. There was a technology risk. We got lucky in another different way in the sense that EVM won out and everything basically became EVM. There are teams that bet on, like EOS, Cardano, and Solana, it’s done well, but anyone who’s bet on a non-EVM like XRP and Elrond, all those guys are dead now. Definitely, we got lucky on that, choosing EVM, the technical thing. I would say those are some of the rest, but the biggest brutal thing is financing and market risk. After spending two years on Covalent, working day in and day out, we had bootstrapped no outside capital. I got really disillusioned with the whole space. Not just me. There were a lot of people who exited during that bear market, just like you saw how people exited in the previous bear market. A mentor of mine said, you should just take some time off and go get some perspective to see if this is right for you or not. So I went and climbed Mount Everest. It was a pretty hard journey, but I spent a lot of time by myself, like eight, nine, ten hours walking by myself. There was a whole group, but I was just in my own thoughts and I had a lot of time to think about it. I had some brilliant insights in the Himalayas. One of the insights I had was that you scraped all this blockchain data, why don’t you just use it to see if there’s any attraction and then outbound and reach out, and see if they want your product? So we came back. I came back in October, then in November, December, January, and February, I just ground through those four months. I didn’t take a Christmas break and we got product market fit and we started making revenue, and then we closed consensus, and then that’s a flywheel. So again, another chance just like a different perspective. We had all the data. We could literally see what protocols have what fraction. So why don’t you go and just reach out to them? I would say those are the big resistance and challenges. Then after that, there’s always a challenge that people don’t understand the value of an indexer because all data is public. People are like, what’s the difference between Etherscan and Covalent? What is the difference between The Graph and Covalent? But those are ongoing, that’s part of the journey. But initially, I would say the biggest challenge was just getting this off the ground, it was so hard, and took almost three years before we could see light at the end of the tunnel. Those days were long and hard.

Blair: Yeah. But that’s also very impressive because, in this space, it’s still very nascent, so we are still seeing entrepreneurs struggling with product market fit. Sometimes I feel like entrepreneurs need to be really selective on things that they’ve been doing, not just for their interests or maybe some sort of perception.

Ganesh: There’s something called product founder market fit.

Differences with Other Data Solutions

Blair: Yeah, that’s something I want to talk about. Can you also provide us with a brief overview of your product scope? Because I do see you have a unified API and GoldRush. Additionally, in comparison to developers like manually handling data retrievals and processing through RPC, what kind of cost reductions or efficiency improvements can developers expect with your product? I’m not a technical person, but I guess that could be a question for people wondering what’s the difference between those two. Also, how is it different from other blockchain data solutions like The Graph?

Ganesh: Got it. This is a great question. Maybe before even talking about the differences between different kinds of data solutions, the crux of the problem is that blockchains are billboards, they’re not databases. So what happens in a billboard is that you post something, and then after that week is gone, you take down the posting, and then you put another post. That is what blockchains are. The whole point of a blockchain is to get it in for the challenge window, and see if there’s any kind of challenge. After the challenge, you evict it, you eject it, and then you go on to the next. So you evolve the state machine, that is the core thing about blockchain. A lot of people misunderstand this. They don’t understand blockchains are meant for state propagation, not for storing any kind of historical data. So that is one gotcha. The second thing is that every blockchain has its own nuances. Some of them do POS, and some of it is proof of work. You have new kinds of Rollups and different DA solutions. Some people using Call Data, and some people using Blob storage. But from a developer’s perspective, they just want to see token balances, NFTs, your cost basis, and standard stuff. They don’t care about what technology is, it doesn’t matter. So the unification makes sense. This is very novel to Covalent, where we’ve built a unified interface for all blockchains that we index. We index about 200 blockchains today, including testnets. So if you integrate with Ethereum, you change one character, and then you have any other, like Polygon, Arbitrum, Phantom, Optimism, Base, Mantle, and the list goes on. So any EVM chain, just one character changes. You build your UI, and then everything just works. This is very popular with developers because they don’t want to rebuild the stack again and again for all the different chains. Some of the other aspects is that it’s important to understand the data stack itself. You have data products like CoinGecko, which is more for a retail audience, so they give you high-level stats and market cap and circling supply and stuff. It’s not truly on-chain data, because some of that data is also off-chain, but that’s a retail audience. Then you have infrastructure layer indexers like Covalent, and The Graph that offers essentially structured data. Then you have RPC, which is a layer below, like Alchemy and QuickNode that offer raw data. So this is the layer. Our specialty is in the center, which is structured data, because the RPC gives you unstructured and messy data. So that is a key difference. The value of an indexer is to take all this raw unstructured data from RPCs or the blockchain, and then make it structured in a way that’s usable, consumable, and human-readable. That’s how the stack is organized. Then I would say with The Graph, I think there are just two different philosophies in building indexers. The Graph has something called subgraphs. Covalent has a unified API. In the subgraph, you create DApp-specific kinds of endpoints, but each DApp has its own schema and structure. In Covalent’s approach, it’s a unified schema. It’s not specific to a DApp or something. The use cases, the attraction and the kinds of customers, they’re all completely different, but they both kind of solve the same problems. What is so interesting is after about five years, The Graph is becoming more like Covalent and Covalent is becoming more like The Graph, because everyone tries to expand their scope.

How the Flywheel Works

Blair: That’s very interesting. I noticed that you guys have highlighted a lot of actionable items in your Covalent Vision 2024. It’s been a quarter and you guys mentioned that Ethereum Wayback Machines are pretty pivotal. I can get that. How were all those items determined by your team? Because it’s a lot. Can you also provide an overview of your current progress? Which one will be your primary focus among all those items?

Ganesh: I think there is a method to this madness. It looks like it’s a lot, but it’s actually like a jigsaw puzzle and it’s a holistic kind of program. Let’s take a step back and understand this flywheel. So Covalent goes and indexes blockchains. We index the blockchain, so the developers and the DApps on those blockchains use Covalent. When they use the product, now these DApps want to go multi-chain. So they consume more data from Covalent, which means it unlocks more use cases. When it unlocks more developers and more use cases, more blockchains want to come and tap into the use cases and the developers. So lastly, I would say 50, 60, 70 blockchains we’ve indexed, it’s all been inbound. We don’t do any kind of outbound. They want to say, come, they have all of these products and attractions. An example is Rainbow Wallet. Rainbow Wallet is a very popular wallet. All of the data is Covalent. They will not go to a new chain unless Covalent supports it. So we get a lot of requests from DApps. For example, we announced Blast indexing, but that’s not because the Blast team asked us or we went to Blast or whatever. It’s because Rainbow asked us. Because on Rainbow’s side, it’s one character change. They just change one character and suddenly Blast is supported. Everything is supported on Blast. So they don’t have to rebuild anything. It’s so easy to use. That’s the flywheel. So everything around this is like a flywheel that’s spinning around and around. The key thing here is where the token comes in. All of the revenue that the demand side, the developers, is a pay-as-you-go kind of API, so it’s free to start and then you start paying revenue. That revenue flows back to the operators who run the nodes and therefore the CQT stakers. So that’s really how this whole flywheel spins and how the decentralized economy starts to develop. It may seem a lot, but everything about our community program, the fee buyback and the switch, the Ethereum Wayback Machine, the list of products we have on the demand side, the developer grants programs we have, the more indexing we have for all the rollups and rollup as a service, are all part of this giant flywheel. It just spins faster and faster. It’s all part of the same program. It just looks like it’s disjoint.

Product Development Progress

Blair: It looks like it’s all over the place, but I would say everything ties together, like you said. How is everything going? I guess we just want to see if there’s any sort of product development that we’re expecting to see in the future. Can you give us a sneak peek at that?

Ganesh: Absolutely. So the key thing here is one of the things that we highlighted in our review from last year, the fee switch mechanism, which is the revenue that is coming from an exogenous source, basically customers paying revenue, that’s used to buy back CQT and distribute it to the operators. So that switch went alive about 45 days ago, and it’s buying $1,000 worth of CQT every day. Perhaps in the show notes or something, I can share the wallet address. It’s just like buying $1,000 every day. Sometimes CQT is 20 cents, sometimes it’s like 40 cents. It doesn’t matter. As the demand side revenue increases, it’s going to essentially establish the floor for CQT because it is buying anyone who is trying to sell. It’s just that floor. So that’s live now. That’s an exciting update. That’s the final picture going in. The other thing is this staking migration, moving back to Ethereum. So far we’ve been using Moonbeam for our settlement and so on. I think generally the Polkadot ecosystem is not really where it needs to be. So we’re moving, staking back to Ethereum. So all the audits have been done for that. Then what’s next is the EWM testnet, the incentivized testnet. That is almost ready. And then doubling down on the AI and the DA narratives building more products and getting involved with those communities. So everything is going according to the plan.

Use Cases of AI models

Blair: Wow. I hope everything pulls off because it sounds like a lot of work. I captured from your social media that Covalent also strides in advancing AI with very extensive historical and real-time Web3 datasets. How does this process work? Can you also name some specific use cases for AI models? Because I know there’s been Web3 and AI crossover for a long time, but we are debating on some sort of real legit use cases currently. Can you maybe name a few?

Ganesh: Absolutely. So the key aspect of the large language models, the LLMs, is structured data. That is the input to all of this. It’s the reservoir of data that is required to train these LLMs. The whole point of Covalent is having all of this structured data. You can go and feed all of this structured data into these language models, and then you can fine-tune existing foundational models, whatever you want, and then you can start to do inference on it. That’s really how the pipeline is. It’s very similar to getting the structured data and then running a query node and then querying the structured data. That’s a database product. You’re just moving from big data to big models. That’s like a transition. It’s a very natural kind of extension for us. We were quite surprised when the market started to use Covalent for these use cases. It makes sense. Structured data is like clean formatted, normalized data. Who would not want that? So we started seeing a lot of use cases. We put out a post recently on all of the AI use cases that are being built today. We could perhaps put it in our show notes or something. An example is Smart Wheels. Smart Wheels is a platform that does on-chain copy trading. You can follow any kind of wallet, and then they do a summary of multiple wallets, and then they use AI to figure out if it’s a trap or if it’s a scam or not. Smart Wheels is an example of an excellent project that’s doing really fun stuff there. Another example is Leica. Leica.AI uses AI for analytics. So you see a lot of projects here. Again, we are not on the analytics layer or retail side, but they’re using all this data to train, and then they can do sophisticated analytics on tokens if you want to do your research or so on. Leica is a cool product for this. Another cool thing I recently heard about is Entendre Finance. This offers anomaly detection and predictive analytics, and this has a lot of attraction for financial management. In the back office, they’re looking at your payroll, expenses, and all that stuff. They can use AI to do fraud detection, essentially. Another example is bitsCrunch. bitsCrunch is a project that recently went public. They did a coinless sale. They have Animoca and Coinbase as investors. They use Covalent data for fraud analytics and all that stuff. So the base foundation data there is Covalent, behind all of these projects. Those are some of the use cases, just like how we started Covalent before there was DeFi, NFT, and GameFi. The market evolves in different ways, but that’s on the application layer. We’re on the infrastructure layer. So we empower all of these use cases.

How CQT Plays Within the Ecosystem

Blair: Wow, that’s very impressive. It’s good to see that you’re empowering those innovations and those people are just moving the needle and making a difference because of Covalent. Well, you mentioned CQT several times in our talk today. Can you also elaborate on the specific role that your token plays within the ecosystem? I guess that could be another unique differentiator comparing your tokenomics with other products. Also, recently you have launched a token buyback program to bring off-chain revenue on-chain. Can you share more insights?

Ganesh: Absolutely. So CQT stands for the Covalent Query Token. It’s a staking and governance token that belies this entire covalent economy. From our experience of being in the market, your developers and the consumers of your product don’t want to use your token for payments. It’s kind of friction. These people’s tokens are like a 2017-era relic. There’s no point. So everything on the demand side, all of the revenue that customers are charged is in US dollars. It’s fixed. There are no challenges, forecasting budgeting or whatever. So the US dollar is then used as an on-chain mechanism to buy CQT. when I mentioned that thousand dollars a day, that thousand dollars is coming from customers. Then the CQT that is bought, based on just like a market buy is distributed to the decentralized operators who actually do the work. They earn in CQT. So think of it, maybe a close analogy is that I’m hiring some contractors in the Philippines, and I’m paying in the Philippines like local currency because that’s what they use to spend. I could pay them US dollars, but they’ll sell it for their local economy. That’s how it is, the whole Covalent economy is based on CQT. Now the other utilities are like we’re going to be introducing a liquid staking program, so the delegation program is another utility here. You can go as a token holder, go delegate against one of these operators. There are about 14 operators, and we’re just going to put out a post to recruit more operators. They run the actual infrastructure. You can delegate against their infrastructure. We have a whole tokenomics program there. Everything about the incentive for long-term data availability, even in the AI use case, if you see, there are lawsuits against the New York Times, which sued OpenAI because they’re using all these New York Times articles to train their data. So there’s any kind of bias or any kind of revenue, then the royalties have to flow back, which means you need the entire track record of all of the mutations that have happened on the base model. All of that, like blockchain, is a perfect use case for stuff like that. Back to the token, it’s a regular ERC20 token. It’s available on OKEx, Uniswap, Sushiswap, KuCoin and Gate. You can hold the token or you can participate in the economy. As a delegator, you can take a token and earn a yield. Or if you have enough technical know-how to run the infrastructure, you can become an operator. Besides that, I think that’s generally how the system works on the back end. Besides, the DeFi utilities provide LP and so on.

Strategic Plan for Revenue Generation

Blair: Yeah, it is a very well-designed mechanism, especially for all of the stakeholders in this game. They’re incentivized. Well, given your very ambitious revenue generation goal, can you please outline some sort of your strategic plan? Considering your steady growth with institutional users, do you foresee the unified API significantly driving that growth? There are two big pillars in your product line right now, one is GoldRush, another one is unified API. Which one will be the secret recipe?

Ganesh: I think we’ve taken a different approach with the revenue generation. On the demand side, we have three products. We have the unified API, Increment, and GoldRush. The way we design these products is to think of them as multiple ingredients, or the same set of ingredients, multiple recipes. You have this database of structured data, and then you have unified API, Gold Rush, which is a model of block explorer, you have Increment, which is like a Dune-like dashboarding product, but they’re all based on the same data. So that’s been our approach to having multiple use cases and personas based on the same index data. With regard to revenue generation, we have pretty ambitious targets. We’ve been growing consistently month after month. Now, in terms of the unlocks, there are a couple of unique opportunities down the pipe. The first is RPC stuff. What is happening in space is that all the RPC vendors are not storing historical archival data. So now the whole industry is kind of consolidating on Covalent because the whole point of Covalent and long term like Ethereum Wayback Machine and all that stuff is to hold the entire history of the blockchain. We have very custom architecture to enable that. So we signed a deal with Infura. Infura is now starting to spill that traffic to us. We’re seeing other kinds of like, I can’t name these players, but they’re all starting to migrate their backend to Covalent as a stack. So we should see significant revenue growth from that initiative, for example. Besides that, we have some gaps. We outlined this in our Covalent Vision. We’re very upfront with some of the shortcomings that we have in our data stack. One of the biggest gaps is trace data. For the toughest forensics and accounting cases, trace data is a gap that we have. We’re making strides on that and we’ll plug that gap. The whole team is organized in a way where whatever actions they do are going to drive revenue down the line. That’s a completely different part of Covalent where they’re incentivized and they’re motivated by different reasons. So I’m pretty confident that we’ll hit all of our targets. It’s a matter of product delivery and product pipeline and going to market and sales and stuff, which is quite distinct. I think it’s not a lot of companies have that kind of machine built for products and building outside of the token side of things.

Viewpoint on Centralized Data Indexing

Blair: Thank you for sharing all those insights and backstage stories, because it sounds like you guys have pretty sophisticated and very well-designed mechanisms in all aspects. Looking forward to seeing more innovations happening on Covalent. Let’s see the bigger picture of the whole data indexing or whatever they’ve been calling. How would you assess the current state of the on-chain data market, specifically focusing on decentralized data indexing? Because there is also centralized data indexing available in the market.

Ganesh: I’m just going to be transparent here. I think the centralized data indexers come and go. We saw dozens of indexers come in last cycle, and they’re all mostly dead today. We’re seeing a lot of indexers enter the market now, and I don’t know what’s going to happen to them. I think centralized indexers are not really within the ethos of decentralized technologies, especially if you want to feed this index data back into smart contracts. The trust assumptions in your data need to be at the same level as how the data got to the blockchain in the first place. If you break that trust assumption, it has a limited total addressable market. That’s really how this works. For example, if you look at a Celestia or your Eigen DA or Avail, the trust of Celestia needs to be the exact as the L1 that is securing. Otherwise, people will hack Celestia and put fraud on L1. So it’s the same kind of setup here. It really matters. I think centralized indexers can go get some customers, maybe a couple of million dollars of revenue, maybe for some simple use cases. But for the toughest use cases, which is the whole point of crypto, you need trust and security guarantees. We never think of centralized indexers as competitors ever, because we’ve been around for a while, we see these guys come and go, they make a lot of noise. There was another project that Paradigm invested in last year called NXYZ. They raised $40 million and after a year, they shut down. It happens all the time. We see these centralized indexers are just not going to work in this space. With decentralized indexers, there is a lot of blames about decentralization. But if you look closely behind the scenes, there are some points of centralization, including Covalent. We’ve been very transparent about how we are going on this progressive decentralization story. If you think about the vision of who’s trying to introduce something brand new, Covalent is the only indexer that has cryptographic security. Every mutation on the data has cryptographic proof that is submitted, that anyone can audit. This has been running at scale for multiple years. The first roll of the network launched summer of 2022. It was April, and it’s exactly two years from now, even through the normad hack, even through all of this, like changes and stuff, the network itself has never stopped. So I think, they say that a lot of projects die because of a lack of focus, not because their core stuff works. We don’t think about other projects like what they’re doing, and so on. We are laser-focused on what the industry needs, what our customers say they want, and what are the toughest problems to solve that will forward the industry. Everything about cryptographic security. No one asked for this two years ago when we built this. Two years ago is when we shipped. We’ve been working on this for like four or five years now. But this is what the industry needs and this is how you push space forward. We are pioneers in this journey, so it’s important for us to get that out.

Notable Trends

Blair: I really admire your mindset. I’m feeling the same because those centralized players sometimes made a lot of noise, but one day it would backfire them in a way. I know you don’t want me to ask you about any sort of timing thing, but are there any sort of notable trends on your radar that you want to share with everyone? Regarding your business metrics, in terms of your data indexing volume from layer1s or any other kind of metrics, because there are a lot of speculations around this cycle.

Ganesh: I would say in terms of Covalent, the revenue is real. Actual customers paying money to use the protocol and the data. That’s a testament to the quality of data and the quality of service. No other index out there has revenue of this scale. We have Fidelity, and Ernst & Young as customers. That just tells you how trustworthy the data is. The second thing is in terms of the impact. We did a calculation a few weeks ago. There are over 250 million wallets that consume or are enriched by Covalent’s data. So this is all of the wallets, all of the custodians. If you look at Jump’s custody products, they’re all like Covalent. For things like Ambient Finance, which is a big project on Scroll, AirSwap, and SushiSwap, all of those wallets use Covalent’s data to get enriched structured data. That’s 250 million wallets in this space. So that’s real. That’s like the number of unique wallets we’ve seen use Covalent’s data. I would say the proofs that are submitted on chain, anyone can download these proofs and rebuild the entire Ethereum state from the Genesis block. That is real. You don’t even have to talk to us. You can just download the proofs and rebuild the stack. I would say these are the things that are real on Covalent. In terms of trends, in ETHDenver I was on a couple of panels. Definitely I think the cycle is the DA cycle, the data availability cycle. I would say there’s a big push on AI stuff as well. That’s a macro trend. That’s very exciting. I would say the LSD and LRT seem to be on top of mind for a lot of people. I’m not a financial guy, so I don’t understand the intricacies of how these liquid restaking tokens work and what the risk factors are. But there just seems to be a lot of attention on those tokens. Perhaps with Eigen Layer and restaking and all that stuff, there’s going to be a big move this year. But we only stick to things that we have expertise in and what our sweet spot is, which is data, data availability and AI.

Blair: Big thanks for sharing your expertise today. Because of the nascency of the web3 industry, there are a lot of tweaks and turns in the space. We’re already seeing all those massive, fresh, new capitals or innovations flowing into our world with all those different experiments. Well, let’s see how it goes.

Two Thoughts

Ganesh: I want to leave you with two thoughts for your audience. The first point is that Covalent has about 60000 developers. Infura has probably half a million developers. So Covalent has like 10% of what Inferno has. GitHub has 30 million developers, Covalent has 0.1% of GitHub. That’s how early we are. We haven’t really done anything. The second thing is that the bull market is like, Should I invest in these meme coins? Should I invest in LRT, LST? I would say the biggest investment you can make is in yourself, in your knowledge, your research, and your conviction. If you believe in yourself, you should double down on yourself. For people who’ve done that in just my limited history, they’ve done well for themselves. So I want to just leave that message to our community and our audience here.

Blair: Wow, that’s very authentic. Thank you so much for everything. It’s very fulfilling.

Ganesh: Blair, thank you so much for this excellent service that you do because I think we need more genuine builders and people with strong convictions. If listeners haven’t had a chance, please read both English and Mandarin translations of the research report. Quite extensive, quite detailed, and it goes deep. Thank you so much for everything that you guys do for the industry.