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Welcome to Crypto Long & Short! This week, Colton Dillion, CEO of Hedgehog, a robo-adviser, sketches out emerging crypto investment fields for 2024, from real-world assets to DePIN.
Then, David Attermann, at M31 Capital surveys the multi-faceted crypto-AI field and discusses the best current investments. As always, get the latest crypto news and data from CoinDeskMarkets.com. – Benjamin Schiller, head of opinion and features at CoinDesk |
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Beyond the ETF: Crypto Innovations to Watch in 2024 |
It's the beginning of a new year and anything is possible, but if you ask around, all anyone can talk about is the imminent approval of spot bitcoin ETFs. We get it, it's exciting and creates potential for retail to get exposure to digital assets without learning any of the hard parts of crypto. But all that alpha has been scraped clean at this point. Where else should we be looking for value in the ecosystem? If you believe in the fundamentals of various digital assets like we do at Hedgehog, it can be helpful to craft a narrative around collections of tokens and try to identify the key performance indicators that are going to drive demand for the underlying asset. While past results are no guarantee of future returns, 2023 offers lessons for recognizing unrecognized alpha in the coming year.
The good folks at @cryptokoryo have done some amazing work putting together a Dune dashboard that makes it easy to monitor performance of various asset baskets, and they've selected some sensible defaults that can help you to visualize what your own narratives might look like.
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The big winners from 2023 include liquid staking derivative tokens on layer 2 protocols (eg, ALCX, ASX, PENDLE) followed closely by DeFi 2.0 protocols (eg, DYDX, FXS, INST), the only included narratives that outperformed simply holding Bitcoin. However, Money Market protocols (eg, AAVE, COMP, QI) and Decentralized Physical Infrastructure Networks ("DePIN," eg, FIL, RNDR, DIMO) followed closely behind. One could speculate that the unifying feature of these narratives is a combination of leverage and liquidity, necessary elements to generate better yield than the 5% Treasury rates we were seeing last year, and traits that are fundamentally superior when accessed on a shared database and runtime like a blockchain. However, DePIN doesn't fit neatly into this thesis. Perhaps, alongside narratives like Decentralize Science ("DeSci," eg, VITA, HAIR, GROW) and Real World Assets ("RWAs," eg, MKR, MPL, CPOOL), these more technologically and regulatorily sensitive applications are finally starting to hit their stride with major hardware deployment and licensing milestones, leading to real world adoption. |
It can also be helpful to look at where devs are deploying contracts and turning over token inventory. Much of the ETH volume has started to migrate to its L2 chains where transactions are faster and cheaper, and many have even started touting an appchain thesis, where the future will belong to individual apps who own their own L2, like Coinbase's BASE. Based on the price action of their hottest assets, it seems Avalanche, Arbitrum, and Optimism have been showing strong growth potential. |
No matter which thesis you choose to put your capital behind, remember that it can take years for research and development efforts to pan out and translate into end-consumer adoption. As much as we may like to speculate as degens, a steady and patient hand can dramatically outperform over the long term. Just think, you would have had to hold BTC for 15 years to see its entire growth trajectory! Maybe your next thesis has a similar story. |
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Making Sense of Web3's Burgeoning AI Ecosystem |
In just over a year since ChatGPT's debut release, generative AI has arguably become the most influential global narrative today. OpenAI's early success drove a surge in investor interest for large language models (LLMs) and AI applications, attracting $25 billion in funding in 2023 (up 5x YoY!), in pursuit of the potential multi-trillion-dollar market opportunity. |
As I've previously written, AI and crypto technologies complement each other well, so it's not surprising to see a growing AI ecosystem emerging within Web3. Despite all the attention, I've noticed a lot of confusion about what these protocols do, what's hype vs. real, and how they all fit together. This report will map out the Web3 AI supply chain, define each layer in the tech stack, and explore the various competitive landscapes. By the end you should have a basic understanding of how the ecosystem works and what to look out for next. Web3's AI tech stack |
Generative AI is powered by LLMs, which run on high-performance GPUs. LLMs have three main workloads: training (model creation), fine-tuning (sector/topic specialization) and inference (running the model). I've segmented this layer into general-purpose GPU, ML-specific GPU, and GPU aggregators, which are characterized by their different workload capabilities and use-cases. These P2P marketplaces are crypto-incentivized to ensure secure decentralization, but it's important to note the actual GPU processing occurs off-chain. |
- General-purpose GPU: Crypto-incentivized (decentralized) marketplaces for GPU computing power which can be used for any type of application. Given its general-purpose nature, the computing resource is best suited for model inference only (the most used LLM workload). Early category leaders include Akash and Render, but, with many new entrants emerging, it's unclear how protocol differentiation will play out. Although compute is technically a commodity, Web3 demand for permissionless, GPU-specific compute should continue to grow exponentially over the next decade-plus as we integrate AI more into our daily lives. Key long-term differentiators will be distribution and network effects.
- ML-specific GPU: These marketplaces are more specific to machine learning (ML) applications and can therefore be used for model training, fine-tuning, and inference. Unlike general-purpose marketplaces, these protocols can better differentiate through the overlay of ML-specific software, but distribution and network effects will also be key. Bittensor has an early lead, but many projects are launching soon.
- GPU Aggregators: These marketplaces aggregate GPU supply from the previous two categories, abstract away networking orchestration, and overlay with ML-specific software. They are like Web2 VARs (valued-added resellers) and can be thought of as product distributors. These protocols offer more complete GPU solutions that can run model training, fine-tuning, and inference. Io.net is the first protocol to emerge in the category, but I expect more competitors to emerge given the need for more consolidated GPU distribution.
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The previous layer enables permissionless access to GPUs, but middleware is needed to connect this computing resource to on-chain smart contracts in a trust-minimized manner (i.e., for use by Web3 applications). Enter zero-knowledge proofs (ZKPs), a cryptographic method by which one party (prover) can prove to another party (verifier) that a given statement is true, while avoiding conveying to the verifier any information beyond the fact of the statement's truth. In our case, the "statement" is the LLMs output given specific input. |
- Zero-Knowledge (ZK) Inference Verification: Decentralized marketplaces for ZKP verifiers to bid on the opportunity to verify (for compensation) that inference outputs are accurately produced by the desired LLM (while keeping the data and model parameters private). Although ZK technology has come a long way, ZK for machine-learning (zkML) is still early days and must get cheaper and faster to be practical. When it does, it has the potential to dramatically open the Web3 and AI design space, by allowing smart contracts to access LLMs in a decentralized manner. Although still early, =nil;, Giza, and RISC Zero lead developer activity on GitHub. Protocols like Blockless are well positioned whichever ZKP providers win since they act as aggregation & abstraction layers (ZKP distribution).
- Developer Tooling & Application Hubs: In addition to ZKPs, Web3 developers require tooling, software development kits (SDKs) and services to efficiently build applications like AI agents (software entities that carry out operations on behalf of a user or another program with some degree of autonomy, employing representation of the user's goals) and AI-powered automated trading strategies. Many of these protocols also double as application hubs, where users can directly access finished applications that were built on their platforms (application distribution). Early leaders include Bittensor, which currently hosts 32 different "subnets" (AI applications), and Fetch.ai, which offers a full-service platform for developing enterprise-grade AI agents.
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And finally, at the top of the tech stack, we have user-interfacing applications that leverage Web3's permissionless AI processing power (enabled by the previous two layers) to complete specific tasks for a variety of use-cases. This portion of the market is still nascent, and still relies on centralized infrastructure, but early examples include smart contract auditing, blockchain-specific chatbots, metaverse gaming, image generation, and trading and risk-management platforms. As the underlying infrastructure continues to advance, and ZKPs mature, next-gen AI applications will emerge with functionality that's difficult to imagine today. It's unclear if early entrants will be able to keep up or if new leaders will emerge in 2024 and beyond. |
Investor outlook: While I'm bullish on the whole AI tech stack, I believe infrastructure and middleware protocols are better investments today given the uncertainty in how AI functionality will evolve over time. However it does evolve, Web3 AI applications will no doubt require massive GPU power, ZKP technology, and developer tooling and services (i.e. infrastructure & middleware). Disclosure: M31 Capital has positions in several tokens mentioned in this article. |
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From CoinDesk Deputy Editor-in-Chief Nick Baker, here is some news worth reading: |
- ETFs ETFs ETFs: All that seems to matter in crypto at the moment is whether the U.S. Securities and Exchange Commission will approve spot bitcoin ETFs or not. I'm writing this in advance of the decision, so what evergreen thing do I focus on? How about the hopes and dreams that've been expressed about these products. There's the natural comparison to the introduction of gold ETFs two decades ago. Those made it far easier for regulator folks and even pros to invest in gold, and the precious metal's price zoomed higher in the years that followed. Some see the potential for bitcoin ETFs to do the same. For instance, Standard Chartered says bitcoin could double to $100,000 by year-end and approach $200,000 in 2025 if they win SEC approval. That said, not everyone is bullish. One prominent crypto figure sees a giant plunge soon, for reasons unrelated to bitcoin or ETFs. (He cites one of the most TradFi of TradFi things: the Federal Reserve's reverse repo program program.)
- BULLS VS. BEARS: Here's a handy reference: CoinDesk's guide to the bull case and bear case for bitcoin ETFs. Which do you suppose got more readership?
- ETHEREUM LEFT BEHIND: Amid all the bitcoin ETF excitement, ether (ETH) has gotten left behind. Priced in BTC terms, it's at a 32-month low. Is that bullish or bearish for ETH?
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Mapping the Crypto-AI Investment Landscape