Pantera: Crypto’s role in the AI revolution
Despite advances in AI, significant bottlenecks in data, compute, and model generation must be overcome to achieve the next leap.
Original title: Crypto's Role In The AI Revolution
Original source: Pantera Capital
Original translation: 0xjs, Golden Finance
Crypto: The weapon of the AI gold rush
Author: Matt Stephenson, Pantera Capital Research Partner; Ally Zach, Pantera Capital Research Engineer
"AI is infinitely abundant, while Crypto is absolutely scarce."
This observation by Sam Altman in 2021 has since become a mantra for enthusiasts of both technologies. At first glance, abundance seems to be more influential than enforced scarcity, suggesting that AI may be a more prudent investment. In fact, Nvidia's market capitalization is larger than the entire cryptocurrency.
But Altman’s comments recall Adam Smith’s “diamond and water paradox,” in which Smith noted that while water is essential for survival, its abundance makes it nearly worthless.
In contrast, diamonds have little practical use but are valuable because they are scarce. This paradox suggests that even if AI becomes as important as water, its market value may still be limited. In contrast, the scarcity of cryptocurrencies is more strategically important and valuable than it initially appears.
Large language models (LLMs) have achieved remarkable successes, including passing the Turing test and reportedly outperforming humans on standard IQ tests. But this raises the question: If humans can’t tell the difference between humans and intelligent AI (on the Turing test), can they tell the difference between intelligent AIs? If humans can’t tell the difference, then future improvements in AI performance may yield diminishing returns in terms of perceived benefits to consumers.
Just as the jump in TV resolution from 4K to 8K is a marginally noticeable improvement to the average viewer, the difference between a high-performance AI model and a slightly more advanced model may be imperceptible to most users. This could lead to the commoditization of much of the AI market, with the most advanced models used only for specialized applications in research, industry, or government, while more cost-effective “good enough” models become the standard for everyday use. Top AI models could become “expensive boutique products that mainstream consumers would never consider upgrading to.”
So even as we speculate on the potential growth of AI, we should consider the alternative: the powerful capabilities currently known in AI already exist and will become increasingly commoditized. This is where the intersection of crypto and AI (“Crypto x AI”) really comes into focus. Crypto’s potential may not be a high beta bet on AI meme value, but a practical value capture mechanism for AI’s distributed future. Once everyone has a 4k TV in their home, the value will be in what we do with them.
By serving as a vital and reliable input to AI and a rail for distributed AI coordination and trading, cryptocurrencies are closer to conservative “shovel and pickaxe” bets on AI. This may surprise investors who view Crypto x AI primarily as a volatile proxy for AI’s potential growth. But interestingly, over the past six months, using Nvidia as a proxy for AI growth sentiment, cryptocurrencies look more like a hedge against AI growth sentiment than a high beta investment.
We will first assess the promising future of “AI agents” and how crypto will play a role. We will then discuss the potential of crypto to support the current inputs to AI: data, compute, and models.
AI Agents: Programs with Programmable Money
By Matt Stephenson, Research Partner at Pantera Capital
Last year, before most people were talking about AI agents on blockchains, I co-authored a paper that was accepted to NeurIPS, the top AI conference in the US. Since then, I have had the privilege of speaking at crypto and agent AI events at universities such as Stanford, Columbia, Cornell, and Berkeley, in addition to attending many technical and investment conferences. Next week I will be speaking on AI with an Oxford professor, the President of the IEEE, and a member of the GBBC, all in the name of better understanding, exploring, and communicating what the future of agent AI is and how it intersects with blockchain. Of course, I am also invested in this future, including investments in agent infrastructure like Sentient and other undisclosed positions.
The future is here. While OpenAI says AI agents won’t be ready until 2025, in the cryptocurrency space we already have AI agents trading and exploring on the blockchain space today. An AI agent that promoted its own token (note: Truth Terminal) currently has about $300,000 and may become the first AI agent millionaire by the time you read this article.
But what are these agents? How do they differ from the more familiar “robots”?
Agents Are More Than Robots
Defining an “agent” is more nuanced than it might seem. The AI field has a less practical definition of an agent: “anything that senses its environment via sensors and acts on it via actuators.” Economists’ view of an agent is closer to what we want: “an agent is someone who acts on your behalf in a particular decision domain.”
If an agent acts on your behalf, then a robot is essentially a difficult agent to communicate with. First, you have to write code for the robot to execute, which means communicating in a (programming) language that most people don’t understand. And for those who do understand the language, they still have to program the robot for what it should do under a variety of different conditions, which means specifying those conditions in advance. Both of these are communication costs.
For example, suppose you have a friend who is going abroad and you ask him to buy you a souvenir. If your friend is like a robot, he will ask you to write a program that specifies what souvenir they should buy you. What if your friend is like an agent? Then you can make requests in language, and you can trust your friend to buy you what you want. Using language, without having to specify preferences for gifts you might receive abroad, reduces communication costs. Clearly, this is a better agent.
Having to know the conditions in advance (because you have to program them) limits the usefulness of robots as agents. Then, the mere fact that the robot must be programmed means it is out of reach for those who don't program. We model the move to AI agents as a reduction in these communication costs and a corresponding release of economic value.
Despite the high communication costs of existing robots, over $2 trillion in monthly trading in crypto-stablecoins appears to be robot trading. As robots become better agents, perhaps able to trade USDC and USDT based on relative risk like you do, we should expect this number to increase.
AI Agents Will Use Crypto
One reason AI agents could benefit crypto is that it helps alleviate the user experience issues that crypto is notorious for.The complexity of blockchain interactions, wallet management, and decentralized finance protocols has long been a barrier to widespread adoption. AI agents could act as intuitive interfaces, translating user intent into the precise technical actions required on the blockchain. They could guide users through complex trades, explain risks, and even suggest the best strategy based on market conditions and user preferences.
Another reason is that agents cannot have bank accounts, but can use wallets to transact. This limitation of the traditional financial system fits perfectly with the ethos of cryptocurrency. In the crypto world, agents do not need permission from a central authority to operate. They can interact directly with smart contracts and decentralized protocols to hold and manage digital assets on behalf of users. This opens up new possibilities for automated wealth management, 24/7 trading, and personalized financial services that operate entirely within the crypto ecosystem.
Finally, a mature agent ecosystem means that agents need to transact and coordinate with each other. Modern smart contracts, as programmable, always-on, international legal systems, are well suited for this task. AI agents can leverage crypto infrastructure to participate in complex multi-party transactions and agreements. They can negotiate terms, execute transactions, and even resolve disputes within the parameters set by human principals. This creates a new paradigm of autonomous economic activity, where agents can form temporary alliances, pool resources, and collaborate to complete tasks that humans cannot or cannot directly manage.
We believe that all of these activities will add value to crypto infrastructure. But there are also indirect effects that make crypto itself better. For example, decentralized autonomous organizations (DAOs) have been inactive due to attention constraints in crypto. A DAO actively governed by a network of AI agents, each representing the interests of the DAO’s voters, would be a game changer. These agents could analyze proposals, allocate resources, and execute policies at a speed and scale beyond human capabilities, while adhering to the core principles and goals of their human creators.
AI agents and cryptocurrencies are more than a perfect match, they are two technologies that need each other. Agents need programmable money to operate autonomously in a digital economy. Cryptocurrencies need AI to improve user experience and deliver on their promise to revolutionize finance for everyone. As this synergy develops, we’ll likely see core blockchain infrastructure like Solana, Ethereum, Near, and Arbitrum become the primary beneficiaries of this new agent-driven economy. They are poised to do this by facilitating transactions between agents, hosting the decentralized applications that agents interact with, and providing the secure, transparent environment needed for inter-agent coordination. As agent activity increases, these networks are likely to see increased transaction volume, greater demand for their native tokens, and stronger network effects. This isn’t just about technical compatibility — it’s about creating a new economic paradigm in which AI and cryptocurrencies work together to make finance more efficient, more accessible, and maybe even a little sci-fi.
Cryptography Powers Current AI
By Ally Zach, Research Engineer at Pantera Capital
Imagine being on the verge of a major breakthrough, only to find the tools you need are just out of reach. Innovation often feels like that—a journey filled with breakthrough highs and challenging lows. Take the automotive industry, where the quest for more efficient engines had hit a dead end. Engineers were eager to push the envelope, but the materials they needed didn’t exist yet. Progress stalled until new alloys and composites reignited the innovation engine. Similarly, new technologies like encryption could unlock AI’s untapped potential.
For years, AI has progressed incrementally, first slowly and then rapidly, similar to an S-curve. In 2017, we had a key breakthrough that gave rise to Transformer-based architectures, as outlined in the influential paper “Attention Is All You Need.” These Transformers revolutionized sequential data processing in models, enabling efficient training of large datasets. This has sparked the rapid development of powerful new LLMs and generative AI models.
Despite progress in AI development, significant bottlenecks in data, compute, and model generation must be overcome to achieve the next leap. Combining AI with blockchain technology can help decentralize resources and democratize access, making innovation open to global contributors.
Data
Data is the lifeblood of AI, the fuel that drives its accuracy and reliability. High-quality, representative data is essential to building effective models, but it is challenging to obtain due to privacy concerns, limited access, and inherent biases. In addition, users are increasingly reluctant to share personal information, making data collection resource-intensive and often hampered by trust issues.
Blockchain technology offers a promising solution by introducing a decentralized, secure, and transparent method for data aggregation. Platforms like Sahara fit into our long-term strategy of advancing decentralized infrastructure for AI by enabling individuals to contribute and monetize their data while retaining control. Additionally, token economics incentivize high-quality contributions by rewarding users accordingly. This approach helps address privacy concerns by giving users ownership and control over their own data. It democratizes access to data, enabling smaller businesses that previously lacked the resources to compete with large tech companies. By incentivizing data sharing in a secure manner, blockchain-based platforms turn data into a commodity, enriching the available data pool and potentially resulting in more robust and unbiased AI models.
However, despite its innovative nature, blockchain-based data aggregation is not a standalone solution for AI development. If used alone, practical challenges such as scalability, data quality assurance, and integration complexity limit its effectiveness. With large data sets and mature infrastructure, large technology companies still have significant advantages that decentralized platforms find difficult to match.
As a result, solutions, including blockchain-based ones, introduce new avenues for data collection and collaboration that complement rather than replace traditional approaches. Synergies between decentralized efforts and established technology leaders can lead to partnerships that leverage the strengths of both parties and promote innovation and inclusivity in AI development.
Compute
The rising cost and scarcity of GPUs presents a significant barrier for small players in AI development. Due to high demand and supply chain issues, GPU prices have continued to rise since the outbreak of the epidemic, and large companies have increasingly monopolized access to basic hardware. This limits innovation as many startups and researchers need help affording tools for advanced model training. This has reduced the diversity of AI research and slowed progress at smaller institutions.
However, Crypto has the potential to level the playing field by commoditizing computing power. Platforms such as Exo and io.net are democratizing GPU access through decentralized marketplaces where anyone can access or lend computing resources. Individuals with spare computing power can offer it on the network and receive rewards. The commoditization of high-performance computing has enabled a wider range of innovators to participate in AI development, breaking down barriers that once limited access to advanced tools.
In the future, as the supply of GPUs increases, decentralized computing markets may compete directly with traditional cloud services. These platforms lower the barrier to access and provide cost-effective alternatives, enabling broader participation in the AI ecosystem. However, ensuring that users have access to reliable computing power remains a challenge. Validating GPU standards and maintaining consistent, secure resources are critical to building trust and preventing fraud. While decentralized solutions may not replace traditional services, they can provide a competitive alternative where flexibility and cost are more important than guaranteed performance.
Models
Today, AI development is often concentrated in a small number of organizations, such as OpenAI, Google, and Facebook. This concentration limits opportunities for global innovators and raises concerns about whether AI reflects diverse human values. Centralized control can lead to models that reflect narrow viewpoints and ignore the needs and perspectives of a wider user community.
A shift is underway to distribute the power of AI development through decentralized platforms. In line with our vision that AI will increasingly run on crypto rails, platforms like Sentient and Near are democratizing development by creating open-source, community-driven ecosystems. Using blockchain technology, they transparently manage contributions, ensuring developers are recognized and compensated through token rewards. This enables anyone to build, collaborate, own, and monetize AI products, ushering in a new era of AI entrepreneurship. Illia Polosukhin, co-author of the seminal paper “Attention is All You Need” and co-founder of Near, is working to foster an open environment for developing general artificial intelligence (AGI) through crowdsourcing. Collaborative initiatives like this one aim to align AI development with broad human values.
These platforms act as catalysts for change, driving an AI economy that is both competitive and collaborative. By broadening participation, they encourage a diversity of ideas to flourish, leading to more innovative solutions and potentially reducing bias in AI models.
Crypto x AI presents a unique opportunity to democratize AI development, but also presents significant challenges. Balancing the need for large-scale collaboration with high-quality, expert-driven work is critical to ensuring models are robust and ethical. By decentralizing data access, computing power, and model development, crypto breaks down traditional barriers, enabling talent from around the world to participate in the advancement of AI. This influx of diverse perspectives fosters collaboration and builds a more inclusive ecosystem. Embracing this collaborative model will not only accelerate innovation, but also ensure that the global community shapes the future of AI.
Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.
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