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Coinbase Report: Are Crypto AIs a Mirage Or For Real?
Mar. 28, 2024. 5 mins. read.
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Amidst adversities, AI tokens surpass mainstream cryptocurrencies and AI stocks. Analysis unveils their resilience, thriving on crypto market dynamics and AI sector growth, enticing investors with sustained momentum.
Artificial Intelligence (AI) has been making rapid strides in recent years, with breakthroughs like ChatGPT, Midjourney and Claude capturing the public imagination. At the same time, the world of cryptocurrency and blockchain technology is expanding, vying for the attention of a still-young digital economy. Can these two cutting-edge fields co-exist and aid each other’s evolution? The question presents both exciting opportunities and complex challenges.
A new report by Coinbase, a leading US-based cryptocurrency exchange, which also launched the surging Base layer-2 network, delves into the current state of the crypto-AI landscape. The report highlights that while there is significant potential in the overlap between technology’s two brightest sectors, the path to widespread adoption is not that straightforward. Different sub-sectors within this intersection have vastly different opportunities and development timelines.
One key observation is that decentralization alone is not enough for an AI product to succeed in the crypto space. It must also reach feature-parity with centralized alternatives. Crypto-based AI solutions must offer compelling advantages beyond just being decentralized.
The report also suggests that the value of AI tokens may be overstated due to the current hype around AI. Many AI tokens may lack sustainable demand-side drivers in the short to medium term, despite the excitement surrounding them.
Key Trends in Crypto AI
Open Source Models Carry On
The AI sector has a thriving open-source ecosystem, with platforms like HuggingFace.co hosting a wide range of publicly-available models. This open-source culture coexists with a competitive commercial sector, ensuring that non-performant models are quickly weeded out.
Smaller AI Models Gain Traction
Despite this, smaller AI models are increasing in quality and cost-effectiveness. Fine-tuned open-source models can even outperform leading closed-source models in certain benchmarks. This trend, combined with the open-source culture, enables a future where performant AI models can be run locally, offering a high degree of decentralization.
AI Integrations Strongly Benefit Existing Platforms
The report notes that existing platforms with strong user lock-in or concrete business problems are well-positioned to “disproportionately” benefit from AI integrations.
- For example, GitHub Copilot‘s integration with code editors enhances an already powerful developer environment. Similarly, embedding AI interfaces into various tools like mail clients, spreadsheets, and CRM software are natural use-cases for AI.
- In such scenarios, AI models augment existing platforms rather than creating entirely new ones.
- AI models that improve traditional business processes internally often rely on proprietary data and closed systems, making them likely to remain closed-source.
Hardware and Compute Trends
In the AI hardware and compute space, there are two distinct trends:
One is shifting computation from training to inferencing: with more models now available, the focus moves towards making queries to these already-trained models. This trend favors platforms that can reliably run production-ready models securely.
A second, related trend is that the competitive landscape around hardware architecture is evolving, with new processors from Nvidia, Google, and Groq potentially shifting cost dynamics in the AI industry. Cloud providers that can quickly adapt, procure hardware at scale, and set up associated infrastructure stand to reap the rewards of these developments.
Crypto’s Role in the AI Pipeline: Four Stages
The Coinbase report next examines crypto’s potential impact on four stages of the AI pipeline:
1) data collection and management
2) model training and inferencing
3) output validation
4) tracking
1) Data Collection and Management
Historical blockchain data is a rich source of training data for AI models. However, commercial models tend to use proprietary datasets, posing challenges for decentralized data marketplaces, which need to compete with both open-source data directories and corporate silos.
Decentralized storage also faces hurdles in the AI industry. While decentralized storage can offer potential cost savings, it currently lacks the tooling, integrations, and predictable costs of mature cloud systems. Regulatory and technical challenges around sensitive data storage on decentralized platforms remain significant barriers.
2) Model training and Inferencing
In the model training and inferencing stage, decentralized compute solutions like Render and NuNet aim to leverage idle computing resources to provide an alternative to centralized cloud providers. While some projects have seen increased usage, long-term success faces strong competition from established players. Technical limitations like network bandwidth constraints also pose challenges for decentralized compute networks.
3) Output Validation
Validating AI model outputs, and ensuring trust is another area where crypto-based solutions are being explored. However, the complexity of model benchmarking and the increasing feasibility of running models locally on consumer hardware raise questions about the demand for trustless inferencing solutions.
4) Tracking
Finally, the importance of tracking AI-generated content and proving online identity is growing. While decentralized identifiers and on-chain data hashes can help address these issues, centralized alternatives like KYC providers and AI watermarking techniques are also being developed.
Trading the AI Narrative
Despite the challenges, AI tokens have outperformed major cryptocurrencies and AI-related equities in recent months. The report suggests that AI tokens benefit from strong performance in the crypto market and in the AI industry, leading to upside volatility even during bitcoin drawdown periods. Hype drives demand, and investors will be piling in for some time to come.
However, the lack of clear adoption forecasting and metrics has enabled speculative trading that may not be sustainable in the long run. Eventually, as in every crypto cycle, price and utility will need to converge, either through rising use-cases or falling prices.
Looking Ahead
The marriage of AI and crypto is still in its very early stages, and is likely to evolve rapidly as the broader AI sector develops. A decentralized AI future, as envisioned by many in the crypto industry, is not guaranteed. Crypto-based solutions are technically feasible, but to drive adoption they must provide meaningful advantages over centralized alternatives.
The AI industry itself is undergoing swift changes, fighting more and more headwinds as public opinion often turns against it. Therefore it is crucial to navigate this space carefully. Deeper examination of how crypto-based solutions can offer substantially better alternatives, or at least a clear understanding of the underlying trading narrative, is essential for investors and entrepreneurs alike.
As the AI and crypto landscapes continue to search for a sustainable symbiosis, ongoing research and experimentation will be vital to unlocking the potential of this area while meeting its challenges.
The future of decentralized AI is still being written, and it will be shaped by the ingenuity and perseverance of the innovators working at the forefront of these transformative technologies.
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13 Comments
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