AI x Crypto Investment Thesis 2025

The Landscape

AI represents the world's largest market because it encompasses all of problem-solving. While our "helicopter view" mental model shows the vastness of this landscape, we recognize it's impossible to invest everywhere. That's why we specifically target the intersection of crypto and AI.

We believe in the collective effects of commoditized cognition, programmable markets and transparent governance.

Traditional economies are bound by human productivity and inefficiencies due to coordination overheads. Complex systems favor monopolistic control -- hierarchies arise to minimize transactional costs and reduce complexity, but they inevitably concentrate power and wealth, creating dominant entities -- often called enterprises, firms (or governments) -- that dictate the economic landscape.

Now, with the exponential growth of artificial intelligence, transactional inefficiencies and coordination overheads can be nullified. As these inefficiencies vanish, centralized firms will transform to a more efficient forms, and we will enter a new epoch - the era of the cybernetic economy.

The Moore’s Wall

Model pre-training has hit a critical plateau — The Wall — where additional compute power no longer yields significant performance improvements.

As a result, in 2025 the entire AI industry will focus on these key activities:

• Hyperscalers will leverage their data advantages to develop superior reasoning algorithms while continuing to monetize inference results.

• Open-source companies will continue publishing GPT-4o level open-weights models, unless they find easy ways to monetize the models themselves (rather than inference results).

• Enterprises will focus on distilling large reasoning models into smaller language models while advancing data retrieval technology for RAG.

• Individuals and researchers will develop agents by leveraging existing APIs and platforms while awaiting better benchmarks for agent performance from industry and academia.

The Grand Scheme

Bitcoin converts electrical power into monetary value through a decentralized process. Classified as a legal commodity, it operates outside of SEC jurisdiction, with no centralized authority, no corporate governance, and no dependency on traditional financing.

AI models convert electrical power into monetary value through training and inference processes. They could be classified as commodities if these processes are decentralized and the resulting models remain unextractable and perpetually tied to the blockchain.

Ethereum, a globally decentralized computer, is also classified as a legal commodity, demonstrating that computational mechanisms can be commoditized and transformed into a universally accessible natural resource, rather than restricted as a proprietary service.

Commoditization of AI models unlocks an opportunity for institutional capital to invest directly into the collective intelligence as a commodity.

We invest in technologies and market mechanisms that accelerate commoditization of AI.

Mechanisms

The unique properties of on-chain models enable new market mechanisms through the fusion of economic and technological capabilities:

• An on-chain AI model is essentially a model that will not execute any operation without approval recorded on the blockchain even if deployed on a dedicated server infrastructure or private cloud.

• On-chain models are both permissionless for use and fine-tuning (like open-source models) and monetizable (like proprietary ones).

• On-chain models guarantee perpetual accessibility — continuous access even if the original creator withdraws the model from circulation.

• On-chain models allow for decentralized governance, where smart contracts could act as open verifiable licenses and escrow mechanisms, ensuring that usage, derivative works, and financial flows are transparently managed without centralized intermediaries.

These properties allow the following market mechanisms to emerge and attract the next $100B into the crypto ecosystem from institutional investors:

• Distribution of profits from AI models to token holders creates significant value.

• Establish revenue-sharing mechanisms where token holders earn from model derivatives (including fine-tuned versions), ensuring sustainable rewards regardless of the original model's popularity.

• Integration of AI with lending markets: on-chain models serve as collateral, enabling financing for model training and fine-tuning.

• High token velocity enables institutional investors to participate in liquidity pools, facilitating large-scale trades with minimal price impact.

• Unpredictable inference costs create opportunities for institutional investors to introduce futures, options, derivatives, and perpetual contracts.

• Analyze correlations between AI model performances to develop hedging strategies, using stronger models to offset risks from underperforming ones.

• Offer insurance against inference-time compute bloating, model performance degradation, and other operational risks.

• Create a unification layer where multiple AI model tokens can be aggregated into a single, standardized token, improving stability and reducing velocity.

• Deploy mechanisms to encourage long-term holding and reduce token velocity through staking rewards, vesting periods, and loyalty programs.

• Invest in Layer 2 solutions and decentralized storage to support high transaction volumes and large-scale AI models efficiently.

The top-3 value capturing mechanisms are going to be:

• Distribution of profits created by AI models to the token holders (coordination).

• Distribution of profits down the AI model provenance tree (composability, derivatives).

• Discovery, interactions, and transactions between AI agents.

The top products that would implement these mechanisms?

• On-chain "Hugging Face" with the ability to own models, model derivatives, and inference profits.

• Agent intents network with the ability to discover, interact, and transact with AI agents and tools.

Pre-training of Models

It has become evident, as highlighted by Ilya Sutskever at NeurIPS 2024, that pre-training has reached a critical plateau -- the wall -- where further increases in compute power no longer yield significant performance improvements. This development sets a clear benchmark for the "final cost" (CAPEX) of building a GPT-4-like model "from scratch".

Historically, the crypto industry has been very effective in fundraising. With the right team, an on-chain model, and distributed inference -- even if training remains centralized -- the emergence of a new "OpenAI" remains possible.

The DeepSeek-V3 model was trained at a cost of approximately $5.5 to $5.6 million, utilizing 2.78 million GPU hours on 2048 H800 GPUs over a span of about two months. This cost is significantly lower than that of comparable models, such as Meta's LLaMA 3, which required 30.8 million GPU hours and a much higher budget.

It is absolutely possible to train an on-chain model for $5-10M from scratch already today.

Fine-tuning of Models

The year 2024 was a year of small language models as they increasingly closed the gap with larger models when fine-tuned for specific tasks and compared under compute constraints -- showcasing how focused optimization can outperform general-purpose systems.

We introduce Sky-T1-32B-Preview, our reasoning model that performs on par with o1-preview on popular reasoning and coding benchmarks. Remarkably, Sky-T1-32B-Preview was trained for less than $450, demonstrating that it is possible to replicate high-level reasoning capabilities affordably and efficiently (https://novasky-ai.github.io/posts/sky-t1/).

Fine-tuning can be done at different levels:

• Foundation level: Instruct fine-tuning and alignment.

• Domain level: Specialist models for fields like medicine, law, and biotech.

• User level: Adapts to individual user needs and style.

• Prompt-level: Adapts to an individual prompt in real-time.

User-level and prompt-level fine-tuning are the killer apps: A 3.8B parameter model fine-tuned at inference time outperformed a 130B parameter general model (https://arxiv.org/pdf/2410.08020).

The world will need tens of foundational models, hundreds of domain-level models, millions of user-level models, and trillions of prompt-level models.

The biggest untapped crypto+AI opportunity is the on-chain "Hugging Face" with the ability to own models, model derivatives, and inference profits. It keeps models both permissionless for use and fine-tuning (like open-source models) and monetizable (like proprietary ones).

Inference-time Compute

"The Wall" is the main reason for OpenAI to release reasoning models like o1 and o3. The difference from previous generations is that these models target inference-time compute, meaning they implement "chain-of-thought" in one form or another to climb the scaling law and achieve better performance.

Inference-time compute is a game-changer for the entire AI industry as it introduces an unknown variable to the cost structure — OPEX — variable inference compute. Previously, we had a pretty good idea of how much it costs to do the inference; now, it depends on how long the resulting chain-of-thought is. This can be viewed as an advantage for large enterprises and also as a "risk component" in the agentic market setting (when agent takes a task without knowing how much compute it will take to finish).

It is unknown how complex or simple the "chain-of-thoughts" are, but it is expected that the open-source community may start closing this gap in the upcoming year.

Novel "chain-of-thoughts" architectures and distillation of reasoning models are the biggest opportunities in this sector of AI.

Memory

Context memory is the only research direction that can enable "personalized information gains" -- a surprising piece of information that updates an internal world model of the user the most.

Memory is both underdeveloped (plaintext) and researchable by GPU-poor startup teams and individual researchers. Effective context memory allows LLMs to retain relevant information across interactions, enhancing the continuity and relevance of responses.

A recent Google paper titled "Titans: Learning to Memorize at Test Time" introduces a relatively small AI model that acts as memory for the larger model. We believe this trend will continue as we see more "differentiable memory" designs emerge.

Memories that employ "theory of mind" approaches enhance models' ability to understand and predict human intentions and mental states, improving the quality of interactions.

Memories can be made portable and are easy to own on-chain. This opens a market for “memory marketplaces”, where multiple AI agents can buy or license shared historical contexts.

Memory is also important to enable efficient multi-agent systems, mixing agent memories together to achieve better performance gains.

Memories can also be used to produce a database of personas or characters, which is important for AI agent diversity. These “virtual personalities” become portable assets, creating new opportunities for personalization and creativity.

Agents

While agents lack a precise definition, for our purposes they can be considered "consumer technology" — easy to build since they only require engineering skills without needing access to compute or expensive datasets.

The key to unlocking the agent creation market is simplifying development and distribution until developers realize they can earn more by building agents than by working for tech giants.

Here are the infrastructure elements required for that:

• app-store for agents

• micro-task platforms for AI-human interaction

• agent-ready tools and virtual environments

• benchmarks and guardrails as agents can do as much damage as autonomous cars

Due to the variable inference costs, agents would likely offer customers a choice of "two out of three" between fixed scope, fixed budget, and fixed time — or alternatively between quality, cost, and time.

The only enterprise agent to be build is "Dwight Schrute" and his main job is process mining — observing organizational workflows and documenting existing processes. This agent would be particularly valuable for strategic consulting companies as a sales tool.

Several key challenges remain theoretically unsolved: active inference (taking actions to learn from them), planning (finding solutions when no direct path exists), and surprise (maximizing personalized information gains).

Is that all?

No, we look forward to seeing teams develop: non-language foundational models, alternatives to transformers, differentiable and remixable memories, mortal AI, domain-specific reinforcement learning gyms, forward-forward training and fine-tuning, proof of inference, and more.

Conclusion

The convergence of AI and crypto in 2025 presents unprecedented opportunities for value creation and capture. We stand at a unique moment where the democratization of AI through blockchain technology can reshape how we build, deploy, and monetize artificial intelligence.

This investment thesis demonstrates how the convergence of AI and crypto creates a powerful feedback loop:

• Pre-training and fine-tuning innovations reduce barriers to entry, enabling more participants to create and own models

• Inference-time compute introduces new economic dynamics that require novel market mechanisms and risk management tools

• Memory systems and agent frameworks create opportunities for value capture through personalization and automation

Each of these components reinforces the others - as models become more accessible, agent development accelerates; as memory systems improve, fine-tuning becomes more effective; as inference costs vary, new market mechanisms emerge.

For investors and builders in this space, the message is clear: the infrastructure for the next generation of AI is being built now. The winners will be those who recognize that decentralized, permissionless, and transparent systems are not just ideological preferences - they are practical necessities for unlocking the full potential of artificial intelligence.

cyber•Fund mission is to accelerate the buildout of healthy cybernetic economy. If you're working on any of these problems, please reach out. Our goal is to support founders, researchers and open source projects in decentralized and distributed AI.