Lyrik: Open Web AI Investment Thesis

Ken Miyachi and Josh Daniels
February 27, 2024

In the evolving technological sector, the emerging intersection of Web3 and Artificial Intelligence (AI) is drawing considerable investment attention for its exploratory development and potential impact.  This convergence is at a nascent stage, inviting deeper investigation into what could revolutionize digital interactions across all industries. With Web3's decentralized structure changing digital asset management and AI's advanced data processing altering business and daily life, this thesis aims to succinctly analyze the investment potential in the intersection of these technologies, Open Web AI. Open Web AI is a concept where Artificial Intelligence (AI) and Web3 technologies merge to promote digital self-sovereignty, emphasizing transparency in the AI pipeline and providing individuals choices in their interactions with AI. It embodies the shift towards a decentralized, transparent digital ecosystem that safeguards individual rights and fosters community-driven, democratic governance of AI technologies.

“Blockchains, peer-to-peer payments, Web3, zero-knowledge, very large language models and on-edge AI models: these are not separate technology verticals, but rather interconnected facets of a new digital paradigm of self-sovereignty.”<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-1"  href="#footnote-1" rel="">1</a></div> - Illia Polosukhin, Co-Founder NEAR

The goal of this thesis is to delineate an actionable roadmap towards Open Web AI. Below is a table and venn diagram which categorizes various AI paradigms, serving as a foundational guide for understanding the distinctions and applications discussed throughout the research.

<table>   <thead>     <tr>       <th>Type</th>       <th>Source</th>       <th>Development</th>       <th>Methodology</th>       <th>Computing</th>       <th>Examples</th>     </tr>   </thead>   <tbody>     <tr>       <td>Closed-Source AI</td>       <td>Closed-source</td>       <td>Developed by centralized companies</td>       <td>Proprietary data gathering, training algorithms, and fine-tuning</td>       <td>Centralized training, Centralized inference</td>       <td>Google, OpenAI, Anthropic</td>     </tr>     <tr>       <td>Open-Source AI</td>       <td>Open-source</td>       <td>Developed by centralized companies</td>       <td>Open data sources, training algorithms, and fine-tuning</td>       <td>Centralized training, Decentralized inference</td>       <td>Meta, Mistral, Nous Research</td>     </tr>     <tr>       <td>Decentralized AI</td>       <td>Open-source</td>       <td>Developed by distributed and decentralized organizations or individuals</td>       <td>Non-centralized compute, all methodology is open source</td>       <td>Decentralized training, Decentralized Inference</td>       <td>Bitensor, Ritual, SingularityNET</td>     </tr>   </tbody> </table>

Table 1: Closed-Source vs Open-Source vs Decentralized AI Comparison

Diagram 1: Venn Diagram comparing Closed Source vs Open-Source vs Decentralized AI

The State of Technology and Societal Cycles

“This is the beginning of a new Industrial Revolution and this Industrial Revolution is about the production, not of energy, not of food, but the production of intelligence… it is really up to you to take initiative activate your industry, build the infrastructure as fast as you can so that the researchers the companies your governments can take advantage of this infrastructure to go and create your own AI” - Jensen Huang<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-2" href="#footnote-2" rel="">2</a></div>

In today's landscape, marked by significant technological and societal shifts, Artificial Intelligence (AI) and blockchain technology stand at the forefront, reshaping industries and redefining global data management and value transfer. AI, with its advancements in machine learning, and Web3, marked by innovations like zero-knowledge proofs, are foundational to the new Industrial Revolution. This shift is highlighted by rapid economic and user growth in both sectors. Ethereum recently became the second fastest company to reach $10B in revenue<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-3" href="#footnote-3" rel="">3</a></div>, and ChatGPT reaching 100 million users just two months after launch.<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-4" href="#footnote-4" rel="">4</a></div>

The growth and evolution of both AI and Web3 is paving the way for a transformative convergence. This integration combines Web3's open-source, decentralized principles with AI's analytical capabilities, fostering innovative applications and vast investment and growth opportunities. Notably, Web3's infrastructure, including smart contracts, is typically deployed on public blockchains, allowing for transparency and verifiability. As we move towards more autonomous and decentralized systems, this synergy between blockchain and AI represents both a technological leap and a societal shift, challenging traditional norms and promising to significantly impact various sectors globally.

In 2023, despite the setbacks of the bear market, the cryptocurrency sector continued on its path towards growth and maturity, evidenced by an annualized 52% increase in experienced developers in the blockchain space over the past five years.<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-5" href="#footnote-5" rel="">5</a></div> Furthermore, the market transitioned from a bearish phase into a new cycle of growth, marked by the introduction of new products, increased user adoption, and significant indicators like the surge in overall crypto market capitalization and the launch of Bitcoin ETFs. Advancements in blockchain technology, including improvements in cryptography, scalability, and user experience through technology abstraction, have driven the ecosystem towards a significantly more robust architecture. This includes enhanced performance in monolithic Layer 1 solutions like Solana with Firedancer, the adoption of modular architecture within the EVM ecosystem, and the emergence of new paradigms and chains, such as Bitcoin Layer 2 solutions, parallelized EVMs, and Move-based chains like SUI and Aptos. Collectively, these developments have contributed to a more cohesive Web3 environment, showcasing the diverse yet integrated nature of blockchain technology's evolution. These collective advancements signal a maturing market, setting a solid foundation for scalable data infrastructures and facilitating the integration of blockchain technology with AI.

Diagram 1: Cryptocurrency Market cap from 2020 - 2024

Projected to grow from approximately $400 billion in 2023 to $15.7 trillion by 2029, the AI industry is poised to make a substantial impact on the global economy, showcasing its expanding influence across multiple sectors<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-6" href="#footnote-6" rel="">6</a></div>. This growth highlights a pivotal shift in AI's integration into the technological and business spheres. Amidst this expansion, debates around the pace of AI development—effective versus defensive acceleration—and the centralization versus decentralization of AI have gained prominence. Centralized AI, championed by tech giants such as Google, OpenAI, and Anthropic, draws on significant resources but faces ethical and control-related criticisms.

In contrast, the momentum towards decentralized AI, propelled by blockchain technology, advocates for wider, transparent participation and aims to democratize AI development. This shift seeks to address challenges like news and content verification such as Verify Media<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-7" href="#footnote-7" rel=""></a></div> and the concentration of AI power, promoting an AI future that aligns more closely with societal values. Leading this charge towards open-source are entities like Meta, Nous Research, and Mistral, which prioritize open-source collaboration and innovation.

Diagram 2: Open-Source vs Private AI Model Performance by ARK Invest<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-8" href="#footnote-8" rel="">8</a></div>

With over 300 million global cryptocurrency users and projections to hit 1 billion users by 2028 and AI's expanding integration into all aspects of life, the momentum behind these technologies is clear.<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-9" href="#footnote-9" rel="">9</a></div> <div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-10" href="#footnote-10" rel="">10</a></div> Recent challenges, from financial system vulnerabilities to ethical concerns in AI practices, emphasize the need for robust, transparent solutions, where the synergy between AI and Web3 offers promising prospects. Often, these challenges are framed as doomsday scenarios, suggesting that a drastic change in trajectory is necessary for resolution.

However, this thesis offers a different viewpoint, asserting that the convergence of Web3 and AI holds the potential to significantly enhance existing systems. Through technological and application advancements, Open Web AI will incrementally foster positive change, offering a more optimistic outlook on the future of these technologies.

Open Web AI Will Outperform AI and Web3 Individually

Despite rapid growth in both sectors, the current intersection of AI and Web3 remains relatively small. However, as these technologies continue to evolve, their overlap is expected to expand significantly. The transparent, decentralized nature of Web3 complements AI's capabilities, addressing key challenges like data authenticity and privacy while enhancing system trust and accountability. As AI technologies evolve, the diminishing performance differences among closed-source vs open-source models highlight a shift towards value derived from widespread application rather than exclusivity, a principle inherently aligned with the Open Web AI ethos.

This democratization enables independent monetization opportunities for AI, granting direct access and contribution opportunities to essential resources such as datasets, fine-tuning tools, and hardware, free from the constraints of centralized gatekeeping. The transition towards a decentralized AI ecosystem, quantifiable by a comparison between the Web2 creator economy and the AI contributor economy in Web3, underscores a paradigm where contributors directly monetize their innovations, retaining a more substantial value share. As personalized AI assistants become more ubiquitous, the paramount importance of trust challenges the closed ecosystems fostered by entities like OpenAI, advocating for decentralized AIs transparency and reliability.

The current concentration of talent and investment in closed-source AI is set for a redistribution towards open-source and decentralized models, as the advantages of such systems become increasingly apparent. This shift would accelerate the functionality and accessibility of Web3 platforms but also introduce novel AI-driven business models, facilitated by blockchain technology. The reciprocal enrichment between AI and Web3 is expected to drive broader adoption and innovation, securing a stronghold of value at their intersection. These blockchain enabled business models focused around AI contribution have already shown strong signs of success and momentum. Notable successes such as Morpheus AI, with over $200M in contributions, and Bittensor’s AI-based mining approach, which recently achieved a $4 billion market cap, exemplify the momentum in blockchain-enabled AI business models.

Despite currently representing just 3% of the total AI market, the Web3 AI sector's alignment with both technological innovation and societal needs indicates a fast-approaching increase in market share, signaling a wave of innovation and foundational advancements. This sector offers substantial opportunities for growth and investment, signaling a shift in the way technology is developed, utilized, and valued. The reciprocal relationship between AI and Web3 will foster innovative applications and drive further adoption across various sectors for both technologies. Most crucially, at the core of this thesis, lies the assertion that the outsized value accrual will accrue at the intersection of AI and Web3, with blockchain technology enabling novel business models for AI-based products and services, setting the stage for profound economic and societal impact.

Charting a Practical Roadmap to Fulfill Its Long-Term Promise

Building AI in a decentralized, open manner offers a distinct advantage, particularly in the context of AI's nascent stage. The early phase of AI development presents a risk when initiatives are pursued in stealth, isolated from community feedback and external advancements. Without the insights and contributions from a broader community, there's a danger of missing out on critical developments and perspectives that could shape the technology's trajectory more effectively. Moreover, integrating AI within an open-source framework fosters a collaborative environment that accelerates innovation and refinement. However, the challenge lies in creating a cohesive ecosystem for effective and performant AI as well as designing an elegant user experience for these open-source models.

To realize the potential of the intersection between AI and Web3 technologies, a clear and strategic roadmap is essential. This roadmap must address the unique challenges identified by industry leaders in the space, fostering an ecosystem that attracts top talent and encourages a sustainable and resilient development framework of decentralized AI solutions.

Key Challenges to Interrogate

  1. Balancing Open Source and Security in AI: Vitalik Buterin highlights the paradox where cryptography benefits from open-source for security, whereas AI's openness could increase vulnerability to adversarial attacks. Resolving this requires innovative approaches to open-source development in AI that protect against such vulnerabilities.<div class="footnote-hovercard-target"><a class="footnote-anchor" id="footnote-anchor-11" href="#footnote-11" rel="">11</a></div>
  2. Bridging the Talent Gap: The negative perception among AI talent about Web3 poses a significant challenge. For decentralized AI to rival centralized systems, attracting and retaining top talent is crucial. This involves education, creating compelling incentives and providing the necessary tools and platforms for innovation. This talent gap permeates beyond just AI engineers into product, design, and leadership talent. The brand of web3 overall requires a revamp after the collapse of FTX and other high-profile fraud and abuse that has occurred in the space.
  3. User Experience: There are inherent user experience benefits of having a vertically integrated and closed software system as opposed to open source (i.e, Linux vs OSX). Web3 needs feature parity, at minimum with web2, and then additional advantages that benefit the developer, users and entrepreneurs, which also applies to decentralized AI.
  4. Governance: Orchestrating decentralized and open-source communities can reduce velocity and increase friction in decision making due to opaque hierarchy and differing opinions.

Roadmap to Ecosystem Development

Decentralized ML platforms like Bittensor, create incentivization Frameworks which offer a glimpse into how decentralized networks can incentivize AI development. By rewarding contributions in a distributed manner, these frameworks can attract AI engineers to build solutions within the Web3 space. There is also a significant improvement in Decentralized Physical Infrastructure Networks (DePIN) like Akash can lower the barriers to entry for AI development by providing affordable computational resources. This democratizes access to the necessary hardware, making AI projects more feasible for a wider range of developers. The Roadmap to realize the potential of decentralized AI will center on incremental improvement to address the problems outlined above. To ensure the success and resilience of open web AI, it is imperative to establish a robust foundation that addresses key challenges and leverages the unique strengths of decentralized technologies.

Mapping the Landscape of Decentralized Physical Infrastructure Networks

Diagram 2: DePIN Landscape outlining physical infrastructure, DePIN modules, and other Digital Resources (source:

Below is a table that offers a clear, sequential roadmap for overcoming each identified challenge. This roadmap is designed to guide the development of a thriving and fruitful ecosystem capable of competing with, and eventually surpassing, centralized AI solutions. By systematically addressing aspects such as security, talent acquisition, user experience (UX), business models, and governance, we can lay down the essential bedrock for a future where Open Web AI not only flourishes but also sets new standards for innovation and inclusivity in the technological landscape.

This approach is designed to address foundational aspects of Open Web AI development with the necessary foresight and precision. By focusing on development of security, talent, user experiences, and governance, it lays the framework for integrating AI and decentralized technologies in a way that is not only successful but also resilient to future challenges.

While this chart is detailed in an idealistic light, it's important to recognize the reality that much of what's envisioned is actively unfolding, and these solutions will evolve concurrently as the technology landscape moves forward. The field's greatest collective triumph rests on the commitment of all its stakeholders — community, investors, builders, and more — to advancing the position of open-source and decentralized dialogues and methodologies. Historically, the fluid collaboration seen in closed, private organizations posed as a benchmark; however, the maturity of the web3 and the progression of work within the global open-source and decentralized communities have started to set a unique mark. Today, there’s an unusual rigor and source of success in the open-source and decentralized community workspace, evidencing not just a reality of effective engagement but a case where value-added contributions and velocity are at an all-time high.

Collectively, the willingness to mobilize, share, and implement ideas across this open architecture is primed to establish a legal foundation for a host of integrated advancements. This not only provides a mode for this outlined roadmap to be adopted more amicably but also convincingly posits an exciting call to action for the intertwined destinies of AI and Web3.

Diagram 3: Gantt Chart of a roadmap to achieve a successful Open Web AI Ecosystem

The Gantt chart outlines a strategic roadmap to foster the OpenWeb AI ecosystem. Significant work has already gone into all four categories in advancing decentralized AI to get to where we are now. The most critical component (right now) of achieving a successful Open Web AI Ecosystem is bridging the talent gap. It's understood that attracting and empowering a skilled workforce is crucial to drive innovation in decentralized AI, setting the stage for development that is on par with, if not superior to, centralized AI systems. Initiatives like economic incentives for AI engineers and recognition mechanisms for contributions in decentralized AI are marked as initial steps to cultivate a strong talent pool.

Subsequent phases focus on developing sophisticated tools and improving security and user experience (UX), ensuring that decentralized AI applications offer the full spectrum of Web3 benefits while being user-friendly and secure. The timeline culminates with the establishment of governance structures, crucial for maintaining ethical standards, regulatory compliance, and long-term success, as evidenced by organizations like OpenAI. This comprehensive approach ensures that each phase builds upon the last, creating a robust and sustainable foundation for the OpenWeb AI thesis to thrive.

By addressing these challenges and following a structured roadmap, the vision of a thriving ecosystem at the intersection of AI and Web3 can be brought to fruition. The goal is not only to match the capabilities of centralized AI solutions but to surpass them by obtaining feature parity with centralized AI solutions, and leveraging the unique advantages of decentralization to offer additional value propositions. This includes enhanced security, privacy, user control, and community-driven innovation. With the right framework, incentives, and focus on user experience, the decentralized AI space can attract the best talent, foster groundbreaking developments, and pave the way for a new era of technological advancement.

To bridge our roadmap with the envisioned future, it's crucial to spotlight key investment areas that will catalyze this transformation. These include the development of decentralized models, datasets, and AI marketplaces; the expansion of Decentralized Physical Infrastructure Networks (DePIN); the integration of AI smart agents with smart contracts; and the enhancement of AI security and verification infrastructure. Investing in these domains will lay the groundwork for an Open Web AI Ecosystem that is not only innovative but also equitable and sustainable.


The intersection of AI and blockchain technologies is ushering in a transformative era in various sectors. This convergence is particularly pivotal in addressing the emerging challenges of AI-generated content, such as deep fakes, and the growing need for robust security and identity verification systems. Decentralized data and model marketplaces are democratizing AI, offering new opportunities for innovation and participation, ultimately accelerating AI development as a whole. Actionable AI through agent-driven smart contracts is enhancing the efficiency and intelligence of blockchain networks, while decentralized physical infrastructure networks (DePIN) are addressing the computational bottlenecks in AI development. Enhanced security and identity verification systems are becoming increasingly essential in maintaining information integrity and protecting intellectual property in the digital age. As these technologies continue to evolve and intersect, they hold the promise of creating a more equitable, transparent, and innovative future, reshaping the computational landscape and offering transformative solutions across industries. The synergy of AI and blockchain not only presents a wealth of opportunities for advancement but also poses a responsibility to ensure these technologies are developed and utilized responsibly and ethically for the greater benefit of society.

To realize this vision, there is a call to action for builders within decentralized AI to foster open communication channels and actively collaborate towards executing this roadmap. Open collaboration communities already in existence, such as Morpheus AI, OpenAdapt AI, and Bittensor, exemplify the vibrant projects with active communities dedicated to developing Decentralized AI. Furthermore, the launch of the BitMind Collective, in conjunction with Edge City at ETH Denver, signifies a stepping stone towards unified efforts in this domain. Engaging in these communities and initiatives is not just an opportunity but a pivotal step for all stakeholders in decentralized AI to contribute to a collective journey towards an innovative, open, and more connected digital future.


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