Muhammad Kashif

November 26, 2025

Why Ethereum Is Losing Institutional Favor

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Ethereum, once the unquestioned leader of the smart contract revolution, stood for years as the natural choice for banks, hedge funds, enterprises, and large financial institutions experimenting with blockchain technology. As the first major network to make decentralized applications and programmable smart contracts possible, it attracted developers, liquidity, and attention from the world’s most powerful investors. Why Ethereum Is Losing. In its early days, Ethereum was seen as the future of decentralized finance and the backbone for institutional blockchain adoption. However, as blockchain technology has rapidly evolved and competitors have matured, the narrative has shifted. Institutions, which once viewed Ethereum as the default solution, are now exploring alternative networks that are faster, cheaper, more scalable, and in some cases more aligned with regulatory and compliance requirements.

To why Ethereum is no longer the top choice for institutions, it is essential to examine the fundamental changes taking place in the blockchain ecosystem. Institutions now have significantly more options than they did in the past, and many of these options address the limitations that have held Ethereum back. High gas fees, network congestion, environmental concerns, and regulatory uncertainties have all contributed to a changing institutional landscape. At the same time, Ethereum still maintains a strong position, but the days of uncontested dominance are over. The question is not whether Ethereum still matters—it absolutely does—but why institutions are broadening their focus and, in some cases, shifting away from Ethereum in favor of platforms that better meet their evolving needs.

Ethereum’s Early Institutional Dominance

Ethereum’s early success with institutions can be attributed to its ability to do what Bitcoin could not. While Bitcoin was revolutionary as a decentralized store of value, Ethereum introduced smart contracts, a transformative innovation that allowed code to self-execute on the blockchain. This breakthrough opened the door to decentralized applications, tokenized assets, automated financial products, and the early foundations of what would become the massive DeFi sector. Institutions that were curious about blockchain technology found Ethereum appealing because it offered functionality, programmability, and innovation potential unmatched by any other network at the time.

Throughout its early years, Ethereum benefited from the largest developer community in the blockchain industry. This meant new tools, applications, and services were constantly being built, providing a stronger infrastructure for institutional experimentation. Ethereum also captured the majority of stablecoin volume, decentralized exchanges, and liquidity pools. For institutions wanting to interact with blockchain-based markets, Ethereum was the place where the most activity happened. Because it had such a strong brand and such deep liquidity, institutions could feel confident that they were entering an ecosystem with relevance, future growth potential, and wide support from custodians and infrastructure providers.

However, the very success that made Ethereum dominant also created problems. As the network grew, congestion became common. As more decentralized applications launched and user demand skyrocketed, Ethereum’s limited throughput became a bottleneck. Although institutions tend to be long-term thinkers, they also require a degree of predictability, performance, and cost-efficiency that Ethereum often struggled to provide.

The Scalability Problem: High Gas Fees and Network Congestion

One of the clearest reasons Ethereum is no longer the top choice for institutions comes from its well-documented scalability challenges. Ethereum’s base layer has limited bandwidth, and when the network becomes congested, transaction fees—known as gas fees—can spike to extremely high levels. There have been periods when processing a single transaction could cost hundreds of dollars, making it impractical for institutions that want to move significant amounts of assets efficiently or frequently.

For organizations that manage large portfolios, execute high-frequency trades, or run automated smart contract strategies, unpredictable fees are a major concern. Institutions need reliability and cost predictability, especially when executing operations at scale. Ethereum, because of its congested network and fluctuating costs, has not always been able to provide these guarantees. Despite the shift to Proof-of-Stake and ongoing improvements, the base layer still faces the same structural limitations. This means that institutions operating on Ethereum must either accept high fees or shift their activity to Layer 2 networks. Many institutions are reluctant to do so because multiple layers introduce complexity, risk, and integration challenges.

Ethereum’s throughput challenges also mean that transactions sometimes take longer than institutions prefer. Lightning-fast settlement is not just a convenience; for financial institutions, it can be essential. When alternative blockchains can confirm transactions in seconds for a fraction of the cost, it becomes easy to see why many organizations are exploring new options.

The Rise of Faster and Cheaper Layer 1 Competitors

The emergence of high-performance Layer 1 blockchains is one of the most significant reasons institutions have expanded beyond Ethereum. Platforms like Solana, Avalanche, BNB Chain, and others have marketed themselves as faster, cheaper, and more scalable alternatives. These networks often process thousands of transactions per second, offer extremely low fees, and provide near-instant settlement. For institutions focused on speed, throughput, and cost-efficiency, these platforms can be more appealing than Ethereum’s congested base layer.

What makes this shift particularly impactful is that these competing blockchains are no longer experimental. They have matured into full-fledged ecosystems with decentralized finance platforms, tokenized assets, derivatives markets, and development environments that rival Ethereum. As liquidity grows on these networks and institutional infrastructure improves, institutions feel increasingly comfortable diversifying into or even prioritizing these alternative ecosystems.

Another important factor is the speed with which some competitors have embraced enterprise use cases. Instead of trying to adapt a general-purpose blockchain to institutional needs, many networks are building features designed specifically for businesses. These may include custom consensus mechanisms, governance models tailored for organizations, and improved data privacy frameworks. Ethereum, while powerful, was not originally designed with institutional specialization in mind, and this has created opportunities for competitors to position themselves as better fits for corporate users.

Layer 2 Complexity and Institutional Hesitation

To address its scalability issues, Ethereum has turned to Layer 2 solutions, such as optimistic rollups and zero-knowledge rollups. These scaling networks offer faster and cheaper transactions by processing activity off the main Ethereum chain and then settling the data on the base layer. From a technological perspective, Layer 2 solutions are essential to Ethereum’s long-term scalability. However, from an institutional adoption perspective, they introduce new complexities that some organizations find difficult to manage.

Instead of dealing with a single network, institutions must now interact with multiple Layer 2 environments, each with its own bridging solutions, liquidity pools, security assumptions, and operational challenges. Institutions generally prefer simplicity and standardization, and the fragmentation of Ethereum’s ecosystem can create complications that discourage adoption. The need to manage bridging between networks, understand differing fee markets, and ensure secure operational processes makes Ethereum’s multi-layer ecosystem harder to navigate.

Although Layer 2 networks derive security from Ethereum itself, they still represent additional layers of technology that must be audited, monitored, and understood. Traditional institutions often prefer a single, unified environment where risks are minimized and performance is consistent. Until Ethereum’s Layer 2 ecosystem becomes more streamlined and standardized, these complexities may continue to push institutions toward alternative solutions.

Regulatory and Compliance Challenges

Regulation is another critical factor in determining why Ethereum is no longer the top institutional choice. Ethereum is a public blockchain, meaning all transactions are visible on the ledger. While transparency is an advantage for decentralization, it is not always ideal for institutions that must protect client privacy, sensitive financial data, and confidential internal processes. Public visibility can create compliance and privacy concerns that make it difficult for certain institutional use cases to operate on Ethereum’s public layer.

Additionally, institutions must comply with strict KYC, AML, and reporting requirements. If regulators view Ethereum-based assets or certain decentralized finance activities as high-risk or potentially unregulated, institutions may reduce or limit their engagement. The uncertain regulatory environment surrounding some Ethereum-based tokens and DeFi protocols has pushed institutions to look for platforms that offer clearer compliance pathways.

Private and permissioned blockchains have gained interest because they provide controlled environments with defined governance and restricted access. Some organizations prefer hybrid or permissioned networks that allow them to maintain confidentiality and meet regulatory requirements without exposing sensitive information to the public. Ethereum does offer enterprise solutions through frameworks such as Enterprise Ethereum and private chain options, but competing blockchain platforms have been more aggressive in positioning themselves directly as institutional-grade solutions.

Shifting Institutional Priorities and Multi-Chain Strategies

Institutional priorities have changed significantly over time. In the past, institutions adopted blockchain primarily for experimentation and innovation. Ethereum, with its robust ecosystem and early leadership, was the natural choice for pilot projects. Today, however, institutions are more strategic and selective. They consider specific use cases such as cross-border payments, tokenized real-world assets, digital identity systems, and decentralized finance through a different lens. Each use case may align better with a particular blockchain’s strengths.

As a result, institutions increasingly prefer a multi-chain strategy. Instead of choosing a single platform, they distribute activity across several networks based on their performance, cost structure, and regulatory alignment. Ethereum still plays an important role in this landscape, especially for DeFi and tokenization, but it is no longer the only serious option. Institutions now evaluate blockchain platforms as part of a broader ecosystem rather than defaulting to Ethereum because of its early dominance.

Another important shift is the desire for specialized networks. Not all blockchains aim to be general-purpose platforms. Some are built specifically for high-frequency trading, institutional settlement, or enterprise-level customization. Where Ethereum lacks specialization, other networks have stepped in with purpose-built architectures designed to meet precise institutional needs. This shift toward specialization is one of the main reasons institutions are exploring other blockchains more aggressively than before.

Ethereum’s Institutional Strengths and Continued Importance

Despite increased competition and its declining status as the sole top choice, Ethereum remains one of the most important networks in the institutional blockchain world. It continues to hold the largest decentralized finance ecosystem, the widest pool of liquidity, and the most established community of developers. Institutions that want exposure to DeFi, staking, or tokenization often still rely heavily on Ethereum due to its depth and maturity.

Ethereum’s Proof-of-Stake upgrade and ongoing scalability roadmap show that the network is committed to addressing its limitations. As rollups mature, transaction costs decrease, and interoperability improves, Ethereum may regain some lost ground among institutions. Its strong brand, long-term vision, and large community ensure that it will remain a foundational element of the blockchain ecosystem regardless of shifts in institutional sentiment.

However, while Ethereum will likely remain central to the future of blockchain innovation, it must adapt to the realities of a more competitive ecosystem. Institutions now demand speed, scalability, predictable costs, and regulatory clarity. Ethereum must evolve to meet these expectations while maintaining the decentralization and security that made it valuable in the first place.

Will Ethereum Regain Its Institutional Dominance?

The future of Ethereum’s relationship with institutions depends on how effectively it can simplify its scaling solutions, reduce friction in Layer 2 onboarding, and deliver lower transaction costs. Institutions may return in greater numbers if Ethereum provides a streamlined, scalable, and unified experience across its ecosystem. The ongoing development of rollups, cross-chain standards, and improved user experiences is a positive sign, but the competition is fierce. Other blockchains have strong technical advantages, and many are tailoring their products directly to institutional audiences.

The blockchain world is now firmly multi-chain, and Ethereum must coexist with other networks rather than dominate them. Whether or not it regains its institutional leadership will depend on the success of its upgrades, the strength of its developer community, and the ability of its ecosystem to maintain relevance in an increasingly diverse and competitive environment.

Conclusion

Ethereum’s evolution from a pioneering smart contract platform to one part of a broader multi-chain ecosystem reflects the rapid growth of blockchain technology. While once the uncontested leader for institutional experiments and innovation, Ethereum now faces competitors that offer higher throughput, lower fees, and specialized solutions for enterprise needs. High gas fees, network congestion, Layer 2 complexity, regulatory concerns, and the rise of faster Layer 1 networks have all contributed to institutions rethinking their approach to blockchain adoption.

Today’s institutions are guided by strategic use cases, regulatory pressures, and operational efficiency. Ethereum remains a key player, but it is no longer the only path forward. Instead, it is part of a diversified landscape where multiple blockchains serve different purposes. Ethereum’s future success with institutions will depend on its ability to continue evolving, delivering scalable solutions, and meeting the demands of a market that now values performance, specialization, and flexibility.

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Muhammad Kashif

7:38 am

Tohoku University and Fujitsu Utilize Causal AI to Discover Superconductivity Mechanism of Promising New Functional Material

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Scientific discovery has always advanced at the intersection of theory, experimentation, and technology. In recent years, artificial intelligence has emerged as a powerful force reshaping how researchers understand complex physical phenomena. A landmark development in this evolution is the collaboration where Tohoku University and Fujitsu utilize Causal AI to discover superconductivity mechanism of promising new functional material. This breakthrough represents more than a single scientific success; it signals a paradigm shift in how advanced materials are studied and understood.

Superconductivity has long fascinated scientists due to its potential to revolutionize energy transmission, computing, transportation, and electronics. However, uncovering the mechanisms behind superconductivity in newly discovered materials has remained a challenging task. Traditional analytical approaches often struggle to interpret the enormous complexity of interacting variables at the atomic and electronic levels. By applying Causal AI, researchers have gained a new lens through which cause-and-effect relationships can be revealed with unprecedented clarity.

This article explores how Tohoku University and Fujitsu applied causal artificial intelligence to unravel the superconductivity mechanism of a promising new functional material. It examines the scientific background, the limitations of conventional methods, the role of AI-driven causality, and the broader implications for materials science, industry, and future technological innovation.

The Scientific Importance of Superconductivity

Understanding Superconductivity in Modern Physics

Superconductivity refers to a physical phenomenon in which certain materials conduct electricity with zero resistance when cooled below a critical temperature. This property enables the lossless transmission of electrical energy and the creation of powerful magnetic fields. Despite decades of research, superconductivity remains one of the most complex topics in condensed matter physics.

The challenge lies in understanding how electrons pair and move cooperatively through a material’s lattice without resistance. Each new superconducting material introduces unique atomic structures and electronic interactions, making it difficult to generalize mechanisms across different compounds. This complexity underscores why the discovery that Tohoku University and Fujitsu utilize Causal AI to discover superconductivity mechanism of promising new functional material is so significant.

Why New Functional Materials Matter

New functional materials expand the boundaries of technological possibility. Superconductors, in particular, hold promise for applications ranging from quantum computing to energy-efficient power grids. Identifying materials that exhibit superconductivity under more practical conditions, such as higher temperatures or lower costs, is a central goal of materials science.

The ability to uncover the mechanism behind superconductivity in a new material not only validates its potential but also provides a roadmap for designing even better materials in the future. This is where AI-driven analysis becomes transformative.

Limitations of Traditional Research Approaches

Complexity of Multivariable Interactions

Conventional experimental and computational methods often rely on correlation-based analysis. While correlations can suggest relationships, they do not explain causation. In complex materials, hundreds of variables such as atomic composition, lattice structure, electron density, and magnetic interactions coexist. Isolating which factors actually cause superconductivity is extraordinarily difficult.

This limitation has slowed progress, as researchers must test countless hypotheses through time-consuming experiments. The fact that Tohoku University and Fujitsu utilize Causal AI to discover superconductivity mechanism of promising new functional material directly addresses this challenge highlights the novelty of their approach.

The Data Interpretation Bottleneck

Modern experiments generate massive datasets through simulations, spectroscopy, and material synthesis. While high-performance computing can process this data, interpreting it in a scientifically meaningful way remains a bottleneck. Researchers often struggle to distinguish signal from noise or identify hidden causal relationships.

Causal AI offers a solution by going beyond pattern recognition to reveal why certain phenomena occur, not just when they occur.

What Is Causal AI and Why It Matters

Moving Beyond Correlation

Causal AI is a branch of artificial intelligence designed to identify cause-and-effect relationships rather than simple correlations. Unlike conventional machine learning models that predict outcomes based on patterns, causal models attempt to understand underlying mechanisms.

When Tohoku University and Fujitsu utilize Causal AI to discover superconductivity mechanism of promising new functional material, they are essentially teaching AI to ask scientific questions. The system evaluates how changes in one variable directly influence others, allowing researchers to isolate the true drivers of superconductivity.

Explainability and Scientific Trust

One of the most important advantages of causal AI is explainability. In scientific research, results must be interpretable and verifiable. Black-box models are often unsuitable because they cannot explain their conclusions. Causal AI, by contrast, provides logical pathways that researchers can validate experimentally.

This transparency makes causal AI particularly well suited for advanced materials research, where trust and reproducibility are essential.

The Collaboration Between Tohoku University and Fujitsu

Academic and Industrial Synergy

The partnership between Tohoku University and Fujitsu represents a powerful synergy between academic research and industrial innovation. Tohoku University brings deep expertise in condensed matter physics and materials science, while Fujitsu contributes cutting-edge AI technologies and computational infrastructure.

By combining these strengths, the collaborators created an environment where AI could be applied directly to fundamental scientific questions. The fact that Tohoku University and Fujitsu utilize Causal AI to discover superconductivity mechanism of promising new functional material demonstrates how interdisciplinary collaboration can accelerate discovery.

Shared Vision for Future Technologies

Both institutions share a vision of leveraging AI to solve real-world scientific and industrial challenges. Their work on superconductivity reflects a broader commitment to integrating AI into the research pipeline, from hypothesis generation to experimental validation.

This collaboration sets a precedent for future partnerships between universities and technology companies in the field of AI-driven materials discovery.

Discovering the Superconductivity Mechanism

Applying Causal AI to Material Data

In this project, causal AI was applied to extensive datasets describing the physical and electronic properties of the new functional material. The AI system analyzed relationships between variables such as atomic arrangement, electron interactions, and temperature-dependent behavior.

Unlike traditional methods, causal AI identified which factors directly triggered superconductivity rather than merely coexisting with it. This allowed researchers to pinpoint the underlying mechanism with a level of clarity previously unattainable.

Key Insights Uncovered

The analysis revealed critical interactions that govern the onset of superconductivity in the material. By isolating these causal factors, the researchers gained a deeper understanding of how electrons pair and move within the material’s structure.

These insights not only explain why the material becomes superconducting but also suggest how similar mechanisms might be engineered in other compounds. This outcome reinforces why Tohoku University and Fujitsu utilize Causal AI to discover superconductivity mechanism of promising new functional material is a milestone achievement.

Implications for Materials Science

Accelerating Discovery Cycles

One of the most profound implications of this work is the acceleration of discovery cycles. Instead of relying solely on trial-and-error experimentation, researchers can use causal AI to guide experiments more efficiently. This reduces costs, shortens development timelines, and increases the likelihood of success.

As a result, materials science may shift from a largely empirical discipline to a more predictive and design-oriented field.

Enabling Rational Material Design

Understanding causal mechanisms enables rational material design. Researchers can intentionally manipulate variables known to cause superconductivity, rather than hoping for favorable outcomes through random variation. This capability could lead to the creation of materials with tailored properties for specific applications.

The success achieved when Tohoku University and Fujitsu utilize Causal AI to discover superconductivity mechanism of promising new functional material illustrates the potential of AI-guided design strategies.

Industrial and Technological Impact

Energy and Power Applications

Superconducting materials have enormous potential in energy transmission, reducing losses and improving efficiency. By clarifying superconductivity mechanisms, this research supports the development of more practical superconductors for power grids and renewable energy systems.

Industries focused on energy infrastructure stand to benefit significantly from AI-driven materials insights.

Quantum Computing and Electronics

Superconductors are foundational to quantum computing and advanced electronics. Understanding their behavior at a fundamental level enhances the reliability and scalability of quantum devices. The application of causal AI could lead to breakthroughs in device performance and stability. This connection underscores the broader technological relevance of the discovery made by Tohoku University and Fujitsu.

The Future of Causal AI in Scientific Research

Expanding Beyond Superconductivity

While this research focuses on superconductivity, the methodology is broadly applicable. Causal AI can be used to study magnetism, catalysis, battery materials, and other complex systems where causation is difficult to determine. The success of this project may encourage wider adoption of causal AI across scientific disciplines.

Redefining the Role of AI in Discovery

AI is no longer just a tool for data analysis; it is becoming an active participant in scientific reasoning. By identifying causal relationships, AI systems can help formulate hypotheses and guide experimental design. This shift represents a new era in which human intuition and artificial intelligence work together to unlock nature’s secrets.

Conclusion

The achievement where Tohoku University and Fujitsu utilize Causal AI to discover superconductivity mechanism of promising new functional material marks a turning point in materials science and AI-driven research. By moving beyond correlation and embracing causality, the researchers have demonstrated a powerful new approach to understanding complex physical phenomena.

This breakthrough not only advances our knowledge of superconductivity but also showcases the transformative potential of causal AI in scientific discovery. As interdisciplinary collaborations continue to grow, the integration of explainable AI into research promises faster innovation, deeper understanding, and more sustainable technological progress. The future of materials science, guided by causality and computation, is now firmly within reach.

FAQs

Q: Why is causal AI important for discovering superconductivity mechanisms?

Causal AI is important because it identifies direct cause-and-effect relationships rather than simple correlations. In superconductivity research, this allows scientists to determine which physical interactions truly trigger superconducting behavior, leading to clearer explanations and more reliable conclusions.

Q: How does this research differ from traditional AI approaches in materials science?

Traditional AI approaches often focus on pattern recognition and prediction without explaining why results occur. In contrast, causal AI provides explainable models that reveal underlying mechanisms, making the findings scientifically interpretable and experimentally verifiable.

Q: What makes the collaboration between Tohoku University and Fujitsu significant?

The collaboration is significant because it combines academic expertise in physics and materials science with industrial leadership in artificial intelligence. This synergy enabled the successful application of causal AI to a complex scientific problem that neither institution could have solved as effectively alone.

Q: Can causal AI be applied to other areas of scientific research?

Yes, causal AI can be applied to many fields, including chemistry, biology, energy research, and engineering. Any domain involving complex systems with interacting variables can benefit from causal analysis to uncover fundamental mechanisms.

Q: What are the long-term implications of this discovery for technology?

The long-term implications include faster development of advanced materials, improved energy efficiency, and breakthroughs in technologies such as quantum computing and electronics. By enabling rational material design, causal AI may significantly accelerate technological innovation.