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In 2023, ‘chatGPT’ became one of the most searched terms on Google, with a volume of over 119 million searches. This mirrors the similar sentiment echoed in the blockchain space in 2019 and 2020. This newly sparked interest in generative AI began a new era where AI reached the masses. Within a year, we saw an influx of similar platforms, making AI more widespread than before.
Just as blockchain grappled with challenges such as scalability, interoperability, and regulatory compliance, AI faces its own share of hurdles related to reliability, transparency, and energy consumption. At Consensus 2024, these challenges and the potential synergies between AI and blockchain were hot topics, with experts worldwide exploring new ways to address AI’s black box problem and energy consumption issues through blockchain technology.
The history of AI dates back to ancient times, when philosophers referred to self-operating objects as automatons. While in 1949, Edmund Berkley’s Giant Machine laid the groundwork for AI, it was only in 1956 that the term ‘artificial intelligence’ was first used at the Dartmouth Conference, marking the birth of AI.
For decades, predictive AI models have been used in the energy industry for forecasting energy demand and managing responses. But the persistent challenge of quality input data has been undermining AI’s full potential and limiting its mass-scale use cases. A large part of this problem lies in AI’s lack of explainability and the black box challenge.
LLMs, while powerful, are often black boxes, making decisions without transparent reasoning. This lack of explainability leads to trust issues, as users cannot discern how these models arrive at their conclusions. The prevalence of "fake news" and the inability to verify the authenticity of AI-generated content further exacerbate these challenges. The recent controversy over Gemini AI’s black Vikings images is a perfect example of how AI’s bias can lead to flawed representation and racial stereotypes.
AI is capable of performing complex and tedious tasks with great efficiency. Yet AI workflows lack explainability in their decision-making, which means we simply do not know how black box AI models make decisions. This points us to a crucial question: How much can we trust AI’s decisions if we can’t verify the origin of their output?
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Black box AI models, which make decisions without explaining their reasoning, are being challenged by explainable AI (XAI) and glass box AI models, which offer more insight into decision-making processes. However, while glass box models provide transparency, their susceptibility to cyberattacks raises concerns, particularly for industries handling sensitive data. For instance, a large energy company relying on glass box models could face widespread damage from malicious attacks.
Black box AI models remain crucial for security purposes due to their inherent opacity. Their inability to easily reveal internal mechanisms makes them less vulnerable to exploitation compared to glass box models.
The hype surrounding generative AI today parallels the excitement blockchain experienced in 2019 and 2020. Both technologies, while sometimes competing for venture capital funds, are not mutually exclusive but rather complementary. Blockchain's role in enhancing AI's trustworthiness, security, and efficiency is a testament to their synergistic potential.
Blockchain integration can potentially enhance transparency in black-box AI models without compromising security. By leveraging blockchain's immutable and decentralised data storage capabilities, AI systems can achieve greater trustworthiness.
While Web3 and AI are distinct, they intersect in significant ways. Web3 emphasises decentralisation, user control, and transparency, whereas AI focuses on automation, efficiency, and intelligence. The integration of AI in Web3 can enhance the functionality of decentralised applications (dApps) and smart contracts, making them more intelligent and responsive.
Another key problem to solve in the realm of AI models is their tremendous energy consumption. Training large language models (LLMs) such as GPT is extremely energy-consuming. Training a single AI model can emit as much carbon as five cars in their lifetimes, as per the MIT Tech review. GPT-3 is estimated to use 1,300 megawatt-hours (MWh) of electricity, equivalent to the annual power consumption of 130 US homes.
Energy-efficient solutions like Powerledger blockchain can tackle AI’s energy consumption and transparency challenges. By using highly scalable and low-energy-consuming blockchain technology, AI models can transition to truly sustainable and transparent practices of large language training. Powerledger’s blockchain-backed platform can allow AI companies to offset the exact amount of energy used in AI training.
Decentralised Physical Infrastructure Networks (DePIN), like those offered by companies such as Acurast, provide decentralised computational power at much higher efficiency. Blockchain-based DePIN can solve underlying resource issues and allow LLMs to be hosted in a decentralised manner. This not only improves efficiency but also contributes to the sustainability of AI operations.
As we explore the intersection of AI and blockchain, we find several compelling use cases, particularly those in the energy sector, that are of importance to us at Powerledger.
AI algorithms optimise P2P energy trading by matching supply with demand in real-time, while blockchain securely records transactions between consumers. This system allows for a decentralised energy market where individuals can trade energy directly with each other, leading to more efficient energy distribution and potentially lower costs for consumers. AI can continuously learn and adapt to market conditions, ensuring optimal trading strategies and improving overall market efficiency.
Blockchain can create transparent records of carbon emissions, and AI can analyse this data to optimise carbon trading, emission reduction strategies, and support regulatory compliance. This synergy enables real-time tracking of emissions and ensures that all data is accurate and immutable. Companies can use this information to make more informed decisions about their carbon footprint and sustainability practices, while consumers can trust the authenticity of sustainability claims.
Beyond energy management, another crucial application area for AI and blockchain is identity verification. This is particularly important in realms such as tokenized carbon credits and general order book exchanges for environmental assets, where Know Your Customer (KYC) compliance is vital. AI-powered identity verification can significantly cut costs and increase efficiency by automating the process while ensuring robust security.
Traditional identity verification methods often rely on cumbersome processes, from presenting physical IDs to answering personal questions. However, with the advent of blockchain technology and the introduction of AI, this paradigm is shifting dramatically.
The areas we have explored are just a few examples of how AI and blockchain can intersect to create powerful, innovative solutions. These topics, while significant, are not exhaustive and warrant further research and development. At Powerledger, we encourage everyone to build solutions in this realm and are keen to partner with teams and startups that bring AI expertise but want to build on a sustainability-first Layer 1 protocol. The integration of AI and blockchain holds immense potential, and together, we can drive forward a more sustainable and transparent future.
The intersection of blockchain and AI holds immense potential to address contemporary issues like sustainability, transparency, and efficient resource management. As these technologies continue to evolve, they will pave the way for innovative applications and solutions, driving the future of digital transformation.
A prime example of rapid technological adoption is OpenAI's ChatGPT, which onboarded over 100 million users within just two months—a feat that took Facebook over four years to achieve. This rapid adoption demonstrates the public's readiness and eagerness to embrace transformative technologies.
In the energy sector, Powerledger is at the forefront of integrating AI and blockchain to create a more efficient and transparent future. By leveraging blockchain's transparency and AI's efficiency, Powerledger offers solutions for energy management, P2P trading, and grid security, addressing both sustainability and reliability. The synergy between blockchain and AI is set to revolutionise various sectors, with their combined strengths providing robust solutions to some of the most pressing challenges.
As we look to the future, the integration of these technologies will continue to push the boundaries of innovation, creating a more sustainable and transparent world. At Powerledger, we encourage everyone to build solutions in this realm and are keen to partner with teams and startups that bring AI expertise but want to build on a sustainability-first Layer 1 protocol. Together, we can drive forward a future where AI and blockchain not only coexist but thrive, leading to a more efficient, transparent, and sustainable world.
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