Blockchain and AI: The Next Great Industrial Revolution Is Here

What if the same underlying technology that secures Bitcoin could protect the exciting artificial intelligence (AI) boosted use cases emerging today in healthcare, supply chain, and beyond?

Whole-scale industries are being transformed overnight, and many others, at risk of being replaced in the wake of this new digital-first economy. The AI renaissance may concern status quo defenders, but for those willing to adapt and embrace change, the possibilities are limitless. From driving novel scientific discoveries in healthcare to vastly improving global supply chains, the opportunities to upgrade archaic human-based systems with AI and blockchain-based infrastructure are expanding rapidly.

The intersection of AI and blockchain is at the vanguard of this new era of technological innovation. These two technologies are powerful and disruptive in their own right, but combined, they are a driving force behind this technological enlightenment. AI’s powerful data processing capabilities, coupled with secure, decentralized blockchain infrastructure, offers transformative solutions to once-intractable problems.

Let's take a quick tour through the history of these two disruptive technologies and examine some of the most exciting projects in production.Blockchain, AI And The Next Industrial Revolution

A Brief History of Blockchain

While many people would simply believe that the birth of blockchain technology began in 2008 with the release of the Bitcoin Whitepaper, forty years of cryptographic research preceded it. The work of early cypherpunks, privacy advocates, and libertarians like Ralph Merkle, David Chaum, Nick Szabo, Bram Cohen, and Julian Assange all contributed to the groundwork that modern-day blockchain technology is built on.

Yet, despite these early contributions, Bitcoin succeeded where previous iterations of peer-to-peer value transfer protocols had failed, and inpired a new generation of developers who would go on to establish the foundations of the blockchain industry we know today. Four years after the release of Bitcoin, the Ethereum Whitepaper was released, introducing the concept of smart contracts, and two years after that, the open-source blockchain development consortium Hyperledger Foundation was launched. Today, this foundation allows AI to securely automate complex processes on a decentralized network.

A Condensed History of AI

AI has an even longer and storied history, dating back to 1950 when Alan Turing first defined artificial intelligence through his thought experiment as any technology whereby a computer’s responses could not be distinguished from those of humans. 1966 saw the invention of MIT professor Joseph Weizenbaum’s Eliza, the first iteration of a language chatbot. At the end of the 1960s AI development went into a hiatus for the following two decades with minimal development. 

Emerging from this long AI winter in 1997 was IBM’s chess-playing Deep Blue. This new era of AI development shocked the world when Deep Blue defeated world champion Garry Kasparov in a game of chess, demonstrating the advancements that had quietly been made in the sector. The remainder of the 1990s and early 2000s saw most of the focus on AI being used for learned training in commercial robotic products like Roomba (2002) and Aibo (1999). 

The following decade would see the early institutional integration of language models into commercial products. AI software like Apple’s Siri (2011), IBM’s Watson (2011), and Amazon’s Alexa (2014). This period also saw the founding of notable projects like IBM’s Food Trust (2017), Google DeepMind Alphafold (2018), and OpenAI (2015). The latter publicly released its ChatGPT product to the public in 2022, which initiated the most recent modern-day explosion of AI development. 

Since the public release of ChatGPT at the end of 2022, AI projects have seen a seismic shift in funding. This investment is expected to go parabolic in the coming years. Precedence Research is reporting that “global artificial intelligence (AI) market size was USD 538.13 billion in 2023, calculated at USD 638.23 billion in 2024 and is expected to reach around USD 3,680.47 billion by 2034, expanding at a CAGR of 19.1% from 2024 to 2034.”

The Future Of AI In Healthcare

One of the most important features of general-purpose AI models is their ability to effectively analyze, predict, and construct models from large data sets. 

No industry is benefiting from this aspect more than healthcare. Google’s DeepMind Alphafold protocol is on the cutting edge of this sector. Earlier in 2024, the co-founders of Alphafold3, David Baker, Demis Hassabis, and John Jumper, won the Nobel Peace Prize in chemistry. Their model effectively decodes the protein structures made up of amino acids at the foundational layer of DNA. These protein structures can then be reconstructed, folded into larger proteins, and manipulated to perform specific tasks in the human DNA. 

This innovation has immense potential for curing chronic genetic diseases by targeting specific proteins and replacing them with healthy ones. Alphafold's strides and potential for eradicating diseases like cancer could be further enhanced by integrating blockchain technology. By doing so, Alphafold could improve the security of sensitive information while enabling global collaborative research and accelerating the pace of discovery in protein folding.

Blockchain technology is also being used in healthcare to tackle issues orthogonal to the problems AI is solving.

Blockchain is being used to address the issues of isolated patient data silos, costly intermediaries, lack of data provenance, and sub-par centralized cybersecurity. Companies like PharmaLedger are leading the charge in creating a system of trust across the healthcare sector. The Digital Trust Ecosystem in Healthcare (DTE-H) that PharmaLedger has created allows each participant (doctors, patients, regulators, companies) to access the previously fragmented information channels, improving security and reducing wasted time and resources that historically inflated costs. For anyone who’s experienced the disjointed, slow, paperwork-heavy experiences of modern healthcare systems, the value of this innovation cannot be overstated.

Other companies like Insilico are combining general-purpose AI models in their research with the power of decentralized blockchain tech to create an all-encompassing product for use across their three areas of interest: biology, chemistry, and clinical trials. 

Advised by 2013 Nobel Laureate Michael Levitt, Insilico is leveraging the work at Alphafold to drive drug discovery in medicine. Using general-purpose AI models in their products, PandaOmics, Chemistry42, and InClinico enable Insilico to create a full suite of drug discovery software from beginning to end in a fraction of the time and cost. The pharmaceutical industry has historically been cost-prohibitive and labor-intensive. Insilico demonstrates that the costs and protracted timelines of deep biology analysis, drug design, and clinical trials can be overcome with the introduction of these new AI models.

The Next Generation of Supply Chain Management

One of the earliest industries to adopt blockchain technology was the supply chain. Tracking a product across its entire lifecycle is beneficial for improving efficiency and critical when dealing with perishable goods. The CDC reports that annually, 1 in 6 Americans (48 million) get sick, 128,000 are hospitalized, and 3,000 die of foodborne diseases.

The high rate of food-borne illnesses led to the Food and Drug Administration (FDA) passing the Food Safety Modernization Act (FSMA) in 2011, which, under Rule 204(d), requires digital traceability records for food products to be kept. 

The FSMA partly led to the creation of IBM’s Food Trust in 2017. The Food Trust is a modular blockchain solution for the transparency, safety, and traceability of the supply and manufacturing of foods. A year before the launch of the Food Trust, IBM partnered with Walmart and built a food tracking software based on the Hyperledger Fabric. The result of that partnership was that Walmart could track the exact source and location of a product (mangoes) in 2.2 seconds, compared to previously, which took 6 days, 18 hours, and 26 minutes. This ability to track products to the exact source has long-reaching impacts on food safety, illness mitigation, fraud, and overall supply chain efficiency. 

The introduction of AI into IBM’s Food Trust has allowed for the further development of its Intelligence Suite, allowing it to leverage its reverse lot tracing, data quality monitoring, and one-click reporting features like never before. These innovations are only the beginning of AI's potential in the supply chain. Additional AI tools could be leveraged in the future to enhance decision-making processes within these blockchain-based supply chains. 

The Synergistic Future of AI and Blockchain

While this article provides only a few examples of the unbounded potential of this next generation of AI technology, it is clear that projects that leverage blockchain technology alongside their AI protocols will only improve their products. Immutable, decentralized, and secure data storage is the bedrock of the architecture on which the future of AI rests.

The history of these two great technologies dates back seventy-five years, yet it has only been in the last few years that we have started to uncover the true potential of their convergence. Combined with the level of funding analysts predict to enter the AI space, we are only beginning to scratch the surface.