by Michael Reuter | 3 April 2026 | AI, Autonomous Agents, Featured
A Practical Guide for Businesses
In April 2026, two timely pieces cut through the AI hype cycle. Ethan Mollick, writing in The Economist, warned that “the IT department [is] where AI goes to die.” His core argument: AI is a profoundly odd, risky, and powerful technology, a next-word predictor that unexpectedly writes code, gives strategic advice, or shows empathy, and companies are killing its potential by trying to “de-weird” it. Traditional IT processes, risk-averse governance, standardized KPIs, and legacy systems force AI into the mold of conventional enterprise software, stifling experimentation and emergent value.
At the same time, the Financial Times published “Investors are betting on AI chaos. History suggests otherwise.” Author Richard Waters noted that markets are pricing in revolutionary disruption – new winners, old losers – but past technology revolutions (PCs, internet, cloud) show savvy incumbents often muddle through, adapt, and even thrive. The real story is rarely the total chaos investors crave.
Together, these perspectives paint a clear picture of the most likely future of AI in business: not a dystopian job apocalypse or unicorn-disrupting chaos, but a pragmatic, evolutionary integration. AI will augment human work, reshape workflows, and deliver real value, but only for organizations that treat it as the strange, probabilistic tool it is while building the right foundations. Incumbents with strong data capabilities will have the edge.
Why AI “Dies” in Traditional IT, and Why That’s the Wrong Approach
Mollick’s essay resonates because it diagnoses a widespread problem we see daily in enterprise deployments. AI isn’t deterministic software with predictable outputs. It’s generative, context-dependent, and often surprising. When companies hand it to IT teams focused on security, compliance, uptime, and cost control, the natural response is to:
- Wrap it in rigid approval processes
- Demand ROI projections before pilots
- Force it into existing tech stacks without workflow redesign
- Prioritize “safe” use cases over creative experimentation
The result? Pilots that never scale. According to recent analyses (including Deloitte’s 2026 State of AI in the Enterprise), while worker access to AI tools has surged, the number of organizations moving projects into full production remains modest. Barriers like data quality, skills gaps, infrastructure readiness, and risk management continue to stall progress.
HBR has similarly observed that many companies report “widespread AI usage but disappointing returns,” with adoption stalling at the integration stage. The problem isn’t execution – it’s treating AI like a new CRM module instead of a fundamentally new way of working.
History Shows Incumbents Can Win If They Adapt Smartly
The FT piece offers reassurance: AI won’t necessarily destroy every incumbent. Past waves of technology (from electricity to the internet) initially sparked predictions of massive disruption, yet established players who invested in complementary capabilities—new skills, processes, and organizational structures—came out stronger.
In 2026, the winners won’t be the pure-play AI startups alone. They will be enterprises that:
- Combine their domain expertise and proprietary data with AI’s capabilities
- Redesign workflows around human-AI collaboration (what some call “co-intelligence”)
- Scale from pilots to enterprise-wide agentic systems under proper guardrails
PwC’s 2026 AI Business Predictions and similar reports emphasize a “disciplined march to value”: top-down enterprise strategies, measurable business outcomes, and governance that doesn’t kill experimentation.
The Most Likely Future: Pragmatic, Data-Driven, and Agentic
By late 2026 and into 2027, we expect the following trajectory:
- From pilots to production at scale — Organizations doubling the share of AI projects in production, driven by agentic AI (autonomous agents that execute multi-step workflows).
- J-curve productivity — Initial flat or negative returns as companies rewire processes, followed by steep gains once complementary innovations (new roles, data pipelines, decision protocols) are in place.
- Governance catching up — Mature frameworks for agentic AI, data quality, and responsible use becoming table stakes. Shadow AI will decline as secure, enterprise-grade platforms mature.
- Incumbents leveraging data moats — Companies with clean, governed data and domain expertise will outperform pure AI-native players in regulated or complex industries.
This future is neither utopian revolution nor failure – it’s an evolutionary transformation, provided organizations avoid the “IT department trap.”
Optimal Use of AI in Businesses: Five Practical Principles
Drawing from Mollick, historical lessons, and 2026 enterprise reports (Deloitte, McKinsey, PwC), here’s how forward-thinking companies are winning:
- Embrace the weirdness—experiment deliberately
Give teams space to discover unexpected uses. Mollick advocates leadership that encourages crowdsourced experimentation and “labs” to scale promising ideas. Treat AI like a creative collaborator, not just an automation tool.
- Build on rock-solid data foundations
Data quality and governance remain the #1 barrier cited across reports. Without trustworthy data pipelines, AI outputs are unreliable. This is where specialized partners excel – unifying siloed data, implementing real-time pipelines, and ensuring privacy/compliance.
- Redesign workflows and roles around human-AI co-intelligence
Don’t automate jobs – augment them. Successful organizations are re-architecting processes so humans focus on judgment, creativity, and relationships while AI handles analysis, drafting, and routine execution.
- Deploy secure, governed agentic AI
Autonomous agents are the next frontier, but they require bounded orchestration, threat modeling, and compliance-by-design. Enterprises need platforms that support multi-agent systems without introducing new risks.
- Measure what matters – and iterate
Move beyond vanity metrics. Track business outcomes (revenue impact, cost savings, customer satisfaction) and accept that ROI may follow a J-curve.
How Datarella Helps Businesses Navigate This Future
At Datarella, we’ve spent years helping organizations move beyond AI hype and pilot purgatory. Our expertise in AI agent development and security, full-stack application modernization, Web3-enabled decentralized solutions, and privacy-preserving data architectures directly addresses the challenges outlined above.
Whether you need:
- Secure, production-ready autonomous agents
- Data platforms that make AI reliable and compliant
- Integration of AI into legacy systems without the usual friction
- Or decentralized approaches that enhance trust and data integrity
We combine deep technical capability with practical business understanding to help you safely and scalably embrace AI’s weirdness.
The future of AI in business isn’t about replacing your IT department or betting everything on chaos. It’s about evolving how your organization learns, decides, and creates value—by treating AI as the strange, powerful tool it is, while building the data, governance, and cultural foundations it demands.
Ready to move from pilots to production without letting AI “die in IT”? Let’s talk. Contact Datarella to explore how we can help you capture the real, pragmatic upside of AI in 2026 and beyond.
by Simon Zehentreiter | 12 June 2025 | AI, Autonomous Agents, Blockchain, Gaia-X, MOBIX, moveID, SSI
After three years of intense collaboration, innovation, and field testing, the moveID project—part of the Gaia‑X 4 Future Mobility initiative—has made significant strides toward redefining how mobility ecosystems work. At its core, moveID aimed to create a decentralized, user-centric infrastructure where vehicles, infrastructure, and service providers interact seamlessly using Self-sovereign Identity (SSI) and AI agents.
Building the Foundation for Trusted Machine Communication
Working alongside industry leaders such as Bosch, Airbus, Continental, and leading Web3 projects, we contributed to building the technical and conceptual foundation for trusted machine-to-machine communication in the mobility sector. This included the secure exchange of credentials, decentralized data marketplaces, and AI-powered autonomous service interactions—all compliant with European data and privacy standards.
Demonstrating Real-World Impact: MOBIX Park & Charge
A standout achievement was the development and public demonstration of MOBIX Park & Charge, a fully operational system enabling electric vehicles (EV) to autonomously find parking spots, access charging stations, and handle payments. First showcased at IAA Mobility 2023, the system integrated SSI, blockchain-based payments, and AI agents in a live, real-world environment.
Scaling Toward Smart Cities
Beyond the demo, MOBIX has evolved into a scalable smart-city solution. By turning private EV chargers and parking spots into publicly accessible assets, we’re addressing key challenges in urban congestion and infrastructure scalability, while opening up new economic opportunities for individuals and municipalities.
Where Web3 Meets AI
The project also served as a powerful example of the convergence between AI and Web3. By combining intelligent agents with decentralized infrastructure, we demonstrated how machines can not only interact but also negotiate, transact, and self-optimize—laying the groundwork for more ethical and transparent digital ecosystems.
A Foundation for the Future
In sum, moveID wasn’t just about mobility. It showcased how decentralized identity, autonomous agents, and AI can reshape how devices, services, and users interact—not just in cities, but across industries. As the project concludes, its outcomes provide a strong foundation for future applications in smart infrastructure, data sovereignty, and the broader digital economy.
by Simon Zehentreiter | 25 April 2025 | AI, Blockchain, COSMIC-X
In part two of the Cosmic-X blogpost series, we explained how we use the Secret Network blockchain and a custom Wallet Service to ensure the integrity and privacy of machine-generated data in Industry 4.0 environments. In this final part, we’ll show how we integrated the Wallet Service with live machines from SW and an AI service from inovex. Together, they power a proof-of-concept demonstrator for secure and accurate anomaly detection in machine components.
Visualizing Sensor Data
The demonstrator has three core features. First, the data exploration tool lets you visualize sensor data from three different machines. For granularity, you can filter by machine, component, sensor, and the timeframe you want to monitor.
Verification & Anomaly Detection
Next, the anomaly detection feature, coupled with data integrity verification. Like before, you can filter by machine, component, sensor, and timeframe. Additionally, you choose from three anomaly detection algorithms—Local Outlier Factor (LOF), DBSCAN, and Isolation Forest—and adjust their hyperparameters. After that, once you lock in your configuration and submit the query, the system fetches data from a central time series database. It then converts the data into the standardized format described in our previous post.
To ensure trust, the Wallet Service verifies the dataset by comparing a freshly generated fingerprint to the one anchored on the blockchain. It uses the standardized batchID for this lookup. If the fingerprints match, the AI service proceeds with the anomaly detection. Whenever the number of anomalies exceeds a defined threshold, the system flags the component as worn out. Consequently, it submits an automatic spare part order to the ERP systems of the manufacturer and the customer.
Data Integrity Log
In this demonstrator, users manually trigger the configuration and execution of the anomaly detection. In contrast, a production system would automate and continuously run these steps. The third feature is a data integrity log to give users better visibility of what is happening. This audit trail has three levels: At the top level, it shows the health status of each machine and the last verified batch used for anomaly detection.
Next, it breaks down each machine into components, displaying health statistics for each
Finally, it presents detailed logs of every anomaly detection run and whether the data integrity check succeeded.
As we wrap up this blog post series, what began as a technical experiment has evolved into something much broader. It points toward a future of industrial intelligence that values transparency and built-in trust. By embedding trust directly into machine data and equipping AI with verified information, we do more than detect anomalies. We enable machines to communicate, collaborate, and maintain themselves. Ultimately, this proof of concept is a first step toward an Industry 4.0 landscape that is autonomous, secure, and transparent, where trust is not an afterthought but a foundation.
Curious how our blockchain-based data-integrity solution can help your business? Check out our one-pager for a quick overview of its key benefits!
by Simon Zehentreiter | 19 December 2024 | AI, Blockchain, COSMIC-X, Gaia-X
In the first part of this Cosmic-X blogpost series, we evaluated various blockchain platforms for their suitability in Industry 4.0 and explained why we chose the Secret Network with its confidential computing capabilities. Today, we’ll explore how we use the Secret Network to secure machine data integrity from its origin to its consumption.
Need for Data Integrity
Securing data integrity in Industry 4.0 is crucial because systems and devices rely on accurate data to function effectively. Tampered or incorrect data can lead to poor decisions, operational failures, and vulnerabilities in key sectors like manufacturing and logistics. With IoT and AI driving Industry 4.0, maintaining data accuracy ensures reliable operations, protects sensitive information, and prevents cyber threats that disrupt businesses and critical infrastructure.
Anchoring data close to its source is essential for securing integrity across the entire data processing chain, which often involves multiple distributed systems. For machines, this means securing the data before it leaves the device. At the same time, the system must protect the anchored data from tampering after export. Blockchain’s immutable nature aligns perfectly with this paradigm. That’s why we built a Wallet Service on top of the Secret Network. This service integrates seamlessly into any machine to secure its data integrity in a decentralized and privacy-preserving manner.
Wallet Service
The Wallet Service acts as a gateway for communication with the Secret Network. It deploys onto any machine infrastructure that supports Docker. By using the Wallet Service, machines interact directly with the blockchain and its smart contracts. The service assigns each machine a unique identity through a public-private key pair. With its private key, the machine signs and broadcasts transactions to anchor its data on the Secret Network. The blockchain’s encryption ensures that no unauthorized third party can access the data. For details on how the network reaches consensus despite encryption, refer to our previous post.
Integration
To simplify integration, the Wallet Service offers a straightforward REST API with two endpoints. The ingress endpoint accepts a batch of data in a defined structure for anchoring. After receiving the data, the Wallet Service hashes it and stores the resulting hash in the service’s smart contract through a transaction on the Secret Network. This process creates an immutable fingerprint, allowing anyone to verify the integrity of a data batch through the Wallet Service’s verification endpoint. Since data verification typically occurs in systems other than the one that supplied the data, the Wallet Service supports deployment anywhere. In distributed data processing scenarios like Cosmic-X, entities that consume data instantiate a Wallet Service to verify data integrity before making decisions. For example, an AI service provider might deploy a Wallet Service in its cloud environment to verify data before using it for training or inference.
Requirements
Two conditions must be met for this workflow to function: first, the verifying Wallet Service must have the appropriate viewing key from the machine that supplied the data. Otherwise, it cannot decrypt and query the fingerprints stored in the smart contract. Second, the format and schema of the data batch must remain standardized across the processing chain. To achieve this, we developed a Data Integrity Protocol as the foundation of the Wallet Service.
Data Integrity Protocol
To anchor and verify data batches reliably, the Wallet Service requires a standardized protocol. Both the data anchoring and verification processes must adhere to a common data format, schema, and canonicalization standard. For Cosmic-X, we chose JSON as the data format and RFC 8785 as the canonicalization algorithm. Canonicalization ensures reliable cryptographic operations on JSON data by defining methods for handling whitespace, data types, and objects.
Batch Structure
Considering use case requirements and the limitations of edge and cloud environments in Cosmic-X, we define a data batch as one hour’s worth of sensor data collected from a machine. The figure below shows an extract of a data batch collected from one of the use cases. The batch includes a metadata object used only for the Wallet Service’s business logic. This metadata contains key-value pairs such as the batchId and placeholders for the payload hash and the transaction hash on the Secret Network blockchain. The payload, which the system hashes during anchoring, consists of discrete sensor measurements. Each measurement uses a composite key created by concatenating the variable name with the Unix timestamp of its recording. The measurements include key-value pairs for variable name, timestamp, absolute value, and data type.
The batchId is the most critical part of a data batch. Since the Wallet Service uses it to anchor and later locate the data batch for verification, the batchId must be unique. In this setup, the batchId combines a machine ID with a Unix timestamp representing the time range of measurements in the batch, rounded to the nearest hour. For example, if machine 2080839 collects measurements from 11:01:23 to 11:59:43 on May 16, 2024, the batchId becomes 2080839_1715853600.
In the next post, we’ll showcase how we integrated the Wallet Service with three live machines and an AI service to enable secure and accurate anomaly detection in machine components.
Curious how our blockchain-based data-integrity solution can help your business? Check out our one-pager for a quick overview of its key benefits!
by Simon Zehentreiter | 8 November 2024 | AI, Blockchain, COSMIC-X, Gaia-X
With the Cosmic-X project nearing its conclusion, it is finally time to lift the curtain on the blockchain solution that Datarella has built over the last two years to enable confidential computing and data sharing in Industry 4.0. In this first entry of a series of technical posts about designing, implementing, and integrating an edge-to-cloud blockchain solution, we discuss the evaluation process for selecting a suitable blockchain platform for Cosmic-X and how that platform operates on a protocol level to provide an open, transparent, and secure infrastructure for industrial use cases.
Evaluating Blockchain Platforms
Today, many different blockchain platforms exist, but their suitability for industrial use cases remains specific or, at times, limited. To achieve the best match between the requirements of Cosmic-X and the possibilities of blockchain technologies, the team conducted an extensive evaluation process. This evaluation compared both private and public blockchain platforms based on security, privacy, scalability, and interoperability.
Current-generation blockchain platforms predominantly perform well in security and scalability, yet privacy and interoperability often fall short. To achieve privacy in industrial scenarios like Cosmic-X, organizations have almost exclusively used private or consortium blockchains such as Hyperledger Fabric in the past. However, these approaches inherently involve high infrastructure costs for the operating parties, as well as centralization and limited interoperability. In contrast, public blockchains offer resilience, cost efficiency, and a degree of interoperability. Though only recently have they started focusing on privacy and data protection. Blockchain protocols with confidential computing capabilities remain relatively new and untested. Nevertheless, when weighing the advantages and disadvantages of the two approaches, a privacy-focused public network emerges as the preferred solution in an industrial context.
For a public network to meet Cosmic-X’s privacy and data protection requirements, it must support the multi-tenancy paradigm. Multi-tenancy enables a single instance of a software application to serve multiple clients while ensuring logical isolation. Different clients share an underlying infrastructure, which optimizes resource use and reduces infrastructure costs. Further, it enhances efficiency in data access, management, and collaborative data sharing.
Through this evaluation, the Cosmos-based Secret Network emerged as the blockchain platform best suited for Cosmic-X. The Secret Network functions as a public blockchain specifically developed for confidential computing. By combining established encryption techniques with trusted execution environments, it provides so-called Secret Contracts. This type of smart contract establishes consensus on computation without disclosing incoming or outgoing data. Integrated access control mechanisms enable third-party access and create an auditable processing chain. Thus, the Secret Network satisfies the need for multi-tenancy capability while retaining all the benefits of a public network.
How the Secret Network Works
The Secret Network leverages Intel Software Guard Extensions (Intel SGX) to create Trusted Execution Environments (TEE) that enable Secret Contracts. These smart contracts, based on the CosmWasm framework, allow for fully private computation of data. Outside a TEE, the transaction payloads and the network’s current state are encrypted at all times. Only the data owner and an authorized third party can decrypt and view data inputs and outputs. A combination of symmetric and asymmetric encryption schemes—ECDH (x25519), HKDF-SHA256, and AES-128-SIV—achieves this end-to-end encryption. Each validator in the network must run an Intel SGX-compatible CPU and instantiate a TEE that follows the network’s rules.
When an encrypted transaction arrives in the shared mempool of the network, a validator forwards it to their TEE, where a shared secret is derived and used to decrypt the transaction. The WASMI runtime then processes the plaintext input. Finally, the validator re-encrypts the updated contract state and broadcasts it to the network through a block proposal. If over two-thirds of the current network voting power agree on the result, the network appends the proposed block to the Secret Network blockchain.
For access control, the Secret Network offers Viewing Keys and Permits. A Viewing Key acts as an encrypted password that grants a third party permanent access to data related to a specific smart contract and private key. A Permit allows a more granular approach, restricting viewing access to specific parts of data for a set period. Consequently, despite its encrypted nature, the network remains fully auditable.
In the next post, we’ll explore how we leverage the Secret Network to secure machine data integrity directly from its point of origin to its consumption by a Machine Learning Model.
by Michael Reuter | 8 July 2024 | AI, Blockchain, Featured
In the digital transformation era, the concept of digital twins has gained significant attention. Meanwhile, companies are creating virtual replicas of physical objects or systems, enabling real-time monitoring, analysis, and optimization. However, as the reliance on digital twins increases, it becomes clear that robust digital twin security measures are needed to ensure authenticity and trustworthiness. Consequently, our innovative approach secures digital twins with cutting-edge event-tracking technology.
Cyber-Physical Trust: The Future of M2M Communication
We’re revolutionizing the security landscape by integrating blockchain technology and M2M communication to provide trust and authenticity. In addition, blockchain technology ensures the integrity and transparency of digital twins, enabling secure interactions between machines and humans. As a result, we envision a future where cyber-physical trust becomes the standard, surpassing traditional processes. Notably, this shift will have a significant impact on the way we approach security.
Firm-Specific Track & Trace Tools
We empower companies to create applications without requiring extensive technical knowledge. Specifically, our SDK seamlessly integrates into existing infrastructure and embeds the digital twin with necessary code for secure interactions. Furthermore, we provide comprehensive setup guides and API references for smooth onboarding. Meanwhile, developers can choose their desired authentication hardware, extract the required code, and embed it into the app environment. Subsequently, our system tracks digital twin usage, maintaining high security. In the future, we plan to implement additional business logic via smart contracts, enabling data provision and payments. Additionally, event tracking creates an immutable record for decision-making.
The Future of Trust and Authenticity in Digital Twin Security
To summarize, digital twins are essential for real-time monitoring, analysis, and optimization of physical objects. As their use increases, it becomes clear that robust security measures are needed. In response, our innovative approach secures digital twins with advanced event-tracking technology, integrating blockchain and M2M communication to ensure trust and authenticity. Moreover, we provide an SDK that integrates into existing infrastructure, allowing companies to create secure applications easily. Ultimately, our system tracks digital twin usage to maintain high security, with plans for smart contracts and payments. In conclusion, event tracking creates an immutable record for informed decision-making.
by Michael Reuter | 8 July 2024 | AI, Blockchain
In the digital age, data has become the cornerstone of innovation. However, this surge in data-driven innovation is not without its challenges. Concerns about user confidentiality and the potential misuse of personal information are increasingly being highlighted. The ever-present risk of breaches also poses a significant threat. Additionally, our interconnected digital ecosystems have exacerbated the rise of misinformation and fake news. There is hope though through the convergence of Web3 and AI.
Two European non-profit organizations, INATBA and EBA, have unveiled the Report on AI and Blockchain Convergence. This report serves as a starting point for further discussions on the tensions surrounding Web3.
What’s in the Report?
The convergence of Web3 and AI can help segregate authentic from inauthentic content. Blockchain technology has matured beyond its initial cryptocurrency applications. It is now a fundamental tool in enhancing security, transparency, and efficiency across various industries. Blockchain has played a key role in redefining supply chain management and financial services. It has also enabled secure digital transactions.
Meanwhile, AI has progressed from theoretical concepts to practical applications. Major strides in machine learning have enabled AI to process and analyze data at unprecedented speeds and accuracy. This has led to innovations in fields such as autonomous driving and personalized medicine. The integration of AI and blockchain technologies can create a transformative synergy.
AI can enhance the flexibility of smart contracts. Blockchain’s decentralized architecture can diversify AI data sources, reducing inadvertent biases in AI outputs. AI can also streamline and enhance blockchain’s scalability. It can detect and rectify anomalous behaviors, and potentially prevent hacks and other illegal activities.
However, the convergence of AI and Web3 also raises significant challenges. The methodologies these AI models employ have raised questions about data source reliability and information quality. The ownership of this access is also a concern. The journey of blockchain adoption is laden with challenges, including scalability issues and integration complexities.
Navigating the regulatory landscape is also a complex task. In light of these advancements and challenges, it is essential to recognize the need for meticulous regulatory and ethical considerations. The integration of AI and blockchain technologies has the potential to elevate transparency and reduce the role of intermediaries.
Unlocking the Potential of AI and Blockchain Convergence
However, ensuring the credibility of AI decisions and the accountability of blockchain transactions is necessary for operational excellence and public trust. In conclusion, the convergence of AI and Web3 represents a transformative step towards creating more ethical and effective technological solutions. By combining the strengths of each technology, we can address pressing challenges faced by digital innovations today.
These challenges include concerns around privacy, security, and ethical decision-making. AI brings unparalleled capabilities in data processing and pattern recognition. It drives efficiency and innovation across various sectors. However, its limitations, such as potential biases and lack of transparency, highlight the need for a complementary solution.
Web3 offers the perfect counterbalance to AI’s limitations. It ensures transparency, enhances data security, and empowers users with control over their information. Together, AI and Web3 are transforming industries and setting new standards for ethical technology development. Their integration fosters a more accountable and trustworthy digital ecosystem.
Decisions are not only data-driven but also socially responsible and aligned with ethical standards. The historical parallel of the Internet’s convergence with various technologies underscores the potential of this combination. The integration of AI and blockchain has the potential to bring about significant social transformations in the coming years and decades.
A Future of Sustainable Technological Advancements
However, global institutions and national governments must work together to mitigate fragmentation risks. They must ensure that AI model development inputs are complementary to human flourishing. The road to global coordination for blockchain standards and regulatory treatment is far more advanced. The same approach should be applied to AI.
As we look to the future, the synergy between AI and Web3 holds the key to unlocking sustainable technological advancements. Emerging trends such as decentralized finance and smart healthcare show promising applications. We encourage stakeholders in the AI and blockchain ecosystems to come together and leverage their collective expertise.
By working together, we can tackle the challenges and harness the opportunities discussed throughout the INATBA / EBA report. Ultimately, the integration of AI and blockchain encourages us to reimagine the possibilities of digital innovation. This journey towards integrating AI and blockchain propels us towards technological excellence and ensures that our advancements contribute positively to society and the environment.
by Michael Reuter | 25 June 2024 | AI, Blockchain, Featured, Ocean Protocol
The transition from Web2 to Web3 represents a significant shift in how we manage and control information online. Web3 offers a potential solution to issues like privacy, surveillance, and misinformation by utilizing blockchain technology. This technology gives users more control over their data. Together, AI and Blockchain can be even more powerful.
However, user friction has slowed Web3’s adoption. To accelerate adoption, we can integrate AI and blockchain technology. Consequently, this integration improves the user experience and reduces user friction. Additionally, decentralized AI built on blockchain can offer users personalized online experiences while protecting their privacy and control over their data.
Blockchain and AI are complementary technologies that address each other’s limitations. Specifically, blockchain’s built-in consensus protocol ensures data accuracy and integrity because it verifies data at multiple points. When AI is trained on flawed data, it produces flawed results. Therefore, we can design blockchain platforms to distribute power more evenly, reducing the risk of a few AI companies or models making opaque but consequential decisions. To leverage the power of AI and blockchain cohesively, we must overcome technical challenges. Next, we can track the co-development of these two technologies by examining their progression in three phases:
Data, Information, and Knowledge
Meanwhile, AI can accelerate Web3 adoption by offering personalized experiences based on user prompts. For Web3 to go mainstream, the next generation of consumer-facing applications must match the user experiences of Web2. To achieve this, we must prioritize personalization, which optimizes marketing spend. Decentralized knowledge graphs – as they are developed in the Artificial Superintelligence Alliance ASI – may be the key to bringing personalized experiences from Web2 into Web3. Knowledge graphs map relationships between objects, facts, events, situations, and other data. In addition, we can make knowledge graphs more accessible and verifiable by using decentralized knowledge graphs and open, permissionless blockchain networks. By integrating AI and Blockchain, we can create a foundation for Web3 built on trustworthy data. As a result, this new decentralized internet addresses issues prevalent in our current centralized internet.
AI and Blockchain Together Improve Internet Governance
- Decentralization: Web3 is built on blockchain technology, which is decentralized and transparent. We can use AI to ensure that data is stored and processed securely and transparently, reducing the risk of a single entity controlling or manipulating data. In this way, we can promote a more democratic and equitable Internet governance.
- Trust and Transparency: AI verifies the authenticity and accuracy of data on the blockchain, ensuring that information is trustworthy and transparent. With this information, users can make informed decisions and reduce the spread of misinformation. Moreover, AI can detect and prevent malicious behavior, ensuring that the internet remains a safe and trustworthy environment.
- Autonomy and Agency: Web3’s decentralized architecture and AI’s ability to automate decision-making processes empower users to take control of their personal data and online experiences. Users can then make choices that align with their values and preferences. Consequently, users have more autonomy and agency over their online experiences.
- Security and Privacy: AI detects and prevents cyber threats, ensuring that Web3 applications are secure and private. By doing so, AI protects users’ sensitive information and prevents unauthorized access or manipulation. Additionally, AI optimizes Web3 applications, enabling them to scale efficiently and process vast amounts of data in real time.
- Scalability and Efficiency: As a result of AI optimization, the internet’s overall performance and responsiveness improve. Furthermore, AI helps design and optimize token-based incentive systems that encourage desired behavior and promote a healthier internet ecosystem.
- Data-Driven Decision-Making: AI analyzes data generated by Web3 applications, providing insights that inform data-driven decision-making. With these insights, we can make decisions based on objective data rather than personal biases or assumptions. Therefore, we can make more informed decisions that benefit users.
- Accountability and Governance: AI ensures accountability and transparency in Web3’s decentralized governance models by detecting and preventing malicious behavior. As a result, the internet remains a safe and trustworthy environment.
In conclusion, by combining AI and Web3, we can create a more decentralized, transparent, and secure internet that prioritizes user autonomy, privacy, and agency. We can make decisions based on objective data and respond more effectively to the needs of users. Ultimately, this new decentralized internet can promote a healthier and more equitable online ecosystem.
