When the concept of “Web 3.0” first emerged about a decade ago the idea was clear: Create a more user-controlled internet that lets you do everything you can now, except without servers or intermediaries to manage the flow of information.

Where Web2, which emerged in the early 2000s, relies on centralized systems to store data and supply compute, all owned—and monetized by—a handful of global conglomerates, Web3 turns that structure on its head. Instead, data and compute are decentralized through technologies like blockchain and peer-to-peer networks.

What was once a futuristic concept is quickly becoming a more concrete reality, even at a time when Web2 still dominates. Six out of ten Fortune 500 companies are exploring blockchain-based solutions, most taking a hybrid approach that combines traditional Web2 business models and infrastructure with the decentralized technologies and principles of Web3.

Popular use cases include cloud services, supply chain management, and, most notably financial services. In fact, at one point, the daily volume of transactions processed on decentralized finance exchanges exceeded $10 billion.

Gaining a Web3 edge

Among the advantages of Web3 for the enterprise are greater ownership and control of sensitive data, says Erman Tjiputra, founder and CEO of the AIOZ Network, which is building infrastructure for Web3, powered by decentralized physical infrastructure networks (DePIN), blockchain-based systems that govern physical infrastructure assets.

More cost-effective compute is another benefit, as is enhanced security and privacy as the cyberattack landscape grows more hostile, he adds. And it could even help protect companies from outages caused by a single point of failure, which can lead to downtime, data loss, and revenue deficits.

But perhaps the most exciting opportunity, says Tjiputra, is the ability to build and scale AI reliably and affordably. By leveraging a people-powered internet infrastructure, companies can far more easily access—and contribute to—shared resource like bandwidth, storage, and processing power to run AI inference, train models, and store data. All while using familiar developer tooling and open, usage-based incentives.

“We’re in a compute crunch where requirements are insatiable, and Web3 creates this ability to benefit while contributing,” explains Tjiputra.

In 2025, AIOZ Network launched a distributed compute platform and marketplace where developers and enterprises can access and monetize AI assets, and run AI inference or training on AIOZ Network’s more than 300,000 contributing devices. The model allows companies to move away from opaque datasets and models and scale flexibly, without centralized lock in.

Overcoming Web3 deployment challenges

Despite the promise, it is still early days for Web3, and core systemic challenges are leaving senior leadership and developers hesitant about its applicability at scale.

One hurdle is a lack of interoperability. The current fragmentation of blockchain networks creates a segregated ecosystem that makes it challenging to transfer assets or data between platforms. This often complicates transactions and introduces new security risks due to the reliance on mechanisms such as cross-chain bridges. These are tools that allow asset transfers between platforms but which have been shown to be vulnerable to targeted attacks.

“We have countless blockchains running on different protocols and consensus models,” says Tjiputra. “These blockchains need to work with each other so applications can communicate regardless of which chain they are on. This makes interoperability fundamental.”

Regulatory uncertainty is also a challenge. Outdated legal frameworks can sit at odds with decentralized infrastructures, especially when it comes to compliance with data protection and anti-money laundering regulations.

“Enterprises care about verifiability and compliance as much as innovation, so we need frameworks where on-chain transparency strengthens accountability instead of adding friction,” Tjiputra says.

And this is compounded by user experience (UX) challenges, says Tjiputra. “The biggest setback in Web3 today is UX,” he says. “For example, in Web2, if I forget my bank username or password, I can still contact the bank, log in and access my assets. The trade-off in Web3 is that, should that key be compromised or lost, we lose access to those assets. So, key recovery is a real problem.”

Building a bridge to Web3

Although such systemic challenges won’t be solved overnight, by leveraging DePIN networks, enterprises can bridge the gap between Web2 and Web3, without making a wholesale switch. This can minimize risk while harnessing much of the potential.

AIOZ Network’s own ecosystem includes capacity for media streaming, AI compute, and distributed storage that can be plugged into an existing Web2 tech stack. “You don’t need to go full Web3,” says Tjiputra. “You can start by plugging distributed storage into your workflow, test it, measure it, and see the benefits firsthand.”

The AIOZ Storage solution, for example, offers scalable distributed object storage by leveraging the global network of contributor devices on AIOZ DePIN. It is also compatible with existing storage systems or commonly used web application programming interfaces (APIs).

“Say we have a programmer or developer who uses Amazon S3 Storage or REST APIs, then all they need to do is just repoint the endpoints,” explains Tjiputra. “That’s it. It’s the same tools, it’s really simple. Even with media, with a single one-stop shop, developers can do transcoding and streaming with a simple REST API.”

Built on Cosmos, a network of hundreds of different blockchains that can communicate with each other, and a standardized framework enabled by Ethereum Virtual Machine (EVM), AIOZ Network has also prioritized interoperability. “Applications shouldn’t care which chain they’re on. Developers should target APIs without worrying about consensus mechanisms. That’s why we built on Cosmos and EVM—interoperability first.”

This hybrid model, which allows enterprises to use both Web2 and Web3 advantages in tandem, underpins what Tjiputra sees as the longer-term ambition for the much-hyped next iteration of the internet.

“Our vision is a truly peer-to-peer foundation for a people-powered internet, one that minimizes single points of failure through multi-region, multi-operator design,” says Tjiputra. “By distributing compute and storage across contributors, we gain both cost efficiency and end-to-end security by default.

“Ideally, we want to evolve the internet toward a more people-powered model, but we’re not there yet. We’re still at the starting point and growing.”

Indeed, Web3 isn’t quite snapping at the heels of the world’s Web2 giants, but its commercial advantages in an era of AI have become much harder to ignore. And with DePIN bridging the gap, enterprises and developers can step into that potential while keeping one foot on surer ground.

To learn more from AIOZ Network, you can read the AIOZ Network Vision Paper.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

arXiv:2601.02749v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) from passive text generators to autonomous, goal-driven systems represents a fundamental shift in artificial intelligence. This chapter examines the emergence of agentic AI systems that integrate planning, memory, tool use, and iterative reasoning to operate autonomously in complex environments. We trace the architectural progression from statistical models to transformer-based systems, identifying capabilities that enable agentic behavior: long-range reasoning, contextual awareness, and adaptive decision-making. The chapter provides three contributions: (1) a synthesis of how LLM capabilities extend toward agency through reasoning-action-reflection loops; (2) an integrative framework describing core components perception, memory, planning, and tool execution that bridge LLMs with autonomous behavior; (3) a critical assessment of applications and persistent challenges in safety, alignment, reliability, and sustainability. Unlike existing surveys, we focus on the architectural transition from language understanding to autonomous action, emphasizing the technical gaps that must be resolved before deployment. We identify critical research priorities, including verifiable planning, scalable multi-agent coordination, persistent memory architectures, and governance frameworks. Responsible advancement requires simultaneous progress in technical robustness, interpretability, and ethical safeguards to realize potential while mitigating risks of misalignment and unintended consequences.

arXiv:2601.02554v1 Announce Type: cross Abstract: Public debate links worsening job prospects for AI-exposed occupations to the release of ChatGPT in late 2022. Using monthly U.S. unemployment insurance records, we measure occupation- and location-specific unemployment risk and find that risk rose in AI-exposed occupations beginning in early 2022, months before ChatGPT. Analyzing millions of LinkedIn profiles, we show that graduate cohorts from 2021 onward entered AI-exposed jobs at lower rates than earlier cohorts, with gaps opening before late 2022. Finally, from millions of university syllabi, we find that graduates taking more AI-exposed curricula had higher first-job pay and shorter job searches after ChatGPT. Together, these results point to forces pre-dating generative AI and to the ongoing value of LLM-relevant education.

arXiv:2505.19347v3 Announce Type: replace Abstract: Patent similarity evaluation plays a critical role in intellectual property analysis. However, existing methods often overlook the intricate structure of patent documents, which integrate technical specifications, legal boundaries, and application contexts. We introduce PatentMind, a novel framework for patent similarity assessment based on a Multi-Aspect Reasoning Graph (MARG). PatentMind decomposes patents into their three dimensions of technical features, application domains, and claim scopes, then dimension-specific similarity scores are calculated over the MARG. These scores are dynamically weighted through a context-aware reasoning process, which integrates contextual signals to emulate expert-level judgment. To support evaluation, we construct a human-annotated benchmark PatentSimBench, comprising 500 patent pairs. Experimental results demonstrate that the PatentMind-generated scores show a strong correlation ($r=0.938$) with expert annotations, significantly outperforming embedding-based models, patent-specific models, and advanced prompt engineering methods. Beyond computational linguistics, our framework provides a structured and semantically grounded foundation for real-world decision-making, particularly for tasks such as infringement risk assessment, underscoring its broader impact on both patent analytics and evaluation.