Algorand’s Technical Evolution With AlgoKit 3.0—John Woods on RWAs, AI Smart Contracts and Quantum Security

In an interview with CCN, John Woods, Chief Technology Officer (CTO) at the Algorand Foundation, discusses the blockchain’s approach to real-world asset tokenization, its progress on quantum resistance, and the enhanced developer experience enabled by the building blocks in AlgoKit 3.0.

Speaking from a technical and systems-oriented perspective, Woods outlines Algorand’s position in the blockchain space, spotlighting developments like tokenized real estate via Lofty, steps toward regulatory alignment, and upcoming integrations such as MasterCard virtual cards — enabling ALGO holders and stakers to spend assets on everyday expenses. These examples underscore how the network is evolving its infrastructure and focus as it approaches 2025.

How Algorand is Driving Real-World Asset (RWA) Tokenization

For Woods, tokenizing real-world assets is a key step in bringing blockchain into the mainstream. He noted that while digital assets have matured, the connection between traditional and digital assets remains underdeveloped. Woods emphasized Algorand’s focus on bringing real-world assets like real estate and financial products on-chain.

Woods explained how “the idea of creating RWA assets represented by tokens” means that “all of that stuff is done without writing a single line of code. So no-code assets, and that’s why Algorand is very accessible to businesses that don’t come from a blockchain background.”

One example is Lofty, which Woods described as “probably the crown jewel of RWA tokenization on Algorand.” Woods explained how Lofty as “a firm that has pioneered on Algorand the idea of tokenizing real estate.”

He noted that the platform features “some hugely valuable properties,” adding, “It’s not just some shed in the middle of nowhere, there’s actually choice.” 

The ability to represent real-world assets like property on-chain — as seen with Lofty — is possible “without writing a single line of code,” Woods explained. He continued, “So no-code assets, and that’s why Algorand is very accessible to businesses that don’t come from a blockchain background.”

Use Cases for Tokenized RWAs: From Real Estate to Regional Indexes

Woods suggested that the appeal of tokenized real-world assets extends beyond real estate. He explained that “there’s a number of different things that are ongoing, including crops and other things like that.” 

He described how these tokenized assets could be applied broadly, explaining how “you can tokenize property that you own and allow that not just to be sold to, say, one participant, one hop away, but allow it to be traded or used as liquidity in other contexts” such as “loans or insurance and many other contexts.” 

Speaking on future possibilities, Woods added, “How wonderful would it be to be able to tokenize one’s own home or a percentage of it,” and highlighted that one “could buy California, you could long California and short New York,” imagining an index model based on tokenized real estate. 

For users of RWAs, an RWA trend points to a future where ownership becomes more flexible, composable, and usable whether you’re an investor, a homeowner, or someone looking to access new forms of financial utility

How AI and Native Languages Are Shaping Smart Contract Development on Algorand

As blockchain development becomes more accessible through the use of AI, Woods noted that AI tools are beginning to shape how smart contracts are written. Algorand’s choice to support widely used languages like Python and TypeScript makes it easier for developers to get started and for generative AI models to assist. 

Woods explained how “you can essentially talk to any AI today, and it will give you answers that are good in the context of an Algorand smart contract.”

“Unlike ecosystems such as Ethereum, which use language variants like Vyper or Solidity that resemble popular languages but differ in syntax and structure,” he said. 

Woods emphasized that Algorand supports native programming languages, stating, “Not Python-like, not TypeScript-like—it’s actual Python, actual TypeScript.” This approach reduces the entry barrier for developers and enhances compatibility with existing AI tools.

Algorand’s decision to support Python and TypeScript was a strategic move by Algorand, as it offers compatibility with the world’s most widely used programming languages. 

Why Algorand’s Use of Native Python and TypeScript Matters in the Age of AI Development

Woods touched on the rise of “vibe coding,” where developers will use generative AI to build applications by describing what they want, rather than writing every line of code manually. While this opens the door for broader participation, especially for those without deep technical backgrounds, Woods stressed the importance of understanding what the AI produces, mainly when financial applications are involved.

Woods critiqued vibe coding, acknowledging its potential while stressing the importance of understanding the underlying code. 

“When you’re building blockchain apps, you’re usually handling money. It’s crucial to understand the code you’re writing,” Woods said. 

“If you’re vibe coding your way to blockchain-based apps, make sure you understand what comes out of the generative AI before you launch it into production.”

Despite these concerns, Woods sees vibe coding as a way to make blockchain development more accessible. “Absolutely possible,” he said of this approach on Algorand. “We have plugins for VS Code that help you do all sorts of things, and they can definitely be extended to help you vibe-code your smart contracts.”

Algorand’s use of widely known programming languages plays a key role in this accessibility. “We brought Python to Algorand, the same Python that’s used for generative AI, the same Python that’s taught in high schools and universities around the world today,” Woods explained.

Paired with TypeScript, this approach lowers the barrier for developers and improves AI-generated code. As Woods put it, “Not Python-like, not TypeScript-like—it’s actual Python, actual TypeScript.”

Is Algokit 3.0 Quantum Resistant? Unveiling Algorand’s Security Journey

Woods described AlgoKit 3.0 as a significant advancement in developer tooling on Algorand, highlighting its launch on March 26, 2025. Designed to make blockchain development more accessible, Woods emphasized the kit’s flexibility and its future compatibility with emerging technologies, including AI and quantum-resistant components.

When asked whether Algorand is currently quantum-resistant, Woods responded directly: “Algorand isn’t fully quantum secure yet, and we’re about a third of the way there.” 

He explained that Algorand currently uses “quantum-secure Falcon signatures to sign the history of the blockchain every 256 rounds, which is about every 12 minutes.” 

However, he noted that “accounts and consensus” are not yet protected by quantum-safe methods, making these areas a key focus for future development.

Algorand’s Quantum Security Strategy

Securing a blockchain against quantum attacks requires addressing three key vulnerabilities: 

• History of transactions, 
• Consensus mechanism,
• And user accounts.

Woods explained how “at Algorand, we’ve fixed problem number one.” This implementation of Falcon signatures being “Fast Fourier Lattice-based Compact Signatures over NTRU” will secure the Algorand blockchain’s history. Next, Algorand focuses on securing accounts, followed by the consensus mechanism.

A significant challenge with quantum security is size.

“With elliptic curves, a signature is about 64 bytes. With post-quantum cryptography, it might be like 64,000 bytes,” Woods points out. “If you added this quantum stuff everywhere, things would get slower. The blockchain would get bigger faster,” he said.

Market Implications of Quantum Vulnerability Moving Into the Future

When asked about the odds of building effective quantum-resistant systems moving into the future, Woods estimates that the odds of building quantum secure blockchains stand at 85%, 90% confidence.

“We don’t know for sure these schemes work because we haven’t attacked them with quantum computers yet, but we are mathematically assured that post-quantum cryptography works,” Woods explained.

He believes the mere knowledge of quantum vulnerability could trigger market turbulence, explaining how in the future, “it’s not about me personally being attacked or you being attacked. The moment we know as a community, ‘Oh no, this stuff is broken,’ that’s when we’ll see serious turbulence in the market. Before anything’s stolen, that’s when the panic sets in.”

He noted that multiple approaches exist beyond Falcon signatures. “There are many others in the lattice-based family, and also other families of math we can use against quantum computers, multivariate, hash-based, code-based, super singular, isogeny-based.”

Crypto vs. Gold: A $20 Trillion Market Opportunity?

Woods believes cryptocurrency will rival gold’s $20 trillion market cap, up from $2.8 trillion on 26 March 2025.

Woods explained that “Bitcoin is a more modern version of gold, hard, unforgeable, infinitely divisible, and easy to transfer. You need a cargo plane to move gold across borders, but you can carry $10M in Bitcoin by memorizing a sentence.”

Woods envisions crypto’s market cap reaching between $10-20 trillion, driven by censorship resistance, decentralization, and integration with AI.

Looking Ahead: Algorand’s Development Priorities and Vision

As blockchain continues to expand to various sectors, Woods pointed to growing global interest in Algorand’s infrastructure, explaining how “there are nations that are interested in tokenization, not just institutional players.”

On the developer front, Woods emphasized a shift toward open participation in the protocol’s evolution, explaining how he would “like to see more decentralized engineering of the protocol. We need to move to a place that’s more like Monero or Bitcoin, where it’s community-led engineering.”

He also outlined what’s ahead for Algorand’s privacy layer: “Privacy is part of the things we want to do in 2025, including private token transfers,” noting models like Mimblewimble as reference points.

He pointed that positioning matters too. “Algorand is fully engineered in the United States and headquartered in Boston,” Woods said. “Our expectation is that we’re part of that cohort because it quite literally has been built by Silvio Micali and Algorand Technologies in the United States.”

Rather than framing Algorand’s future around hype, Woods kept the vision practical and grounded in infrastructure: 

“We’re trying to build a better engine — one that can touch AI, touch RWAs, and be ready for a quantum world. But we want that engine to be something that many people can understand and use,” he said.

He concludes by reaffirming a focus on real-world utility over speculation.