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ALEO: Road to Mainnet Updates
June 23, 2024
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⚠️ Another one to keep your eyes on... 💎~The Dinarian⚠️

June 2024

We’re now four months along the Road to Mainnet, as our team and community gears up for the final push towards launch. This month, we achieved major milestones that include making our CanaryNet publicly available, testing core functionality against our acceptance criteria, and launching Testnet Beta. 

Our next major milestones are code freeze, which is scheduled for June 15, and finalizing the fully-featured Coinbase Puzzle by the end of June. 

CanaryNet now publicly available

This month, we deprecated the canonical Devnet in favor of having the community run their own isolated devnets to support third-party development and testing to help empower the community to submit bug fixes and protocol upgrades. We also made CanaryNet, previously a closed network, available to the public so they can access new features as soon as possible. When these features are fully validated, they will be upgraded to Testnet Beta.

Testing progress update

In March, we began our weekly core developer meetings with key ecosystem partners and established 7 acceptance criteria required for mainnet, outlined below in more detail under “Mainnet Test Plan.”

Since then, we’ve successfully tested all core functionality. After introducing a few updates, we plan to rerun the acceptance criteria again in Testnet Beta to ensure all new changes are working correctly. We are also implementing the final feature updates to the codebase, including updates to ARC-0041, and are preparing to release the fully featured Coinbase Puzzle.

Deploy on Testnet Beta now

Testnet 3 is officially deprecated and Testnet Beta, a new, public development environment is live, which will allow us to test our acceptance criteria in a more realistic, mainnet-like environment. If you haven’t migrated your app yet, set aside some time and join the folks already running on Testnet Beta.

The updated API endpoints are available below:

To start using and participating in Testnet Beta, you can get Testnet credits from the official Aleo faucets hosted on Leo Wallet and Puzzle Wallet.

Join the ARC-0041 audit contest

Passionate about sniffing out vulnerabilities and interested in making an impact on the security of the Aleo Network? We recently announced our partnership with Sherlock on the ARC-0041 contest, which offers $155,000 for pinpointing and describing code vulnerabilities in ARC-0041. You can register to participate now and begin submitting vulnerabilities on June 10th at 3pm UTC. 

Reaching these milestones has been no small feat, and it has taken no shortage of dedication and hard work. As always, we appreciate how our community comes together to support us as we work together to create a world where a secure blockchain is the new normal.

May 2024

One day, we anticipate that the Aleo Network will secure billions of transactions and data points. This means that certain aspects of the protocol (specifically, the cryptographic logic and consensus as it pertains to value) must be ready from day one. Together with dozens of ecosystem participants, the team is working towards fulfilling a set of acceptance criteria and testing methodology to help provide assurance of the highest standards of security. 

Since our last update in March, we’ve made strides towards our final goal of a mainnet launch. For starters, we implemented ARC-0037 and 0038 with the help of community partners StorSwift, Demox Labs, and Puzzle. These proposals greatly improved the security and usability for validators and delegators and created a new, program-based system for token delegation or “staking.” 

Additionally, the underlying protocol has also been updated to incorporate previous audit fixes, which includes incorporating a fully-synchronous BFT assumption into our Narhwal-Bullshark implementation, updates to the foundational credits.aleo program, and improved client sync performance.  

We’ve validated over 90% of our previously specified test cases in multiple testing environments, including a closed “DevNet” and a semi-open “CanaryNet”. In addition, we have completed testing the Coinbase Puzzle on Devnet and created cannons for public transactions, private transactions, and deployments for easier automated testing.

Testing in progress for ARC-0020

We’ve begun testing a new ARC, ARC-0020. ARC-0020 is a token standard enabling seamless public/private transfer to contracts, which is important for enabling secure DeFi applications on the Aleo Network. It adds several key features, including a one-step approval process for applying off-chain signatures, the ability to send private tokens to smart contracts, and the ability to connect token contracts to the company website using a digital signature. 

Some may be familiar with the popular ERC20 standard, ARC-0020 is similar and different in a few ways. Both define a framework for token interactions on the respective blockchains, featuring similar mechanisms for token transfers, balance inquiries, and third-party transaction approvals. However, ARC-0020 is unique by emphasizing security and efficiency in its operations, using offchain signatures to replace onchain approvals, reducing the amount of data publicly recorded on the blockchain. This approach aims to enhance data security compared to ERC20’s fully public transaction and approval records.

After numerous discussions among various community and ecosystem stakeholders, we’ve reached the current design of ARC-0020. Commenting and voting around ARC-0020 will be on the Aleo Governance platform here. We’ve added unit testing and trialed the initial implementation with ecosystem builders, and hope to implement it soon


Mainnet Test Plan

Two weeks ago, we announced our commitment to transparent updates around the criteria for and progress towards Aleo mainnet. Our belief that every individual has the right to privacy online has led us to this moment: creating and launching a network that is truly zero-knowledge by design.

Today, we’re ready to share our plan for a mainnet launch, the features needed to make it successful, and what you can expect going forward.

Programmable. Private. Permissionless.

We’re committed to launching a mainnet that fulfills Aleo’s core principles — programmable, private, and permissionless. We met with several of our ecosystem partners and established a set of baseline features that were most important to everyone, and from these defined the minimum acceptance criteria needed for a confident mainnet launch. 

Together, we worked to outline the features that could not be compromised on. In order to validate the features and their respective acceptance criteria, test cases were defined and agreed upon by not only members of the Aleo Foundation, but also the broader community of Aleo stakeholders. Each feature detailed below has three specific test cases with increasing amounts of stress to satisfy the minimum acceptance criteria and ensure the network is ready to launch.

aleoBFT Consensus

AleoBFT is our novel consensus mechanism that combines the finality of proof-of-stake with the powerful incentive mechanism of proof-of-work. This hybrid architecture helps us achieve instant finality for block confirmation while utilizing a “coinbase puzzle” to reward provers and incentivize the development of better hardware for zero-knowledge cryptography.

Our goal is a secure and resilient consensus system that minimizes downtime,  is robustly Byzantine fault tolerant, and ensures validators can rapidly synchronize to the latest network state, maintaining overall system integrity and performance.

Client Sync

Client synchronization is crucial for the proper functioning of a network, as it ensures that all participants have a consistent and accurate view of the global state. We're focused on ensuring client nodes can sync from any point in the ledger history to tip quickly and reliably.

This involves optimizing two key methods:

  • CDN snapshots for rapid updates

  • Peer-to-peer gossip for efficient, direct data sharing among nodes

Private & Public Transactions

Public and private transactions, both integral to our mainnet launch, are distinct yet interconnected features we're rigorously testing. Users can generate zero-knowledge proofs to transfer value via the record model, as specified in “ZEXE: Enabling Decentralized Private Computation. For public transactions, users have the capability to transparently update the state using the finalize statement.

Program Deployments

Enabling the development of applications on Aleo is key to our mission, with program deployment playing an essential role. Users will have the capability to create new Aleo programs, utilizing the execute and finalize statements to bring their applications to life. This functionality is foundational to expanding our ecosystem and empowering developers to innovate within Aleo’s secure and privacy-focused environment.

Coinbase Puzzle

The coinbase puzzle is a proof-of-work-type puzzle that is intended to incentivize the development of faster software and hardware for generating zero-knowledge proofs. Provers can submit valid solutions to a coinbase puzzle to receive a reward.

We're integrating the coinbase puzzle to drive advancements in zero-knowledge proof technology, rewarding provers in a way that maintains network stability and sustainable tokennomics.

Staking (ARC 0038

Users have the ability to stake Aleo credits to a validator. Our community partner, Demox Labs, will be leading the effort to implement ARC-0038 on the program level, as well as an audit of that implementation to ensure that the approach is secure. Demox will also be implementing a liquid staking protocol to provide even more flexibility for staking.

Validator/worker separation (ARC 0037)

Our community partner, Provable, has taken the lead on ARC-0037 and published an implementation and design spec that is being reviewed and tested by another one of our community partners, Puzzle.

ARC-0037 aims to reduce the security burden on validators by providing a separate withdrawal address. To address security concerns, during bond_public, validators and delegators designate an unchangeable withdrawal address, securing validator funds in case of compromised "hot" keys. Unbonded credits are directed to this address upon executing claim_unbond_public.

Conducting robust network testing

Several teams from the ecosystem, namely Demox LabsMonadicusSupranationalPuzzle, and Kryha, collaborated with us to extend the testing protocol to ensure the network's readiness for launch. To achieve this, we're introducing a range of testing tools. These tools include a genesis block generator, ahead of time (AOT) transaction and block history generation, and node topology management systems. Additionally, we've launched a canary network in partnership with these ecosystem members. These testing tools help us check how well the network's consensus mechanism works, how smoothly clients sync up, and how fast transactions are processed.

To test these various features against their acceptance criteria, the Aleo Network Foundation team will set up and run a series of test environments, starting with an internal devnet and “canary net”. The devnet is an internal testing environment where the Aleo Network Foundation and Provable are managing the testnet validator rollout, and where initial features are tested before being released upstream. The canary net is a more decentralized, semi-open network that, lets us test the network under more realistic conditions. 

We also plan to launch another testnet, which will be fully open and permissionless, and which should most closely approximate what the Aleo mainnet will look like.

What’s next

Our community has been a part of the Aleo story from the beginning. As we come together to launch mainnet, we are working closely with partners across the ecosystem to support the testing efforts and to verify our readiness. It is our goal to ensure the network meets the needs of our users, not just on day one, but every day after that. It’s truly a team effort, and we want everyone to be aware of our progress towards completing each of these important tests

 

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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

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Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

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The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notesMassachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
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  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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