Staking Regulation Best Practices: An Overview by Alison Mangiero, Executive Director of POSA

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Dinarian888

March 18, 2023

Key Takeaways

Alison Mangiero of POSA provides an overview of staking and its importance for the security and maintenance of blockchain networks, emphasizing that staking should not be considered an investment scheme or subject to securities regulations.
The regulatory landscape for staking is complex, needing a clear differentiation between staking, lending, and other financial products to facilitate appropriate regulatory treatment.
POSA established the Staking as a Service Industry Principles in 2019, providing guidelines for industry participants to maintain focus on network security and avoid regulatory scrutiny. The principles include using non-financial terminology, focusing on security and network participation, and not providing guarantees on rewards earned.
Liquid Staking Tokens (LSTs) should be accurately described and differentiated from derivatives (LSDs) to ensure clarity and avoid regulatory confusion. LSTs should not be considered investment contracts, notes, or securities under U.S. federal securities laws, nor should they be considered swaps under commodity law.
Accurate terminology and clear communication are crucial when discussing staking and related services, making it easier for regulators and lawmakers to understand the technology and its importance in blockchain networks.
Ongoing regulatory developments may include potential banking packages addressing stablecoin and custody regulations and additional enforcement actions targeting custodial staking arrangements.
The industry should prioritize educating regulators and lawmakers on the differences between staking, lending, and other financial products to facilitate appropriate regulatory treatment and promote a healthy regulatory environment.

In a recent panel discussion during the Staking Rewards Institutional Ethereum Staking Forum, Alison Mangiero, Executive Director of the Proof of Stake Alliance (POSA), delivered a comprehensive presentation on staking regulation in the blockchain industry. In her talk, Alison discusses the legal and regulatory landscape surrounding staking, various staking models, and how these regulations can impact the industry.

Understanding the Four Staking Models

Alison outlines four distinct staking models that exist within the blockchain space:

Protocol staking: Users deposit ETH into a smart contract, run Ethereum software, and receive rewards directly from the protocol.
Custodial staking: Users enter an agreement with a service provider to stake ETH using the provider’s infrastructure. The provider takes custody of the ETH.
Delegated self-custodial Software as a Service (SaaS) staking: Users maintain control and ownership of their ETH while using professional software to stake. Custody of tokens is never relinquished.
Smart contract-facilitated liquid staking: Users deposit ETH into a smart contract, which spins up a validator when it receives 32 ETH in total.

Staking is not an Investment Scheme

Alison emphasizes that staking should not be considered an investment scheme, as it is crucial for maintaining and securing blockchain networks. Validators provide technical services, not financial services. Thus, staking should not be viewed as an investment contract or security.

Staking as a Service Industry Principles

In 2019, POSA developed a set of Staking as a Service Industry Principles, which still hold relevance today:

Use non-financial terminology when discussing staking.
Focus on security and network participation.
Do not provide guarantees on the amount of rewards earned.

Alison explains that these principles aim to help the industry provide a clear understanding of staking and its importance in blockchain networks.

Liquid Staking Tokens vs. Derivatives

A working group was formed to analyze liquid staking tokens (LSTs) under US law, concluding that LSTs for digital commodities should not be considered investment contracts, notes, or securities. They should also not be treated as swaps under commodity law. Alison stresses the importance of accurate terminology, advocating for using “liquid staking tokens” instead of the commonly misused term “liquid staking derivatives.”

Liquid Staking Principles

The working group also developed a set of liquid staking principles, which include:

Using appropriate terminology to describe tokens (LSTs vs. LSDs).
Focusing on increasing liquidity without sacrificing the importance of staking for security and network participation.
Developing tools that enable direct staking with access to liquidity.
Refraining from providing investment advice.

These principles aim to guide the industry in developing and offering liquid staking products without crossing regulatory boundaries.

Possible Regulatory Developments

Alison shares her thoughts on possible regulatory developments in the near future, such as a potential banking package that could include stablecoin and custody regulations. She also anticipates additional enforcement actions, although the specifics remain uncertain.

“I think there will be bills introduced in Congress. I think the likelihood of them passing is not likely in the coming months. But there will be some action from people trying to introduce sensible regulations.”
Alison Mangiero

Importance of Accurate Descriptions and Terminology

Alison concludes her talk with a call to the industry to use accurate descriptions and terminology when discussing staking and related activities. She believes that doing so will make engaging with regulators and lawmakers easier while explaining the importance of staking in blockchain networks.

“We should be cautious about the words that we use, the way that we’re describing the activities that we engage in, and also explain why it’s essential for the functioning of these networks.”
Alison Mangiero

Final Thoughts

Alison Mangiero of POSA highlights the importance of staking in the blockchain ecosystem and the need for a clear and accurate understanding of its role in maintaining network security. By following established industry principles and promoting accurate terminology, stakeholders can work together to create a favorable regulatory environment that fosters innovation and growth in the blockchain and staking sectors. As the industry evolves and regulatory landscapes change, education and open communication with regulators and lawmakers will be essential in ensuring that staking and related services are treated fairly and appropriately under existing and future regulations. For future conversations like this, check out our Staking Summit 2023.

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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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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.

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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

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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.

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August 16, 2026

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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:

Warehouse robots contribute operational data.
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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