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Bigger is Better: Why Stellar is the Leader in Cash-to-Crypto On and Off-Ramps
July 26, 2023
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At the Stellar Development Foundation (SDF), we like to talk about interoperability on the Stellar network; more specifically, the network’s ability to connect the fiat economy to the digital economy so that value can flow between them quickly, affordably, and seamlessly. That’s a value proposition any business likes to hear. As for the end users? They now have options to hold their value in more places than ever, thanks to increased access to previously gated financial services.
 

And while value can stay parked in one place and even grow, whether it be a checking account, digital wallet, or piggy bank, value is only useful if people can actually use it

This is where on and off-ramps come in. Crypto is often touted as democratizing access to financial services for underbanked and unbanked people worldwide, but much of the world – roughly 2 billion workers in the informal economy, or over 60% of the world’s adult labor force – don’t have access to financial services required to easily use these digital assets. To them, they’d prefer cash because they can use cash.

Why do cash-to-crypto on and off-ramps matter?

People living in cash-based economies often don't have access to bank cards or accounts. But having access to cash-to-crypto on and off-ramps enable them to hold digital assets, making it viable for them to access financial services such as P2P payments, cross border payments, and value storage – all use cases that might not be possible in a purely physical financial world where their cash can’t travel globally, 24/7. 

By converting from cash to crypto, people can do more with their cash than they could before. 

But to date, these on and off-ramps have been a largely deprioritized piece of the global financial infrastructure. The potential of crypto and blockchain cannot be fully realized until there are more easy, accessible ways for people to get value both into and out of the digital economy.  

The Stellar network is making this happen, one on and off-ramp at a time.

The state of cash-to-crypto on and off-ramps

To better understand how large a role on and off-ramps play in today’s financial systems, SDF commissioned The Block to conduct data-driven research quantifying cash-to-crypto access worldwide. By leveraging publicly available data and the services of data aggregators, The Block was able to quantify the level of access to cash-to-crypto on and off-ramps, a segment of  blockchain-based financial services, across different blockchains. 

The Block also looked at over 100 third-party service providers (e.g., financial institutions such as MoneyGram International) to determine the number of access points they provide to public blockchains, which blockchains they offer access to, and where they are located. 

So what does the research say? The details are illustrated below:

Stellar network leads cash-to-crypto accessibility in off-ramps and in the total number of global on and off-ramps

Generally, the total number of on-ramps outnumber off-ramps by a large margin. It makes sense; after all, they’re servicing higher demand for people to enter a new emerging industry. There's an appetite to onboard, use crypto, and innovate with blockchain.

However, digital assets don’t possess broader utility in the physical economy in part because the number of off-ramps does not match up with the number of on-ramps (yet). While onramping makes it easy to deposit and store value for future use, being able to extract it for everyday use cases is just as important.

Total on-ramp locations by asset - The Block’s “Quantifying Cash to Crypto Access Worldwide” Report
Total off-ramp locations by asset - The Block’s “Quantifying Cash to Crypto Access Worldwide” Report

The Stellar network far outpaced Bitcoin and other blockchains when it came to the number of off-ramps: a staggering 322,000 [as compared to Bitcoin in second with a distant 98,208 off-ramps]. Due to the extensive Stellar anchor network, financial institutions all across the world are plugging into the digital economy, providing their users an easy path in and out of the digital economy. The role of the first-of-its-kind MoneyGram Access service on Stellar is particularly noteworthy, with the report calling out that MoneyGram is the single largest provider of on and off-ramp access. With the Stellar network’s emphasis on real-world utility, it is vital for the network to close the last-mile for end users as much as possible, whether that be through on and off-ramps or other solutions.

Combining the number of on-ramps with off-ramps, the Stellar network led the pack in terms of the absolute number of cash-to-crypto ramps (475,000+) out of all the blockchains examined in this report. 

The Stellar network fills a critical gap in cash off-ramps globally

The Block Report points to an overall cash-to-crypto ramp accessibility challenge in under-served regions specifically. According to the Report, the cash-to-crypto ramp coverage in Africa, Asia, and South America is highly limited compared to coverage in North America and Europe across practically all assets and blockchain networks

Cash on-ramp providers by continent - The Block’s “Quantifying Cash to Crypto Access Worldwide” Report

This disparity is particularly noticeable with respect to off-ramps. Outside of North America and Europe, people are unable to easily withdraw the value they’ve stored digitally, making digital assets inconvenient for everyday use.

Cash off-ramp providers by continent - The Block’s “Quantifying Cash to Crypto Access Worldwide” Report

However, the Stellar network proves the exception when it comes to off-ramps, and the network is readily filling this gap in the Asian, African, and South American markets. In these under-served regions, the geographical distribution of cash-to-crypto ramps on the Stellar network is far ahead of other networks. 

On-ramps (location)Stellar network2nd largest network
Africa8,300+2,900+
Asia7,000+300+
South America19,000+100+
Off-ramps (location)Stellar network2nd largest network
Africa53,300+17
Asia147,500+90+
South America24,800+90+

The report found that compared to other networks, the Stellar network has a uniquely extensive and globally distributed network of cash-to-crypto on and off-ramps that can be used. It is also a uniquely accessible service, as users don’t need a bank account or credit card to leverage it.

However, there is always opportunity to build on and off-ramps more evenly across geographies. And as long as SDF’s mission remains to create equitable access to the world’s financial systems, we will commit to supporting Stellar’s vibrant ecosystem in building and growing solutions on the network so that more of the unbanked and underbanked around the world can access these on and off-ramps.

Leading the charge in cash-to-crypto on and off-ramps

Trends run rampant in the crypto industry, and while products and services have proliferated to give people the ability to participate in the digital economy, the same can’t be said for making crypto useful for the real world. This report illustrates much of that disparity, with large swathes of the world population unable to translate their digital value into fiat. But where there’s disparity, there’s opportunity.

Let’s talk about a more interconnected world and change how we build. 

For consumers, the breadth of the on and off-ramp services available on the Stellar network means more worldwide access to financial services powered by the blockchain and digital assets. The Stellar network is the leader in on and off-ramps. By choosing Stellar as their blockchain of choice, MoneyGram International built MoneyGram Access™ to provide users in over 180 countries the ability to convert crypto into local currency for instant pickup at participating MoneyGram locations – no bank account needed. 

As for technical solutions, it’s easier than ever to become an anchor on the Stellar network. Through the Stellar Anchor Platform, companies offering financial services, such as on and off-ramps and cross-border payments, can connect their payment services to the Stellar network via one integration of the Stellar Anchor Platform. 

Even with the Stellar network being the global leader in terms of the absolute number of on and off-ramps, there’s still a lot of ground to cover. Join us as we help businesses and builders realize their visions to create a more connected world, where everyone, no matter who they are or where they're from, has the chance to thrive. After all, that's the true utility of Stellar.

Dig into the insights of The Block report here. You can also learn all you need to know about on and off-ramps on the Stellar network here

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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.
 
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The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
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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.
 
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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.
 
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Train on Azure.
 
Run foundation models from OpenAI.
 
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It can't.
 
At least not anytime soon.
 
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Navigating the world of blockchain 🧭
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.

🏛️ 1. Core Architecture: The Base Layer

  • Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
  • Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
  • Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
  • Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.

    • Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
    • Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).

🔑 2. Ownership & Security: Wallets and Keys

  • Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
  • Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
  • Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
  • Hot Wallet vs. Cold Wallet:

    • Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
    • Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

  • Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are met—eliminating the need for intermediaries.
  • dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
  • Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
  • Layer 1 vs. Layer 2:

    • Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
    • Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).

💰 4. Financial & Market Concepts

  • Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
  • DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built on smart contracts without traditional banks or financial intermediaries.
  • Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
  • DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.

💡 Quick Cheat Sheet

"Not your keys, not your coins."

If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

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AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

Source

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