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Apple Introduces Digital Savings Account
April 24, 2023
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"Banking should be boring," said Warren recently. While the name Warren might bring to mind Warren Buffet, this time it was Senator Elizabeth Warren who spoke out in late March following the collapse of Silicon Valley Bank and Signature Bank. She called for stricter regulations for all banks, arguing that banking should not be a sector that attracts risk-takers. Coincidentally, Warren's words had barely cooled when Apple issued a press release that made headlines worldwide. The Cupertino-based technology company announced that Apple Card users can now open a savings account with an annual return of 4.15%. There are no fees, no required minimum deposit, and no minimum balance.

Let's take a closer look at the details of this announcement.

Apple is known for producing iPhones, iPads, Macs, AirPods, and Apple Watches, as well as software such as operating systems and apps like iTunes, Apple Music, and iCloud. But what does a technology company have to do with a savings account? Well, Apple has nearly 10 years of experience in financial services.

Apple's Exponential Evolution in the Financial Sector

Apple's foray into the financial sector began in 2012 with the launch of Apple Wallet, which was originally called Passbook. This digital wallet app allows users to manage digital versions of credit cards, debit cards, gift cards, boarding passes, and other documents. Two years later in 2014, Apple Pay followed, which not only allowed users to store their credit card or bank card information in the Wallet app on their Apple device, but also to make payments by bringing their device close to a contactless payment terminal. 2019 saw the launch of Apple Card, an Apple specific credit card that integrates with Apple Pay and the Wallet app on iPhones, allowing users to view and manage their spending and payment history. For this, Apple partnered with Goldman Sachs, which is the card's issuing bank, and Mastercard, which ensures that payments can be made using the card worldwide. In the same year, Apple introduced Apple Cash, a peer-to-peer payment service that allows users to send and receive money via iMessage, split restaurant bills, and pay friends. Apple collaborates with Green Dot Bank for this service, which is the issuer of the debit card. The maximum Apple Cash balance users may hold is $20,000, and Apple does not pay interest on this. Recently, in March 2023, Apple introduced Apple Pay Later, which allows users to split purchases into four payments due at different times without interest and fees. Apple finances the loan and bears the credit risk if the borrower does not repay. These small steps have led to Apple's exponential evolution in the financial sector.

And now, Apple Savings! The tech giant had already announced this service with a press release on October 13, 2022. With this week's announcement, the service is now effectively available, and the offered savings interest rate is known, a whopping annual return of 4.15%. Up to this point, the focus of most of the services mentioned above has been on facilitating digital payments and providing credit. With Apple Savings, they broaden their range of services by also attracting deposits. The maximum balance is $250,000. Technically, one should not prematurely conclude that Apple has now become a full-fledged retail bank, as those who read the fine print will find that the savings account is provided by Goldman Sachs.

You could compare this brand new offering with a similar approach in the telecom sector, where a Mobile Virtual Network Operator (MVNO) offers mobile telecommunication services but does not have its own mobile network. Instead, an MVNO buys network services from an existing mobile network operator and offers them to customers under its own brand name. An MVNO can differentiate itself from other providers by offering lower prices, targeting a specific audience or niche in the market, providing better customer service, offering innovative services and features, and/or building a strong brand. Apple certainly has some of those assets to call itself a full-fledged Virtual Mobile Bank (VMB).

It is important to note that, with the exception of Apple Wallet and Apple Pay, all of these financial services are currently only available in the US. Apple has over 1.3 billion active iPhone users worldwide, with about 10% located in the US. While there is a chance that Apple will expand these services to other regions, it is important to consider the differences in banking culture and consumer protection laws. For example, in Europe, despite the introduction of the Single Euro Payments Area (SEPA) in 2014, each country still has its own banking practices and regulations. To expand its financial services in these regions, Apple could potentially partner with a fintech player like Adyen, which has extensive knowledge and expertise in Europe, to offer payment services. Though, one thing is sure, the European payment space is a hard nut to crack.

Apple's new savings account: a win-win for customers and the company

Without knowing the details of the agreement between Apple and Goldman Sachs, it is unlikely that this new deposit activity will generate significant net interest income for Apple in the short term. However, there are several reasons why Apple can extract added value from this partnership. Firstly, it can attract non-hardware users into its ecosystem through attractive financial services. Secondly, Apple can leverage the data it can access through the digital savings account. Lastly, it can strengthen customer loyalty through the ecosystem of services it offers. As shown in the overview below, the market for cashless payments is rapidly growing in all regions where Apple's products are available. Therefore, it is not surprising that a giant like Apple wants to participate in this market.

Volume of cashless transactions (US$bn)

 

Apple's timely launch of its savings account solution

It is not a coincidence that Apple is launching its savings account solution now. The recent fall of Silicon Valley Bank has caused a significant shift in deposits, prompting depositors to think more about the risk they may be taking by putting all their money with one bank. Diversification is becoming increasingly important, not just for investment portfolios but also for banks. As a result, large financial institutions have seen a significant inflow of new deposits. With its new offering, Apple now positions itself among them. The Cupertino-based company can rely on a lot of goodwill from the American consumer, and it provides seamless digital integration of all its services.

The rapid interest rate hike by the US central bank (FED) presents an additional favorable opportunity for Apple to successfully launch this service. The FED's policy interest rate is currently 5 percent, which is indicative of the interest rate that banks can offer their customers. US banks now receive 5 percent interest when they park their money with the FED, while they only give on average 0.38% interest to the saver (according to https://www.fdic.gov/resources/bankers/national-rates/index.html). The difference between the interest banks receive from the FED and the interest they give to savers is significant. Normally, this difference should decrease due to competition. However, the rapid increase in interest rates by the FED means that this competitive dynamic has not yet fully materialized. Banks are also pleased to finally realize rich margins after years of zero interest rates and little or no interest income, which explains the incentive they have to drag their feet to increase the deposit rate they offer.

Apple's attractive annual return of 4.15% has everything to do with the fact that it does not have to take into account, unlike existing banks, the margins of the 'backbook' or the profits on existing customers. This allows them to embed themselves more deeply as a service provider in the financial market, using their innovative technological disruption and smart challenger technique.

Giants are becoming even more gigantic.

While the focus has recently been on smaller regional banks, the US financial regulator will have its hands full with this initiative, where tech and financial giants are gathering around the campfire. Tech giants such as Apple are expected to continue innovating in the financial sector. Some future developments to watch for include expansions into personal loans, health and life insurance, and possibly the introduction of cryptocurrency, funds, and stock transactions. These developments could provide consumers with more access to credit and affordable insurance, but there are also concerns about privacy and concentration of power. With their huge customer base, technological capabilities, and significant cash resources, these giants have the potential to disrupt the financial services sector. Therefore, it is important for regulators to strike a healthy balance between innovation and consumer protection in a rapidly changing financial sector. It is likely only a matter of time before they will face stricter regulations. Do I already hear calls for breaking up tech giants, following the example of Alibaba in China?

To end with a quote from Warren Buffet, "Banking is a good business, unless you do stupid things..."

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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:
  • 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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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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