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J.P. Morgan Payments’ Georgakopoulos: Internet of Things and Embedded Finance Forge New Commerce Ecosystems
May 10, 2023
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No one wakes up, grabs their phone and declares, I’m going to go omnichannel shopping today.

But here we are, three years after the pandemic took root, navigating online and in-person interactions every day.

As Takis Georgakopoulos, global head of J.P. Morgan Payments, told Karen Webster, even our in-person experiences are changing, based on how we’ve become habituated during the pandemic.

“We’re social animals,” he said. “We like to go into stores. We like to order food. We like to go to restaurants.” But nowadays, even if we go into a store, we want the brick-and-mortar experience to be as easy as it would be online. We want to be recognized, we want to skip the lines — whether buying in-store to have items shipped to the doorstep or ordering online to pick up in-store.

Major brands are shifting innovation spending from a pure online and digital experience to place much more emphasis on the integration between the digital and in-store experience. Many brands underinvested in the in-store space for years, and the experience has simply fallen behind, he noted. These brands are also finding that a digital-only approach is short-sighted, especially in the fast-moving consumer and retail sector.

This is why a seamless omnichannel set up is critical. It helps ensure a consumer can be recognized and receives customized attention whether they go to the store or switch between online, mobile and in-store. In at least some cases, leading brands replace their chief digital officer role with a chief omnichannel officer to ensure focus on the customer journey, regardless if it’s online or in-store.

Forging that seamless cross-channel navigation, Georgakopoulos explained, comes with activating commerce within every connected endpoint — the mobile devices, voice assistants, handheld POS terminals and even “smart” cars that are becoming more commonplace. Social media platforms play a critical role, too, as new commerce ecosystems evolve; consumers now use TikTok and Instagram to search and shop just as much as to connect with friends.

The Next Big Thing Is the Internet of Things — With Payments

The Internet of Things is evolving to have embedded payments in the mix, and smart devices all around us are now being required to include the ability to make payments, and make them instantly.

The secure, interconnected commerce experience, noted Georgakopoulos, needs to be underpinned by data that travels with consumers across each and every touchpoint. We’re not all that far away from the day when, as he illustrated, a vacationer in Las Vegas will travel through the casino, play the slots, go to the swimming pool, head to the restaurant, have the meal, and up to the room … navigating it all without juggling keys or wallets.

“Why do you need a wallet?” asked Georgakopoulos. “Your face is with you, your palm is with you, your wearable is with you. You are used to using all of those things. Why can’t you use them in person as well?” Biometrics can play a big role in eliminating the checkout line, voice commands have the promise of becoming among the most natural conduits of transacting.

Right now?

The continuum — of the customer journey from the store to their phone, back to the store buying, returning — well, the seamlessness, is not there.

But the roadmap is there.

As payments systems get faster — instant, and on 24/7 — and we get ever more accustomed to using smart devices in the home to keep daily life running smoothly and to pay utilities, for example, the more adept we’ll become at using biometrics and other advanced technologies.

“The technologies are maturing,” he said.

Payments tie it all together no matter where commerce is taking place. Marketplaces and platforms, for example, have to make it easy for customers to interact, to have a simple checkout experience — ideally without leaving the page they are browsing. Payment choice is essential to attracting sellers (or, say, drivers, if it’s a platform catering to gig economy workers).

Embedded finance, he mentioned, can offer merchants loans against their sales, and can help give them information about who buys from them, thus helping them adapt their own product offerings. As they reach new audiences and markets, preferred and local payments need to be top of mind.

“If you are an ecosystem or a platform player, you need to be able to do all of this,” he told Webster. “You need to do it securely, and you need to be able to handle [commerce] peaks — because the last thing you want is for your platform to go down.”

No Need to Rewire the Business

The model’s working, at least for some of the larger eCommerce players, Georgakopoulos said. But many other companies have not been able to get to this level of intuitive commerce. They don’t have the capability or infrastructure, or simply the expertise, to get there. The good news is that these firms need not “rewire” themselves.

“That’s why companies such as ours,” he said of J.P. Morgan Payments, “have stepped in, to help these companies to develop these capabilities more simply.” J.P. Morgan Payments has helped, with partners such as the FinTech Sightline, to foster the interaction layer between the consumer’s accounts and credit cards and the provider’s (take a hotel, for example) own systems so that closed-loop ecosystems begin to take shape.

The conventional wisdom may be that the FinTech landscape has been decimated beyond repair. And, indeed, investors are pulling in a bit, valuations have plummeted, and some FinTechs are scrambling for cash in the wake of the Silicon Valley Bank collapse. The companies that relied on relatively cheap capital and low interest rates — but with no focus on profitability — will have a tough time of it.

But, as he stated, J.P. Morgan Payments, moving nearly $10 trillion daily, has found a number of FinTech partners that have built out strong capabilities to help client firms (and, by extension, J.P. Morgan’s client firms) manage know your customer (KYC), anti-money laundering (AML) and other back-end functions. The partnerships enable software as a service and fraud defense as a service.

“If you do this well, and you do it at scale — and globally – this is what gives us the license to do everything else,” Georgakopoulos explained.

That “everything else” includes payments, of course. Payments acceptance is the most basic building block. But then once the money starts coming in, firms have to be able to manage payout, and to make the customer experience as frictionless as possible. No matter the payment method, whether debit, credit or buy now, pay later (BNPL), or whether face, palm or thumbprints are part of the equation, it’s imperative to make sure all the methods work, and that they’re safe and tokenized.

“Payments become an invisible kind of back-end to that whole infrastructure,” he said, “and for the consumers, they can think about what payment methods they want to use in the casino, how much money they want to spend, how long they want to spend there. And you can preset those options.”

Changing B2B — for the Better

The same trends and technologies, he said, have the potential to reshape business-to-business (B2B) transactions, where 40% of hundreds of trillions of dollars are still done by paper check. But just as the pandemic pushed consumers and companies to rethink how and where they find one another, how price discovery is done, and of course, how they transact, the industrial economy is moving online too.

We’re seeing the emergence of embedded finance in commercial settings, helping bring trade credit, net terms and other fund flows into supply chains that improve the very nature of business itself.

“We’re in a world of higher interest rates, and higher inflation and a market environment that focuses much more on profitability,” Georgakopoulos said.

 

“Larger companies desire to become more efficient and small companies that serve those larger companies want to be able to work with as many of those platforms as possible so that they themselves can also grow … everyone from the CFO to the CEO to the head of product, head of technology, are all there at the table,” when it comes to discussions about digitization.

APIs are gaining ground, he mentioned, but there’s a long way to go before B2B finally enters the modern age.

Looking ahead, he told Webster, there will be more room for partnerships between J.P. Morgan Payments and FinTechs. “They’re very good clients, and they also raise the bar in terms of what we need to do.” And the key word for the months and years ahead boils down to one thing: resiliency.

As commerce — retail and commercial commerce alike — moves between the digital and physical realms, “you need to offer value,” Georgakopoulos noted. “And, increasingly, the value is coming through embedded finance.”

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1️⃣ Story One: The Chart 📈

  • Ten days ago, $TAO was left for dead under $190.

  • It tagged $245, held the move, and is now coiling around $233 while everyone argues about $300.

  • That’s what’s trending—and it’s the least interesting layer on the screen.

2️⃣ Story Two: The Work 🛠️

  • Const isn't just tweeting a roadmap. He is training a 110B model in public on Subnet 3.

  • It runs permissionless, pays out only if you beat the current best, and ships open checkpoints anyone can verify.

  • This is the Bitcoin of AI thesis running live—not another thread describing it.

3️⃣ Story Three: The Question Price Can't Answer ❓

  • Grayscale already filed to turn the TAO trust into a spot product.

  • $ZEC just showed what that path does when the market finally prices the wrapper—$TAO is on the same script, just earlier in the queue.

  • But filings and green candles still don't prove the thing that actually matters: How much real money is flowing into subnets from real customers, not ...

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The most important AI model launched this year is completely empty—and that was entirely by design. 🫙⚡️

You likely already scrolled past it once thinking it was just another weight drop, but Teutonic-II 110B Genesis isn't a finished model. It is a clean, randomly initialized sparse Mixture of Experts (MoE) checkpoint that the network is pretraining live from scratch, completely in public, on Bittensor Subnet 3. 🌐🔥

The Raw Architecture 📊

  • Total Scale: ~110B total parameters.

  • Active Routing: ~7.3B active parameters per token.

  • Incentive Engine: Permissionless updates, open datasets, and over 6,000 per day inTAO rewards paying the builders who drive loss down.

The Pay-Publish-Price Test 🧪

Most people look at "110B on Bittensor" and instantly compare it to Llama. That’s the wrong frame. You aren’t looking at an open model—you are looking at an open training market.

💳 Pay: Traditional labs pay millions to train, then donate a static snapshot. This network pays open contributors in live $TAO to continuously push loss lower.

📢 Publish: Instead of shipping one final weight file while ...

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🚨 Jensen Huang, founder & CEO of Nvidia—the largest company in the world—publicly validated Bittensor, and nearly all of crypto is STILL fast asleep on $TAO 😴👇

When the king of AI hardware speaks, you listen. On the All-In Podcast, Jensen highlighted Bittensor’s ability to train large-scale models across a decentralized network of idle GPUs, calling it a modern version of "Folding@Home" and a "crazy technical achievement". 🧠⚡️

Here is why this is massive:

• Nvidia builds the raw compute ⚙️

• Big Tech builds the centralized walled gardens 🏰

• Bittensor ($TAO) builds the open, permissionless marketplace for global machine intelligence 🌐

While crypto Twitter is busy chasing daily meme coin rotations and short-term leverage plays, the key architect of the AI boom just gave a nod to decentralized AI infrastructure.

💡$TAO isn't just another altcoin—it’s an incentive layer for open-source AI models. Most traders won't connect the dots until the rest of the market catches up. Don't sleep on what's being built here. 💎🚀

#Bittensor #TAO #Nvidia #Crypto #ArtificialIntelligence #JensenHuang

00:02:48
🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨

Chutes is gaining attention as a decentralized AI inference platform that claims to combine real usage, cryptographic verification, confidential computing, and open-source infrastructure into a working production system. The thesis is simple: instead of trusting Big Tech clouds with AI workloads, users get a distributed compute layer built around verification and privacy.

🔑 Key points

🔹 Chutes is live in production and reportedly scaled to more than 1,170 active GPU nodes, including large numbers of Nvidia H200s and Blackwell-class hardware.

🔹 The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.

🔹 The team reportedly cut unprofitable usage programs, reduced total token volume, and still improved revenue efficiency, with revenue per GPU rising sharply after removing subsidized traffic.

🔹 Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...

🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨
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A new clash is emerging between legacy finance and crypto legislation after JPMorgan CEO Jamie Dimon reportedly warned that the CLARITY Act could let crypto firms offer bank-like products without bank-level oversight. The dispute is quickly turning into a larger fight over regulation, competitiveness, and who controls the future architecture of digital finance in the United States.

🔑 Key points

🔹 Jamie Dimon reportedly called the CLARITY Act a threat to the financial system, arguing it could allow crypto firms to offer yield-like products while avoiding the capital, reserve, and oversight burdens traditional banks face.

🔹 Senator Cynthia Lummis pushed back publicly, framing the issue as a global strategic race and warning that if the U.S. does not set digital asset standards, other powers will.

🔹 The core tension is whether the bill creates legitimate regulatory clarity or simply opens the door to regulatory arbitrage for crypto platforms operating outside the traditional banking...

🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨
👉 Coinbase just launched an AI agent for Crypto Trading

Custom AI assistants that print money in your sleep? 🔜

The future of Crypto x AI is about to go crazy.

👉 Here’s what you need to know:

💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit

👉 What this means for the future of Crypto:

1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto 👉txns done by AI agents by 2025

🚨 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.

👉 Coinbase just launched an AI agent for Crypto Trading

⛵️ LIFE IS THE WIND — AWARENESS IS YOUR SAIL

Perhaps shifting your reality isn’t about forcing the atmosphere to change.

Anyone who steps onto a sailboat learns a fundamental truth early on: you cannot order the gale to blow from a different direction. You can fight the gusts until your hands bleed, curse the sudden stillness, and demand the weather yield to your plans, or you can become quiet enough to feel the subtle shift in the breeze.

The seasoned sailor doesn’t try to dominate the ocean. He listens to it. He senses the change in pressure before the wave even breaks. He adjusts his trim by fractions of an inch. He knows when to catch the gust, when to alter his angle, and when to drop anchor and simply wait out the fog. A minimal touch on the tiller, executed with precision, accomplishes far more than desperate struggling against a gale.

Perhaps navigating existence demands the exact same touch.

The ego insists, “I must bend the world to my will.”

The observer asks, “Which way is ...

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📈 Bitcoin’s rally faces one key test: reclaiming the 200-day moving average 📈

Bitcoin has rebounded sharply, but analysts are watching whether the price can decisively reclaim and hold its 200-day moving average—a widely followed indicator that could help determine whether the bear market has truly ended.

🔑 Key points

🔹 200-day moving average is the test: A sustained move above the indicator would suggest Bitcoin’s longer-term trend is improving.

🔹 A brief breakout is not enough: Bitcoin needs multiple daily or weekly closes above the level to confirm that resistance has become support.

🔹 Momentum has improved: The recent rally has lifted Bitcoin from deeply oversold conditions and attracted renewed market participation.

🔹 Short covering may have helped: Forced liquidations can accelerate an upside move without proving that long-term spot demand has returned.

🔹 Volume is important: Strong trading activity during a breakout would provide better confirmation than a low-volume price ...

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🖥️ Chutes proposes conviction locks for fairer access to federated AI compute 🖥️

Chutes is exploring a conviction-lock model that would let users commit funds over time in exchange for prioritized or more predictable access to distributed GPU compute.

🔑 Key points

🔹 Compute access is the focus: The proposal targets AI developers and users competing for limited federated GPU capacity.

🔹 Longer commitments show demand: Users who lock funds for longer periods would signal stronger and more predictable demand.

🔹 Priority could replace pure bidding: Access may be determined by time-weighted commitment rather than simply awarding capacity to the highest short-term bidder.

🔹 Demand becomes measurable: Conviction locks could help Chutes identify which workloads and customers are likely to remain active.

🔹 Providers gain planning visibility: Longer commitments may help GPU operators estimate future utilization and allocate hardware more efficiently.

🔹 Users could receive better terms: ...

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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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