There is an unprecedented situation emerging in London, where the relentless hemorrhaging of one of the world’s largest stockpiles of silver is now well and truly under way.
For the last 9 months, this stockpile of silver, held in the LBMA vaults in London, has been consistently falling each and every month, and has now reached an all time low (since vault holdings records began in July 2016).
These vaults comprise the precious metals storage facilities in and around London run by the bullion banks JP Morgan, HSBC and ICBC Standard Bank, as well as the London vaults of three security operators, namely Brinks, Malca-Amit and Loomis. Since the system of vaults is administered and coordinated by the London Bullion Market Association (LBMA), these vaults are collectively known as the ‘LBMA vaults’.
That article covered the vault data up to the end of June 2022, where the London silver holdings had reached the dubious milestone of having dropped below the 1 billion ounce level, specifically falling to 997.4 million ozs (31,022 tonnes).
London sub-Billion Market Association (LBMA)
Since then, however, the situation has only worsened. Latest data for July and August show that the downward trend is still very much intact. During July 2022, London silver inventories fell by another 4.66% month-on-month, with the vaults seeing an outflow of 46.5 million ozs of silver (1447 tonnes). This brought total LBMA London silver holdings down to 950.9 million ozs (29,576 tonnes), and a new all time low since records began. (Note the lowest previous low had been 951.4 million ozs at the end of July 2016).
Now that August 2022 vault data has been released (LBMA release vault data by the 5th business day of a new month), we can see that August saw no reprieve, because in August the London silver holdings fell by another 3.62% month-on-month, with the vaults seeing an outflow of 34.4 million ozs of silver (1070 tonnes). This brings the LBMA silver vault inventories down to 916.5 million ozs (28,506 tonnes).
In other words, during these two months of July and August 2022, the LBMA vaults have lost another 2517 tonnes of silver.
With consistent silver outflows over the last 9 months to the end of August 2022, the LBMA silver vaults have now lost a whopping 254.5 million ozs (7915 tonnes) of silver since the end of November 2021. In other words, from a situation where the LBMA silver inventories had been 36,421 tonnes at the end of November 2021, they are now 21.7% lower at 28,506 tonnes.
To put all of this into context, the Silver Institute estimates that world annual silver mining production will only be 843.2 million ozs this year. That’s 26,262 tonnes. So the LBMA vaults, with 28,506 tonnes as of the end of August 2022, now hold just less than one year’s mine supply of silver.
In addition, except for a blip during November 2021 in which LBMA silver inventories rose by 311 tonnes, the LBMA silver vaults have actually seen outflows for 13 of the last 14 months. This is because silver inventories in London also fell in each of the months of July, August, September and October 2021. Putting all of this together means that since the end of June 2021, the LBMA vaults in London have lost 8200 tonnes of silver (263.3 million ozs), and the vaults now hold silver representing just over one year’s mine production.
While LBMA silver inventories did rise during the first six months of 2021, the net outflow from January 2021 to the end of August 2022 is still 5102 tonnes. And people say there is no silver squeeze?
But that is actually only half the story, because as readers of these pages will know, a majority of the silver within the LBMA vaults is held by Exchange Traded Funds (ETFs) and is already accounted for, and is therefore not (unless it is sold out of ETFs) available to the market. Additionally, this silver in ETFs is not, as the LBMA disingenuously claims, available to “underpin the physical OTC market."
Backing this ETF silver out of the headline figure is thus even more revealing. According to the calculations of GoldCharts’R’Us, as of the end of August there were 18,110 tonnes of silver held by silver-backed ETFs which store their silver in London. This means that of the 28,506 tonnes of silver that the LBMA claims to be held in its London vaults, 63.5% of this is held in ETFs, and only 10,396 tonnes (36.4%) is not held by ETFs. This 10,396 tonnes also represents only about 40% of annual silver mining supply.
Back at the end of June 2022 when the LBMA data claims that there were 31,023 tonnes of silver in the London vaults, the combined silver-backed ETFs which store their silver in London accounted for 19,422 tonnes (62.6%) of this total, leaving a remainder of 11,601 tonnes of silver (37.4%) not held in ETFs. Fast forward to the end of August, and you can see that ETFs now comprise a greater percentage (63.5%) of all the silver in the London vaults. This is because, while there have been outflows of ETF held silver over these two months, there have been even greater outflows of non-ETF held silver.
These calculations were done on 9 September using silver ETF bar lists dated 8 September. This ETF silver is held in the London vaults of JP Morgan, HSBC, Brinks, Malca Amit, and Loomis.
Together these 13 ETFs currently hold 17,759.7 tonnes of silver in the LBMA London vaults.
The LBMA London vaults figures also include silver held by clients of BullionVault and GoldMoney. BullionVault clients hold 491.2 tonnes of silver in the LBMA vaults in London (same as at the end of June, while GoldMoney clients hold 186.8 tonnes in the LBMA vaults (one tonne less than in June). Adding these two figures to the ETF total means that as of 8 September 2022, there were 18,437.6 tonnes of silver held by silver-backed ETFs and private client investors in the LBMA London vaults, which to reiterate, has nothing to do with “London’s ability to underpin the physical OTC market”.
This means that of the 28,506.28 tonnes of silver as of the end of August 2022, only 10,068.7 tonnes of silver is not held in ETFs. And another caveat as usual: of the London silver not held in ETFs, some of this too represents allocated silver holdings of the wealth management sector, such as physical silver held by investment institutions, family offices and High Net Worth individuals.
So as more and more silver drains out of the LBMA London vaults due to continued strong global demand, the free float (the amount of silver that is available to ‘underpin’ trading), is diminishing.
COMEX Silver also in Crisis
Over on COMEX in New York, the silver situation is also precarious, with ‘Registered’ silver inventories in the COMEX approved warehouses practically in freefall, and at a four and a half low. See the following chart. Latest figures for 9 September show that registered inventories (those that are warranted and available to back COMEX silver futures contract delivery) are now only 46 million ozs (1430 tonnes). This is insanely low. For example, more silver left the LBMA vaults during July 2022 (1447 tonnes) than there is currently in COMEX registered silver stockpiles.
Regarding the COMEX category of ‘Eligible’ silver (which merely represents silver stored in the COMEX approved vaults which could be traded if it was put under warrant, but which realistically may have nothing to do with COMEX trading), the amount of silver in the COMEX eligible category hasn’t really fluctuated much so far in 2022 and has ebbed and flowed by about 30 million ozs (930 tonnes) within the 250-280 million ozs range. See the following chart.
With so much silver exiting the London vaults, the silver holdings on COMEX cannot explain this, since the silver leaving London is not showing up in New York. So where is the silver that is leaving London going to?
A Resurgence in Indian Silver Demand
Apart from 2022’s strong global investment and industrial demand for silver which is detailed by the Silver Institute here, there is now huge new physical demand entering at the margin, a case in point being India. Indian silver imports are now seeing some of their strongest monthly figures in recent years. See chart below which includes silver imports into India up to the end of July 2022.
Reports out of India also say that July has been a record month, according to the following interview with Metals Focus India.
Conclusion
The existence of ETF silver in London is key to the ability of the LBMA bullion banks to control the market and the silver price.
LBMA bullion banks / ETF Authorised Participants appear to use London silver ETFs as a top up fund for physical silver, scaring the market by bringing the paper silver price lower and flushing out / triggering institutions and retail to sell ETF units, at which point the bullion banks pick up and convert these units, thereby obtaining extra metal that’s needed to meet physical demand. In fact, as physical silver demand rises, bullion banks will try to get the price lower so as to have access to the silver that is held by the ETFs.
But the bullion banks know that in the West, a higher silver price brings in more ETF buyers, which in turn leads to more of the silver that is in the LBMA vaults being ‘spoken for’ by the ETFs. Which is why the bullion banks have a vested interest in keeping a lid on the silver price, because they don’t want a situation (such as early 2021) where ETF investor demand gobbles up a greater and greater proportion of LBMA silver holdings, as then this silver cannot be used to supply other industrial and investor demand (i.e. global demand outside London). See BullionStar article “LBMA acknowledges “Buying Frenzy” in Silver Market and silver shortage Fears” from April 2021.
This circus trick, where the bullion banks have to keep all the plates spinning at the same time, only works when they can control the various sources of demand and borrow silver from the ETFs. Which they do via controlling the silver price.
But as demand for physical silver continues to accelerate globally and silver continues to flow out of London at an astounding rate (which are factors which the bullion banks seem to have lost control of), is this crunch time again for the LBMA?
Only time will tell, but with physical silver demand firing on all cylinders and massive amounts of silver leaving the LBMA London vaults, the bullion bank tactics of rinse and repeat in creating a ‘paper’ silver price unconnected to physical demand and supply is becoming more and more exposed.
🚨 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. 💎🚀
🤖 AI Won't Destroy Jobs—It Will Create a Labor Shortage! 📉
While most headlines focus on AI-driven displacement, Groq Founder and CEO Jonathan Ross offers a fascinating, contrarian perspective. He argues that instead of mass unemployment, we are heading toward a massive labor shortage driven by three tectonic shifts:
1. Massive Deflationary Pressure: Efficiency gains from automated farming, robotics, and streamlined supply chains will drive down the cost of everyday essentials—from coffee to housing—meaning people will ultimately need less money to thrive. ☕️🏠
2. The Great Economic Opt-Out: As living costs drop and productivity skyrockets, humans will choose to work fewer hours, fewer days a week, and retire much earlier because their lifestyles will be easier to support. ⏳🌴
3. Unimaginable New Industries: Just as agriculture dropped from 98% of the US workforce a century ago to just 2%—paving the way for entirely new careers like software development and content creation—tomorrow's jobs are literally ...
Heinz Hopf discovered this in 1931. Roger Penrose called it "an element of the architecture of our world.' Eric Weinstein brought it up on Joe Rogan - and the silence in the room said everything.
The Hopf fibration maps a 4D hypersphere onto a regular sphere using circles that never intersect but each links through every other exactly once. It shows up in at least 8 areas of physics - including the Bloch sphere geometry that every qubit in a quantum computer lives on.
🚨 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 🚨
🚨 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 🚨
🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨
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...
👉 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
👉 Coinbase just launched an AI agent for Crypto Trading
🧠 TAO holders may be watching the wrong number: Bittensor’s real test is external revenue 🧠
TAO is trading roughly 70% below its 2024 peak, but Bittensor’s underlying economics have changed significantly through the halving, dTAO, Root Reborn, emission gates, and cross-chain expansion.
🔑 Key points
🔹 First halving completed: Block rewards fell from 1 TAO to 0.5 TAO, reducing daily issuance to approximately 3,600 TAO.
🔹 dTAO created subnet economies: Each subnet now issues an alpha token that trades against TAO, with market activity influencing emissions.
🔹 Strong markets attract emissions: Higher alpha prices can draw more capital and increase a subnet’s emissions share.
🔹 Weak subnets face pressure: Inactive or low-demand subnets can lose emissions through burn adjustments and emission gates.
🔹 Root Reborn reduced automatic selling: Alpha dividends owed to root stakers now accumulate in validator-linked baskets instead of being automatically sold for TAO.
📊 Bitwise XRP ETF trading volume surpasses $200 million in three sessions 📊
Bitwise’s XRP exchange-traded fund has generated more than $200 million in cumulative trading volume across its first three sessions, signaling strong initial market interest.
🔑 Key points
🔹 $200 million milestone: The ETF recorded more than $200 million in combined trading volume during its first three sessions.
🔹 Trading volume is not inflows: High activity shows that shares changed hands, but it does not reveal how much new capital entered the fund.
🔹 XRP demand is being tested: The ETF gives traditional investors access to XRP through a regulated brokerage product.
🔹 Institutional access expands: Investors can gain exposure without managing wallets, private keys, or direct exchange accounts.
🔹 Price discovery may improve: ETF trading can create another regulated venue for XRP exposure and institutional positioning.
🔹 Multiple products are competing: Bitwise’s launch enters a growing market of ...
🚀 Bitcoin smashes through $80,000 as $260 million in short positions are wiped out 🚀
Bitcoin broke above $80,000 as a sharp short squeeze forced bearish traders to close positions, accelerating the rally and pushing the market toward higher technical targets.
🔑 Key points
🔹 $80,000 resistance broken: Bitcoin moved above a major psychological and technical level.
🔹 $260 million in shorts liquidated: Forced buybacks added momentum as traders betting against BTC were removed from the market.
🔹 Momentum is accelerating: The breakout followed a period of consolidation and renewed buying pressure.
🔹 Leverage amplified the move: Liquidations can push prices higher quickly, but they also create conditions for sharp reversals.
🔹 $84,000 is the first target: Traders are watching the next resistance zone around the mid-$80,000 range.
🔹 $88,000 could follow: A sustained move above $84,000 may open the path toward the upper-$80,000 region.
🔹 $100,000 remains the larger objective: A move to six ...
🤖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.
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.
🙏To support my work, Helping to keep the signal high and the noise low:
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.
A 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.
A 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.
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