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IMF: Facing a Darkening Economic Outlook: How the G20 Can Respond

By Kristalina Georgieva

As G20 ministers and central bank governors gather in Bali this week, they face a global economic outlook that has darkened significantly.

When the G20 last met in April, the IMF had just cut its global growth forecast to 3.6 percent for this year and next—and we warned this could get worse given potential downside risks. Since then, several of those risks have materialized—and the multiple crises facing the world have intensified.

The human tragedy of the war in Ukraine has worsened. So, too, has its economic impact especially through commodity price shocks that are slowing growth and exacerbating a cost-of-living crisis that affects hundreds of millions of people—and especially poor people who cannot afford to feed their families. And it’s only getting worse.

Inflation is higher than expected and has broadened beyond food and energy prices. This has prompted major central banks to announce further monetary tightening—which is necessary but will weigh on the recovery.

Continuing pandemic-related disruptions—especially in China—and renewed bottlenecks in global supply chains have hampered economic activity.

As a result, recent indicators imply a weak second quarter—and we will be projecting a further downgrade to global growth for both 2022 and 2023 in our World Economic Outlook Update later this month.

Indeed, the outlook remains extremely uncertain. Think of how further disruption in the natural gas supply to Europe could plunge many economies into recession and trigger a global energy crisis. This is just one of the factors that could worsen an already difficult situation.

It is going to be a tough 2022—and possibly an even tougher 2023, with increased risk of recession.

That is why we need decisive action and strong international cooperation, led by the G20. Our new report to the G20 outlines policies that countries can use to navigate this sea of troubles. Let me highlight three priorities.
First, countries must do everything in their power to bring down high inflation.

Why? Because persistently high inflation could sink the recovery and further damage living standards, particularly for the vulnerable. Inflation has already reached multi-decade highs in many countries, with both headline and core inflation continuing to rise.

This has triggered a monetary tightening cycle that is increasingly synchronized: 75 central banks—or about three-quarters of the central banks we track—have raised interest rates since July 2021. And, on average, they have done so 3.8 times. For emerging and developing economies, where policy rates were lifted sooner, the average total rate increase has been 3 percentage points—almost double the 1.7 percentage points for advanced economies.

Most central banks will need to continue to tighten monetary policy decisively. This is especially urgent where inflation expectations are starting to de-anchor. Without action, these countries could face a destructive wage-price spiral that would require more forceful monetary tightening, with even more harm to growth and employment.

Acting now will hurt less than acting later.

Equally important is clear communication of these policy actions. This is about preserving policy credibility as downside risks abound. For example, continued inflation surprises would require a sharper monetary tightening beyond what the market has priced in, potentially causing further volatility and sell-offs in risk assets and sovereign bond markets. This, in turn, could prompt further capital outflows from emerging and developing economies.
The appreciation of the US dollar has already coincided with portfolio outflows from emerging markets: they experienced a fourth consecutive month of outflows in June, the longest such run in seven years. This is putting additional pressure on vulnerable countries.

Where external shocks are so disruptive they cannot be absorbed by flexible exchange rates alone, policymakers should be ready to act. For example: through foreign exchange interventions or capital flow management measures in a crisis scenario—to help anchor expectations. In addition, they should pre-emptively reduce reliance on foreign currency borrowing where debt levels are high. It was to help countries respond in such circumstances that we recently updated the IMF’s institutional view on this issue.

The Fund is stepping up to serve our members in other ways as well. This includes providing advice on managing reserve assets and technical assistance to strengthen central bank communications.

The goal must be to get everyone safely to the other side of this tightening cycle.

Second, fiscal policy must help—and not hinder—central bank efforts to bring down inflation.

Countries facing elevated debt levels will also need to tighten their fiscal policy. This will help reduce the burden of increasingly expensive borrowing and—at the same time—complement monetary efforts to tame inflation.
In countries where recovery from the pandemic is more advanced, shifting away from extraordinary fiscal support will help tamp down demand and thus reduce price pressures.

But that is only part of the story. Some people will need more support, not less.

This requires targeted and temporary measures to support vulnerable households facing renewed shocks, especially from high energy or food prices. Here, direct cash transfers have proven to be effective, rather than distortionary subsidies or price controls that typically fail to reduce the cost of living in a durable way.

Over the medium-term, structural reforms are also crucial to bolster growth: think of labor market policies that help people join the workforce, especially women.

New measures must be budget-neutral—funded through new revenues or expenditure reductions elsewhere, without incurring fresh debt and to avoid working against monetary policy. This new era of record indebtedness and higher interest rates makes all this doubly important.

Reducing debt is an urgent necessity—especially in emerging and developing economies with liabilities denominated in foreign exchange (FX) that are more vulnerable to tightening global financial conditions and where borrowing costs are surging.

Already, sovereign FX bond yields have reached more than 10 percent in around a third of emerging economies—close to the highs last seen after the global financial crisis. Emerging economies with a greater reliance on domestic borrowing, such as in Asia, have been more insulated. But a broadening of inflation pressures and the attendant need to tighten domestic monetary policy faster could change the calculation.

The situation is increasingly grave for economies in or near debt distress, including 30 percent of emerging market countries and 60 percent of low-income nations.

Again, the Fund is here for its members—offering tailored analysis and advice, and a more agile lending framework to support countries in times of crisis. That includes emergency financing, increased access limits, new liquidity and credit lines, and last year’s historic SDR allocation of $650 billion.

Beyond these efforts, decisive action by all involved is urgently needed to improve and implement the G20’s Common Framework for debt treatment. Large lenders—both sovereign and private—need to step up and play their part. Time is not on our side. It is critical that creditor committees for Chad, Ethiopia, and Zambia deliver as much progress as possible at their meetings this month.

Third, we need a fresh impetus for global cooperation—led by the G20.
To avoid potential crises and boost growth and productivity, more coordinated international action is urgently needed. The key is to build on recent progress in areas ranging from taxation and trade to pandemic preparedness and climate change. The G20’s new $1.1 billion fund for pandemic prevention and preparedness shows what is possible, as do recent successes at the World Trade Organization.

Most urgent of all is action to alleviate the cost-of-living crisis, which is pushing an additional 71 million people into extreme poverty in the world’s poorest countries, according to the United Nations Development Programme . As concerns over food and energy supplies increase, risks of social instability are rising.

To avoid further hunger, malnutrition and migration, the world’s wealthier countries should provide urgent support for those in need, including with new bilateral and multilateral financing, especially through the World Food Programme.

As an immediate step, countries must reverse recently imposed restrictions on food exports. Why? Because such restrictions are both harmful and ineffective in stabilizing domestic prices. Further measures are also needed to strengthen supply chains and to help vulnerable countries adapt food production to cope with climate change.

Here, too, the IMF is helping. We are working closely with our international partners, including through a new multilateral food security initiative . Our new Resilience and Sustainability Trust will provide $45 billion in concessional financing for vulnerable countries—aimed at addressing longer-term challenges such as climate change and future pandemics. And we are ready to do more.

The particularly difficult conditions in many African countries at this moment is important to consider. In my meeting with Ministers of Finance and Central Bank Governors from the continent this week, many highlighted how the effects of this, entirely exogenous, shock was pushing their economies to the brink. The effect of higher food prices is being felt acutely as food accounts for a higher share of income. Inflation, fiscal, debt and balance of payments pressures are all intensifying. Most are now completely shut out from global financial markets; and unlike other regions don’t have large domestic markets to turn to. Against this backdrop, they are calling on the international community to come up with bold measures to support their people. This is a call we need to heed.

As the G20 meets to navigate the current sea of troubles, we can all take inspiration from a Balinese phrase that captures the spirit that is needed more than ever— menyama braya, “everyone is a brother or sister.”

https://blogs.imf.org/2022/07/13/facing-a-darkening-economic-outlook-how-the-g20-can-respond/?utm_medium=email&utm_source=govdelivery

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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 ...

00:04:13
🚨 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
🤖 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 ...

00:02:00
🚨 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 🚨
🚨 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...

🚨 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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🫀⚡️ Think your brain is the most electrically active organ in your body? Think again. The human heart produces an electromagnetic signal magnitudes stronger than anything generated in your head. 🧠💥

The Bioelectric Comparison

⚡ Electrical Voltage: The heart's electrical signal amplitude (measured via ECG) is roughly 60 times stronger than the electrical activity produced by the brain (measured via EEG).

🧲 Magnetic Strength: The magnetic field created by cardiac tissue is up to 5,000 times more powerful than the magnetic fields generated by cerebral neural activity.

📡 Detection Distance: Brain signals drop off rapidly outside the skull, but the heart’s magnetic field radiates outward from the body and can be measured several feet away using sensitive magnetometers.

Why is the heart's field so massive? 🛠️

Every beat requires millions of cardiac muscle cells to depolarize in precise, unified rhythm. This massive surge of ionic current creates a continuous, 3D toroidal...

🚨 U.S. Treasury targets Iran-linked crypto network tied to more than $100 million in oil payments 🚨

The U.S. Treasury Department has sanctioned an Iran-linked network accused of using cryptocurrency and shadow financial channels to move money connected to oil sales and evade international restrictions.

🔑 Key points

🔹 More than $100 million processed: The network allegedly facilitated cryptocurrency-linked payments connected to Iranian oil activity.

🔹 Oil revenue is the target: Funds were reportedly moved through intermediaries and front companies to support Iran’s energy trade.

🔹 Crypto was part of a wider system: The alleged network used digital assets alongside traditional banking channels, exchange accounts, and offshore entities.

🔹 Sanctions block U.S. access: Designated individuals and entities are prohibited from accessing U.S. property and financial services.

🔹 Crypto addresses can be blacklisted: Wallets linked to sanctioned actors may be identified and blocked by compliant ...

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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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