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Gold & The Upcoming Recession

We are now seeing the initial stages of a currency, credit, and banking crisis develop.

Driving it are an inflation of prices, contraction of bank credit and a pathological fear of recession.

One can imagine that the major central banks almost wish a mild recession upon us so that they can keep interest rates suppressed and bond yields low.

The key to understanding the course of events is that the cycle of bank credit is turning down, and this time the factors driving contraction are greater than anything we have experienced since the 1930s, and possibly in all modern monetary history.

This article joins the dots between inflation and recession and puts the relationship between money (that is only gold), currencies, credit, and commodity prices into their proper perspective.

The bank credit downturn…
It is increasingly obvious that the economic cost of sanctioning Russia is immense, and there’s now growing evidence of all major economies facing a downturn in economic activity. And we don’t have to rely on GDP forecasts to know why. Intuitively, if food and energy shortages impact us all, higher prices for these items alone will affect our spending on less important items and services.

That’s reasonable enough for sensible citizens. But financial analysts insist on quantifying it with their models. Their principal measure is the total value of all recorded transactions, comprised of GDP. They proceed seemingly unaware of the difference between the value of economic activity to the advancement of the human condition, which can’t be measured, and a meaningless total comprised of only currency and credit, which can. Consequently, all they end up recording is changes in the quantity of currency and credit deployed in the economy.

Of course, there is a broad point that if the quantity of currency and credit contracts, GDP falls. And if it is severe, economic activity tends to fall as well. But to equate the two to the point that a variation of less than a per cent or so from modelled forecasts means anything is nonsense. A proper assessment of the economic condition gets lost.

Instead, an awareness of the role of bank credit is called for. Banks create credit, which feeds into the GDP total when they are optimistic about the outlook for lending. And when they deem the outlook to be deteriorating, they withdraw credit which reduces the GDP total. It leads to a repetitive cycle of boom and bust. We are now entering a period where, at the margin, banks are trying to reduce their exposure to credit going sour. Therefore, GDP will contract And we can assess where it will contract. It really is that simple.

The best thing to do is to stand back and let the excesses of lending and the support for malinvestments wash themselves out of the system. The last time this was done was the brief but very sharp recession of 1920—1921 in the US. The government of the day understood it was not its business to intervene, and anyway, it was not capable of improving thngs.

But increasingly since, monetary policy has become run by central banks which steer their economies through rear view mirrors, reacting to information rather than anticipating. But even if they could anticipate economic trends they lack the commercial nous to manage it. Instead, their stock reaction to declining GDP will be to “stimulate”. Not only do they have a mandate to maintain full employment, but they have a Keynesian belief that a decline in GDP is entirely due to falling demand. Falling demand, they say, leads to lower prices, so the inflation figures in the CPI will fall. Producer prices will fall. All commodity prices will fall. The chart below feeds this line of hopeful thinking.

This basket of commodities has fallen in value by 17% in a month. Panic over. Even wheat and soya prices have fallen. Dr Copper is down. Grasping at these straws, central banks are undoubtedly relieved that inflation might be turning transient after all.

Or so they think. There is no doubt that we are experiencing enormous price volatility. If it was entirely due to consumers deciding not to spend because prices are too high for them, that is one thing. But if it is because banks are withdrawing credit, the consequences are materially different.

A central bank’s concern to maintain consumer spending might discourage banks from contracting credit for consumers, at least initially. Furthermore, their risk models show that while individually consumers using credit are often high risk the magic of securitisation turns these risks collectively into low risk. It becomes a numbers game. So, credit card and other consumer faced lending divisions with very high credit margins are not the first to be targeted. And anyway, that would put the bank’s executives at odds with the central bank.

Instead, in the initial stages of a credit downturn, banks withdraw credit principally from business borrowers who use overdraft facilities. A business that frequently resorts to overdraft facilities is high risk in any bank’s assessment. Weaker businesses are first to succumb to the credit downturn for this reason. Other early victims of credit contraction are financial speculators because their collateral is easily realised. We have seen the decline of US stock indices so far being accompanied by a $200bn reduction in margin lending. There’s still much more to go.

As the economist Irving Fisher pointed out in the 1930s, calling in loans to reduce bank credit can become a self-feeding destruction of value. The bit he failed to understand is that in a serious downturn it can’t be helped, because it is the other side of earlier credit expansion, and it is the unwinding of unsound lending. Both an understanding of what drives periodic contractions of bank credit and the empirical evidence that it has repeated in one form or another approximately every decade since records began, inform us that it should not be stopped but allowed to proceed. Compare the brief 1920—1921 slump in the US with the prolonged 1930s slump, the latter managed by first Presidents Herbert Hoover and then Franklin Roosevelt.

We should also know from understanding that bank credit is a cycle, that the height of the recent expansionary phase measured by the ratio of total bank balance sheet assets to their shareholders’ capital indicates the likely severity of the subsequent credit contraction. It reflects deposit liabilities to a bank’s customers relative to its shareholders assets. Traditionally, asset to equity ratios of more than eight to ten times were deemed risky. Some major banks, particularly in the EU and Japan, are now at over twenty times. While the US banks are less geared, the systemic risks to them from other national banking systems in this financially interconnected world are the highest they have ever been.

For the immediate future we can discern two things. First is that production of goods and services is likely to be more limited than consumption due to an absence of bank credit, knocking on the head the Keynesian misconception that it is a problem of insufficient demand. That is just an initial phase. And following it, the contraction of bank credit can be expected to become more severe, as banks draw their horns in to protect their shareholders from an Irving Fisher style slump. In this subsequent second phase both producers and consumers will face enormous financial difficulties.

Without aggressive intervention by central banks, the correction from excessive over-lending taking bank balance sheets beyond dangerous levels of leverage will simply fuel a GDP slump. Central banks will intervene, not just to deliver on the full employment mandate, but to finance government budget deficits which will soar under these developing circumstances.

Prices in a slump
The last real slump, when the forces driving bank credit contraction were arguably less severe, was in the 1930s following the Wall Street crash. At that time, the dollar and sterling, together the world’s major international currencies, were both on gold standards. Prices of commodities, raw materials and agricultural products collapsed, effectively measured in gold through these two currencies. The political strains led to Britain abandoning its bullion standard in 1932, and the US gold coin standard was suspended for US citizens in 1933, followed by a 40% dollar-devaluation in January 1934.

The effect of the collapse of bank credit was to make circulating media in dollars and sterling scarce, thereby raising their purchasing power. To this extent, gold’s purchasing power also rose, because it was tied to the currencies. While gold gave credibility to the dollar and sterling, it was the contraction of bank credit that drove the slump in prices, while gold got the blame.

We know that priced in gold, over time commodity, raw materials, and agricultural product prices are remarkably stable. Disruption in the price relationship does not come from gold. The following chart of the WTI oil price rebased to 1950 illustrates prices in sterling, dollars, and euros where there are huge variations in prices. Contrast that with gold (the yellow line), where the price today is down about 30% from 1950 with minimal volatility along the way.

Since 1992, which is the earliest common date we have for these series, an unweighted average gold value for them has fallen a net 19% (the black line). Fuel has been the most volatile at up to 2.5 times the 1992 price, but from the previous chart we can see that it was up a net 12 times in US dollars in 2007/08 from 1992. Priced in gold, the relatively little volatility we see in these commodity groups is as close as we can get to free market values in sound money. And even then, we know that gold prices are manipulated in the markets. We can also assume that the origin of this volatility does not come from gold, but from the violent price changes in fiat currencies, their interest rates, and their distortions with respect to demand for commodities.

These findings overturn conventional opinions on price formation. The evidence is that it is not true that fiat currencies are purely objective in their relationship with commodity prices. Forecasters of commodity prices incorrectly assume there is no change from the currency side. But clearly, the fluctuations overwhelmingly emanate from the currencies themselves.

This brings us to the likely effect of an economic slump on prices. Initially in our analysis, we will assume there is little change in the public’s desire to hold fiat currencies relative to the range of commodities and consumer goods. That being the case, we can see that it will be variations in the quantity of currency and credit in circulation driving prices. A contraction in this quantity will tend to lower prices. And Keynesian economists might conclude that precious metals being commodities will also fall in price against fiat currencies, given that fiat currencies are no longer tied to gold.

The flaw in this argument is that there are indeed other factors involved, and the consequences for the quantity of currency and credit in a slump must be taken into account. Irrespective of changes in monetary policy, in socialised economies government budget deficits soar and will need financing by expansion of the currency if bank credit is not forthcoming. In other words, despite the tendency for banks to contract bank credit to the private sector and even if central banks do not amend monetary policies, it will be more than offset by an expansion of currency passed into the economy through the government’s books.

Furthermore, under these circumstances monetary policy will change as well. Following the initial withdrawal of overdraft credit from businesses and bank loans for financial speculation, there is likely to be a softening of consumer demand as lending standards tighten and financial insecurity for consumers escalates. Central banks will notice the tendency for the withdrawal of bank credit to lead to a slump in consumer demand. They will almost certainly reduce interest rates and reintroduce quantitative easing to replace contracting bank credit to stimulate flagging economic activity. They have eased and stimulated in every bank credit cycle at this point since the 1930s, and there’s no reason to think they will do otherwise today.

An increase in currency and credit, not emanating from the commercial banks but from the central bank, with increasing budget deficits will continue to debase the currency in gold terms. The currency will also be debased against commodities. But with some volatility imparted from the currency side, we can see that the general relationship between commodities and gold can be expected to remain intact.

A systemic failure is on the cards
All this assumes that within the context of the bank credit cycle there is not a significant systemic failure. Given that the forces behind credit contraction today are greater than any time since the 1930s, and possibly for all modern monetary history, that is a vain hope. Last week I pointed out the looming catastrophe for the euro system and the euro. A similar tale can be told about the Japanese yen. And sterling is just a poor man’s version of the dollar without its hegemony status.

In the event of a systemic crisis, the role of central banks will be to underwrite their entire commercial banking system. The consequences of letting Lehman go bankrupt on the last cycle of bank credit contraction did not serve as a warning to profligate bankers. Instead, it had us all staring into a systemic abyss, and that mistake will not be repeated. In a systemic crisis today, it will take unprecedented currency and credit creation by the central banks to save the financial world. And it’s that debasement that will end up collapsing fiat currencies.

Meanwhile, we can expect central banks to milk the transitory inflation story for all its worth. Forget the CPI rising at 8%+ they will say. It will soon return to the 2% target as recession bites. But that’s another excuse to ease policy. It might buy just a little more time before the crisis hits. But don’t bank on it.

Manipulation becomes official
Earlier this month, three JPMorgan Chase traders faced a federal trial in Chicago, accused of masterminding a massive eight-year scheme to manipulate international markets for precious metals by spoofing, including gold and silver. JPMorgan had already been fined $920m in 2020.

Coincidently, Peter Hambro who was a gold trader in London in the early days of the derivatives market described how the bullion banks created unallocated gold accounts. One of Hambros’ more interesting comments was about the role of the authorities:

Read more at: https://www.zerohedge.com/markets/gold-upcoming-recession

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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.
 
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Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
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  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
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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.
 
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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.

🙏To support my work, Helping to keep the signal high and the noise low:

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👇 Crypto Donations 👇

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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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