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šŸ’„5 Things That Break With Rising RatesšŸ’„
The Fed being aggressive AF - what could break with interest rates skyrocketing?
October 03, 2022
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Dear Bankless Nation,

When COVID-19 hit, the Fed courageously dove into the thick of the US Treasury market and committed to buying over $1 trillion in Treasury bonds.

The plan was simple: keep rates low, print money, and stimulate the economy.

A global pandemic and a recession mashed together into one mutant blackswan event would have just been too much for the markets to bear.

Thankfully, the stimulus went as planned.

  • Treasury interest rates hit record lows at near zero

  • Global markets quickly rallied as fears of recession evaporated overnight

  • Crypto markets mooned

  • The Payroll Protection Program (PPP) reduced a spiking unemployment rate

  • We developed cures and vaccines to Covid

We did it!!!

Right?

Well… not exactly.

Expansionary monetary policy allowed inflation to hit a 40-year-high of 8%. In response, the Fed is now scrambling to combat inflation by aggressively reversing course and raising interest rates.

But, policy doesn’t always work as intended, and things may break.

Here are the 5 things that could break if the Fed continues on its course:

  1. Inflation (in a kinda positive way)

  2. Housing Markets

  3. Financial Institution Solvency

  4. Sovereign Debt and Forex Markets

  5. Private Credit Markets

1. Inflation

Why is the Fed raising interest rates in the first place?

To fight inflation.

The Fed’s unlimited bid on the US Treasury market during COVID-19 – and to a lesser extent, US Mortgage Backed Securities (MBS) markets – increased the size of its balance sheet from $4.24 trillion to $8.96 trillion.

That is an increase of$4.72 trillion, or111%.

Where did all of that money come from?

Essentially, from thin air.

The Fed repurchased Treasuries and MBS throughOpen Market Operations, which is a fancy monetary policy term for buying and selling Treasuries and MBS.

When conducting such an operation, the Fed credits or debits a bank’s reserve accounts at the Federal Reserve. There is no need for the Treasury Department to create or destroy any money. A simple ledger adjustment at the Fed can simply create trillions of brand new dollars.

Stimulating the economy to drag ourselves from the depths of Covid requiredQuantitative Easing(QE), meaning the Fed purchased Treasuries and MBS – hence the balance sheet expansion.

And wouldn’t you know it, creating new dollars caused inflation!

Can you recall how else we created new dollars during Covid?

That’s right! Those sweet, sweet stimmies and those fatty PPP loans, which evolved into PPP grants and never had to be repaid.

Central banks claimed that inflation was only transitory. But in December 2021, people finally came to terms with the fact that the mighty United States of America is not immune to basic economic principlesafter the CPI reported its biggest annual increase in 40 years.

The Fed, however, continued to expand its balance sheet until the middle of April, at which point they became net sellers of Treasuries and MBS.

Further, the Fed failed to raise interest rates until the middle of March, despite knowing inflation would be stickier than expected afull three and a half months prior!

Raising interest rates increases the opportunity cost of money.

As cost increases, investors are less willing to pay high prices.

Increasing opportunity cost delays consumption.

As we can see in the chart below, the month-to-month change in inflation has been decreasing as of late, therefore, rate increases = successful!

We broke inflation! Probably… Maybe?

So I guess this is a positive break. However, raising rates to reduce inflation comes at the risk ofdeflationand while inflation is not desirable, deflation is even less so, as it may create the spiral of a weakening economy.

Put elegantly by Elon:

2. Housing Markets

US home prices have been on an absolute tear since the end of the pandemic.

Over the past two years, the median home sales price has risen by 36.5%, fueled by historically low interest rates on mortgages.

In January 2021, you could take out a 30-year mortgage at 2.65%.

Today, it’s 6.70%. 😱

To examine the impact of mortgage rates on the purchasing power of consumers, let's take a hypothetical buyer, looking to put 20% down on a 30-year, fully amortizing mortgage, with a budget of $1M.

The low rate scenario uses our record low interest rate mark of 2.65%, while the high interest rate of 6.70% is the current rate offered on 30-year fixed mortgages.

The monthly payment for this hypothetical buyer in today’s high rate interest environment is $1,939 (or over 60%) greater than had they purchased the home in January, 2021.

It’s 60% more expensive to buy a home today than less than 2 years ago!

Unless you can increase your annual income by 60%, if interest rates continue to rise, buyers will be able to afford less, and home values will have to decrease (which they already have).

Initially, decreasing home prices will only impact those who purchased homes and took out high interest loans in 2022 on overvalued real estate, however,a potential housing bubble could cause cascading foreclosures, much the same as a falling token price can cause cascading liquidations.

3. Pension Funds

When rates rise, discount rates increase and the market value of existing assets decline.

Thanks to their known cash flows, fixed income instruments (bonds) are extremely easy to price, given the bond’s yield and future cash flow timing. Many large financial institutions also love fixed income securities, as it provides a known cash flow, enabling them to match assets with obligations.

For a pension, bonds provide known cash flows to match with fairly certain liabilities payments to retirees. While pensions generate income and returns through a variety of investments of varying risk profiles, a primary income generation tool is sovereign bonds, especially those originating from the country in which the pension operates.

British pensions funds have been absolutely shellacked by rising interest rates, aftertax cut plans announced by UK’s new Prime Minister Liz Truss gave rise to investor fearover Britain’s ability to service national debt.

From peak to trough, the tax cut announcement wiped out 10% of the value of British 10-Year Gilt (the UK equivalent of US Treasuries). This rapid drawdown in the market value of assets placed enormous strain on UK pensions’ solvency, and required the BOE to step into the fray, supporting the Gilt via QE (printing money).

If the Fed continues to raise rates, markets will demand higher yields on foreign paper, placing the solvency of holders (i.e. pensions) at risk!

4. Sovereign Debt and Forex Markets

In point #3, we discussed how the BOE was forced to commence QE to defend yields on the Gilt and prevent pension fund insolvency.

How did they do this?

By turning on the money printer, much like the Fed did to support US Treasury yields in the depths of Covid!

But doesn't QE and money printing just increase inflation?

And isn’t inflation what we started raising rates to fight against?

Yes. But hey… the boomers can’t lose their retirement accounts.

It is fairly clear to see the inflationary shocks in the British Pound from the chart below:

While extremely rapid QE measures stabilized Gilts, the result was the value of the Pound temporarily falling off a cliff against the dollar.

Protection of British interest rates came at the cost of a devalued Pound!

Britain is not the only major economy affected by Fed rate increases.

The Bank of Japan (BOJ) has conformed to a policy of yield curve control for decades, the first major central bank to do so since 2016.

Rising rates in the US make Treasuries and Dollars attractive investments, causing upwards pressure on Japanese yields. To combat this pressure, the BOJ has resorted to… you guessed it… QE!

But the BOJ has reached the limits of QE.

The BOJ has imposed exchange rate limits of less than 145 Yen to 1 USD and a targeted yield of 25 bps on their 10-Year bonds.

Currently the Yen to Dollar exchange rate is 144.68 to 1, with 10-Year bonds yielding 24 bps!

The BOJ may soon have to choose between further devaluing the Yen or losing control of the yield curve. Such a change in monetary policy may be seen by markets as a sign of looming disaster, further hammering the Yen and rocketing Japanese yields.

5. Private Credit Markets

If the US Government is forced to pay more in interest when yields increase, I can assure you that private corporations experience the same effects.

Corporate bonds help to finance a wide range of publicly traded and private companies. MicroStrategy has used its debt issuances to finance purchases of Bitcoin while Boeing uses bonds to finance the development of new manufacturing plants in South Carolina. Real estate developers, such as Greystar, may opt to finance developments of apartments using bank loans, instead of issuing bonds.

You get the idea.

Regardless of how the company structures financing, there will be some form of interest rate attached to the note.

These notes are often interest only, or mature before being fully paid down.

New debt must be taken on to repay maturing notes.

Rising interest rates make new debt more expensive for borrowers. Low interest rates allowed for borrowers to finance projects that would be considered unfeasible in today’s high interest rate environment. When it comes time to rollover outstanding debts on unfeasible projects, corporations will resort to selling, as their cash flows from the purchase asset may no longer cover interest obligations.

High quality, two year corporate bonds yielded0.47% in August, 2020.

In August 2022, they yield3.81%, an increase of 711%.šŸš€

Payments on interest-only notes rolled over last month increased by seven times! While proformas often provide some amount of slack for changes in interest rates, an payment increase of that magnitude may force asset sales.

A Difficult Decision

Multiple pressure points exist in the global financial system, and with each Fed rate hike pushing us closer to the edge of the recession cliff, it may only be a matter of time until we see a true crisis, potentially caused by one of the five factors listed above.

The Fed faces a difficult decision: continue to raise interest rates, potentially destabilizing US allies attempting to stimulate and maintain low interest rates, or devalue the Dollar and exacerbate inflationary pressures in the US.

Jerome Powell and the FOMC are walking the knife’s edge.

A singular misstep and…

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šŸ¤–Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?šŸ¤–
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
Ā 
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.
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It's a valid concern.
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Why Decentralized AI Exists
Ā 
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.
Ā 
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.
Ā 
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.
Ā 
Why This Matters for Robotics
Ā 
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.
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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.
Ā 
This is where decentralized systems become interesting.
Ā 
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.
Ā 
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.
Ā 
That vision is beginning to emerge.
Ā 
Bittensor's Move Toward Physical AI
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While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
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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.
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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.
Ā 
In other words, the pieces are starting to appear.
Ā 
Not a decentralized robot network yet.
Ā 
But the infrastructure that could support one.
Ā 
Beyond Bittensor: The Rise of Physical AI Networks
Ā 
Bittensor isn't alone.
Ā 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
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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.
Ā 
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.
Ā 
The goal is not simply decentralization for its own sake.
Ā 
The goal is resilience.
Ā 
If one server fails, the system continues.
Ā 
If one company disappears, the network survives.
Ā 
If one participant leaves, innovation continues.
Ā 
But Here's the Reality
Ā 
Decentralized AI faces the same challenge every decentralized technology faces.
Ā 
Big Tech has resources. A lot of resources.
Ā 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
Ā 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
Ā 
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.
Ā 
The challenge isn't just decentralizing intelligence.
Ā 
It's decentralizing intelligence while maintaining performance.
Ā 
That's much harder.
Ā 
The Most Likely Outcome
Ā 
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.
Ā 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
Ā 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
Ā 
The companies building robots may use NVIDIA hardware.
Ā 
Train on Azure.
Ā 
Run foundation models from OpenAI.
Ā 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
Ā 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
Ā 
The Bigger Question
Ā 
The real question isn't whether decentralized AI can eliminate Big Tech.
Ā 
It can't.
Ā 
At least not anytime soon.
Ā 
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.
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As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
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Because the battle for the future of robotics is no longer about hardware.
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It's about who owns the intelligence.
Ā 
And that battle is just getting started.
Ā 
Ā 

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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.

šŸ›ļø 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).

šŸ”‘ 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.

āš™ļø 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).

šŸ’° 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.

šŸ’” 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.

šŸ™To support my work, Helping to keep the signal high and the noise low:

šŸ‘‰ Cashapp: $thedinarian

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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 overĀ LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as ā€œforward-deployed and agentic AI architect.ā€ 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 LabĀ found that the number of postings onĀ IndeedĀ 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 isĀ AI engineer, which ranked No. 1 onĀ 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 analysisĀ reportedĀ 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 rƩsumƩ keyword.

LinkedIn data cited by theĀ World Economic ForumĀ 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.

AĀ Syracuse University reviewĀ 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 theĀ forward-deployed engineer, an oldĀ PalantirĀ title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to ā€œdo something with AIā€ into software that works.Ā The Next WebĀ reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

AĀ CTO guideĀ 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.Ā Evals engineersĀ design tests to determine whether models perform reliably.Ā AI red teamersĀ try to make systems fail before customers do.Ā Model behavior engineersĀ study why an AI system responds as it does.Ā AI governance leadersĀ manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is theĀ Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There areĀ vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. ā€œVibe engineerā€ is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

ā€œContext engineerā€ is a real discipline involving the data, instructions, memory and tools supplied to AI models. ā€œPrompt engineer,ā€ 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 ā€œbuilder,ā€ ā€œAI-native developer,ā€ ā€œRAG engineer,ā€ ā€œagentic AI engineerā€ and ā€œprincipal agentic GenAI forward-deployed context architect,ā€ 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 ā€œsoftware developer.ā€

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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