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đź’ĄAn Elegant Approach to Consensusđź’Ą
Stefan Thomas @justmoon CEO and founder of Coil, co-creator of Interledger, and former CTO of Ripple
December 16, 2022
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It’s the age-old debate between Proof of Work and Proof of Stake, brought back to the forefront of people’s minds by Ethereum's successful merge back in September.

The critiques of both are well documented. One side will point to the fact that Bitcoin consumes energy at a significant scale. Others will highlight Ethereum’s new realities when it comes to concentration of power. Post merge, Lido plus three of the largest exchanges control over 50% of staked ETH.

Neither solves for governance, evidenced by the fact that both Bitcoin and Ethereum manage governance off-chain.

In this piece, I’ll argue that there’s a more direct solution; one that holds advantages over Proof of Work and Proof of Stake in terms of energy use and governance controls.

What’s neat is that this solution is based on the already existing, informal process that underlies both Proof of Work and Proof of Stake—and any other consensus mechanism for that matter.

That’s because consensus is something that humans do naturally and intuitively all the time. We can formalize that process and automate some of the more tedious parts. This is how we get to a foundational form of consensus without a lot of extra steps.

Proof of Work: How we got here

Decentralized, anonymous ledgers all face the same challenge. In designing a system that allows anyone to participate, you need a way to decide between equally valid ledgers to ensure that everyone stays in agreement. The obvious answer is some kind of voting mechanism. But as with any fair and equitable voting mechanism, you need to prevent any single person or entity from having more votes than they should.

One way to frame this is that the problem we’re trying to solve is a form of digital democracy.

Proof of Work’s approach requires participants to contribute computing power or hashing to the system. We can think of miners "voting" with their computing power by choosing one of the valid blockchains and attempting to extend it. After all, you can’t fake computing power. And as the value of the system grows and competition for computing power intensifies, the cost of outvoting the rest of the system goes up along with it.

That’s how we achieve consensus anonymously—Proof of Work in a nutshell.

Of course, computational power is essentially a proxy for energy consumption, and the last thing the world needs at the moment is wasted energy. We can minimize waste by using stranded or surplus energy but there is no way around the fact that any computer doing proof-of-work could always be doing useful calculations instead.

The last point I’ll make here is on governance. In the early days of Bitcoin, some protocol changes were indeed voted on and decided by miners. But that approach came to a head during the debate around block size and scalability, what Coindesk, at the time, described as a “constitutional crisis.” In some contexts, miners’ incentives aren’t aligned with the rest of the network. In the context of block size for example, miners prefer smaller blocks to force users to pay them higher fees.

Naturally, the community didn’t take that lying down and turned to extra-protocol forms of governance as a response as well as hard forks. Eventually, this put enough pressure on miners such that a compromise was reached. The point is that Bitcoin isn't governed purely by proof-of-work. Important strategic decisions are made through a political process outside of the protocol and not simply by the majority of miners.

Given these limitations, there has always been interest in potential alternatives to Proof of Work.

Proof of Stake: The popular alternative

If we think about consensus mechanisms as forms of democracy, then Proof of Stake would be a plutocracy. You might call it Proof of Wealth.

Instead of computing power, votes in a Proof of Stake system are counted proportional to the number of tokens a person or entity stakes. Assuming tokens have been broadly distributed among many unaffiliated participants, decentralization is achieved without the energy needs of Proof of Work.

Just as you can’t fake computing power, you also can’t create tokens out of thin air. Sure, a well-capitalized organization could buy up tokens to increase their voting power but that’s by design. As a rule, Proof of Stake is a consensus mechanism typically dominated by aggregators of tokens such as exchanges or DeFi platforms.

When those staked tokens are also tied to governance of the ledger itself, it creates a feedback loop, which tends toward inequality and power concentration. The more tokens you have, the more votes you have. If you can turn that power into greater profits, you can turn those profits back into greater power. Keep doing this and you will eventually fully control the system.

This is less of an issue if the system is still in competition with other Layer 1s. We’re generally fine with corporations being governed by insiders such as shareholders or—in the case of co-ops—workers, as long as consumers still have a choice. If the company makes a bad product, you can buy a different one, and if they're an awful employer you can work someplace else. If an evil dictator takes over a corporation, it will lose customers and employees, a natural form of checks and balances.

Problems start when corporations become too entrenched and consumers lose that choice, which is when we typically see unchecked bad behavior. The same applies to a consensus system. While it still competes with other systems, those checks and balances continue to exist. But if it becomes universal, then unchecked concentration of power becomes everyone’s problem.

(It’s one reason why I’m so passionate about Interledger. With cross-blockchain interoperability, you get persistent competition between consensus systems, which serves as an additional layer of checks and balances. We’ll get into that more in a future post.)

Ethereum solves for this by taking governance off-chain, including, as they describe, both “social and technical processes.” But when power transitions from votes and well-defined rules within the system to more informal processes outside the system, it's difficult to guarantee transparency and fair representation. 

Just like Proof of Work, Proof of Stake defers the issue of governance.

Beyond questions around governance, a more common criticism highlights the circular logic inherent in any Proof of Stake system:

In order to know how many tokens each person has, you need to know the status of the current ledger.

In order to know the status of the current ledger, you need to know how the majority of the staked tokens has voted.

Any Proof of Stake system has this problem. Anyone who has access to the keys of previous validators could create an alternative ledger history that’s completely and equally valid. There are workarounds, such as creating regular ledger checkpoints, but this raises further questions—e.g. what is the next checkpoint, how are checkpoints determined, etc. An already nebulous off-chain governance system now must make even more arbitrary decisions.

Consequently, Proof of Stake requires myriad features that account for flaws and potential attack vectors that are inherent in its design. (Lyn Alden has a great writeup on this subject.)

There are potential regulatory hurdles as well. Hours after the Merge, SEC chief Gary Gensler told reporters that he thought Proof of Stake tokens looked like securities due to staking rewards.

All roads lead to Rome

So where does that leave us?

Proof of Work is simple, relatively reliable, and expends a ton of energy.

Proof of Stake is complex, logically awkward, and plutocratic.

Neither solves the question of governance.

Surely, there’s a better way.

In fact, there is—one that’s already working in the real world—but first, let’s take a step back and take a look at how we choose a consensus mechanism in the first place.

Think of it this way: Most people don’t consider the consensus mechanism itself when deciding who they want to be in consensus with. Maybe you heard about a cool gaming NFT project that you want to support. It happens to be on the Ethereum ledger, which is Proof of Stake.

Or maybe you’re looking for alternative assets as part of a diversified investment portfolio. You choose Bitcoin, which is Proof of Work. Or maybe you chose it because it’s the most popular and longest running.

In deciding what chain to participate in, you’ve made the decision based on your particular use case, needs, or target community.

In other words, the first choice you make isn’t about the consensus mechanism itself. Instead, it’s: Who do you want to be in consensus with?

Understanding consensus

Now that we’ve established this central choice that any participant needs to make, let’s take another step back.

What is consensus, anyway?

Here’s my definition: Consensus is a process of voluntary agreement.

In society, consensus establishes the ground rules for cooperation, enabling us to efficiently interact and transact with one another.

For example, I’m able to go to the grocery store to buy food and supplies because of consensus. There’s consensus on things like the monetary system, the legal system, languages, and certain social norms. If we can’t agree on how to make payment, how to settle disputes, or how to communicate, it’s going to be a tough time at the supermarket. Most likely, I won’t be able to buy my groceries and my grocer won’t be able to sell their products.

You and I might have different opinions on how our country should be run. We might be on the opposite sides of a political issue. But if my side loses the vote, I’ll still voluntarily agree to follow your rule so that we can collectively move forward. Despite our disagreements, we find a way to reach consensus such that progress can be made and peace maintained.

Part of it is because not coming to consensus comes with huge costs. Ideally, we’d like to avoid a revolution or civil war. Or in blockchain parlance, a fork.

The key point, again, is that consensus is voluntary. You can claim that you’re actually Napoleon—no one can stop you. But you won’t be in consensus with the rest of society, which will create friction and increase your social and economic interaction costs. Because of this, it’s rare in practice to run into someone who strays too far from the norms of social consensus. The benefits of consensus outweigh the cost of not being Napoleon for most people most of the time.

We want to agree on transactions that have occurred. We might disagree on the exact order of when those transactions came in—this could be simply due to being located at different distances on the globe from where a transaction originated. But we seek agreement anyway because any order—as long as it is universally accepted—allows us to transact.

Proof of Association: A more direct approach

Here’s what we’ve established so far:

First, Proof of Work, Proof of Stake, and so on are consensus systems designed to achieve voluntary agreement.

Second, before we even get to the "how" of consensus, we first need to choose who we want to be in consensus with, which, in turn, is based on who we want to interact and transact with.

Third, consensus is voluntary—people reach consensus because it serves as a foundation for transacting with each other.

Given that, what if I could just describe who I want to be in consensus with and have an algorithm that keeps me in sync with the people I’ve selected?

Spoiler alert: You can—which brings us to the concept behind Proof of Association.

Instinctively, if we knew who we want to be in consensus with, all we would need to do is look at their ledger and make sure that ours is the same. If it is, we’re in sync; we’re in consensus. It is a little bit more complicated in practice, but not much.

The first step is to write down a list of those people or entities you’d like to be in consensus with.

Once you write down that list, you hand it over to a software program that will scan the network and listen for people on your list. When enough of those people vote for a particular ledger—a quorum—consensus is achieved. (Honest nodes commit to never changing their vote.)

Since you’re writing your own list, you don’t need to worry about voting spam. If someone joins the ledger with 10,000 nodes that nobody cares about, they'll simply be ignored.

And because everyone participating—voluntarily, of course—is incentivized to maintain and improve consensus, the system will naturally evolve toward a more robust and decentralized structure. That could mean:

  • Adding more reliable people or entities to your list
  • Removing unreliable people or entities
  • Aligning your list to be similar to the lists of other participants
  • Changing your list toward having a more diverse set of validators across people, organizations, and geographical locations

As a result, such a system will naturally iterate to create ever more trustworthy states. Just like our real-life interactions, trust is developed and strengthened over time. Someone might have a lot of influence over the network because they are included in a lot of other people's lists, but if, for any reason, they break bad and lose the trust of other participants, they can be quickly dropped by the rest of the network in a way that isn't typically possible with Proof of Work or Proof of Stake.

Here, the age-old adage applies—it takes a lifetime to build a good reputation, but it can be lost in an instant. In that sense, the power of even the most important node is always limited. Just as a media outlet which consistently offers unreliable information might lose subscribers, so too will a bad validator. In a system based on voluntary association, there is always a choice.

What's more, if a validator has too much influence, others may proactively diversify their list even if that validator is perfectly honest and reliable. Over time, there is an incentive toward greater and greater decentralization. Or, more precisely, the level of decentralization that most participants think of as optimal.

It's important to note that we're only talking about a single consensus system so long as there is enough overlap between different lists. The overlap doesn't need to be perfect—in fact, the slight differences are what allows for improvements over time. Generally, participants don't want the network to split so everyone is incentivized to try to keep their lists relatively in sync through communication and discourse. If there are irreconcilable differences between groups, their overlap might decrease and they might eventually split into separate networks. This sounds bad, but is actually just a reflection of the preferences of the members of both groups choosing to separate from each other. Consensus is voluntary and can only be maintained as long as people want it to be.

In general, the network and community will ultimately determine for itself the best inclusions for their lists, which will continuously optimize over time—a form of fluid, iterative democracy. You have your chosen representatives in your list. If the times change, you can vote for new ones at any time. Others who transact with you may notice your choice and change their selection in turn.

Writing lists doesn’t use a lot of energy nor does it concentrate power.

And this isn’t just theory. A consensus system based on this process has been operating for the last 10 years—the XRP Ledger.

What’s cool is that over those 10 years, the network has evolved precisely in the ways I just described. Natural incentives mean that the XRP Ledger is consistently becoming more robust and decentralized.

Today, most participants follow 35 validators spanning geographies around the world, including individual participants, exchanges, universities, and companies building on the network, like my own company, Coil. No entity controls more than two validators, or 5.7% of the vote.

Unlike Bitcoin and Ethereum, the governance process is formal and voting happens in-protocol using the same consensus process that is used to confirm transactions.

Over the years, validators have successfully passed 45 amendments to improve the system, including new features such as multisign, escrow, and most recently, NFT support. New amendments are constantly being voted on.

But this is not just about XRP Ledger. If blockchains are to serve important functions in our society, advocates must have better answers to questions around energy usage and governance. Such were the weight of those questions when Ethereum made the bold step of actually switching their consensus system.

I hope that, ultimately, this will lead more people toward Proof of Association. It would not only solve the problems of energy consumption and concentration of power, but also serve as a simpler, more robust, and transparent method of governance for blockchains.

What started as a first principles observation of the consensus process becomes the mechanism itself. The beauty here is that in making the principles of consensus explicit, the consensus mechanism becomes obvious.

 

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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.
 
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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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  • 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.
 
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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.
 
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.
 
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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 đź§­
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).

đź’° 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.

đź’ˇ 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.

A Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

A CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

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

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

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