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Vitalik Buterin Discusses "The Purge", The Next Steps In Blockchain Development
April 03, 2024
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One of the less well-known EIPs in the recent Dencun hard fork is EIP-6780, which removed most of the functionality of the SELFDESTRUCT opcode.

This EIP is a key example of an often undervalued part of Ethereum protocol development: the effort to simplify the protocol by removing complexity and adding new security guarantees. This is a big part of what I have labeled as “The Purge”: the project of slimming down Ethereum and clearing technical debt. There will be more EIPs that have a similar spirit, and so it is worth understanding both how EIP-6780 in particular accomplishes the goal, and what other EIPs there might be in the future.

How does EIP-6780 simplify the Ethereum protocol?

EIP-6780 reduces the functionality of the SELFDESTRUCT opcode, which destroys the contract that calls it and empties its code and storage, so that it only works if the contract was created during the same transaction. This by itself is not a complexity decrease to the specification. However, it does improve implementations, by introducing two new invariants:

  1. Post EIP-6780, there is a maximum number of storage slots (roughly: gas limit / 5000) that can be edited in a single block.
  2. If a contract has nonempty code at the start of a transaction or block, it will have the same code at the end of that transaction or block.

Before, neither of these invariants were true:

  1. SELFDESTRUCT on a contract with a large number of storage slots could clear an unlimited amount of storage slots within a single block. This would have made it much harder to implement Verkle trees, and it was making Ethereum client implementations much more complicated, because they needed to have extra code to handle that special case efficiently.
  2. A contract’s code could go from nonempty to empty through SELFDESTRUCT, and in fact the contract could even be re-created with different code immediately after. This made it harder for transaction verification in account abstraction wallets to use code libraries without being vulnerable to DoS attacks.

Now, these invariants are both true, making it significantly easier to build an Ethereum client and other kinds of infrastructure. A few years down the line, hopefully a future EIP can finish the job and eliminate SELFDESTRUCT entirely.

What are some other “purges” that are happening?

  • Geth has recently deleted thousands of lines of code by dropping support for pre-merge (PoW) networks.
  • This EIP which formally enshrines that fact that we no longer need to have code to worry about “empty accounts” (see: EIP-161, which introduced this concept as part of a fix to the Shanghai DoS attacks)
  • The 18-day storage window for blobs in Dencun, which means that an Ethereum node only needs ~50 GB to store blob data and this amount does not increase over time

The first two significantly improve life for client developers. The latter significantly improves life for node operators.

What are some other things that might need to be purged?

Precompiles

Precompiles are Ethereum contracts that, instead of having EVM code, have logic that must be directly implemented by clients themselves. The idea is that precompiles can be used to implement complex forms of cryptography that cannot be implemented efficiently within the EVM.

Precompiles are used very successfully today, notably to enable ZK-SNARK-based applications with the elliptic curve precompiles. However, there are other precompiles that are being used very rarely:

  • RIPEMD-160: a hash function that was introduced to support better compatibility with Bitcoin
  • Identity: a precompile that returns the same output as its input
  • BLAKE2: a hash function that was introduced to support better compatibility with Zcash
  • MODEXP modular exponentiation with very big numbers, introduced to support RSA-based cryptography

It turns out that the demand for these precompiles is far lower than was anticipated. Identity was used a lot because it was the easiest way to copy data, but since Dencun the MCOPY opcode has superseded it. And unfortunately, these precompiles are all a huge source of consensus bugs, and a huge source of pain for new EVM implementations, including ZK-SNARK circuits, formal-verification-friendly implementations, etc.

There are two ways to remove these precompiles:

  1. Just remove the precompile, eg. EIP-7266 which removes BLAKE2. This is easy, but breaks any applications that do still use it.
  2. Replace the precompile with a chunk of EVM code that does the same thing (though inevitably at a higher gas cost), eg. this draft EIP to do this for the identity precompile. This is harder, but almost certainly does not break applications that use it (except in very rare cases where the gas cost of the new EVM code exceeds the block gas limit for some inputs)

History (EIP-4444)

Today, each Ethereum node is expected to store all historical blocks forever. It has been understood for a long time that this is a highly wasteful approach, and makes it needlessly difficult to run an Ethereum node due to the high storage requirements. With Dencun, we introduced blobs, which only need to be stored for ~18 days. With EIP-4444, Ethereum blocks will also get removed from default Ethereum nodes after some time.

One key issue to resolve is: if old history does not get stored by literally every node, what does store it? Realistically, large-scale entities such as block explorers will. However, it is also possible and not that difficult to make p2p protocols to store and pass around that information, which are more optimized for the task.

The Ethereum blockchain is permanent, but requiring literally every node to store all of the data forever is a very “overkill” way of achieving that permanence.

Simple peer-to-peer torrent networks for old history are one approach. Protocols that are more explicitly optimized for Ethereum use, such as the Portal Network, are another.

Or, in meme format:

 

Reducing the amount of storage needed to run an Ethereum node can greatly increase the number of people who are willing to do so. Reducing node sync time, which EIP-4444 also does, also simplifies many node operators’ workflows. Hence, EIP-4444 can greatly increase Ethereum’s node decentralization. Potentially, if each node stores small percentages of the history by default, we could even have roughly as many copies of each specific piece of history being stored across the network as we do today.

LOG reform

Quoting from this draft EIP directly:

Logs were originally introduced to give applications a way to record information about onchain events, which decentralized applications (dapps) would be able to easily query. Using bloom filters, dapps would be able to quickly go through the history, identify the few blocks that contained logs relative to their application, and then quickly identify which individual transactions have the logs that they need.

In practice, this mechanism is far too slow. Almost all dapps that access history end up doing so not through RPC calls to an Ethereum node (even a remote-hosted one), but through centralized extra-protocol services.

What can we do? We can remove bloom filters, and simplify the LOG opcode so that all it does is create a value that gets hashes into the state. We can then build separate protocols that use ZK-SNARKs and incrementally-verifiable computation (IVC) to generate provably-correct “log trees”, that represent an easily-searchable table of all logs for a given topic, and applications that need logs and want to be decentralized can use these separate protocols.

Moving to SSZ

Today, much of the Ethereum block structure, including transactions and receipts, is still stored using outdated formats based on RLP and Merkle Patricia trees. This makes it needlessly difficult to make applications that use that data.

The Ethereum consensus layer has moved to the cleaner and more efficient SimpleSerialize (SSZ):

Source: https://eth2book.info/altair/part2/building_blocks/merkleization/

 

However, we still need to complete the transition, and move the execution layer over to the same structure.

Key benefits of SSZ include:

  • Much simpler and cleaner specification
  • 4x shorter Merkle proofs in most cases, compared to status-quo hexary Merkle Patricia trees
  • Bounded length for Merkle proofs, compared to extremely long worst-cases (eg. proving contract code or long receipt outputs)
  • No need to implement complicated bit-twiddling code (which RLP requires)
  • For ZK-SNARK use cases, can often reuse existing implementations that have been built around binary Merkle trees

Today, we have three types of cryptographic data structures in Ethereum: SHA256 binary trees, SHA3 RLP hashed lists, and hexary Patricia trees. Once we complete the transition to SSZ, we’ll be down to having two: SHA256 binary trees and Verkle trees. In the longer-term future, once we get good enough at SNARKing hashes, we may well replace both SHA256 binary trees and Verkle trees with binary Merkle trees that use a SNARK-friendly hash - one cryptographic data structure for all of Ethereum.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
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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.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
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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.
 
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If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
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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.
 
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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.
 
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The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
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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.
 
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Navigating the world of blockchain 🧭
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.

🏛️ 1. Core Architecture: The Base Layer

  • Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
  • Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
  • Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
  • Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.

    • Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
    • Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).

🔑 2. Ownership & Security: Wallets and Keys

  • Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
  • Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
  • Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
  • Hot Wallet vs. Cold Wallet:

    • Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
    • Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

  • Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are met—eliminating the need for intermediaries.
  • dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
  • Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
  • Layer 1 vs. Layer 2:

    • Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
    • Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).

💰 4. Financial & Market Concepts

  • Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
  • DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built on smart contracts without traditional banks or financial intermediaries.
  • Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
  • DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.

💡 Quick Cheat Sheet

"Not your keys, not your coins."

If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

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AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

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

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

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

The Department of Unnecessary Titles

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

Other titles seem to have escaped from a brainstorming retreat.

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

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

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

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

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

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