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What is ‘BASE’? Here is What We Know So Far About the L2 Project Being Built by Coinbase
February 24, 2023
February 26, 2023
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This week, the publicly-traded cryptocurrency exchange Coinbase unveiled its own Ethereum layer-2 network called Base and advised the crypto community to stay tuned for the upcoming mainnet launch.

The Base chain will be home to Coinbase’s on-chain products as well as an open ecosystem for millions of new decentralized apps (dApps), said the leading cryptocurrency exchange.

“Base is an Ethereum L2 that offers a secure, low-cost, developer-friendly way for anyone, anywhere, to build decentralized apps. Our goal with Base is to make on-chain the next online and onboard 1B+ users into the crypto economy,” Coinbase said on Twitter.

Coinbase Senior Director of Engineering Jesse Pollak shared that while the platform was only announced on Thursday, a testnet for Base has actually been live since the start of the month.

“Our goal is to launch mainnet in the next few months,” he said. “This is a bet that we can help enable the next million dapps, which are going to bring in the next billion users. We think that will happen on a five- to 10-year horizon, and this is our contribution to making that happen sooner rather than later.”

The goal of Base is to bring about phase 4 of Coinbase’s ‘secret master plan,’ which is to create an open financial system and onboard the next wave of users to the crypto economy. With this move, Coinbase has become the first publicly traded company to launch an Ethereum Layer 2.

Layer 2 networks help make transactions faster and cheaper than the underlying blockchain, such as Ethereum, by processing batches of transactions on a separate chain and then sending receipts back to the mainnet.

Entering a Crowded Sector

Coinbase’s Base is just one of many Ethereum Layer 2s; others in the space include Polygon, Arbitrum, Optimism, Loopring, and Starknet, to name a few.

According to L2Beat, there is currently more than $6 billion worth of ETH on these layer 2s, which is a 17% growth from a month ago. This website tracks 26 different Ethereum scaling networks and notes that Arbitrum One accounts for the majority (53%) of the L2 market share, while Optimism has captured 31% of this segment.

However, unlike these L2s, Base will not be launching a network token, which is used on other networks to pay gas fees and incentivize development.

According to Coinbase, “tokens are not the only way to drive activity,” instead, it believes “building great products is a great way of driving activity by making things actually useful.”

Coinbase Announcement

It’s interesting to note that both Arbitrum and Optimism launched their networks without tokens initially. However, Optimism has since airdropped its governance token to early adopters of its network. This was done in order to distribute governance power to the community and fund development proposals through its DAO. There have been rumors that Arbitrum has been planning something similar for a long time, but no token has yet been released.

However, Base’s entry into the market may not be as competitive for Optimism. According to Coinbase’s announcement, Base will be “a rollup agnostic superchain powered by Optimism.”

The base will also return a portion of its transaction fee revenue to the Optimism Collective. “This move furthers the Collective’s vision for a sustainable future where Impact = Profit,” tweeted Optimism on Thursday.

Following the Coinbase announcement, Optimism’s native token, OP, recorded a jump in price. The $668 million market cap coin is up 11% in the past 24 hours and nearly 240% YTD, as per CoinGecko. Launched in May 2022, just earlier this month, OP hit an ATH of $3.19.

Earlier this month, Optimism revealed its plans to upgrade its network next month, which is a “step towards a multi-chain future.” Dubbed “Bedrock,” the upgrade aims to lower fees, increase transfer speed, and enhance compatibility with the Ethereum Virtual Machine (EVM).

To address the issues of speed and cost associated with layer-1 blockchains, Optimism uses “rollups,” which involve processing transactions on a separate chain and then settling them on the mainnet in batches.

Decentralized Over Time

The Base chain is launched with dozens of partners who have committed to building in and supporting the ecosystem. Some of these include Ethereum block explorer Etherscan, oracle network Chainlink, DeFi protocol Aave, Animoca Bands, and SushiSwap.

Coinbase said it intends for Base governance to be fully decentralized, but it won’t be that way initially. Instead, it would happen rather progressively in the years ahead.

“Coinbase is going to gradually transition into a role where we’re a contributor to Base, we provide services and products that are built on top of Base, and we are not the decision maker for everything in the Base network — that’s being done through more decentralized governance,” Pollak said.

And to best achieve its goals, Coinbase has decided to join Optimism as a core developer on the open-source OP Stack. They have also decided to keep the core team really lean so that “we can have an outsized impact if we’re building on-chain,” said Pollak.

The exchange explained in the official blog post that by leveraging Optimism’s OP Stack and collaborating with Optimism, Base will act as “an open platform that anyone can contribute to, fork, and extend to help the crypto economy scale.”

Optimism Tweet

So, Coinbase will have more control over Base at first with the vision to make Base fully permissionless eventually. And according to the announcement, Base will progress from its current Stage 0 to the next stage rollup this year alone, with the Stage 2 rollup to be achieved in 2024.

While Base will be a separate network, it will still be powered by the underlying blockchain, that is, Ethereum. It will use Ethereum’s security infrastructure, but it won’t be limited to Ethereum; rather will also provide easy and secure access to other layer 2 networks, such as Optimism, as well as other blockchains ecosystems, such as Solana, Avalanche, and Polygon.

“Base offers full EVM equivalence at a fraction of the cost and is committed to pushing forward the developer platform,” explains Coinbase’s blog post on the L2 Base.

Coinbase further plans to integrate the Base chain across its exchange, wallet, NFT marketplace, and developer products.

Along with the new chain, Coinbase also announced the launch of a Base Ecosystem Fund to support early-stage projects working with Base as long as they meet the company’s investment criteria.

COIN Gaining Strength

While in its announcement, Coinbase made it clear that it has “no plans to issue a new network token,” degen traders were quick to find an alternative.

Some degen crypto traders piled into an unrelated token called BASE following Coinbase’s layer-2 announcement. The native token of Base Protocol, BASE saw its price soar about 350% in a few hours to almost its ATH before falling down hard to its previous levels.

Some have suggested that insider trading may have occurred, as the BASE token began to move long before the Coinbase announcement.

Meanwhile, Coinbase (COIN) prices are up 16% since early last week but down 29% since early February highs. COIN is a $14.43 billion market cap stock trading at $62.36 at the time of writing.

Bearish OP Take

In 2023, along with the broad crypto market green, COIN prices also recovered, having started the year under $32. Still, the stock is nowhere near its all-time high (ATH) of $430 hit when it first started trading on Nasdaq under the COIN ticker in April 2021. The COIN stock prices have only been down since currently 85.5% off their peak.

Amidst the positive price movement, Cathie Wood’s investment management firm ARK added around $13.2 million worth of Coinbase (COIN) shares, per an investor email on Thursday.

ARK Innovation ETF (ARKK) added 181,972 shares ($11.4 million) of Coinbase. This was the fund’s biggest COIN order of the year, surpassing the $9.2 million order earlier this month. Meanwhile, 31,547 COIN shares ($1.93 million) were added to Ark’s Next Generation Internet ETF (ARKW).

These purchases came after Coinbase reported its Q4 2022 earnings this Tuesday, which beat analyst expectations.

The San Francisco-based company reported fourth-quarter net revenue of $605 million, up 5% from $590 million in the third quarter. Subscription and service revenues grew 34% to $283 million in Q4, accounting for almost 50% of Coinbase’s overall revenue in the quarter, which was primarily due to interest income that came in at $162.2 million. Transaction volume, however, fell 12% quarter over quarter to $322 million on lower overall trading volume.

In its shareholder letter, Coinbase said crypto markets have improved in Q1 2023 compared to Q4 2022, helping it generate $120 million in transaction revenue in January 2023.

However, during the company’s earnings call, CEO Brian Armstrong cautioned retail investors “not to extrapolate those results forward,” pointing out that last year revealed just how volatile the crypto market can be.

Coinbase expects increased crypto regulation globally in the coming years and anticipates benefiting from it. The company also criticized the fragmented approach of the United States to regulating crypto but continues to work towards more consistent policies.

“Policy is my top priority this year,” said Armstrong, adding that he’s been spending a lot of time in Washington, D.C. “There is a lot of excitement about the potential of this technology, and there is a lot of desire for people to have this built here in America.”

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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Navigating the world of blockchain 🧭
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

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

🏛️ 1. Core Architecture: The Base Layer

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

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

🔑 2. Ownership & Security: Wallets and Keys

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

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

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

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

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

💰 4. Financial & Market Concepts

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

💡 Quick Cheat Sheet

"Not your keys, not your coins."

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

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AI Is Coming for Your Job Title

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

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

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

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

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

The Titles Employers Actually Want

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

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

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

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

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

The Jobs With the Science-Fiction Salaries

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

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

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

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

The Department of Unnecessary Titles

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

Other titles seem to have escaped from a brainstorming retreat.

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

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

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

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

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

Source

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