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How AI is Transforming Music Creation in Web3
June 20, 2023
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In April 2023, Warner Music Group chief digital officer and executive vice president of business development Oana Ruxandra told CoinDesk’s The Hash that she expects music tools driven by artificial intelligence (AI) to “open up the world like we haven’t before,” inspiring “new forms of creativity and sub-genres” across the music and entertainment industries.

While Ruxandra’s outlook is optimistic, she also acknowledged the concerns of many musicians: “We have to be very vigilant,” she said, noting the importance of protecting the creativity and rights of artists. Just days before Ruxandra’s appearance on The Hash, an AI-generated deep-fake music track titled Heart On My Sleeve gained traction by mimicking the voices of songwriters Drake and the Weeknd – even though neither artist had participated in its creation. Instead, the song’s creators trained the artificial intelligence bot using music by the artists, which angered label owner Universal Music Group over.

Other musicians have been more welcoming to the new technology. Less than a week later, electropop musician Grimes invited her fans to create their own AI-dubbed songs using her voice and extended the offer to split royalties 50/50, demonstrating one creative solution to the AI deep-fake conundrum.

Keeping intellectual property challenges in mind, there’s still no doubt that AI music tools can place new forms of expression at an artist’s fingertips. Sometimes, AI can even be used to enhance music production by filling in technical or intellectual gaps in an artist’s abilities, helping them bring ambitious concepts to life in a matter of clicks. These tools can also perform sound engineering tasks more efficiently, lowering barriers and the time it takes to release music.

As we look toward Web3, companies and artists are taking AI even further by pairing music with immersive, interactive and user-generated experiences in the metaverse and beyond.

AI music tools in Web3

A number of crypto-native musicians and platforms have already found creative ways to integrate AI tools into their practice.

Take VNCCII, for instance, the metaverse-first alter-ego of Sydney-based female producer Samantha Tauber. Utilizing the industry-leading real-time 3D creation tool, Unreal Engine, Tauber dons her avatar to stream live broadcast interviews from the metaverse, in addition to performing in virtual concerts and shows. Like any set or costume change, the digital component of VNCCII’s artistic identity is expanding the borders of her artistry.

Web3 music company PIXELNYX combines augmented reality (AR) experiences with metaverse gaming and is focused on helping artists build memorable experiences for fans. Co-founded by the electronic music producer Deadmau5, who has been known for sending fans on quests through The Sandbox and hosting shows in Decentraland, PIXELYNX aims to evolve our traditional notions of fandom through the use of AI, Web3 and user-generated content (UGC).

In April, PIXELNYX released Korus, a tool that allows users to create AI-powered music companions using officially-licensed artist content.

When used in this spirit, AI music tools can aid, augment or enhance an artist’s creative style. While the tools are not good enough yet to replace artists, they are impressive and constantly “learning” through continued human interaction. Replacing musicians with AI has never been a popular take, as proven by the pushback Spotify received after testing its own version of artificial music curation. Yet despite the controversy surrounding AI, today’s musical artists may be able to benefit from using AI-assisted music production in ways that respect the craft.

Ideation and collaboration

WarpSound, an adaptive AI music platform, has found several ways to integrate blockchain-based collectibles and digital avatars into its business offerings. The company, which produces music content, non-fungible tokens (NFTs) and social experiences, is soon releasing a software API that composes original music note-by-note in a range of styles.

Founder and CEO Chris McGarry, an entrepreneur and media executive who previously severed as the music lead at Facebook’s virtual reality unit Oculus, says WarpSound’s tools help artists find new inspiration and source material that invigorates their creative processes. The company is the recipient of The Sandbox’s Game Maker Fund, which supports game designers in The Sandbox metaverse, and plans to build a home venue inside the platform where artists can experiment with generative music.

WarpSound also worked with Mastercard as the AI music partner for their Artist Accelerator program, where McGarry says he’s observed new benefits to the creative process.

“Last week, I was in a set of virtual studio sessions with artists participating in the program,” said McGarry. “We were working with our generative AI music interface to present a set of musical ideas, then having the artist shape those and iterate until they landed on something, the essence of which resonated with them, that they were motivated to work with.”

WarpSound has also partnered with the Tribeca Film Festival and YouTube to create interactive and playful music experiences between artists and audiences.

Composing and arrangement

If your music project is less about live performance and more about the finished product — maybe you’re composing original music for a podcast, metaverse event, YouTube channel, Web3 video game or educational content — you can use AI to speed up the process of composition and arrangement. Of course, the world’s most talented virtuosos can likely do musical scales in their sleep, but with so many elements to sound and video production, it’s becoming standard practice to use AI to insert quick scales, arpeggios, runs and harmonies to original music.

Tools like Riffusion allow users to provide text prompts that are transformed into music. Soundful is another AI platform that allows people to generate and download royalty-free tracks.

If you want to go one step further and add lyrics, the popular do-it-all tool ChatGPT can write a two-verse song with a pre-chorus, chorus, bridge and outro in just under 30 seconds with minimal prompting. Of course, the lyrics may be a touch simplistic or cheesy — but aren’t some of the best songs?

In most cases, songs generated by AI are reproducible without the need to pay licensing fees, given they were made by machines and therefore not protected under U.S. intellectual property law. Most platforms, however, charge a subscription fee.

These sounds can then be minted as NFTs and sold on marketplaces like OpenSea. Platforms like Royal.io also allow artists to join the site and offer their songs as fractionalized NFTs that offer royalty payouts to fans.

The limits of AI music production

You may have already heard that musical AI tools aren’t yet that sophisticated, especially when compared to the latest AI text-to-image generators (which have already been used to spin out whole comic book collections) and Open AI’s chatbot, Chat GPT (which reportedly passed the Bar exam).

Audio production indeed requires more computing power than static text and image outputs and therefore is lagging behind, according to experts in the field. Alexander Flores, head of tech and strategy at the music research network, Water & Music, says that tech innovation generally travels from the least data-intensive formats to the richest. In the case of AI, it makes sense why chatbots are perhaps faster to develop than AI audio and video rendering.

In one online discussion thread, a Reddit user pointed out these limitations, emphasizing that while a writer can proofread and edit an AI chatbot’s outputs in seconds, it takes several minutes to listen to a song, and sometimes even hours to edit it. Machines are also slower to learn from AI datasets since audio files that feed them rarely have comprehensive text descriptions to teach the AI about the file’s attributes (genre, tempo, key, instrumentation, etc.). Meanwhile, text and image-based AIs can swiftly trawl through thousands of words and visuals.

“How long it takes to consume the content matters a lot,” said Flores. “With a song, you're locked in for three minutes. You can't speed it up because then you're not experiencing the actual song as it was written.”

In addition, images are static, while songs are more dynamic: “Audio is just much higher dimensional,” said Stefan Lattner, managing researcher at Sony CSL, a creative technology lab, in a panel at Water & Music’s inaugural Wavelengths Summit. “While images have a fixed number of pixels, in audio you have a variable number of seconds that you want to generate.”

Nonetheless, Water & Music calls creative AI the most disruptive technology for the music business since Napster, the peer-to-peer file-sharing application that made music distribution virtually free, as well as borderless and permissionless – a concept familiar to crypto-natives.

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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:
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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.
 
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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
 
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It can't.
 
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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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