Bitcoin Ordinals: A Quick Guide

Posted on by Werner

Non-fungible tokens (NFTs) used to be the sole domain of Ethereum and other smart contract networks like Cardano and Solana. During the 2021 bull run, the world caught crypto JPEG fever thanks to collections like Crypto Punks, Bored Ape Yacht Club (BAYC) and 2024 darling Pudgy Penguins (now in a Walmart near you), and Ethereum was the infrastructure that captured most of the value. 

In early 2023, a new challenger kicked open the saloon doors, making a crazy entrance into the Wild West of crypto: Ordinals. The crazy part comes with that it lives on the Bitcoin blockchain, which is notoriously slow (only seven transactions per second) and with a hardcore Bitcoin maxi community strongly resistant to any change. 

Despite this, it’s been up only for Bitcoin Ordinals, as over 64 million have been minted by the time of this article. 

What are Bitcoin Ordinals? 

Ordinals in their current form were introduced by Bitcoin developer Casey Rodarmor on 21 July 2022 with the publication of his Ordinal Theory paper.

Bitcoin Ordinals are a way of assigning a unique identifier to each satoshi (sat), the smallest unit of Bitcoin, based on the order in which it was mined. By assigning an ordinal number to each sat, it becomes possible to create unique, distinguishable assets on the Bitcoin blockchain. This means that every single sat can be individually tracked and associated with specific data, such as images, videos, or other digital artifacts. 

According to Rodarmor, Ordinals’ genesis can be traced back to Satoshi Nakamoto’s creation of the Bitcoin blockchain in 2009. That means that especially early Ordinals have a historical significance within the blockchain’s timeline. He also noted that the concept of ordinals was independently discovered multiple times, predating the NFT boom by nearly a decade, most notably:

  • Litecoin creator Charlie Lee’s 2012 proposal on the Bitcoin Talk forum to integrate proof-of-stake using the ordinal algorithm
  • jl2012’s introduction of a decimal notation scheme that mirrored key features of ordinals.

Although neither proposal was fully realized, they underscore the enduring interest and potential of ordinals within the Bitcoin community, long before their recent surge in popularity.

Rodarmor wrote,

These independent inventions of ordinals indicate in some way that ordinals were discovered, or rediscovered, and not invented. The ordinals are an inevitability of the mathematics of Bitcoin, stemming not from their modern documentation, but from their ancient genesis. They are the culmination of a sequence of events set in motion with the mining of the first block, so many years ago.

How do Bitcoin Ordinals work?

Creating a Bitcoin Ordinal involves ‘inscribing’ data onto a specific sat, hopefully the rarer the better. This is done by embedding the inscribed data into the witness data of a Bitcoin transaction, using the ordinal number of the sat as a reference point.

To understand how this works, let’s break down the technical details:

  1. Ordinal numbers: Each sat is assigned an ordinal number based on its position in the blockchain. The first sat in the genesis block is assigned the number 0, the second sat is assigned 1, and so on. This numbering scheme continues sequentially throughout the entire Bitcoin blockchain.
  2. Inscribing: An inscription is the process of embedding data (such as an image or video) into a Bitcoin transaction using the ordinal number of a specific sat. This is done by including the data in the witness data of a taproot script-path spend.
  3. Taproot and SegWit: Bitcoin Ordinals leverage comparatively new upgrades to the Bitcoin protocol, specifically Taproot and Segregated Witness (SegWit). These provide more flexibility in the types of transactions that can be recorded on the blockchain, making it possible to embed larger amounts of data.
  4. Transfers and ownership: Once an inscription is made, the associated sat can be transferred or sold just like any other Bitcoin. The ordinal number and associated data remain linked to the sat throughout its lifetime on the blockchain.

Credit: Tesfu Assefa

Rodarmor Rarity Index

According to the Rodarmor Rarity Index, created by Casey Rodarmor, the developer of Ordinal Theory, satoshis can be classified into different categories based on their rarity. About 99% of sats are considered Common Sats. 

Uncommon Sats are the first sat of each newly mined block, occurring roughly every 10 minutes. Rare Sats are the first sat of the block mined after a Bitcoin network difficulty adjustment, which happens every 2,016 blocks or about two weeks. Epic Sats are the first satoshi mined in the block immediately following a Bitcoin halving event, occurring every 210,000 blocks or roughly four years. 

Legendary Sats are the first satoshi mined when a difficulty adjustment and halving event coincide, which will happen only once every 24 years, with the first instance scheduled for 2032. Finally, the Mythic Sat is the first-ever satoshi mined by Satoshi Nakamoto in the genesis block in 2009.

Magic Eden Rare Sats

Magic Eden drive most Ordinals sales, and have created their own version of the Rodarmor Rarity Index. Let’s go over it: 

  • Nakamoto Sats: Highly sought-after sats mined by Bitcoin’s pseudonymous creator, Satoshi Nakamoto.
  • First Transaction Sats: Sats originating from the first-ever Bitcoin transaction on January 12, 2009, when Satoshi Nakamoto sent 10 Bitcoins to Hal Finney.
  • Palindrome sats: Sats whose numbers can be read the same forwards and backwards (e.g., 16661 or 23832), adding a layer of rarity and curiosity.
  • Vintage Sats: Sats mined within the initial 1,000 blocks of the Bitcoin blockchain, marking the dawn of Bitcoin.
  • Pizza Sats: Sats from the iconic transaction on May 22, 2010, where a programmer paid 10,000 Bitcoins for two Papa John’s pizzas worth $27 at the time, and thereby created a monetary value for BTC.
  • Block 9 Sats: Some of the oldest sats in circulation, mined in one of the earliest blocks and offering a tangible connection to Bitcoin’s beginnings.
  • Block 78 Sats: Sats from the block mined by Hal Finney, marking the first instance where someone other than Satoshi Nakamoto (or is it???) contributed to the blockchain’s growth.

The Essential Ordinals and Rare Sat Tool Case

Some of the best tools for exploring the world of Ordinals include Ordpool.space for tracking Ordinals in the mempool, Liquidium.finance for Ordinals DeFi, and Ordiscan.com for checking your wallet for assets like runes. 

Geniidata and Ord.io are popular Ordinals explorers, while Sating.io and Automated Sat Hunter by Deezy Labs helps you scan your wallet for rare sats. For launching your own Ordinals project, consider Ordzaar, and for inscribing data, Ordinalsbot is the go-to. 

Conclusion

Bitcoin Ordinals represent a significant development in the evolution of Bitcoin, enabling the creation of unique, NFT-like digital assets native to the Bitcoin blockchain. While the concept is still relatively new, it has the potential to unlock a wide range of use cases and drive innovation in the Bitcoin ecosystem, which includes anything from digital art to gaming to even digital identity. With Bitcoin layer-2 chains also improving in leaps and bounds, the future looks extra-ordinally bright (sorry, couldn’t resist!) for this new digital asset class. 

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Can AI Aid in the Recovery Of Lost Bitcoin?

Posted on by Werner

Every time Bitcoin hits a new all-time high, we hear that about ‘about 20%’ of the Bitcoins minted are lost, never to be recovered, with some seriously sob stories thrown in for good measure. They were lost for various reasons, and more continue to go missing, albeit less often these days. Most of those missing Bitcoins have been lying dormant for several years. In Bitcoin’s early days, stashes of Bitcoin weren’t seen as the honeypots they are today, and people got careless, only to pay the ultimate price of losing their BTC. Now, with the age of AI upon us in 2024, can new technologies help retrieve those missing fortunes? 

Careless Early Miners

In the early years of blockchain, from 2009 to 2014 or so, some individuals had found a hobby using spare PCs to mine Bitcoin. This wasn’t terribly lucrative in fiat terms at the time, but many of those miners racked up what would now be enviable collections of dozens, hundreds, or thousands of Bitcoins. By now, most of us have heard about the missing hard drives that contain many millions of dollars worth of Bitcoin. These days, one solitary Bitcoin is worth a tidy sum in fiat money, and accumulators speak of ‘stacking Sats’ (1 SAT = 0.00000001 BTC) rather than accumulating full Bitcoins.

Recovery Efforts

Have any of those stray Bitcoins been recovered? Yes, a small percentage of them have, and there is hope that state-of-the-art decryption techniques and the latest GPUs can be put to use to reclaim more of them. Companies have been set up to assist people in recovering missing Bitcoin wallets, and these efforts have seen some modest success in cases where there is at least a bit of a trail that could lead to the missing Bitcoins.

Bitcoin cryptography is strong. A private key represents a unique number between 1 and 2 to the power of 256. That’s more than the total number of atoms in the universe. With current technology, a brute force attack to test a colossal number of potential keys until landing on the right one would take millions of years.

Is it hopeless? Is it not even worth trying? That depends. If the owner of missing Bitcoins has any hints at a seed phrase, records of where it may have been transferred to or stored, or a physical storage device, that could greatly cut down the time it would take to get to a positive result.

AI to the Rescue?

Artificial Intelligence has been a hot topic of conversation for a while now, and cryptocurrency is not immune from AI influence. When it comes to cracking a Bitcoin wallet, the brute force method will be futile on its own, but AI models are being developed that can drastically shorten the time it takes to crack a private key. 

With machine learning and the most powerful GPUs, patterns can be identified within vast collections of data that can significantly bring down the time it takes to uncover encryption keys.

Enter PassGPT, a cutting-edge password guessing AI model that could be the saviour that unlocks your lost millions. Built on OpenAI’s GPT-2 architecture and trained on millions of exposed credentials, PassGPT has a unique edge over other password-guessing tools. By using a technique called progressive sampling, it constructs passwords one character at a time, making it 20% more effective than even the most advanced GAN models.

But PassGPT isn’t just a one-trick pony. It can analyse password strength, identify patterns, and even guess passwords in multiple languages. And it can come up with new passwords that aren’t even in its training data.

So, how could this help you recover your lost Bitcoin? By adapting PassGPT to focus specifically on Bitcoin wallet passwords and feeding it data on common password patterns used by crypto enthusiasts, its effectiveness could be supercharged.

Of course, this technology raises some security concerns, but when used responsibly, it could be a lifeline for those who have lost access to their Bitcoin due to a forgotten password. As generative AI continues to evolve, solutions like PassGPT could become an invaluable tool in the ongoing challenge of balancing security and accessibility in the world of cryptocurrency.

Side-Channel Attacks on Hardware Wallets

Hardware wallets are a common and extremely secure way to store private keys offline. When used judiciously and prudently, they work very well – and best practices should be followed to ensure that private keys are backed up in another separate location, just in case the device goes missing or the owner becomes incapacitated. If an older or less sophisticated hardware wallet has become inaccessible, a side-channel attack offers a glimmer of hope.

A side-channel attack is a method of exploiting any physical characteristics of a hardware wallet that can be detected via electronic sensors. Characteristics including timing, power consumption patterns, and electromagnetic and acoustic emissions which can sometimes reveal enough information to obtain fragments of the PIN code to access the wallet or crack the private keys. Although only fragments may be uncovered, they can still drastically reduce the time taken for cracking tools (including AI and machine learning) to search for the private key.

Technology is advancing all the time, and different hardware wallets will have different levels of vulnerability to side-channel attacks. One of the most well-known hardware wallet makers, Ledger, claims that its wallets are immune to such attacks, so mileage of side-channel attacks may vary. Ledger’s main competitor, Trezor, also claims that crypto functions have been re-written by their cryptography professionals to eliminate such vulnerabilities. Additional measures such as ‘secure elements’, which ensconces the private key permanently inside a computer chip, also makes breaking cold storage encryption near impossible. 

Credit: Tesfu Assefa

Limitations of Silicon

The silicon computer chips that power all of our digital devices have progressed at an astounding rate over the years, roughly in line with Moore’s Law. Taiwan Semiconductor and Samsung are planning to produce 1.4-nanometer chips by around 2027. These chips will lead to some astounding AI performance, but we are getting awfully close to the physical limits of silicon, and we will have to seek other technologies for computing power to continue advancing beyond silicon.

The Quantum Solution/Threat

Currently lurking in the waiting-room of the future is quantum computing. Right now, it’s cumbersome, expensive, and is years from being available commercially, but boy can it compute. It has been expected that quantum computers will be able to compute 158 million times faster than the most advanced existing silicon-based computer.

Researchers at the Centre for Cryptocurrency Research and Engineering of Imperial College London have calculated that a quantum computer with 1500 or more error-corrected qubits will be able to crack a Bitcoin private key. IBM has published a roadmap where they reckon computers with thousands of error-corrected qubits will be a reality by around 2029.

Opposingly, at the popular hacking news website, Hackernoon, they estimate that it would still take a quantum computer 10³² years of a brute force assault to hack Bitcoin. Some developers are proposing a Bitcoin encryption upgrade to ensure quantum resistance, but that would likely require at least a soft fork to implement.

Conclusion

The possibility of AI and quantum computing helping to retrieve lost Bitcoins are very exciting to some people, but threatens to destroy the value of Bitcoin, since the available supply could swell by a few million BTC, especially if they crack Satoshi’s wallet. The biggest fear is quantum computing though, and it’s good to see Bitcoin core devs putting contingency plans in place. 

Till then, good luck to the likes of James Howells, who has been trying to retrieve a hard drive with 8,000 Bitcoin on it for over a decade. That’s around $560 million right now. 

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Bitcoin Hits All-Time High Notes in Early 2024

Posted on by Werner

Bitcoin is back into your friends’ and family’s chats, as the world’s most valuable digital asset edged its prior all-time high price record in dramatic fashion. The months of sustained growth that also saw other crypto niches like AI cryptocurrencies (riding the artificial intelligence trend) and memecoins (easy-to-understand mainstream tokens) balloon in value appeared unstoppable after most of the Grayscale spot ETF redemptions were done and investors are frothing at the sky-high predictions they are starting to hear across social media and cable news. 

After spending over two years trading far far below its November 2021 peak, Bitcoin defied the naysayers by skyrocketing past $73,000 to set a new supreme price milestone of $73,750 on March 14th.

This latest achievement capped an extraordinary bull run for the crypto market leader, which saw its price increase by over 50% so far in 2024 amid frenzied trading activity. On the day it reached its new record high, $100+ billion worth of Bitcoin changed hands as investors pile in, hoping to ride the wave higher.

Credit: Coinglass

Bitcoin’s new personal best above $73,000 follows on from record-setting earlier in the month, when it hit $69,000 for the first time on 5 March. That proved too great to turn resistance into support, and caused a cascade of liquidations, with its price plunged by double-digits, falling over 15% to $59,000, before paring some of the losses in a rebound to a V-shaped recovery to the new high, and to the $71,000 where it currently trades. 

This whiplash-inducing price action underscores the intense tug-of-war still playing out across crypto markets, where TradFi whales have now entered the ring and brought with them some heavy bags to throw around. 

So what forces converged to propel the world’s largest cryptocurrency to such lofty new heights in the first quarter of 2024? As is often the case in these bullish periods, the rally was fueled by the perfect storm of optimistic narratives and real-world regulatory and economic developments. We already covered many of these in last year’s 2024 Crypto Bull Run article, and I recommend you go revisit it. 

Bitcoin spot ETFs create insatiable BTC demand

Credit: The Block

Restoring interest back in Bitcoin this year were the highly-anticipated launches of spot Bitcoin exchange-traded funds (ETFs) by major financial players like BlackRock, Fidelity, and Grayscale. After years of denial by US regulators, multiple spot Bitcoin ETFs finally received approval in January 2024.

These ETFs allow institutional and retail investors to gain exposure to Bitcoin’s price movements through a regulated, familiar product that directly holds the underlying crypto assets. Huge sums of investment capital flooded into these ETFs right out of the gate, creating immense buy pressure in the spot Bitcoin markets as the funds raced to accumulate enough BTC to back their fund shares.

Just two months after launching, the Bitcoin spot ETFs had already vacuumed up a staggering combined total of over $45 billion in assets under management, representing around 684,000 Bitcoin tokens: over 3% of the total circulating supply. Inflows showed no signs of stopping, with BlackRock’s fund alone reaching $10 billion in just seven weeks.

Bitcoin Halving

Another major factor driving Bitcoin’s rally has been the building anticipation of the next ‘halving’ event for the cryptocurrency, expected in April 2024. This systemic halving of Bitcoin’s mining reward happens automatically every four years, resulting in a 50% reduction in new supply hitting the market. The latest halving will see BTC rewards drop to 3.125, which is down four halvings from 2011’s block rewards of 50 BTC. With over 19 million Bitcoin already mined (and possibly a quarter lost) there is less and less to go around.  Previous halvings reliably preceded massive price increases as the decreasing supply dynamic helped fuel further buying demand.

Many analysts and industry experts have forecasted Bitcoin to surge well past $100,000 within 12-18 months after April’s halving, based on the historic patterns of past cycles where prices eventually climbed 10× or higher following the supply shocks. Speculators piled in early to front-run the perceived upside.

FASB Accounting

Bitcoin’s rally also got a boost in late 2023 from the official embrace of long-awaited new accounting rules for US public companies around cryptocurrencies. Starting in 2025, new FASB guidance will allow businesses to value certain crypto assets at fair market value on their balance sheets each reporting period, marking a huge upgrade from the current treatment of Bitcoin as an ‘indefinite-lived intangible asset’.

This rule change laid the groundwork for even broader institutional adoption of Bitcoin, removing one of the final barriers for public companies and investment funds looking to add exposure without complex workarounds.

US Elections, SEC and Interest Rate Cuts

Another important consideration to make is the impact of US policymakers’ adoption of Bitcoin and crypto. While the SEC under Herr Gensler views most cryptos as securities, it views Nakamoto’s coin as a commodity thanks to its proof-of-work origins. The Fed, whose monthly FOMC meetings became horror movie nights for crypto investors in 2022 and 2023, will likely soon have to relent and start reducing those inflation-crunching high interest rates, or combat a recession next. And of course, America will decide on the next President this year, and this should see crypto bashing through Operation Chokepoint 2.0 this year. Even Donald Trump is warming to Bitcoin now. 

Credit: Tesfu Assefa

What’s different this time round? 

While the Bitcoin frenzy and associated wildly optimistic price forecasts provoked understandable flashbacks to the 2017 and 2021 crypto bubbles for market veterans, the 2024 rally did have some key fundamental differences.

This time, sustained upward price pressure came not from shadowy derivatives platforms like FTX and opaque stablecoin ecosystem hazards like you-know-who, but from transparent, battled-hardened regulated funds and publicly-traded companies allocating directly to Bitcoin’s core layer-1. Record volumes and open interest levels on trusted exchanges – centralized and decentralized, spot and derivative – reflected genuine liquidity.

And whereas the 2021 peak was fueled by hype around experimental blockchain technologies and corporate marketing gimmicks, Bitcoin’s 2024 renaissance had the more grounded narrative of finally fulfilling its long-awaited promises as a now matured value reservoir and decentralized financial network, emboldened by the embrace of legacy institutional capital via ETFs, which may soon extend to Ethereum.

Of course, only time will tell if Bitcoin can maintain these lofty price levels or if the market mania will once again dissolve into despair. It’s easy to get caught up in the FOMO, and even easier to hit that sell button at a loss when market euphoria wears off and those 20% drops or weeks of sideways action break your resolve. 

But in its latest epic price run, crypto’s top dog (sorry Doge) demonstrated its incredible ability to capture imaginations and animate markets around the world like no other asset.

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Ghostbuster: Unprecedented Accuracy in AI-Generated Text Detection

Posted on by Kedist

Text generated by language models, like ChatGPT, is getting better and better at mimicking human language. But doubts have been raised about the authenticity and trustworthiness of writing produced by AI. In response, scientists at the University of California, Berkeley have created Ghostbuster, a sophisticated technique for identifying text written by artificial intelligence.

Methodology

Ghostbuster uses an innovative technique that involves using a number of less powerful language models and running a systematic search over their features. It may determine if a document is artificial intelligence (AI) created by training a linear classifier on specific attributes. Interestingly, Ghostbuster can identify text produced by unknown or black-box models because it doesn’t need token probabilities from the target model. Three additional datasets were made available by the researchers for benchmarking detection across different domains.

Figure 1: An outline of the Ghostbuster model training procedure. The researchers fed each document into a series of weaker language models to obtain token probabilities. Then, they ran a structured search over combinations of the model outputs and trained a linear classifier on the selected features. (Credit: Berkeley Artificial Intelligence Research (BAIR))

Performance and Comparison

Ghostbuster performed exceptionally well in assessments, outperforming competing detectors like DetectGPT and GPTZero by a wide margin with an in-domain classification score of 99.0 F1. It showed better generality over language models, prompting techniques, and writing domains. These astounding results demonstrate Ghostbuster’s dependability and its capacity to identify AI-generated material.

Ethics

There are a lot of ethical questions raised by the use of AI-generated text detection methods. Such models’ false positive rates, which mistakenly identify genuine human work as AI-generated, can have serious consequences. Prior research has revealed some biases, such as the disproportionate marking of writings written by non-native English speakers as AI-generated. Nonetheless, Ghostbuster helps to address these ethical issues thanks to its enhanced performance and generalization skills. Ghostbuster is a technological and moral advance since it ensures more accurate identification while lowering false positives.

Challenges and Future Directions

The paper notes that there are still difficulties in identifying language produced by artificial intelligence, especially when dealing with hostile prompting and paraphrasing attacks. But Ghostbuster’s emphasis on full paragraphs or papers produced by language models offers a viable direction for further investigation. It is imperative to prioritize transparency and fairness in the creation and implementation of AI-generated text detection systems to guarantee impartial treatment and prevent unwarranted harm.

Constraints

Despite Ghostbuster’s outstanding performance, it’s important to recognize its limitations. The quality and diversity of the weaker language models utilized in the detection process can affect the efficacy of the system. Furthermore, adversarial strategies might develop and provide problems for the accuracy of the system. To overcome these restrictions and further expand the system’s capabilities, more research is required.

Credit: Tesfu Assefa

Conclusion

In summary, Ghostbuster is a noteworthy development in the area of artificial intelligence-generated text detection. Its exceptional performance and ethical advancements make it an effective tool for recognizing text generated by artificial intelligence in a variety of sectors. Ghostbuster addresses potential biases and lowers false positives, promoting the safe usage of AI-generated text detection systems. Continued research and development is essential to overcoming obstacles, enhancing system performance, and guaranteeing the moral use of AI-generated text identification tools. With the prevalence of text produced by artificial intelligence (AI), Ghostbuster provides a useful way to ensure the reliability and trustworthiness of written material while giving ethical issues priority.

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Crypto AI Revenues Could Exceed $10 Billion by 2030

Posted on by Werner

The integration of crypto incentives and blockchain technology with artificial intelligence (AI) could drive widespread adoption and advancement of decentralized AI and AI cryptos over the next decade, according to a new report by digital asset manager VanEck. Its timing couldn’t be better as we’ve been seeing an absolute explosion of AI-related cryptocurrency prices so far in 2024, the most recent rally coming on the back of OpenAI’s Sora launch.

In a recent in-depth report ‘Crypto AI Revenue Predictions by 2030’, VanEck analysts made several bullish predictions about the growth and the revenue potential of businesses operating at the intersection of crypto and AI. While any predictions should be taken with a grain of salt, considering where we’re at in the current crypto 2024 bull cycle, the report asks a lot of important questions and offers some surprising answers in the process. 

Van Eck analyst Gabor Gurbacs has taken a controversial stand against AI, predicting its misuse will result in the biggest spam attacks the world has ever seen. Gurbacs sees Bitcoin as its only salvation, so read on to find why. 

Public Blockchains Uniquely Positioned to Aid AI Progress

The Van Eck report is extremely bullish on the benefits that blockchain can bring to AI. He feels that AI and AI Agents “provide the raison d’être” for blockchain technology. It’s a big statement, but backed up by some sound reasoning: blockchain networks like Bitcoin and Ethereum possess several key attributes that make them well-suited to address existing challenges facing the AI industry, most notably:

  • Transparency – Being public ledgers recording all transactions, blockchains enable oversight related to data usage, model ownership, etc. This supports trust and accountability.
  • Immutability – Records made on blockchains cannot be altered afterwards. This immutability lends integrity to data used for critical model training and testing.
  • Ownership – Tokens, NFTs, and smart contracts allow us to clearly define ownership rights related to data, models, and even model outputs.
  • Adversarial Testing Environment – The adversarial, incentivized environments of crypto networks that financially reward hacking and optimization force rigorous, real-world testing of systems.

He projects that these properties of blockchain will speed up the adoption of, and trust in, AI over the next seven years.

Crypto Market Share of AI Could Reach Billions Annually

Applying economic and productivity growth assumptions to previous McKinsey research, Van Eck predicts a total addressable market (TAM) for global productivity gains enabled by AI automation will clock a crazy $5.85 trillion in 2030. Assuming 33% adoption by businesses worldwide, annual AI revenues could top $250 billion within the decade.

Moreover, the report forecasts crypto’s potential market share across major AI business categories:

  • Software: $6.27 billion
  • Infrastructure/Compute: $1.9 billion
  • Identity: $878 million
  • Safety/Compliance: $1.12 billion

Tallying these up, it’s predicted that annual crypto AI revenues could approach $10.2 billion by 2030 in VanEck’s base case. Under more aggressive assumptions about adoption and market capture, AI cryptocurrencies’ stake could exceed $50 billion.

How blockchain helps AI solve identity issues 

Artificial intelligence is increasingly critical to the global economy, akin to an essential utility. Web2 giants like Amazon and Google dominate current AI infrastructure, while blockchain technology supports specific, high-demand needs with its decentralized approach, offering flexibility and customization for AI development. This scenario positions blockchain as a dynamic adjunct in the AI infrastructure market, similar to how ride-sharing platforms like Uber complement, rather than replace, traditional transport services.

The importance of secure AI identities is rising, with blockchain playing a key role in preventing Sybil attacks by establishing verifiable digital identities. This defense is crucial as AI applications extend into areas like autonomous vehicles and healthcare, where safety is paramount. 

Blockchain’s immutable records serve as reliable “proofs of safety,” essential for high-stakes accountability and compliance. Despite blockchain’s potential to revolutionize AI safety and identity verification, a significant portion of this market is expected to stay centralized, preferring established reliability and trust in sensitive sectors.

Bitcoin miners find surprising synergy with the AI sector

Bitcoin miners, traditionally focused on mining proof-of-work cryptocurrencies like Bitcoin, are diversifying into the AI sector due to their shared high energy consumption needs. These miners have historically invested heavily in energy infrastructure, often utilizing cost-effective but carbon-intensive power sources. This positions them uniquely to offer lower-cost energy solutions for AI’s backend infrastructure, contrasting with Big Tech’s move towards renewable energy and vertically integrated operations. 

As AI’s energy demands potentially outpace current projections, bitcoin miners’ cost advantage in electricity could become increasingly significant, prompting a shift towards providing high-margin AI services, particularly in GPU provisioning.

Companies like Hive, Hut 8, and Applied Digital are leading this transition, with some reporting substantial revenue growth from AI-related operations compared to traditional bitcoin mining. 

For instance, Hive’s AI operations are notably more profitable on a per-megawatt basis. However, despite the promising shift and potential for revenue diversification, bitcoin miners face challenges in scaling up for AI, including skills gaps in data center construction, the need for a specialized salesforce, and limitations imposed by network latency and bandwidth in remote locations. These hurdles could impede their pivot to AI, despite the opportunities in the sector.

Credit: Tesfu Assefa

Notable Blockchain Use Cases Emerging Across the AI Landscape

Public blockchains and crypto token incentives have already sparked solutions addressing several pressing needs for progress across the AI landscape:

Decentralized Compute/Infrastructure

Projects like Akash, Render and Bittensor provide decentralized cloud computing platforms and infrastructure to help supply scarce GPUs for AI model training and deployment. This helps to pull down current limitations around access, cost, security, and customization options.

Model Optimization

Protocols like Numerai use tokenized incentives to organize data science competitions aimed at building optimized models for tasks like quantitative finance or natural language processing. Crypto tokens reward the most accurate and effective models.

Data Integrity

Emerging zero-knowledge proof solutions from startups like MODA allow AI model owners to mathematically prove certain claims about data usage or model performance without revealing proprietary intellectual property or sensitive information. This supports copyright protections and model accountability.

Digital Identity

Initiatives like WorldID (with its Worldcoin cryptocurrency) – spearheaded by AI thought leader Sam Altman from OpenAI and Sora – controversially leverage blockchain and biometrics to establish verified digital identity firmly linked to real humans. As automation increases, reliably determining humanity could help ensure security for computer networks and systems.

Conclusion

In summary, crypto-based networks already demonstrate clear potential to overcome some of the hardest barriers holding back innovation and mainstream adoption in artificial intelligence. However, integrating these two exponentially advancing technologies comes with persistent technical and adoption challenges.

If solutions continue maturing at their current brisk pace, crypto and AI seem well positioned to drive tremendous value for one another over the next 7 to 10 years. But uncertainties and speculation still cloud the most aggressive growth projections put forth. According to VanEck, the base case for 2030 remains strong, but the roadmap to billions in annual revenue still requires some visionary bets on both technologies to pay off.

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