Crypto attracts people with one promise:

Fast money.

But that promise creates the problem.

Most traders enter the market expecting:

• Immediate profits
• Minimal effort
• Quick success

And when reality doesn’t match expectations, they make mistakes.

The Missing Foundation

The majority of traders fail because they lack:

• A structured trading strategy
• Proper risk management
• Understanding of market psychology

They jump straight into execution without preparation.

That’s like trying to fly a plane without training.

How AI Changes the Starting Point

This is where AI trading tools create a shift.

Instead of starting with guesswork, traders can:

• Learn from structured data
• Use guided strategies
• Avoid common beginner mistakes

This dramatically shortens the learning curve.

Final Thought & My Take

Most traders don’t fail because trading is impossible.

They fail because they start the wrong way.

And AI + crypto is changing that starting point.

=========================================================================

What would I do right now to get involved in Ai+crypto? Start small. DCA (dollar cost averaging with just a few Ai coins like TAO, AKT, RNDR, & FET. These are my favorites.

To ai + crypto,

Chris

P.S. – wanna earn passive income in crypto but not quite sure how to begin? This 9 module step by step video course is exactly how I got started (only $97 USD).

The intersection of AI + crypto is quickly becoming one of the most powerful narratives in the market.

But here’s the reality most people miss:

By the time a project becomes widely known, much of the upside is already gone.

The biggest gains in low cap crypto projects don’t come from following trends—they come from identifying early-stage narratives before they explode.

Right now, AI is driving a massive shift across industries:

• Automation
• Data monetization
• Compute demand
• Autonomous agents

And crypto is building the infrastructure to support it.

That’s where the opportunity is.

In this post, we’re breaking down 9 under the radar AI crypto projects that are still relatively early, have strong narratives, and could benefit from the next wave of adoption.

1. Phala Network (PHA) — Private AI Compute

Phala Network (PHA) is focused on solving one of the biggest problems in AI:

Privacy.

As AI models become more powerful, they require access to sensitive data. Phala enables confidential AI computation using secure enclaves, allowing data to be processed without being exposed.

Why it matters:

• AI + privacy is a massive future sector
• Enterprises need secure data processing
• Strong infrastructure use case

Bull case:

If privacy becomes a core requirement for AI adoption, Phala Network could become a key player.

2. Cortex (CTXC) — AI Running On-Chain

Cortex (CTXC) is building a system where AI models can run directly on-chain.

This is a major step forward for decentralized applications, allowing smart contracts to integrate machine learning.

Why it matters:

• Bridges AI + smart contracts
• Enables intelligent dApps
• Still a niche concept (early stage)

Bull case:

If AI-powered smart contracts become standard, Cortex could be positioned early in that infrastructure layer.

3. Ocean Protocol (OCEAN) — Data for AI

Ocean Protocol (OCEAN) focuses on one of the most valuable assets in AI:

Data.

It allows users and organizations to monetize and share data securely for AI training.

Why it matters:

• AI runs on data
• Data marketplaces are still early
• Huge enterprise use case

Bull case:

As demand for AI training data grows, Ocean Protocol could become a foundational layer.

4. Fetch.ai (FET) — Autonomous AI Agents

Fetch.ai (FET) is one of the leaders in the AI agent narrative.

It enables autonomous digital agents that can:

• Execute tasks
• Trade
• Interact with systems

Why it matters:

• AI agents are one of the biggest emerging trends
• Automation across industries
• Strong developer ecosystem

Bull case:

If AI agents become widely adopted, Fetch.ai could see massive growth.

5. Arkham (ARKM) — AI-Powered On-Chain Intelligence

Arkham (ARKM) uses AI to analyze on-chain data and track wallet behavior.

Think of it as a data intelligence platform for crypto.

Why it matters:

• Transparency is key in crypto
• Traders need better data tools
• Strong real-world utility

Bull case:

If on-chain analytics becomes mainstream, Arkham could be a major player.

6. Talus Network (TALUS) — AI Agent Infrastructure

Talus Network (TALUS) is an early-stage project focused on building infrastructure for AI-driven decentralized applications.

Why it matters:

• Very early (low competition)
• Focus on AI automation systems
• Aligns with AI agent narrative

Bull case:

If AI agents become core to Web3, early infrastructure plays like Talus could benefit significantly.

7. Zypher (ZYPHER) — AI + Zero-Knowledge Privacy

Zypher (ZYPHER) combines two powerful trends:

• Artificial intelligence
• Zero-knowledge proofs (ZK)

This allows for privacy-preserving AI verification.

Why it matters:

• ZK tech is growing rapidly
• Privacy + AI is critical
• Very early narrative

Bull case:

If privacy becomes central to AI systems, Zypher could be ahead of the curve.

8. Ozak AI (OZ) — Predictive AI + DePIN

Ozak AI (OZ) focuses on predictive analytics powered by AI combined with DePIN (decentralized infrastructure).

Why it matters:

• Predictive AI has massive demand
• DePIN is a fast-growing sector
• Early-stage project

Bull case:

If predictive analytics becomes a core crypto tool, Ozak could gain traction.

9. Hash AI (HASH) — Low Cap AI Compute Play

Hash AI (HASH) is focused on decentralized compute power for AI workloads.

Why it matters:

• AI requires massive compute resources
• GPU demand is exploding
• Low market cap = high potential

Bull case:

If decentralized compute gains adoption, Hash AI could benefit from the trend.

Key Narrative: Where the Real Opportunity Is

Across all these projects, a clear pattern emerges.

The strongest AI crypto projects fall into three categories:

• Compute (processing power for AI)
• Data (training and access)
• Agents (automation and execution)

Projects that combine these elements tend to have the highest long-term potential.

Risk Reality: Not All Will Succeed

It’s important to be realistic.

These are low cap crypto projects, which means:

• Higher risk
• Lower liquidity
• Uncertain adoption

Some will fail.
Some will stagnate.
A few may outperform significantly.

The goal is not certainty.

It’s positioning early in strong narratives.

Conclusion: Early Positioning Is Everything

The biggest mistake in crypto investing is waiting for confirmation.

By the time a project is trending:

• The narrative is established
• The crowd has arrived
• The upside is reduced

The real opportunity lies in:

• Identifying early-stage AI crypto projects
• Understanding the narrative
• Positioning before attention

The projects listed here are not guarantees.

But they represent where the market could be heading.

And in crypto, those who recognize the direction early are the ones who benefit the most.

While most cryptocurrencies focus on payments, decentralized finance, or digital assets, Bittensor aims to solve a much larger challenge: how to build a global network where machine intelligence can be created, shared, and monetized collaboratively.

This vision has attracted attention from developers, investors, and traders who believe that the intersection of blockchain technology and artificial intelligence could become one of the most important technological movements of the next decade.

In this article, we explore the following:

• the history of TAO;
• how the Bittensor network was created;
• the major milestones in its development;
• and why it has gained so much attention in the crypto and AI communities.

The Vision Behind Bittensor

Bittensor was designed to create a decentralized marketplace for machine intelligence. In traditional AI development, large companies control data, computing resources, and the training of models. This centralized structure gives a small number of corporations significant power over how artificial intelligence is developed and deployed.

The creators of Bittensor believed that AI development should be open, collaborative, and permissionless, similar to how Bitcoin decentralized money.

Instead of relying on a single organization, Bittensor allows anyone in the world to contribute computing power or machine learning models to a shared network. Participants are rewarded with TAO tokens, which function as both an incentive mechanism and a way to access the network’s collective intelligence.

In this system, computers act as both producers and validators of machine learning results. Nodes submit answers to queries, and the network ranks them according to the usefulness of their output. The best-performing participants earn rewards in TAO.

This creates a competitive environment where the most useful AI models rise to the top.

The Founders and Early Development

The Bittensor project was founded by Jacob Robert Steeves and Ala Shaabana, two researchers with strong backgrounds in machine learning and computer science.

Jacob Steeves previously worked as a machine learning engineer and had experience in advanced neural network research. Ala Shaabana, meanwhile, held academic positions focused on artificial intelligence and machine learning systems.

The founders began exploring the concept of decentralized machine intelligence around 2019, when they formally launched the Bittensor project under the OpenTensor Foundation.

However, the idea itself was developed earlier. The team published research proposing a peer-to-peer market for machine intelligence, where neural networks could learn from each other and rank each other’s contributions.

This idea became the conceptual foundation for the Bittensor network.

The Launch of TAO and the Bittensor Network

The Bittensor network and its native cryptocurrency TAO officially launched in 2021.

Unlike many cryptocurrency projects that raise money through venture capital funding or token sales, TAO was distributed through a fair launch model, meaning no tokens were pre-mined or allocated to insiders.

Instead, tokens are earned by contributing value to the network.

Participants who run nodes can earn TAO by providing useful machine learning outputs or by validating the work of other nodes. The system rewards participants according to the informational value they bring to the network.

This incentive structure resembles the mining model used by Bitcoin, but instead of securing transactions, miners help build and improve artificial intelligence systems.

The token supply is capped at 21 million TAO, mirroring Bitcoin’s fixed supply model.

Early Network Iterations and Upgrades

Like most blockchain projects, Bittensor went through several iterations before reaching its current structure.

The first experimental version of the network was launched in January 2021 under the codename Kusanagi.

However, developers soon discovered performance and consensus issues. The network was temporarily halted in May 2021 while engineers worked to resolve these problems.

Later that year, the project relaunched under a new version called Nakamoto, which introduced improvements to the network’s consensus mechanism.

In March 2023, Bittensor transitioned away from earlier architectures and launched its own independent blockchain to better support large-scale machine learning computations.

This transition represented a major milestone in the project’s evolution.

How Bittensor Works

The Bittensor network operates using a mechanism known as Proof of Intelligence.

In traditional blockchain systems, miners compete to solve mathematical puzzles. In Bittensor, miners compete to produce useful machine learning outputs.

The system works roughly like this:

1. Nodes generate machine learning responses to network queries.
2. Other nodes evaluate and rank these responses.
3. The network distributes TAO tokens based on the usefulness of each contribution.

This ranking system creates a decentralized reputation economy for artificial intelligence.

Participants who consistently provide valuable information receive higher rankings and earn more TAO tokens.

The network also includes validators, which verify that the system remains fair and that nodes are not manipulating the ranking process.

The Rise of AI-Focused Crypto

The rapid growth of artificial intelligence has created enormous interest in projects that combine AI and blockchain technology.

As large language models and AI agents become more powerful, the demand for computing power, training data, and machine learning infrastructure continues to increase.

Bittensor positions itself as a platform where these resources can be coordinated in a decentralized way.

Instead of relying on a handful of tech companies, developers can use the Bittensor network to access AI services or contribute their own models.

This idea has helped position TAO as one of the most talked-about AI cryptocurrencies in the market.

The Introduction of Subnets

One of the most important developments in the Bittensor ecosystem was the introduction of subnets.

Subnets are specialized networks within Bittensor that focus on specific tasks.

Each subnet may be designed for a particular AI function, such as:

• Data indexing
• Model training
• AI inference
• Image generation
• AI agent development

This modular design allows developers to build new AI applications on top of the Bittensor network.

Over time, the number of subnets has grown significantly as the ecosystem expands.

Market Growth and Investment Interest

As the AI narrative gained momentum in the crypto industry, TAO began attracting significant investor attention.

The project’s combination of artificial intelligence infrastructure and blockchain economics positioned it as a unique asset within the digital asset ecosystem.

Major investors and crypto venture firms have shown interest in the project, and infrastructure companies have begun building services around the Bittensor network.

At the same time, the broader crypto market started paying closer attention to AI-related tokens.

The idea that decentralized networks could coordinate machine intelligence at a global scale sparked debates about whether projects like Bittensor could compete with centralized AI companies in the long term.

Tokenomics and Incentives

The TAO token is central to the Bittensor ecosystem.

It performs several key functions:

• Rewarding miners and validators
• Providing access to AI services within the network
• Supporting governance and decision making
• Securing the network through staking

The emission schedule distributes new TAO tokens to participants who contribute valuable machine intelligence.

Daily emissions are split between miners and validators, ensuring that both computational work and network security are incentivized.

This economic structure is designed to encourage long-term participation and continuous improvement of AI models on the network.

Recent Developments and Future Potential

In recent years, the Bittensor ecosystem has continued to evolve with new upgrades and improvements.

One of the most notable changes has been the introduction of dynamic TAO, which ties token rewards more closely to the productivity of specific subnets.

This upgrade aims to create a more efficient system where AI networks compete for resources based on their real-world usefulness.

Meanwhile, institutional interest in AI infrastructure continues to grow, and some investment firms have explored launching financial products linked to TAO.

If the demand for decentralized AI platforms continues to expand, Bittensor could play a significant role in the future of both industries.

Final Thoughts

The history of TAO and the Bittensor network reflects one of the most ambitious attempts to combine artificial intelligence and blockchain technology.

From its origins as an academic concept to the launch of a global decentralized AI network, Bittensor has evolved into a unique project within the cryptocurrency ecosystem.

Its goal is not simply to create a digital currency but to build a marketplace where machine intelligence itself becomes a tradable resource.

Whether Bittensor ultimately succeeds in reshaping the AI landscape remains to be seen. However, the project has already demonstrated how blockchain incentives can be used to coordinate large networks of computing power and machine learning models.

As both artificial intelligence and decentralized technologies continue to advance, the story of TAO may become one of the most important chapters in the history of crypto innovation.

With reports that the NYSE is developing infrastructure to support 24/7 trading, including tokenized stocks and ETFs, Wall Street is confronting a radical transformation: a market that never sleeps.

This shift represents more than longer trading hours. It signals a fundamental rethinking of how markets operate, who participates, and which technologies will dominate the future of finance.

Welcome to the possible death of the closing bell.

Why the NYSE Is Moving Toward 24/7 Trading

Traditional stock markets were designed around human limitations. Floor traders needed daylight, settlement systems required manual reconciliation, and information moved slowly.

None of those constraints exist anymore.

Key Forces Driving 24/7 Trading

• Global participation: Investors now span every time zone
• Crypto market precedent: Digital assets trade nonstop
• Tokenization technology: Enables near-instant settlement
• AI and automation: Markets no longer require human presence

The NYSE is responding to a world where capital flows continuously and investors expect access at all times — not just between 9:30 a.m. and 4:00 p.m. Eastern Time.

4

What Does “Tokenized Stocks and ETFs” Mean?

Tokenization converts traditional financial assets — like stocks and ETFs — into digital tokens recorded on a blockchain or distributed ledger.

Each token represents ownership in a real-world asset, but with key advantages:

Benefits of Tokenized Assets

• 24/7 tradability
• Fractional ownership
• Near-instant settlement (T+0)
• Lower operational costs
• Improved transparency

Instead of waiting days for trades to settle, tokenized assets can clear in seconds. This makes round-the-clock trading not only possible, but efficient.

The Closing Bell: Why It Existed in the First Place

The closing bell wasn’t just ceremonial — it was practical.

Markets needed:

• Time to reconcile trades
• Human oversight and compliance checks
• Overnight risk buffers

In a world of paper certificates, phone calls, and physical trading floors, downtime was necessary.

But today’s markets are:

• Digitized
• Automated
• Algorithmically managed

The original reasons for closing markets are rapidly disappearing.

What Happens When Markets Never Close?

A 24/7 NYSE fundamentally changes how markets behave.

1. Continuous Price Discovery

News no longer waits for the opening bell.

Earnings leaks, geopolitical events, economic data, and global crises already move prices after hours. A nonstop market allows prices to adjust instantly — not gap violently at open.

2. Reduced Opening and Closing Volatility

Currently, some of the most volatile moments occur:

• At market open
• At market close

Continuous trading could smooth these spikes by spreading volume across time rather than compressing it into narrow windows.

3. Global Market Synchronization

A 24/7 NYSE aligns U.S. equities with:

• Asian markets
• European markets
• Crypto markets

This creates a truly global financial system where capital never pauses.

The Rise of AI in a 24/7 Market

Humans cannot trade around the clock.

Machines can.

In a nonstop market environment, AI-driven trading systems become essential, not optional.

Why AI Will Dominate 24/7 Trading

• Humans need sleep; AI does not
• AI reacts faster to breaking information
• Algorithms manage risk continuously
• AI scales across thousands of assets

In many ways, 24/7 trading accelerates the transition from human-led markets to machine-managed ecosystems.

What This Means for Human Traders

The death of the closing bell doesn’t eliminate humans — but it redefines their role.

Roles Likely to Shrink

• Manual day traders
• Execution-focused traders
• Short-term arbitrage specialists

Roles Likely to Grow

• Strategy designers
• Risk supervisors
• AI system monitors
• Long-term portfolio managers

Humans move from execution to oversight and judgment.

Retail Investors: Opportunity or Disadvantage?

Retail investors face both new opportunities and new risks.

Potential Benefits

• Trade outside work hours
• React to news in real time
• Access fractionalized assets

Potential Risks

• Emotional overtrading
• Burnout from nonstop markets
• Competing against AI at all hours

For retail participants, discipline becomes more important than ever.

Liquidity, Volatility, and Market Stability

One of the biggest questions surrounding 24/7 trading is market stability.

Will Liquidity Fragment?

Trading volume may spread across time zones, potentially thinning liquidity during off-peak hours. This could increase volatility during certain windows.

However, as participation grows globally, liquidity may stabilize and even improve.

Will Crashes Be Worse?

24/7 markets could:

• React faster to panic
• Trigger algorithmic cascades

But they could also:

• Avoid overnight surprise gaps
• Absorb shocks gradually

The outcome depends on regulation, safeguards, and circuit breakers.

Regulatory Challenges of a 24/7 NYSE

Regulators face a massive adjustment.

Key Regulatory Questions

• Who monitors markets at 3 a.m.?
• How are halts and circuit breakers enforced?
• What jurisdiction governs global participants?
• How is market manipulation detected nonstop?

A 24/7 exchange requires 24/7 regulation — likely supported by AI surveillance systems.

How This Impacts Traditional Financial Institutions

Banks, brokers, and custodians must adapt or risk obsolescence.

Required Changes

• Always-on infrastructure
• Automated compliance systems
• Real-time settlement capabilities
• AI-driven risk management

Institutions built for batch processing and business hours will struggle in a real-time world.

The Psychological Shift: Markets Without Pauses

One overlooked consequence of 24/7 trading is psychological.

Markets used to offer:

• Natural breaks
• Time for reflection
• Cooling-off periods

Without pauses, participants must self-regulate more than ever.

This may lead to:

• Increased burnout
• Greater reliance on automation
• A cultural shift away from constant monitoring

Is the Closing Bell Really Dead?

Not immediately — but its meaning is changing.

The bell may remain ceremonial, but functionally:

• Trading may continue elsewhere
• Tokenized markets may operate nonstop
• Liquidity may migrate over time

Much like how physical trading floors became symbolic, the closing bell may soon represent tradition rather than necessity.

A Glimpse Into the Future of Markets

If the NYSE successfully implements 24/7 trading with tokenized stocks and ETFs, the implications are enormous:

• Stocks trade like crypto
• Settlement becomes instant
• AI becomes central to market function
• Finance becomes truly global

This is not just a technical upgrade — it’s a structural evolution.

Final Thoughts: The End of an Era, the Beginning of Another

The development of 24/7 trading by the NYSE marks a historic turning point.

The closing bell once defined when markets lived and died each day. In a tokenized, automated, always-on financial system, that boundary dissolves.

The future of markets will not be measured in trading hours — but in responsiveness, resilience, and intelligence.

The bell may still ring.
But the market will never stop listening.