AI Tokens Surge 30% in March: TAO Soars 107%, Bitfarms Sells All BTC to Focus on AI Computing Power

In March 2026, the AI narrative sector within the crypto market underwent a significant revaluation. According to market data, the total market capitalization of AI tokens surged from approximately $14.1 billion to $19 billion in just one month, marking an increase of over 30%. Decentralized computing power network projects, led by Bittensor (TAO), stood out with a remarkable 107% gain in a single month. Meanwhile, application-layer projects like SIREN posted an astonishing surge of over 540%.

At the same time, a pivotal event unfolded in the traditional crypto mining sector: Canadian mining firm Bitfarms announced it had liquidated its entire Bitcoin holdings and was pivoting fully to AI computing infrastructure. This decision not only reflects an evolution in the profit model of crypto mining but also serves as a real-world example of the deepening integration between AI and blockchain technology. This article provides an in-depth analysis of the structural changes in the AI sector from multiple angles, including data performance, market narratives, mining company transformation strategies, and future scenario projections.

Computing Power Reimagined: The Convergence of Crypto Mining and AI

The rally in AI tokens in March was not an isolated event. It was driven by a dual evolution in both the foundational infrastructure and application-layer value logic of the crypto world. Two core drivers stand out: First, the market has repriced the practical value of decentralized computing networks (such as Bittensor and Render Network) in the AI development process. Second, traditional Bitcoin mining firms, facing pressure on their profit models, have begun redirecting surplus computing infrastructure toward AI training and inference, creating a new capital narrative.

Bitfarms’ transformation epitomizes this trend. As one of North America’s major Bitcoin miners, its decision to liquidate BTC holdings and pivot to AI computing operations signifies a shift from a single "mining-and-holding" loop to providing high-performance computing services for external AI clients. This move reshapes market perceptions on two fronts: First, miners’ assets are transitioning from highly volatile native crypto assets to AI computing rental businesses with stable cash flows. Second, this "computing power migration" creates new synergies for AI token projects—especially decentralized computing platforms.

Divergence in AI Token Volume and Price

To clarify the structural changes in the AI sector, we’ve summarized the performance of major AI tokens in March. All data is sourced from Gate market data, as of April 1, 2026.

Sector Performance Overview:

In March, the total market cap of the AI token sector grew from $14.1 billion to $19 billion, a 34.75% increase. This growth was mainly driven by significant gains among leading projects, accompanied by a notable surge in trading volume, indicating heightened capital focus on the sector.

Monthly Performance of Leading Projects:

The data reveals clear structural stratification:

• Infrastructure Layer (TAO, RENDER): TAO posted a remarkable 107% jump in March, with its market cap holding above $2.9 billion, underscoring the market’s long-term confidence in decentralized AI infrastructure. Although TAO saw a slight pullback over the past 24 hours (-0.23%), its 30-day gain remains strong at 70.12%. RENDER, as a distributed GPU computing network, rose 21% in March with a current market cap of $918 million. Its price has climbed 29.77% over the past 30 days, reflecting sustained demand for computing power.
• Application Layer (SIREN, FET): Application-layer projects displayed greater volatility. SIREN soared over 540% in March but saw a sharp correction of more than 80% within 24 hours, highlighting intense short-term speculation. FET, representing the AI agent segment, gained 44% in March with a current market cap of $528 million. Its price trend has been relatively steady, with a 30-day gain of 53.94%, reflecting ongoing market interest in AI automation use cases.

The AI token sector experienced broad gains in March, with both infrastructure and application-layer projects performing well, though with notable differences in price appreciation and volatility. The market narrative is shifting from pure speculation to pricing "useful computing power." Projects with tangible GPU resources or developer ecosystems (such as TAO and RENDER) are attracting more sustained capital inflows, while application-layer projects are more event-driven. If demand for AI computing continues to spill over into decentralized networks, infrastructure projects could become "store-of-value" assets within the sector, with further upside potential for their market caps.

Three Main Market Perspectives on the AI Narrative

The rapid rise of AI tokens and the wave of mining company transformations have sparked three dominant viewpoints in the market—optimistic, skeptical, and pragmatic.

Optimists: AI Is Crypto’s Next Trillion-Dollar Sector

This camp believes that the convergence of AI and crypto (Crypto x AI) is the most promising long-term narrative since DeFi and NFTs. Proponents emphasize that as global demand for AI computing power grows exponentially, the resource monopoly and high costs of centralized cloud providers (such as AWS and Google Cloud) will create significant opportunities for decentralized computing networks (like Bittensor and Render Network). The entry of mining companies is seen as an early signal of traditional crypto computing power migrating to AI, accelerating the trend.

Skeptics: Narrative Outpaces Substance, Bubble Risk Looms

Skeptics point out that many AI token projects are still in their early stages, with network activity and revenue far from justifying current valuations. TAO’s 30-day gain of over 70% and SIREN’s fivefold monthly surge are seen as driven more by speculative capital than by real business growth. Furthermore, mining companies face technical, customer acquisition, and competitive challenges in pivoting to AI computing, and their profit models remain unproven. This view holds that the market is overpricing future expectations, raising the risk of a correction.

Pragmatists: Divergence Will Dominate, Focus on Computing Asset Value

This perspective falls between the previous two, suggesting that the AI narrative won’t collapse entirely, but significant divergence within the sector is inevitable. The key criteria will be whether projects have "verifiable computing resources" or "genuine developer activity." For example, TAO’s expanding subnet ecosystem and growing developer base provide fundamental support. For the RENDER network, actual GPU usage and rendering job volume are critical value indicators. When it comes to mining company transformation, the focus will be on whether their AI businesses can generate stable revenue and cash flow, rather than just changing the narrative.

From "Concept" to "Computing Power": Bridging the Gap

In the narrative of AI and crypto integration, it’s essential to distinguish between what’s already a reality and what remains a distant vision.

• Real Demand for Computing Power: Global demand for high-performance GPUs for AI training and inference continues to rise, with tight centralized computing resources and high costs being undeniable facts.
• Precedents for Mining Company Transformation: Beyond Bitfarms, several publicly listed mining firms (such as Hive and Hut 8) have already begun building out AI data center operations, demonstrating the initial viability of this business model.
• Observable Network Activity: TAO’s subnet count, registered users, and on-chain transaction volume all showed steady growth in Q1 2026, indicating real activity.
• Performance and Cost Advantages of Decentralized Computing: While decentralized networks may offer advantages in resource scheduling, task execution stability, and privacy protection, their ability to establish a sustainable competitive edge over centralized cloud providers still requires validation through large-scale commercial adoption.
• Effectiveness of Token Economic Models: Whether AI tokens can capture value effectively—such as by using tokens to pay for computing resources or staking to gain network rights—while avoiding inflation and dilution as the network scales, is a key concern for long-term investors.
• Technical Barriers to Mining Company Transformation: Converting Bitcoin mining farms into AI data centers involves more than just swapping ASIC miners for GPU servers; it requires comprehensive upgrades in network architecture, cooling systems, and operational management, making the transition highly challenging.

The current AI narrative is grounded in two real-world variables: genuine demand for computing power and the transformation of crypto mining. However, the market tends to price in long-term potential all at once, leading to short-term overvaluation. Investors and observers should pay closer attention to quantifiable metrics such as the number of deployed GPUs, real customer case studies, and network revenue to distinguish between "computing power" and "concept."

Industry Impact Analysis: The Dual Effects of Mining Company Transformation on the Crypto Ecosystem

Bitfarms’ decision to liquidate BTC and pivot to AI computing has structural implications for the crypto industry, primarily in two areas:

Comparison: Bitcoin Mining vs. AI Computing Power Leasing

Impact on the Crypto Ecosystem:

Potential Effects on BTC Network Security

1. Bitfarms’ transformation means some computing power will permanently exit the Bitcoin network, potentially causing short-term fluctuations in total network hash rate.
2. If more mining firms follow suit, Bitcoin’s total network hash rate could plateau or even decline structurally. This would temporarily lower the cost threshold for a 51% attack, but given the decentralization of major mining pools and the sunk cost of ASIC miners, a sudden collapse in hash rate is unlikely. Over the long term, this may drive Bitcoin mining to concentrate in regions with more efficient, lower-cost energy.

Catalyst for the AI Token Sector

1. Mining company pivots to AI computing provide a real-world benchmark for AI token projects, especially decentralized computing platforms. Mining firms have existing facilities, power infrastructure, and operational expertise, potentially serving as bridges between traditional computing resources and decentralized networks.
2. Two integration models may emerge: First, mining companies directly purchase and deploy high-performance GPUs, then connect to decentralized networks like Render Network or Bittensor as computing providers to earn token rewards. Second, mining firms transform into AI cloud service providers, with their business growth correlating positively with the market cap of AI tokens, creating new capital synergies.

Scenario Projections: Multiple Evolution Paths

Based on current data and market structure, we outline three potential scenarios for the future development of the AI token sector and mining company transformation trends:

Scenario 1: Ideal Growth

• Triggers: AI computing demand continues to exceed expectations; leading decentralized networks (TAO, RENDER) successfully attract developers and clients, with network revenue and active addresses growing over 50% quarter-over-quarter; more mining companies announce transformations and disclose concrete AI computing deployment plans.
• Path: The AI token sector’s market cap continues to expand, with infrastructure projects shifting from "narrative-driven" to "revenue/computing scale-driven" valuation models. Leading projects like TAO could see their market caps reach new highs. Mining company transformation creates a sector-wide effect, with listed company stocks and related tokens rising in tandem.
• Risks: Rapid token price increases may drive up network usage costs, stifling application-layer growth; regulators may scrutinize the use of decentralized computing and introduce restrictive policies.

Scenario 2: Divergence and Volatility

• Triggers: Macroeconomic shifts put pressure on risk assets; some AI token projects (such as the highly volatile SIREN) see sharp price corrections due to weak fundamentals; mining company pivots encounter technical or client acquisition hurdles, falling short of expectations.
• Path: Significant divergence emerges within the sector. Projects with real computing resources and developer ecosystems (like TAO and RENDER) maintain relatively stable prices, moving sideways or posting modest gains, while pure narrative-driven projects see major corrections and reshuffling of market cap rankings. Market focus shifts from "who’s next" to "who can deliver consistently." The mining company transformation narrative cools, and investors pay closer attention to the AI revenue share in company earnings reports.
• Risks: Cooling market sentiment could lead to prolonged sideways movement or gradual declines, requiring investors to wait for the next wave of technological or application breakthroughs.

Scenario 3: Narrative Breakdown

• Triggers: Global AI development hits major technical bottlenecks or faces a regulatory freeze, slowing computing demand growth; decentralized networks suffer severe security incidents or token economic model failures; widespread mining company transformation failures result in massive asset write-downs.
• Path: The AI token sector’s market cap contracts sharply, with confidence in the Crypto x AI narrative collapsing. Capital exits rapidly, and liquidity dries up. Leading projects like TAO and RENDER fall below key support levels. Mining companies revert to Bitcoin mining but miss the industry cycle.
• Risks: Systemic risk could prolong the return to long-term value, and some projects may exit the stage entirely.

Conclusion

The robust performance of the AI token sector in March 2026, coupled with strategic pivots by mining companies like Bitfarms, points to a core trend: the integration of the crypto world and the artificial intelligence industry is moving from conceptual speculation to tangible connections at the infrastructure level. Decentralized computing networks represented by TAO and RENDER, anchored by real computing resources, are becoming the primary value carriers of this integration trend.

However, market narratives often outpace fundamentals. While investors should pay attention to structural opportunities in the AI sector, they must also stay focused on data and facts. In the coming quarter, it will be crucial to monitor the following variables: network activity and revenue growth of leading AI projects, further mining company transformation decisions and execution, and the impact of macro liquidity on crypto risk assets. Only by balancing computing demand, technological adoption, and capital flows can the deep integration of AI and crypto evolve from a fleeting market hotspot into a lasting structural force driving industry development.