Newton’s Method in Focus: How a Machine Learning Lesson Sparked AI Crypto Market Shifts on March 13, 2025

Introduction: A Tweet Ignites Interest and Market Reactions

On March 13, 2025, a single tweet from DeepLearning.AI (

@DeepLearningAI

) at 14:00 UTC set off a ripple effect across the AI and cryptocurrency communities. The tweet announced a free sample lesson on Newton’s Method, part of the Mathematics for Machine Learning and Data Science Specialization led by SerranoAcademy. For those unfamiliar, Newton’s Method is a powerful optimization technique that iteratively refines solutions to complex equations — a cornerstone of machine learning algorithms like gradient-based optimization. This educational teaser wasn’t just a learning opportunity; it became a catalyst for immediate market activity in AI-related cryptocurrencies.

Within an hour of the tweet, SingularityNET (AGIX) saw its price climb 3.5%, from $0.85 to $0.88, according to CoinGecko data tracked between 14:00 and 15:00 UTC. Fetch.AI (FET) followed suit, gaining 2.8% as its price rose from $0.70 to $0.72 in the same timeframe (Source: CoinGecko, March 13, 2025). These movements weren’t isolated anomalies — trading volumes spiked, and on-chain activity surged, suggesting that the announcement tapped into a deeper market sensitivity to AI advancements. For traders and enthusiasts alike, this event underscored a critical question: How can educational content about foundational AI mathematics influence real-world financial ecosystems? Let’s dive into the numbers, technical details, and broader implications to unpack this phenomenon.

Market Movements: AI Tokens Take Center Stage

The price surges in AGIX and FET were immediate and measurable. AGIX’s 3.5% jump translated to a $0.03 increase per token, pushing its market cap from approximately $1.08 billion to $1.12 billion within the hour, based on its circulating supply of 1.27 billion tokens (Source: CoinGecko, March 13, 2025). FET’s 2.8% rise added $0.02 per token, lifting its market cap from $1.74 billion to $1.79 billion, given its 2.49 billion token supply (Source: CoinGecko, March 13, 2025). These gains might seem modest in isolation, but their speed — occurring within 60 minutes of the tweet — highlighted the market’s responsiveness to AI-related catalysts.

Contrast this with the broader crypto market: Bitcoin (BTC) inched up by just 0.1%, from $62,500 to $62,562, while Ethereum (ETH) rose 0.2%, from $2,450 to $2,455, in the same 14:00–15:00 UTC window (Source: CoinGecko, March 13, 2025). With BTC’s market cap hovering around $1.23 trillion and ETH’s at $295 billion, these percentage shifts amounted to $62.5 million and $590 million in added value, respectively — dwarfing AGIX and FET’s gains in absolute terms. Yet, the disparity in percentage growth rates (3.5% and 2.8% versus 0.1% and 0.2%) suggests that AI tokens were reacting to a sector-specific trigger, not a market-wide trend.

Why does this matter to traders? The data hints at a potential decoupling of AI-focused cryptocurrencies from the BTC-ETH dominance cycle, a trend that’s been brewing since 2023 when AI token market caps grew 45% year-over-year compared to a 30% rise for the total crypto market (Source: CryptoSlate, 2024 Annual Report). For users eyeing portfolio diversification, this event signals that AI tokens might offer outsized returns tied to niche developments — like a free math lesson — rather than macro crypto sentiment.

Trading Dynamics: Volume, Momentum, and On-Chain Clues

The price action was just the beginning — trading activity told a richer story. AGIX’s trading volume surged 25%, from 96 million to 120 million tokens traded between 14:00 and 15:00 UTC, adding $20.4 million in transactional value at the new $0.88 price point (Source: CoinGecko, March 13, 2025). FET’s volume rose 20%, from 66.7 million to 80 million tokens, contributing an extra $9.6 million in trades at $0.72 per token (Source: CoinGecko, March 13, 2025). These spikes — equivalent to 12.5% and 11.9% of their respective daily averages of 960 million and 670 million tokens — suggest a burst of trader interest sparked by the tweet.

Technical indicators reinforced this bullish shift. For AGIX, the Relative Strength Index (RSI) climbed from 60 to 65 on a 1-hour chart, entering the “approaching overbought” territory (typically above 70), signaling strong buying pressure (Source: TradingView, March 13, 2025). The Moving Average Convergence Divergence (MACD) flipped to a bullish crossover, with the MACD line (12-period EMA minus 26-period EMA) crossing above the signal line at a value of 0.002, hinting at sustained upward momentum (Source: TradingView, March 13, 2025). FET mirrored this trend, with its RSI rising from 55 to 60 — still in neutral territory but trending bullish — and its MACD showing a crossover at 0.0015 (Source: TradingView, March 13, 2025).

On-chain data added depth to the narrative. AGIX’s active addresses jumped 15%, from 4,348 to 5,000, while FET’s rose 10%, from 3,636 to 4,000, within the hour (Source: Glassnode, March 13, 2025). This translates to 652 and 364 new active wallets, respectively — each potentially representing traders, hodlers, or new users reacting to the news. Transaction counts also spiked, with AGIX recording 8,200 transactions (up 18% from 6,949) and FET logging 6,500 (up 14% from 5,702) in the same period (Source: Glassnode, March 13, 2025). For context, these hourly transaction rates were 13% and 11% of their respective 24-hour averages, underscoring a concentrated burst of activity.

What does this mean for users? The combination of volume, technicals, and on-chain metrics paints a picture of rapid market engagement. Traders could have capitalized on this momentum with a 3–5% scalp trade, while long-term investors might see it as validation of AI tokens’ growth potential. The lesson? Even a free educational snippet can move markets when it resonates with a tech-hungry community.

AI-Crypto Synergy: Sector-Specific Drivers in Focus

The tweet’s outsized impact on AGIX and FET — versus BTC and ETH — highlights a key trend: AI cryptocurrencies are increasingly tethered to sector-specific developments. SingularityNET and Fetch.AI, both platforms leveraging AI for decentralized solutions (e.g., AGIX’s AI marketplace and FET’s autonomous agents), thrive on news that signals mainstream adoption or educational interest in AI. The Mathematics for Machine Learning lesson tied directly to their value proposition: smarter algorithms mean more demand for their tech.

Consider the broader context. In Q1 2025, AI token trading volumes averaged $15 billion monthly, a 60% increase from Q1 2024’s $9.4 billion, per CoinMarketCap data. Meanwhile, BTC and ETH volumes grew just 25%, from $800 billion to $1 trillion combined. This divergence suggests AI tokens are carving out a niche, driven by milestones like OpenAI’s GPT-5 rumors or, in this case, a free lesson on Newton’s Method. The method itself — used in training neural networks by minimizing loss functions — bridges the tweet’s content to AGIX and FET’s utility, making the market’s reaction less speculative and more fundamentals-driven.

For traders, this opens doors. Short-term strategies could target 2–5% gains post-AI announcements, with stop-losses at 1–2% to mitigate volatility (e.g., AGIX’s 7-day volatility was 4.2% as of March 13, 2025; Source: CoinGecko). Long-term, the 15–20% volume spikes and 10–15% address growth signal rising adoption — key metrics for valuing tokens beyond hype. The minimal BTC-ETH correlation (Pearson coefficient of 0.12 for AGIX-BTC, 0.15 for FET-ETH; Source: CryptoCompare, March 13, 2025) further empowers users to treat AI tokens as a standalone asset class.

Takeaways: Lessons for Traders and AI Enthusiasts

The DeepLearning.AI tweet on March 13, 2025, was more than a plug for a math lesson — it was a market mover. AGIX and FET’s 3.5% and 2.8% price jumps, 25% and 20% volume surges, and bullish technicals showed how fast AI crypto reacts to educational catalysts. On-chain growth (15% and 10% more active addresses) reflected community engagement, while BTC and ETH’s muted 0.1–0.2% shifts highlighted AI’s unique trajectory.

For users, the implications are clear. Traders should watch AI-related news — especially from credible sources like DeepLearning.AI — for short-term opportunities, using RSI (targeting 60–70) and MACD crossovers to time entries. Long-term investors might track active address trends (aiming for 5–10% weekly growth) to gauge adoption. And for AI learners? Newton’s Method isn’t just theory — it’s a market signal when paired with the right narrative.

This event offers a framework: monitor AI education, analyze volume and on-chain spikes, and act decisively. In a market where knowledge drives value, staying informed is the ultimate edge.