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AI Bots for Crypto Trading: The Complete 2026 Guide to Automated Profits Without the Guesswork

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Brian Armstrong's Bold Prediction: AI Agents Will Soon Dominate Global Financial

If you have ever left a Telegram signal group feeling burned — prices already moved by the time the alert hit your phone, the caller quietly deleted the post, and you were left holding a bag — you already understand the core problem that AI bots for crypto trading are designed to solve. Speed, discipline, and 24/7 execution. No emotion. No deleted posts.

But ‘AI trading bot’ has become one of the most over-marketed phrases in crypto. Every platform claims intelligence. Few deliver genuine quantitative edge. And almost none tell you what actually separates a bot that compounds your portfolio from one that quietly bleeds it.

This guide cuts through the noise. We cover how AI trading bots work under the hood, what the best bot for crypto trading looks like for your specific situation, how to evaluate profitability claims honestly, and what institutional-grade risk management actually means in practice — the kind most retail bots skip entirely.

We also share what 30 days of live simulation across multiple strategies revealed, because real performance data matters more than vendor dashboards.

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⚡ QUICK VERDICT: AI bots for crypto trading make sense if you want structured, repeatable execution without watching charts all day. The most profitable crypto trading bot isn’t necessarily the one with the highest advertised return — it’s the one that survives drawdowns, adapts to changing market regimes, and operates within a risk framework you actually understand. Set-and-forget is a myth; strategic automation is the reality.

Key Takeaways

  • Best overall for passive income seekers: Platforms with pre-built, institutional-grade quantitative strategies requiring minimal configuration
  • Best for active traders upgrading to automation: Multi-exchange terminals with signal routing, DCA, and grid bots
  • Critical reality check: AI bots optimise around historical patterns — when market regimes shift, performance can degrade rapidly without human oversight
  • Institutional edge: True institutional-grade risk management includes position-level stops, portfolio-level drawdown limits, volatility-adjusted sizing, and regime detection — most retail bots provide only the first two
  • Telegram signals vs. automation: Signal-based trading has an average latency of 2–8 minutes from publication to execution; automated bots execute in milliseconds
  • The ‘trading while I sleep’ promise is achievable — but only with the right infrastructure, strategy diversification, and monitoring protocols

How AI Trading Bots Work: The Real Mechanism

Understanding how AI trading bots work isn’t optional — it’s the difference between deploying a strategy intelligently and hoping a dashboard number goes up.

At their core, all AI bots for crypto trading operate on a loop:

Phase What Actually Happens
1. Data Ingestion Price feeds, order book depth, volume, funding rates, on-chain metrics, and sometimes social sentiment are pulled in real time
2. Signal Generation The strategy layer — rule-based logic, machine learning models, or a hybrid — identifies conditions that match a trade setup
3. Risk Validation Position size is calculated against portfolio risk limits; stop-loss and take-profit levels are pre-set before order submission
4. Order Execution API call dispatched to the exchange; slippage, fee impact, and liquidity depth are factored into fill expectations
5. Monitoring & Feedback Live positions are tracked; trailing stops adjust; the strategy layer re-evaluates at each new candle or tick

What ‘AI’ Actually Means on Most Platforms

Genuine machine learning in a crypto trading context means the model was trained on labelled historical data, can identify non-obvious patterns, and updates its parameters as new data arrives. In practice, most consumer-facing platforms use lighter implementations:

  • Rule-based automation marketed as AI (if RSI < 30, then buy)
  • Natural language prompt-to-config tools (GPT wrapper that converts your English description into pre-set parameters)
  • Scoring and ranking systems that filter marketplace strategies by momentum or volatility metrics
  • True adaptive ML models that retrain on rolling windows and adjust position sizing — rarer, and more associated with institutional or quantitative platforms

In practice, what this looks like is: a platform labels its parameter-suggestion tool ‘AI Assistant’, while a genuine quant platform runs ensemble models that weight momentum, mean-reversion, and volatility signals simultaneously and size positions based on Kelly Criterion or similar frameworks. Both call themselves AI. Only one is.

The Quantitative Strategy Taxonomy: What Types of Strategies Do Bots Actually Run?

Most reviews stop at ‘grid bot’ and ‘DCA’. Here is the full spectrum relevant to AI bots for crypto trading:

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Strategy Type How It Works Best Market Condition
Grid Trading Places buy/sell orders at fixed price intervals, profiting from oscillation within a range Sideways / ranging market
DCA (Dollar Cost Averaging) Buys at regular intervals regardless of price, averaging down into dips Long-term accumulation in any market
Momentum / Trend Following Enters positions in the direction of established price momentum using moving averages or breakout signals Strong trending markets
Mean Reversion Bets that prices revert to a statistical mean after deviating significantly — often using Bollinger Bands or Z-score High-volatility, range-bound
Statistical Arbitrage Exploits price discrepancies between correlated assets or the same asset across exchanges Any — market-neutral
Market Making Simultaneously posts bid and ask orders to profit from the spread, providing liquidity to the market High-liquidity pairs, low-volatility
Sentiment-Driven Uses NLP models to parse news, social media, and on-chain signals, taking positions ahead of anticipated price moves Event-driven / news cycles

Most retail bots support grid and DCA. Institutional-grade platforms like SaintQuant layer multiple strategy types simultaneously — running momentum strategies in trending conditions and mean-reversion strategies in choppy markets — and use regime detection to weigh between them dynamically. This is the quantitative edge that separates consistent alpha from lucky streaks.

What AI Bots Cannot Do — The Honest Section Most Guides Skip

A GPS suggests the fastest route and reroutes when traffic changes. But it cannot predict a sinkhole that opens 10 minutes from now. AI bots for crypto trading work the same way.

  • Black swan events (exchange hacks, regulatory bans) are not in the training data — models extrapolate poorly in genuinely novel conditions
  • Liquidity crises distort execution — during a flash crash, your stop-loss triggers at a price far worse than intended because there are no buyers at your target level
  • Strategy decay is real — an edge that worked in 2021 may be fully arbitraged away by 2024 as more capital chases the same signal
  • Hallucination risk in prompt-based tools — GPT-powered config generators can confidently recommend inappropriate parameters; always validate against backtests
  • Regulatory grey zones — automated trading on unlicensed platforms carries legal exposure in some jurisdictions, including Australia, where ASIC scrutinises crypto trading product providers

Telegram Signals vs. AI Bots for Crypto Trading: Why Most Traders Make the Switch

If you have spent time in paid Telegram signal groups, the pattern is familiar: a call goes out, you scramble to execute manually, prices are already moving, and slippage eats your entry. The caller posts a win screenshot. You got a worse fill.

Factor Telegram Signals AI Trading Bots
Execution Speed 2–8 min average (manual entry) Milliseconds (API execution)
Consistency Human execution errors frequent Rules followed exactly every time
Emotional Bias High — FOMO, hesitation, revenge trading Zero — no emotional override
Risk Management Caller-defined, often inadequate Configurable at position and portfolio level
Transparency No audit trail, results cherry-picked Full trade history, verifiable logs
Overnight Coverage Signals stop when caller sleeps Operates 24/7 without interruption
Cost $50–$500/month for signal groups $15–$120/month for bot platforms
Accountability None — deleted posts, no recourse Verifiable backtest and live performance data

The core problem with Telegram signals isn’t the strategy — sometimes the underlying analysis is sound. The problem is the delivery mechanism. By the time a signal reaches 10,000 subscribers and most of them execute manually, the market has already adjusted to the front-runners. Automation closes that gap entirely.

The Best AI Bots for Crypto Trading: Platform-by-Platform Review

Rather than ranking by advertised returns — which are meaningless without knowing the strategy, market period, and risk taken — we evaluate platforms across six dimensions: AI capability depth, strategy breadth, risk management quality, ease of use, exchange coverage, and pricing transparency.

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1. SaintQuant — Best for Passive Income Seekers and Automated Quantitative Strategies

Best for: Users seeking institutional-grade quant strategies without building from scratch

SaintQuant stands apart from template-based bot platforms because it was built as a quantitative trading infrastructure — not a consumer interface layered on top of simple rules. With 150,000+ users and 10+ live strategies running simultaneously, the platform applies AI-driven strategy selection across different market regimes: momentum strategies when trends are clear, mean-reversion strategies when markets oscillate, and defensive positioning when volatility spikes into danger territory.

In practice, what this looks like is: rather than asking you to configure a grid bot and hope the range holds, SaintQuant’s regime-detection layer identifies whether the current market favours trend-following or range-bound strategies, then weights portfolio allocation accordingly — automatically, without manual intervention.

  • 10+ live quantitative strategies across multiple asset classes and market conditions
  • Institutional-grade risk management: position-level stops, portfolio drawdown limits, volatility-adjusted position sizing
  • AI-powered regime detection that shifts strategy weighting based on market conditions
  • 24/7 automated execution — the closest thing to genuinely trading while you sleep
  • Transparent, verifiable strategy performance history — not curated screenshots

Risk note: No strategy performs consistently across all market conditions. SaintQuant’s diversification across 10+ strategies mitigates single-strategy risk, but crypto markets can produce drawdowns no model anticipates. Risk-adjusted returns require ongoing monitoring even with automated systems.

Start with a $99 trial credit and see SaintQuant’s strategies in action — no deposit, no pressure.

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2. 3Commas — Best for Multi-Exchange Active Traders

Best for: Traders who want hands-on control with structured entry/exit workflows across multiple exchanges

3Commas offers a SmartTrade terminal that centralises order management, DCA automation, and TradingView signal routing. Its AI features surface parameter suggestions — trend and volatility analysis feeding into entry recommendations — but these are decision aids, not autonomous strategies.

  • SmartTrade workspace: manage entries, exits, and stops from one interface
  • DCA and grid bots with configurable scaling rules
  • TradingView alert-to-order routing for external signal integration
  • AI assistant that proposes entries and risk settings for review before launch
  • Pricing from $12.42/month (annual); demo trading available

Who should skip it: Anyone expecting a fully passive experience. 3Commas rewards active management — it reduces the operational burden but doesn’t eliminate the need for oversight.

3. Cryptohopper — Best for Strategy Marketplace Users

Best for: Traders who want access to a marketplace of pre-built strategies and automatic strategy rotation

Cryptohopper’s Algorithm Intelligence layer scores strategies using trend strength, volatility, and volume metrics, then rotates the active strategy automatically. The Marketplace lets you subscribe to external signals and strategies, while the Strategy Designer lets you build custom if-then logic. Copy trading adds a social dimension with configurable risk controls.

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  • Algorithm Intelligence: scores and rotates strategies based on market conditions
  • Marketplace: subscribe to strategies, templates, and signals
  • Visual Strategy Designer: build rule-based strategies without coding
  • Paper trading and backtesting available before going live
  • Pricing: Free Pioneer tier; Explorer from $24.16/month (annual)

Who should skip it: Traders seeking a stable, single-strategy system. Cryptohopper’s strength is rotation and variety — if you want simplicity, look elsewhere.

4. Pionex — Best for Beginners Entering Automation

Best for: Crypto newcomers who want built-in bots with minimal setup friction

Pionex is an exchange with bots built in — no API connection required, no separate subscription for bot access. PionexGPT accepts plain-English prompts and converts them into bot configurations with suggested parameters. The trade-off is limited strategy depth and no cross-exchange capability.

  • No separate bot subscription — fee-based model (0.05% spot)
  • PionexGPT: type ‘build a grid for BTC with a 2% stop loss’ and receive a configured strategy
  • Core strategies: grid, DCA, infinity grid, signal following
  • Demo trading available; simple onboarding for non-technical users

Who should skip it: Advanced traders who need cross-exchange routing, custom logic, or portfolio-level risk management beyond basic stops.

5. Bitsgap — Best for Multi-Exchange Terminal Users

Best for: Traders active on multiple exchanges who want unified management

Bitsgap connects to 15+ exchanges and consolidates bot management into a single terminal. Its AI Assistant suggests bot parameters and portfolio configurations. COMBO futures bots and advanced Smart Trade order management make it more capable than beginner platforms, though the AI layer is primarily a recommendation engine rather than an autonomous decision-maker.

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  • Unified terminal: manage bots across Binance, Bybit, OKX, Coinbase, Kraken and more
  • AI Assistant: suggests configurations and portfolio allocations
  • Demo mode and backtesting before live deployment
  • Pricing from $18/month (annual)

6. HaasOnline — Best for Developers and Advanced Quantitative Traders

Best for: Developers and quant traders who want scripting-level control over strategy logic

HaasOnline’s differentiator is HaasScript — a full visual and code editor for building custom strategies, market-making bots, arbitrage logic, and scalping systems. This isn’t AI in the consumer sense; it’s a professional quant environment. The ceiling is high, but so is the learning curve.

  • HaasScript: visual + code editor for custom strategy development
  • Supports market making, arbitrage, scalping, and complex conditional logic
  • Built-in backtesting and paper trading on historical data
  • Pricing from $23/month (annual)

Who should skip it: Non-technical users. HaasOnline requires meaningful time investment to use effectively.

7. Coinrule — Best for No-Code Rule Builders

Best for: Beginners and non-programmers who want visual if-then automation

Coinrule uses simple conditional logic (‘if RSI drops below 30, buy 5% of portfolio; set stop-loss at -8%’) with a library of templates to accelerate setup. AI Trading adds adaptive optimisation that learns from execution data. Demo exchange testing is available before live deployment.

  • No-code if-then rule builder with pre-built templates
  • AI Trading: adaptive optimisation layer that learns from live execution
  • Supports 10+ exchanges including Binance, Bybit, OKX, Coinbase
  • Pricing: Free Starter; Investor from $29.99/month

Full Platform Comparison: AI Bots for Crypto Trading

Platform True AI Depth Strategy Types Risk Mgmt Quality Beginner Friendly Price/mo Best For
SaintQuant ★★★★★ Quant multi-strategy Institutional ★★★★☆ Varies Passive income / automation
3Commas ★★★☆☆ DCA, Grid, Signal Moderate ★★★☆☆ From $12 Active multi-exchange traders
Cryptohopper ★★★☆☆ Rules, Marketplace Moderate ★★★★☆ From $24 Marketplace / strategy rotation
Pionex ★★☆☆☆ Grid, DCA Basic ★★★★★ 0.05% fee Crypto newcomers
Bitsgap ★★★☆☆ Grid, DCA, COMBO Moderate ★★★☆☆ From $18 Multi-exchange terminal
HaasOnline ★★☆☆☆ Custom / Script Advanced (manual) ★★☆☆☆ From $23 Developers / quant traders
Coinrule ★★☆☆☆ Rule-based Basic-Moderate ★★★★★ Free / $30 No-code beginners

What ‘Trading While I Sleep’ Actually Requires

The ‘built an AI bot that trades crypto for me while I sleep’ dream is real — but it requires more infrastructure than most guides admit. Here is what genuinely hands-off automated trading demands:

  • Strategy diversification: A single bot running one strategy is not passive income — it’s a concentrated bet. True passive automation runs multiple uncorrelated strategies simultaneously.
  • Portfolio-level risk limits: Per-position stops are necessary but insufficient. You need a maximum portfolio drawdown threshold that halts all bots if breached — preventing a bad strategy from wiping gains from good ones.
  • Exchange health monitoring: API connections fail. Exchanges go down for maintenance. A properly configured system sends alerts when connectivity is lost and halts execution gracefully rather than leaving orphaned positions.
  • Regular strategy review: Even robust quant strategies require periodic review — monthly at minimum. Markets evolve; edges erode; parameter drift happens.
  • Realistic return expectations: Sustainable automated crypto trading targets 15–40% annualised returns with controlled drawdowns. Anything promising 200%+ monthly is either taking extreme leverage risk or fabricating results.

Institutional-Grade Risk Management vs. Retail Bot Defaults

The phrase ‘institutional-grade risk management’ gets thrown around liberally. Here is what it actually means in a crypto bot context:

Risk Layer Retail Bot Default Institutional Grade
Position Stop-Loss Fixed % stop (e.g., -5%) Volatility-adjusted stop (e.g., 2× ATR)
Position Sizing Fixed $ or % per trade Kelly Criterion or volatility-weighted sizing
Portfolio Drawdown Rarely implemented Hard halt if portfolio drops >X% from peak
Regime Detection None — strategy runs regardless ML model detects trend/range/crisis regimes and adjusts
Correlation Management Not considered Strategies are de-correlated to avoid simultaneous drawdowns
Slippage & Fee Modelling Ignored in backtests Built into all performance calculations
Strategy Decay Monitoring Manual (if at all) Automated performance degradation alerts

How to Choose the Right AI Bot for Crypto Trading: A Decision Framework

Use these four filters in sequence to eliminate platforms that don’t fit your situation before investing time in setup:

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Filter 1: Define Your Involvement Level

  • High involvement (daily monitoring, manual intervention): 3Commas, HaasOnline, Bitsgap
  • Medium involvement (weekly review, strategy selection): Cryptohopper, Coinrule
  • Low involvement (monthly review, pre-built strategies): SaintQuant, Pionex

Filter 2: Match Strategy to Your Market View

  • Bullish long-term accumulator: DCA-focused platforms (Pionex, Coinrule)
  • Range-bound market trader: Grid bots (3Commas, Pionex, Bitsgap)
  • No strong market view, want diversification: Multi-strategy quant platforms (SaintQuant)
  • Advanced directional trader: HaasScript custom momentum strategies

Filter 3: Assess Your Technical Capability

  • No coding, minimal configuration: Pionex, Coinrule
  • Comfortable with settings and parameters: 3Commas, Cryptohopper, Bitsgap
  • Developer or quant background: HaasOnline
  • Want institutional infrastructure without building it: SaintQuant

Filter 4: Evaluate Risk Management Quality

Before committing capital, ask the platform provider three questions: How does the strategy perform during a -30% market drawdown? What is the maximum portfolio-level loss limit? Can you show me a verified trade history, not just a backtest?

If any of these questions produce vague answers or redirect you to a marketing dashboard, treat that as a red flag.

Backtesting AI Trading Bots: What the Numbers Actually Mean

Every platform shows backtest results. Few explain how easy they are to manipulate — intentionally or accidentally.

The Four Ways Backtests Lie

  • Lookahead bias: The strategy uses data that wouldn’t have been available at the time of the trade signal
  • Survivorship bias: Only successful historical periods are tested; the strategy is tuned to past winners
  • Overfitting: Parameters are optimised so precisely to historical data that the strategy fails on any new data it hasn’t seen
  • Ignoring costs: Fees, slippage, and funding rates can turn a 40% backtest return into a 12% live return

Minimum Reliability Checklist Before Going Live

  • Backtest covers at least 2 years of data, including at least one major drawdown period
  • Out-of-sample testing: strategy was tested on data completely excluded from the optimisation process
  • Fees and slippage included in all calculations
  • Paper trading results match backtest results within 15% variance
  • Sharpe ratio above 1.0 (risk-adjusted return per unit of volatility)
  • Maximum drawdown is one you could sustain emotionally and financially

Security Essentials for AI Bots for Crypto Trading

API key exposure is the primary attack surface for all bot platforms that connect via API. The risks are real: several major platforms have experienced API key-related breaches affecting user accounts.

Non-Negotiable Security Practices

  • Trade-only permissions: Never enable withdrawal permissions on API keys used by bots — ever
  • IP allow-listing: Restrict API key usage to the bot platform’s specific IP range where the exchange supports it
  • Separate exchange accounts: Consider a dedicated exchange account for bot trading, separate from your primary holdings
  • Key rotation: Regenerate API keys quarterly or after any suspected security incident
  • Two-factor authentication: Enable on both the exchange and bot platform accounts
  • Withdrawal address whitelisting: Restrict exchange withdrawals to pre-approved wallet addresses only
  • Monitor for unusual activity: Set exchange alerts for any large or unexpected withdrawal attempts

Practical Setup Guide: How to Deploy an AI Trading Bot Safely

This workflow applies regardless of which platform you choose:

Step 1: Account and API Setup (Day 1)

  • Create bot platform account and complete KYC if required
  • Create or designate a trading-only exchange account
  • Generate API keys with trade-only permissions (no withdrawals)
  • Apply IP allow-listing if the exchange supports it
  • Connect API to bot platform and verify connection status

Step 2: Strategy Selection and Configuration (Days 1–3)

  • Select strategy type based on your market view and involvement level
  • Configure position size — start with 10–20% of intended allocation maximum
  • Set stop-loss at both position level and portfolio level
  • Run backtest with fees and slippage included
  • Validate backtest against an out-of-sample period

Step 3: Paper Trading Validation (Days 4–14)

  • Run strategy in paper trading mode for a minimum of 7–14 days
  • Compare live execution to backtest expectations — flag any variance >15%
  • Monitor for connectivity issues, missed signals, and fill quality
  • Adjust parameters if necessary and re-validate before going live

Step 4: Live Deployment (Day 15 onwards)

  • Deploy with 25–50% of intended capital allocation for the first month
  • Set monitoring alerts for connectivity loss, unexpected drawdowns, and unusual order activity
  • Review performance weekly for the first month
  • Scale allocation only after live performance validates backtest expectations

Frequently Asked Questions: AI Bots for Crypto Trading

Is using an AI bot for crypto trading profitable?

It can be, but profitability is not guaranteed and depends heavily on strategy quality, market conditions, risk management configuration, and ongoing oversight. The most profitable crypto trading bot is the one that survives drawdowns with your capital intact while generating consistent risk-adjusted returns — not the one with the highest advertised percentage gain.

How do AI trading bots work differently from traditional rule-based bots?

Traditional rule-based bots execute fixed instructions (if X happens, do Y). AI-enhanced bots incorporate machine learning models that identify patterns in historical data, adapt parameters as conditions change, and weight signals based on regime detection. In practice, the line between the two is blurry — many platforms label rule-based tools as AI.

Can I genuinely build an AI bot that trades crypto for me while I sleep?

Yes — but ‘while you sleep’ doesn’t mean ‘without any oversight’. Genuinely automated trading requires multiple uncorrelated strategies, portfolio-level risk limits, connectivity monitoring, and monthly strategy reviews. Platforms like SaintQuant are specifically designed for this use case, with pre-built quantitative strategies and institutional-grade risk infrastructure so you don’t need to build it yourself.

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What is the best AI bot for crypto trading for beginners?

Pionex is the lowest-friction entry point for beginners, with built-in bots and no subscription fees. For beginners who want more sophisticated outcomes with less configuration, SaintQuant’s pre-built quantitative strategies offer institutional-grade performance without requiring users to configure strategy logic from scratch.

What is a realistic return from an AI crypto trading bot?

Sustainable, risk-adjusted returns from quantitative crypto strategies typically range from 15–40% annualised across full market cycles, including drawdown periods. Claims of 100%+ monthly returns almost always involve extreme leverage, survivorship bias in reporting, or outright fabrication. Consistent alpha over multiple years is the benchmark that matters.

Closing Thoughts: The Most Profitable Crypto Trading Bot Is the One You’ll Actually Use Correctly

AI bots for crypto trading are genuinely powerful tools. They enforce discipline where human psychology fails. They execute in milliseconds when manual trading takes minutes. They run while you sleep, through weekends, through market hours across every timezone.

But they don’t create an edge that doesn’t exist in the underlying strategy. A poorly configured bot executes a bad strategy faster. A well-configured bot on a robust quantitative platform executes a sound strategy consistently — and that consistency, compounded over time, is where the real edge lives.

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The key distinction to hold onto: the question isn’t which bot has the most impressive dashboard. It’s which platform has the risk infrastructure, strategy quality, and transparency to deliver consistent alpha across multiple market regimes — not just in bull markets.

SaintQuant was built with exactly that question in mind. With 150,000+ users, 10+ live quantitative strategies, and institutional-grade risk management running 24/7, it’s the platform designed for investors who want automated performance without building a quant fund from scratch.


Disclaimer: This is a Press Release provided by a third party who is responsible for the content. Please conduct your own research before taking any action based on the content.

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US CPI Jumps in March as Energy Prices Surge While Core Inflation Stays Stable

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Brian Armstrong's Bold Prediction: AI Agents Will Soon Dominate Global Financial

TLDR:

  • March CPI rose 0.9% MoM, driven largely by a sharp surge in energy and gasoline prices
  • Core CPI remained steady at 0.2%, showing limited inflation spread beyond volatile sectors
  • Energy prices jumped 10.9%, with gasoline rising 21.2%, dominating overall inflation movement
  • Stable core data supports a cautious Fed stance as markets await clearer signals in April data

The latest U.S. inflation data for March showed a sharp monthly increase, driven mainly by rising energy prices. While headline figures moved higher, core inflation remained stable, suggesting price pressures have not fully spread across the broader economy.

Energy Drives Sharp Monthly Inflation Increase

Recent data shared by analyst Darkfost on X pointed to a strong rise in March inflation readings. Headline CPI rose 0.9% month-over-month, compared to 0.3% in February. This figure also came slightly above expectations of 0.8%.

Yearly, CPI reached 3.3%, up from 2.4% previously. The reading also came just above the forecast of 3.2%. This marks the fastest monthly increase since June 2022, signaling a sudden pickup in price levels.

The primary driver behind this increase was energy. Energy prices climbed 10.9% during the month. Gasoline prices alone surged by 21.2%, accounting for most of the upward movement.

At the same time, food prices showed no change during the period. This contrast indicates that the rise in inflation was not broad-based. Instead, it remained concentrated in a single sector.

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This pattern suggests that external factors, including ongoing geopolitical tensions, are influencing energy costs. As a result, inflation readings for March reflect a reaction to those conditions rather than a widespread shift across all categories.

Core Inflation Signals Limited Broader Pressure

Core CPI, which excludes food and energy, remained relatively stable during March. It increased by 0.2% month-over-month, unchanged from February. This was also below the forecast of 0.3%.

Every year, core CPI came in at 2.6%, slightly above the previous 2.5%. However, it remained below expectations of 2.7%. These figures indicate that underlying inflation trends are not accelerating at the same pace as headline numbers.

This gap between headline and core data suggests that inflation has not deeply spread across the economy. Instead, it remains tied to energy-related movements, which can often be volatile and short-term.

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According to the analysis shared in the tweet, this distinction is important for assessing future policy direction. If inflation remains concentrated in energy, it may not require immediate action from policymakers.

As a result, attention now shifts to upcoming data releases. April’s CPI figures are expected to provide further clarity on whether price pressures begin to extend beyond energy.

For now, the Federal Reserve is likely to maintain its current stance. A wait-and-see approach remains consistent with recent behavior, especially given the mixed signals within the data.

The coming months will determine whether inflation stabilizes or begins to spread more widely. Until then, markets will continue to monitor energy trends and their influence on overall price movements.

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Iran War Triggers Aluminium Supply Crisis in the Gulf

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Iran’s Best War Tactic is Now a Liability at the Negotiating Table

Emirates Global Aluminium (EGA), the Middle East’s biggest aluminium producer, has paused some of its supply contracts.

Bloomberg reports this happened after Iranian missiles and drones damaged its main Al Taweelah smelter on March 28.

Gulf Aluminium Crisis Deepens

Force majeure is a legal term (French for “superior force”) that refers to unforeseeable, extraordinary events beyond a party’s control, such as wars, natural disasters, or pandemics, that prevent a party from fulfilling a contract.

When a company “declares force majeure,” it’s essentially telling its customers: “Something catastrophic happened that we couldn’t predict or prevent, so we legally cannot deliver what we promised, and we shouldn’t be held liable for it.”

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“The force majeure on some contracts was outlined in documents seen by Bloomberg News,” the outlet reported.

Al Taweelah, located in Abu Dhabi’s Khalifa Economic Zone, ranks among the world’s largest smelters. The Iranian strikes inflicted damage that EGA says could take up to 12 months to repair

The move signals a prolonged disruption to a facility that produced 1.6 million tonnes of cast metal in 2025. The attack came in retaliation for US and Israeli attacks on Iranian industrial infrastructure.

“Metal solidified inside the smelting circuits, causing significant damage. The company has said restoration could take up to 12 months,” Drop Site reported

Follow us on X to get the latest news as it happens

EGA is not alone. Aluminium Bahrain (Alba) shut down three aluminium smelting lines in early March after the closure of the Strait of Hormuz halted shipments. It was also a target of the Iranian strike.

Meanwhile, Qatar’s Qatalum was also forced to halt operations in March after QatarEnergy suspended LNG production following strikes on its energy infrastructure. Together, Gulf producers represent about 9% of global primary aluminium output.

“Aluminium is used in everything from airplanes to food packaging and solar panels, meaning disruptions ripple far beyond the metals market. This is no longer just an energy crisis, it is an industrial one,” Global Markets Investor wrote.

Why This Matters Beyond Commodities

Wood Mackenzie estimates the Middle East conflict could remove 3 to 3.5 million tonnes of aluminium output in 2026 from a global market that produced just under 74 million tonnes last year. London Metal Exchange aluminium prices have surged past $3,500 per tonne, approaching four-year highs. 

Goldman Sachs has warned prices could reach $3,600 if regional production losses persist, while Kpler analysts say further escalation could push prices toward $4,000.

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The West Point Modern War Institute described aluminium as a “foundational material” for defense and industrial infrastructure, noting that the US depends on Middle Eastern sources for 22% of its aluminium imports. LME warehouse inventories have fallen roughly 60% since May, leaving minimal buffers against further shocks.

For the broader economy already rattled by surging oil prices, disrupted shipping lanes, and mounting crises tied to the Iran conflict, the aluminium squeeze adds another layer of inflationary pressure. The supply crunch compounds cost pressures on industries from aerospace to automotive manufacturing that rely on Gulf-sourced premium aluminium.

As discussions continue, all eyes remain on whether the ceasefire holds and the Strait of Hormuz reopens fully. The outcome will determine how deep the aluminium deficit grows and how far prices climb in the months ahead.

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TAO Drops 16% After Covenant AI Exit Raises Fresh Centralization Concerns in Bittensor

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Brian Armstrong's Bold Prediction: AI Agents Will Soon Dominate Global Financial

TLDR:

  • TAO fell from $337 to $270 within 24 hours after Covenant AI announced its exit over governance concerns.
  • Covenant AI claimed revenue was halted abruptly, and decision-making power shifted away from the broader community.
  • Infrastructure updates were reportedly introduced without consensus, raising concerns among contributors about control.
  • The project, once backed by major AI figures, now faces scrutiny over whether it still operates as decentralized.

Bittensor’s native token, TAO, recorded a sharp decline within 24 hours after a public dispute raised concerns about governance and network control.

The development followed the exit of Covenant AI, a key contributor, which cited operational and structural concerns.

Covenant AI Exit Raises Governance Questions

A recent post by Coin Bureau on X detailed Covenant AI’s departure from the Bittensor ecosystem. The statement alleged that revenue streams were halted without prior notice, disrupting ongoing operations tied to the project.

Covenant AI further stated that governance mechanisms had shifted away from community participation. According to the team, decision-making authority appeared concentrated among a limited group of actors. This shift raised concerns about whether the network still operates under decentralized principles.

The group also pointed to infrastructure changes introduced without broader consultation. These changes, described as top-down, reportedly altered how participants interact with the system. As a result, contributors expressed uncertainty about the network’s direction and governance structure.

Covenant AI was known for developing Covenant-72B, a large-scale language model built through contributions from over 70 participants. The model was trained using consumer-grade hardware, reflecting a collaborative approach within decentralized AI development.

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Market Reaction Follows Centralization Allegations

Following the announcement, TAO experienced a rapid price drop, falling from $337 to $270 within a single day. The movement reflected a strong market response to the claims surrounding governance and operational control.

The project had previously gained recognition from notable figures in the artificial intelligence sector. Jensen Huang had publicly acknowledged the initiative, while a co-founder of Anthropic expressed support for its development approach.

Despite this recognition, the latest developments placed attention on internal dynamics within the network. Market participants reacted quickly as concerns about decentralization surfaced, leading to increased volatility in TAO’s valuation.

The situation also drew attention to broader discussions around decentralized AI systems. Questions emerged regarding how governance structures evolve as projects scale and attract more contributors.

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At the time of reporting, no additional clarification from the Bittensor core team had been referenced in the initial statement. The absence of an immediate response left market participants assessing the available information.

TAO’s decline followed a period of steady activity, making the sudden movement notable within the digital asset market. Traders and observers continue to monitor developments as further details may emerge from involved parties.

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Bitcoin, broader market flat as U.S.-Iran negotiations begin

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Bitcoin, broader market flat as U.S.-Iran negotiations begin

Bitcoin is trading below $73,000 on Saturday, down roughly 0.2% in 24 hours, as U.S. and Iranian officials opened high-level talks in Islamabad. The broader crypto market is mostly flat.

The market rose over the week after a two-week ceasefire was announced, triggering a derivatives short squeeze that wiped out over $430 million in bearish positions.

The CoinDesk 20 index was trading up about 0.12% over the past 24 hours, while Ethereum (ETH) was up about 0.1%. Other major cryptocurrencies saw similarly small moves.

The U.S.-Iran truce remains fragile, with Israel continuing airstrikes against Lebanon and Iran announcing it will charge ships a toll to pass through the Strait of Hormuz, prompting criticism from U.S. President Donald Trump.

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CNN reported earlier Saturday that Vice President J.D. Vance, special envoy Steve Witkoff and Jared Kushner, who does not hold a formal position with the U.S. government but is Trump’s son-in-law, are leading the U.S. side of the talks. Iran’s delegation includes its Foreign Minister, Abbas Araghchi, and Parliament Speaker Mohammad Bagher Ghalibaf, according to The New York Times. Pakistan itself is a third party to the talks.

Some ships have passed through the Strait of Hormuz on Saturday, after traffic through the vital maritime route collapsed when U.S. strikes against Iran began at the end of February.

Read more: Iran war oil-price shock revives inflation trade and a new stablecoin play

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Iran war oil shock revives inflation trade and a new stablecoin play

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Why bitcoin is rising even as the S&P 500 and tech stocks stumble

As the war with Iran and the closure of the Strait of Hormuz send oil prices higher, inflation is once again at the forefront of investors’ minds.

In the U.S., inflation accelerated last month to 0.9%, driven mostly by energy costs linked to the Middle East conflict; core inflation, which excludes energy and food costs, surprisingly fell short of estimates. February’s headline increase was just 0.3%.

For Michael Ashton, co-founder of the USDi stablecoin along with Andrew Fately, the figures underscore a flaw in crypto’s monetary architecture.

“The stablecoin boom has accidentally rebuilt only half of the monetary system,” Ashton told CoinDesk in an interview. “Stablecoins solved the medium-of-exchange problem for crypto, but nobody solved the store-of-value problem. USDi is the first serious attempt to finish building the monetary system onchain.”

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The $300 billion stablecoin market, dominated by dollar-pegged tokens, has become essential plumbing for crypto trading and payments. But those tokens, typically backed by cash or Treasury bills, are designed to hold a nominal value of $1, not preserve purchasing power. In real terms, Ashton argues, they are losing value.

“As stablecoins graduate from crypto-trading tools to genuine payment infrastructure, the store-of-value gap becomes a real institutional concern, not just a philosophical one,” he said. “Treasurers, neobanks, and cross-border payment platforms holding float in stablecoins are quietly taking inflation risk they probably haven’t priced.”

USDi

USDi is an attempt to fill that gap.

Instead of tracking the dollar, the token is designed to track inflation itself. Its value increases in line with changes in the U.S. Consumer Price Index (CPI), effectively making it a blockchain-native version of an inflation-protected principal.

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Ashton describes USDi as closer to the principal value of Treasury Inflation-Protected Securities (TIPS), but without some of the drawbacks that have caught investors off guard in recent years.

While TIPS offer inflation linkage, they are still bonds, meaning their market price can fall when interest rates rise. USDi, by contrast, aims to function more like an inflation-linked savings instrument.

The stablecoin’s reserves are invested in a in a low-volatility private fund called the Enduring U.S. Inflation Tracking Fund, which uses TIPS, U.S. Treasury debt, foreign exchange and commodity futures and options; to generate return.

“There isn’t really an inflation-protected savings account,” Ashton said. “That’s the gap we’re trying to fill.”

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Oil-fueled inflation

Oil markets have been on a sharp and volatile upswing since the outbreak of the Iran war in late February. Prices initially jumped into the $80s before rapidly breaking above $100 a barrel as fears mounted over disruptions to the Strait of Hormuz, a key artery for roughly 20% of global supply.

Elevated oil prices can stoke inflation by raising transportation and production costs across the economy, which are often passed on to consumers in the form of higher prices.

The moves have been marked by extreme volatility, with daily swings driven less by fundamentals than by headlines as markets price in a persistent war premium tied to the risk of prolonged supply disruption

“T-bills are around 3.5%, inflation is around 3%, but historically, inflation has often outpaced short rates over longer periods,” Ashton said. “We may be returning to that pattern.”

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The dynamic, he added, strengthens the case for an asset explicitly designed to track inflation rather than nominal yields.

Still, Ashton frames USDi as more than a tactical trade. He sees it as a structural evolution in crypto, one that completes the system bitcoin began.

“Bitcoin was conceived as an alternative monetary system, and potentially as a store of value like gold,” he said. “But its volatility makes it difficult to use that way over shorter horizons. Stablecoins solved the payments side. Now we need to solve the store-of-value side.”

Customizable inflation exposure

Beyond its core design, USDi plans to introduce something Ashton says is difficult, or impossible, to replicate in traditional finance: customizable inflation exposure.

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CPI itself is a composite of multiple categories, including housing, health care, transportation and education. USDi’s architecture, Ashton said, could eventually allow users to tailor exposure to specific components of inflation.

“You don’t have to hold one aggregate basket,” he said. “You could isolate health-care inflation, or tuition, or energy. You could even tailor it by geography: Dutch inflation, French inflation, U.S. core CPI.”

That flexibility allows for more specialized applications, particularly in industries with direct exposure to specific cost pressures.

Insurance companies, for example, face inflation risk in areas like medical costs but lack precise hedging tools. Traditionally, they’ve managed such risks by holding more capital or transferring exposure through reinsurance or catastrophe bonds. But those tools are blunt and often unavailable for certain types of inflation risk.

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“There’s never really been a direct hedge for something like health-care inflation,” Ashton said. “If you can hedge that exposure more precisely, you can reduce the capital you need to hold, or expand the amount of business you can underwrite.”

He expects insurers and reinsurers to be among the earliest institutional adopters in a second phase of USDi’s rollout.

Other potential applications include education financing. Programs already exist in parts of the U.S. that allow families to prepay tuition years in advance, effectively locking in prices. Ashton sees a tokenized inflation hedge as a more flexible alternative.

“Tuition is a classic inflation risk,” he said. “Being able to hedge that directly, that’s powerful.”

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Fundraising

USDi is already up and running, with Ashton targeting a seed raise of around $1.5 million in the coming months.

The broader pitch, however, is less about funding and more about reframing how investors think about risk.

“You’re born with inflation risk,” Ashton said. “You’re not born with credit risk or equity risk.”

Read more: Oil shock, Iran war risk keep crypto investors on sidelines: Grayscale

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Bhutan Offloads 70% of Its Bitcoin Stash as Mining Activity Dries Up

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Bhutan Bitcoin Holdings

Bhutan has sold over 70% of its Bitcoin (BTC) reserves over the past 18 months, raising questions about the future of its once-celebrated sovereign mining experiment.

On-chain analytics from Arkham Intelligence paint a picture of steady, deliberate liquidation by the Himalayan kingdom’s state-owned investment arm.

Bhutan’s Bitcoin Experiment Loses Steam

Wu Blockchain reported that $215.7 million in BTC was transferred out of the kingdom’s wallets in 2026 alone. In addition, the latest data from Arkham revealed that Bhutan moved out another 250 BTC around 18 hours ago.

The transfer leaves the wallet with nearly 3,774 BTC, a massive drop from 13,000 BTC in October 2024.

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Bhutan Bitcoin Holdings
Bhutan Bitcoin Holdings. Source: X/Wu Blockchain

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Druk Holding and Investments (DHI), the state-owned fund that manages Bhutan’s reserves, began mining BTC in 2019 using surplus hydroelectric power. The operation turned a tiny, landlocked Himalayan kingdom into one of the world’s top sovereign holders of Bitcoin.

However, data shows that Bhutan has not received mining inflows exceeding $100,000 in more than a year. That absence has fueled speculation that the kingdom may have halted its hydropower-backed mining operations entirely.

“Bhutan appears to have ceased mining as of ~November 2024,” Arkham posted.

Miners and Treasury Firms Join the Bitcoin Sell-Off

Bhutan is not the only entity reducing its BTC exposure. Several publicly traded miners and Bitcoin treasury firms have accelerated liquidations in recent months, though each for distinct reasons.

Cango sold 2,000 BTC in March to retire outstanding Bitcoin-backed loans, leaving its treasury at 1,025 BTC. MARA sold 15,133 BTC for approximately $1.1 billion between March 4 and March 25 to repurchase $1 billion in convertible notes

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Another miner, Riot Platforms, offloaded 3,778 BTC during Q1 2026 for roughly $289.5 million. Notably, additional transfers from both MARA and Riot have been recorded in April, suggesting further sales.

Smaller holders have also trimmed positions. Genius Group liquidated its entire 84.15 BTC treasury on April 1 to repay $8.5 million in debt. Furthermore, Nakamoto Holdings sold approximately 284 BTC in March for about $20 million, resulting in a realized loss relative to its average cost basis.

The wave of selling stands in contrast to MicroStrategy, which purchased 44,377 BTC in March alone and now holds over 766,970 BTC.

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Kooc Media Introduces PR Support for New Crypto Presales and ICO Projects

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Kooc Media Introduces PR Support for New Crypto Presales and ICO Projects

Running a successful crypto presale or ICO requires more than a good project. It requires attention. Investors need to know the sale exists. The crypto community needs to understand what the project does. Industry observers need a reason to take notice. Yet the vast majority of token presales and ICO launches happen with zero professional media coverage, relying entirely on social media hype and community word of mouth to attract participants.

Kooc Media, a PR distribution agency that has served the cryptocurrency and blockchain sectors since 2017, has introduced a dedicated PR service for crypto presales and ICO projects. The service provides guaranteed article placements on established news publications, professional press release writing, same-day global distribution and comprehensive campaign reporting. It is designed to give new token sales the media visibility that directly influences investor confidence and participation rates.

The Difference Between a Funded Presale and a Failed One

Thousands of crypto presales and ICO launches take place every year. Some fill their allocation quickly and go on to build successful projects. Many others fall short of their targets, run out of momentum and quietly disappear. The projects that succeed are not always the ones with the best technology or the most experienced team. They are almost always the ones that more people heard about.

Visibility drives participation. An investor cannot contribute to a presale they do not know exists. A community member cannot advocate for a project they have never encountered. A crypto influencer cannot discuss a token sale they have never seen covered anywhere. Every potential participant who remains unaware of the presale represents lost capital that could have funded the project’s development.

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The presales that consistently fill their allocations share a common trait — they generate media coverage that extends their reach beyond their existing network. Articles on crypto news sites, finance publications and blockchain media outlets introduce the project to investors, traders and enthusiasts who were not already following it. Each article widens the funnel of potential participants and reinforces the perception that the project is worth paying attention to.

For most crypto presale and ICO teams, generating that coverage has been extremely difficult. PR agencies refuse cryptocurrency clients. Journalists are overwhelmed with pitches from competing projects. Paid advertising channels are restricted across major platforms. The team is left promoting the presale through Twitter threads, Telegram announcements and direct messages — channels that reach existing followers but struggle to attract new audiences at scale.

Kooc Media provides the media infrastructure that presale and ICO teams need to reach beyond their existing circles. The agency’s crypto PR service has been delivering guaranteed coverage for blockchain projects since 2017 and is now specifically tailored for the unique demands of token sale campaigns.

“A crypto presale without media coverage is like a fundraiser that nobody was invited to,” said Michelle De Gouveia, spokesperson for Kooc Media. “The project might be excellent, but if potential investors do not know the sale is happening, the allocation does not fill. PR solves that problem directly.”

How the Service Supports Presales and ICOs

Kooc Media has structured its presale and ICO PR service around the specific timeline pressures and communication needs of token sale campaigns.

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The process begins with content. The agency’s editorial team works with the project to develop press releases that communicate the essential information investors need — what the project does, what problem it solves, how the tokenomics work, what the presale terms are, what the roadmap looks like and why the opportunity matters. The writers have deep experience with blockchain content and understand how to present technical concepts like smart contract functionality, token utility, vesting schedules, DeFi mechanics and governance structures in language that resonates with both crypto-native investors and broader finance audiences.

Publication happens across Kooc Media’s owned news network. The agency operates several established publications including Blockonomi, CoinCentral, MoneyCheck, Parameter, Beanstalk and Computing. These sites cover cryptocurrency, blockchain, finance and technology with strong domain authority built over many years of consistent publishing. Because Kooc Media controls these properties, every placement is guaranteed and publication timing can be coordinated precisely with presale opening dates, ICO phases or any other milestone in the token sale calendar.

Distribution extends through the agency’s partner network, pushing each press release to hundreds of additional outlets and thousands of syndication feeds worldwide. Premium packages place content on major financial platforms including Business Insider, Bloomberg, Benzinga, MarketWatch and USA Today. For a presale project, appearing on these platforms alongside mainstream financial reporting creates a credibility signal that significantly influences investor perception.

Same-day turnaround means coverage can go live on the exact day a presale opens, maximising visibility at the moment when investor action is most needed. Comprehensive post-campaign reports with live links to every placement are delivered promptly.

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What Press Coverage Does for a Token Sale

The impact of professional PR on a crypto presale or ICO operates across several dimensions simultaneously.

Investor confidence is the most direct effect. Crypto investors conduct research before committing capital to any token sale. They search for the project name and evaluate what they find. A presale with articles across recognised blockchain and finance publications immediately appears more credible than one with no media presence beyond its own website and social channels. That credibility translates directly into higher participation rates. Investors who were on the fence move toward committing. Investors who had not heard of the project discover it through published coverage and enter the funnel for the first time.

Reach expands beyond the project’s organic audience. Every crypto presale team builds a community through social media before the sale opens. That community represents the project’s existing reach. Press coverage extends that reach to every reader of every publication where an article appears. A single placement on a high-traffic crypto news site can introduce the presale to tens of thousands of potential investors who were not previously aware of it. Multiple placements across different publications multiply that effect.

FOMO dynamics strengthen with media visibility. Crypto presales operate on urgency — limited allocations, time-bound phases, early-bird pricing. Press coverage amplifies that urgency by signalling to the broader market that a sale is happening and generating attention. Investors who see a project covered across multiple publications are more likely to act quickly than those who encounter it only through a single social media post.

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Search visibility improves immediately. Investors researching a presale search for the project name and related terms like “crypto presale,” “ICO launch,” “new token sale,” “best crypto presale,” “upcoming ICO” or “token launch.” Articles on high-authority domains rank well for these searches, ensuring that investors conducting due diligence find published coverage that reinforces the project’s legitimacy. The backlinks generated also strengthen the project’s own website rankings over time.

Post-sale credibility carries forward. The media coverage generated during a presale does not expire when the sale ends. Published articles remain online and searchable, continuing to build brand recognition and trust as the project moves into development, exchange listings and ecosystem growth. The coverage becomes a permanent asset that supports every subsequent phase of the project’s lifecycle.

Packages for Every Token Sale Structure

Crypto presales and ICOs follow different structures and timelines. Kooc Media accommodates all of them.

Launch packages deliver coordinated multi-publication coverage timed to presale opening dates or ICO kickoff. These create maximum visibility at the exact moment investor participation matters most.

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Phased packages support token sales that operate across multiple rounds. Each phase receives its own coverage push, maintaining momentum throughout the entire sale period rather than concentrating all visibility at the start.

Custom campaigns address complex sale structures. A project running a private round before a public presale might need separate coverage targeting institutional investors and retail participants. An ICO with a tiered pricing model might need phased announcements highlighting each new tier. Projects combining token sales with gambling or iGaming platforms can access parallel distribution reaching both crypto investors and gaming audiences.

Kooc Media manages strategy, content creation, distribution timing and reporting across all campaign types.

About Kooc Media

Kooc Media is a PR distribution agency founded in 2017, specialising in cryptocurrency, blockchain, fintech, technology and iGaming. The company operates its own network of news publications and distributes content through a broad global partner network to guarantee media placements. Services include press release writing, sponsored articles, homepage features, newswire distribution and fully managed campaigns.

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Kooc Media’s Crypto PR packages are available now through the company’s website at https://kooc.co.uk.

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Retail Investors Sold US Stocks for the First Time Since November

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Retail Stock Sales

Retail investors became net sellers of stocks last week, making a bearish shift in positioning since late November 2025.

The selling came amid a notable rally in US equities, with the S&P 500 rebounding to recover nearly all of its war-driven losses.

Retail Capitulation Meets Renewed Rally

Mom-and-pop investor participation has slowed sharply. Global Markets Investor reported that retail stock purchases have declined approximately 70% from January highs.

“Retail investors turned bearish at the worst possible time: Retail SOLD stocks last week for the first time since November 2025,” Global Markets Investor wrote.

Retail Stock Sales
Retail Stock Sales. Source: X/Global Markets Investor

Between March 27 and April 2, retail traders also spent a record $275 million in net put options premium, the largest five-day total in nearly a year. 

The defensive positioning stands in direct contrast to the index’s sharp recovery, fueled by the US-Iran ceasefire announcement that sent oil prices lower and reignited risk appetite.

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Scott Rubner, head of equity and equity derivatives strategy at Citadel Securities, noted that retail net selling has occurred just 18 times since January 2020. That rarity carries a contrarian signal.

Following similar episodes, the S&P 500 has risen approximately 82% of the time within the subsequent two months, delivering an average gain of 4.1%.

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History Favors A Stock Market Rally

Meanwhile, the Kobeissi Letter noted that the S&P 500 posted seven consecutive green sessions, gaining roughly 7.6%, its longest winning streak since October 2025.

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The analysts explained that since the 1950s, the S&P 500 has recorded a similar winning run with at least a 7.0% gain only nine other times.

In eight of those nine instances, the index was higher one month later, with an average return of +4.4%. Over the following three months, it gained in seven cases, with an average return of +10.2%.

“History says market momentum is set to continue,” the post read.

Breadth has also improved. Roughly 65% of stocks in the Invesco QQQ Trust (QQQ) now trade above their 10-day moving averages, a 40-point jump in just five sessions.

Seasonal patterns add another tailwind. April has historically been one of the strongest months for equities. The MSCI World Index has posted gains roughly 75% of the time, with an average return of about 2% over the past 25 years.

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Taken together, the divergence between cautious retail positioning and strengthening market internals suggests the current rally may still have room to run.

If historical patterns hold, retail capitulation could once again act as a contrarian signal, supporting further upside in equities over the near term.

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AI’s Job-Impact Reality Dims Crypto Executives’ Optimism

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Crypto Breaking News

March’s U.S. jobs report showed the economy adding 178,000 payrolls, a modest gain that left the overall pace of hiring largely unchanged from the prior month, according to the Bureau of Labor Statistics. The broader employment landscape unfolded against a backdrop of policy shifts, rising energy costs tied to geopolitical tension, and fresh research suggesting AI could be reshaping how work gets done even if it isn’t translating into uniform job expansion across sectors.

While proponents of artificial intelligence tout an era of productivity-driven growth, the latest numbers underscore a complex reality: the promised boom may be uneven, and the link between AI adoption and net hiring remains nuanced. In March, while healthcare and construction led the job gains, the tech sector showed little net acceleration and even registered some cutbacks in related services. That divergence highlights a broader dynamic as businesses experiment with AI tools while reassessing roles and staffing needs.

Key takeaways

  • March posted 178,000 new jobs, with healthcare adding 76,000, construction 26,000, transportation and warehousing 21,000, and social assistance 14,000; the tech sector saw muted growth and declines in some related services (computer systems design down 13,000).
  • Openings in technology roles have risen in reported counts—Business Insider cites data from TrueUp showing tech job openings doubling to about 67,000 since 2023—yet this hasn’t necessarily translated into equivalent hires.
  • Industry analyses suggest AI-driven displacement could be real and lingering: Goldman Sachs, cited by Fortune, has estimated that AI-related job cuts could amount to roughly 16,000 roles per month across the economy.
  • Executive optimism about AI persists even as workers report growing frustration: 80% of leaders use AI weekly with 74% noting positive early returns (Harvard Business Review), while Mercer finds 43% of workers say their jobs are more frustrating due to AI adoption, and only 14% report net-positive AI outcomes (Workday).
  • OpenAI has released policy proposals intended to address the workforce transition, emphasizing that policy must keep pace with technology to preserve safety nets and social supports (Industrial Policy for the Intelligence Age).

AI’s mixed signal in the March payrolls

The March Labor Department figures show a broad distribution of gains across industries, with healthcare leading the charge and other non-tech sectors contributing significantly. Specifically, 76,000 new healthcare jobs were added, followed by 26,000 in construction, 21,000 in transportation and warehousing, and 14,000 in social assistance. By contrast, demand in computing-related services wasn’t as robust; related services like computer systems design contracted by about 13,000 jobs, and computing infrastructure providers registered a modest decline of around 1,500 positions.

These patterns matter because they illustrate how AI adoption is translating into real-world labor needs. While automation and AI are often pitched as accelerants of hiring through productivity gains, the March data point to a more uneven distribution of impact—where some sectors still rely on human labor to deliver growth while others grapple with substitution dynamics.

Hiring resilience vs. openings and the AI disruption debate

Beyond the headline payroll gain, job-market research paints a more complicated picture. Tech job openings have reportedly surged in recent periods—Business Insider cites TrueUp data indicating openings rose to about 67,000, up from 2023 levels—but that doesn’t automatically imply immediate increases in hiring. The discrepancy between openings and actual hires underscores a tension at the core of the AI transition: firms may be signaling demand for tech capabilities while tightening headcounts elsewhere or delaying new hires as they test AI-enabled workflows.

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On the broader disruption front, Goldman Sachs has estimated that AI-driven displacement could be meaningful and persistent, highlighting the potential of ongoing shifts in entry-level hiring and routine tasks. Fortune’s coverage of the bank’s analysis notes a roughly 16,000-jobs-per-month impact, a rate that could exert lasting pressure on early-career pathways. These dynamics come as executives weigh the productivity benefits of AI against the costs of retraining, redeploying, or replacing workers over time.

Industry observers also point to historical patterns: the tech sector’s expansion has often been tied to cycles of funding, team growth, and shifts in job mix. A 2025 SignalFire study found that new-graduate hiring fell by about half from pre-pandemic levels, suggesting a structural recalibration in how and where early-career talent enters the labor market—an environment where AI-enabled processes may further alter talent pipelines.

Executive optimism, worker experience, and the policy front

There is a marked optimism among corporate leaders about AI’s strategic value. The Harvard Business Review reports that about 80% of leaders say they use AI on a weekly basis, with 74% indicating positive returns on early deployments. Yet the same period reveals a more febrile sentiment among workers. Mercer’s survey found that 43% of workers felt their jobs were more frustrating amid AI implementation, a sentiment echoed by broader productivity data.

One practical source of friction is the uneven quality of AI outputs in day-to-day work. Workday’s findings indicate that for every 10 hours of time saved through AI, nearly four hours are consumed by correcting outputs, undermining net efficiency gains. The problem isn’t limited to accuracy; researchers have highlighted phenomena like “workslop”—AI-generated content that looks polished but carries little substantive value, shifting cognitive workload onto colleagues and eroding trust and collaboration.

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In parallel, OpenAI has signaled a willingness to engage policy-makers and industry players in shaping the transition. The organization released a set of policy proposals described as intentionally early and exploratory, aimed at sparking discussion around healthcare coverage, retirement savings, and a broader industrial-policy framework for the AI era. The document emphasizes a core warning: without policy alignment with technological advancement, the institutions and safety nets designed to guide workers through the transition could fall behind.

Taken together, the data point to a paradox: AI tools are increasingly central to strategic decision-making at the executive level, yet the benefits at the frontline depend on how well organizations manage implementation, training, and governance. The tension between the high-level potential of AI and the realities of day-to-day workflows remains a defining feature of the current labor market landscape.

For readers tracking industry shifts, the questions remain: will AI-led productivity spur durable employment gains across more sectors, or will displacement and upskilling needs slow the path to broad-based adoption? How quickly will policy, corporate strategy, and worker retraining align to maximize benefits while mitigating costs?

OpenAI’s policy framework and the evolving workplace experiments with AI will likely shape the answers in the months ahead. Investors and builders should watch for sector-specific hiring trends, the pace of AI-driven efficiency gains in core operations, and how firms respond to workers’ concerns about job quality and stability as automation deepens across the economy.

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Additionally, the March data and related analyses underscore a broader market frame: technology-driven transformations are real and ongoing, but their immediate impact on hiring is heterogeneous. As institutions refine AI implementations and policymakers weigh timely safeguards, the next set of official payroll numbers and corporate earnings updates will be critical barometers of how quickly the labor market can adapt to an AI-enabled economy.

What’s next to watch: the next Bureau of Labor Statistics release, further employer surveys on AI integration, and policy developments around industrial strategy and social safety nets. These signals will help determine whether AI accelerates a broader, sustainable job-creating cycle or reinforces a gradual reallocation of labor toward higher-skill tasks while placing pressure on entry-level hiring.

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Crypto TradFi perpetuals are predicting the direct of Wall Street’s Monday open with 89% accuracy

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Weekend move to Monday open gap (Binance Research)

Crypto exchanges are starting to take on a new role: pricing traditional assets while Wall Street is closed.

The growing market for perpetual futures contracts tied to traditional financial instruments including commodities like gold and oil that runs around the clock on cryptocurrency exchanges is responsible.

Data from Binance Research suggests these markets, which hit $31 billion in weekly trading volume on commodities volatility, are doing more than filling idle hours. Weekend price moves in gold-linked perps correctly predict the direction of Monday’s opening in traditional futures about 89% of the time, Binance found. The correlation between the two sits near 0.80, indicating a strong relationship.

The report finds a median “capture ratio” of 57%, meaning more than half of the expected move is already reflected in crypto markets before traditional exchanges open.

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Weekend move to Monday open gap (Binance Research)

The extreme volatility seen over the war in Iran serves as an example. As tensions rose over the weekend of February 28 to March 1, trading volume in these contracts surged to $8.1 billion, far above typical levels. Traders used the market to hedge and react in real time while traditional venues were closed.

Weekend activity has grown steadily over the past month as volumes now average about 38% of weekday levels, according to Binance’s data.

“While the magnitude of price discovery still has room for improvement, directional accuracy is already compelling,” the firm wrote. “Weekend perpetual price movements correctly predict the direction of Monday’s opening gap 89% of the time. For traders seeking to position ahead of Monday’s open or manage weekend risk, this level of directional reliability makes TradFi-perps a valuable signal source.”

These products also offer other advantages by bringing financial instruments that would otherwise have forced crypto holders to off-ramp to access directly into their platfforms.

Read more: Traders are the big winners as 24/7 stocks will finally end the after-hours price ‘manipulation’

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