To keep up with the speed of the technology world, sometimes we have to stop running, lift our heads, and just look at the horizon. For the past few years, headlines screaming “AI is coming!” have been replaced by a quiet acceptance: “AI is everywhere.”
For me, the upcoming period isn’t about the storm; it’s about the climate change that follows it. We are past the “Wow” phase; now we are in the “How does this actually work for me?” phase. The tech table is set with much tougher, more expensive, and more surprising topics this time around.
From the bloody arena of Google vs. OpenAI to the clumsy humanoid robots trying to enter our kitchens, here are the new realities of the tech world.
1. The AI Revolution: The “Bubble” Isn’t Popping, It’s Hardening
Saying that Artificial Intelligence has infiltrated every aspect of our lives is no longer a prophecy; it’s just a mundane fact I accept while drinking my morning coffee. I have bad news for my pessimistic friends who keep saying “The AI bubble will burst”: The bubble didn’t burst; it turned into steel.
The biggest leap awaiting us is likely GPT-6. This won’t just be a version update. My expectation is a massive jump in “reasoning” capabilities—the ability to understand cause and effect, not just predict the next word.
The AGI (Artificial General Intelligence) Threshold: We are all wondering about that dream of a “machine that thinks like a human.” While some experts hear its footsteps, I tend to agree with analysts like Tim Bajarin. For a machine to connect deep dots like a “polymath”—from music to physics, biology to art—I think we need to wait until at least 2028. The spark hasn’t fully ignited yet.
2. Google vs. OpenAI: The Clash of Titans
This year, the tech arena turns into a gladiator match. On one side, the lord of data, Google; on the other, OpenAI, the company that put the word “Chat” into our daily vocabulary.
Google is determined to shove Gemini into literally every hole. What makes me think (and slightly scares me) is that the fine line between traditional search results and AI summaries is vanishing. We are reaching a point where users—myself included—won’t be able to distinguish if information came from a website or if Gemini’s brain just cooked it up on the spot.
OpenAI, on the other hand, is breathing down Google’s neck by turning ChatGPT into a search engine. In this battle where elephants dance, it seems inevitable that smaller players will unfortunately get crushed or swallowed up.
3. The Hardware Crunch: Crisis and Opportunity
Software is flying high, but hardware is hitting a bottleneck. For us gamers and end-users, a RAM crisis is looming.
Bad News for Gamers: Nvidia is currently so busy and profitable selling AI chips to data centers that developing “affordable graphics cards” for you and me has fallen to the bottom of their agenda. Get ready for climbing prices.The ARM Revolution on Windows: The balance of power in the Windows world, held by Intel and AMD for decades, is shifting. Qualcomm-based, ARM-architecture laptops are no longer “toys.” With their balance of battery life and performance, they are finally starting to seduce mobile workers like me.
4. Is Apple Finally Waking Up?
Apple usually arrives late to the party, but they always arrive best-dressed. The projects Tim Cook has been “secretly” working on for years are finally coming to light.
iGlasses (Probable Name): The Vision Pro was a flex; the real product is coming now. I’m expecting stylish, functional AR glasses that we can actually wear in daily life without looking ridiculous.iFold and a Smarter Siri: Apple is expected to finally enter the foldable market (iFold?), but more importantly, Siri getting Gemini support might finally save it from the “dumb assistant” reputation. This is the win users have been waiting for.
5. Robots are Knocking (But They Do Dishes Slowly)
As for humanoid robots… Yes, the videos look cool. Yes, models with $20,000 price tags are hitting the market. But let’s be real for a second.
If a robot takes 45 minutes to unload a dishwasher, it is not ready to be indispensable in my home. I can do that in 5 minutes! While AI is speeding up their learning process, I believe we are still in the “Early Access” phase for a Jetsons-style domestic life. They are clumsy, they are slow, but they are here.
Final Thoughts: Tech is Maturing
In summary, the coming year isn’t just about “faster and stronger.” It’s a year where technology leaves adolescence and matures, pushing its boundaries. From AI actors in cinema to social media losing its grip on the youth, everything is being redefined.
I’ve strapped myself in, and I can’t wait to watch (and write about) this shift. So, what do you think will change our lives the most? The genius assistants in our pockets, or the clumsy robots trying to enter our kitchens?
So, the official numbers for 2025 are in, and the crown has officially passed. BYD sold 2.25 million EVs, while Tesla is sitting at 1.64 million. That’s a gap of over half a million cars.
We all saw the charts, but I want to talk about why this happened. I’m going to be brutally honest here—this isn’t just about BYD being “good.” It’s about Tesla getting lazy.
My Unfiltered Take (The “Ugu” Angle):
The “Toyota Camry” Effect: Let’s face it. The Model 3 and Model Y are fantastic machines, but they have become the “default NPC car” of the roads. They are everywhere. The design hasn’t fundamentally changed in years. Tesla lost its “cool factor” by becoming too common without refreshing the look. It feels like Apple trying to sell the iPhone 11 for five years straight.The Distracted CEO: While BYD engineers were sleeping in the factory to perfect the Blade Battery and launch 10 new models, Elon was… well, fighting on X (Twitter), getting political, and obsessed with the Cybertruck—a niche product that doesn’t solve the mass market volume problem. Focus matters.BYD is the “Hungry Wolf”: I used to think Chinese EVs were just cheap alternatives. That’s not true anymore. I’ve sat in a BYD Seal recently, and the build quality scared me (in a good way). They are moving at “China Speed,” while legacy auto (and now Tesla) feels like it’s moving in slow motion.
The Bottom Line: Tesla needs a Model 2 (the $25k car) yesterday. If they don’t drop a mass-market bombshell in 2026, this gap is going to turn into a canyon.
What do you guys think? Am I being too harsh on Tesla? Or do you also feel like the magic is fading a bit? Would you trade your Tesla key card for a BYD key fob today?
BitVentures Limited has officially launched its Digital Assets segment after acquiring high-efficiency Bitmain mining fleets with 0.5 MW of power capacity.
The new operations will use Antminer S21 XP and L9 models to mine Bitcoin, Litecoin, and Dogecoin across US-based data centers.
This initial deployment is positioned as a proof-of-concept ahead of potential expansion into staking and node operations.
On Friday, Hong Kong-based BitVentures Limited officially launched its Digital Assets business segment after signing purchase and hosting agreements for cryptocurrency mining fleets. The company’s board approved the move to establish a direct presence in the blockchain infrastructure space.
According to the official release, BitVentures will acquire around 0.5 MW of power capacity, targeting a Bitcoin hashrate of 30 PH/s and a combined Litecoin and Dogecoin hashrate of 0.5 TH/s. The entry aims to lay the groundwork for broader digital asset services through a varied mining approach.
Acquisition of high-efficiency hardware
The acquisition includes mining hardware from Bitmain, including Antminer S21 XP Hydro models, S21 XP air-cooled units, and L9 miners. These machines are set to be deployed in several secure, high-uptime data centers across the United States.
The company plans to roll out the equipment in stages starting this month to achieve full operational capacity by the end of the first quarter of 2026. By using current-generation ASICs, BitVentures focuses on energy efficiency to keep margins stable, even during market fluctuations.
Historical pivot and standards
BitVentures has historically operated outside core digital asset mining, making this a notable strategic shift. The technical specifications of the new fleet play an important role in this transition.
The Bitcoin miners operate at 12–13.5 J/TH, while the Litecoin and Dogecoin hardware runs at 0.21 J/MH. These figures point to a focus on low-cost production, which has become essential for institutional mining operations following recent network halving events and the rising global hash rates.
Future roadmap and ecosystem expansion
The company said BitVentures plans to be more than just a mining operator. The company has noted that this initial 0.5 MW deployment serves as a proof-of-concept for a broader roadmap.
Future phases may involve staking services, running blockchain nodes, and forming various ecosystem partnerships. Management believes scaling these operations will happen gradually in phases, depending on market conditions and the profitability of the initial fleet.
Lawrence Wai Lok, CEO of BitVentures Limited, said, “This transaction is a proof-of-concept deployment that marks the formal launch of the Company’s Digital Assets segment. Subject to ongoing market conditions, the company intends to continue to invest in digital assets, and methodically scale its operations in tranches, creating a foundation for future expansion into broader digital asset services, including potential staking, node operations, and ecosystem partnerships.”
He added, “We believe that establishing an efficient mining footprint may position the Company favorably to pursue broader opportunities within the digital asset ecosystem.”
BitVentures’ entry into the mining sector through U.S.-based data centers aligns with a growing trend of international firms looking for diverse revenue streams through blockchain infrastructure.
Also Read: Turkmenistan Legalizes Crypto Mining and Exchanges Under New Law
Published: January 02, 2026 at 9:00 am Updated: December 19, 2025 at 9:03 am
by Ana
Edited and fact-checked:
January 02, 2026 at 9:00 am
To improve your local-language experience, sometimes we employ an auto-translation plugin. Please note auto-translation may not be accurate, so read original article for precise information.
In Brief
AI-powered backtesting tools help crypto traders simulate strategies under realistic, changing market conditions, improving robustness and stress-testing performance across different volatility regimes.
Backtesting has always been a cornerstone of systematic trading, but in crypto markets it comes with unique challenges. Unlike traditional assets, crypto trades nonstop, experiences violent regime shifts, suffers from fragmented liquidity, and evolves structurally every cycle. A strategy that worked during a DeFi summer or NFT boom can collapse entirely in a different volatility regime. That’s why simple indicator-based backtests are often misleading in crypto.
AI-powered backtesting tools attempt to solve this problem by modeling uncertainty more realistically. Instead of assuming static relationships, machine learning systems adapt to changing market conditions, simulate slippage and liquidity constraints, and test strategies across multiple behavioral regimes.
Quant researchers frequently point out that robust backtesting today isn’t about maximizing historical returns, but about stress-testing ideas under noisy, adversarial conditions — something AI excels at when applied correctly.
Below are real, production-grade AI-powered tools currently used to backtest crypto trading strategies, ranging from retail-friendly platforms to institutional research frameworks.
Trade Ideas — AI Strategy Discovery & Historical Simulation
Trade Ideas is best known for equities, but its AI engine — “Holly” — represents a broader shift toward probabilistic backtesting driven by machine learning. Rather than testing static rule sets, the platform evaluates thousands of strategy variations across historical datasets to identify which patterns persist across different regimes.
Trade Ideas’ AI backtesting focuses on expectancy, not perfect prediction — measuring how strategies perform across a distribution of outcomes rather than cherry-picked periods. This probabilistic mindset is particularly relevant in crypto, where tail events dominate returns.
Best for: Traders experimenting with AI-generated strategy ideas and probability-weighted backtests.
QuantConnect — Lean Engine with AI & ML Extensions
QuantConnect is one of the most powerful backtesting platforms available, offering the open-source Lean Engine that supports Python, C#, and machine learning libraries. Crypto traders can backtest strategies across multiple exchanges while integrating AI models such as random forests, neural networks, and reinforcement learning agents.
Walk-forward analysis and out-of-sample validation are critical to avoiding overfitting — a principle embedded deeply in the platform’s tooling. By allowing users to retrain models dynamically during backtests, QuantConnect simulates how strategies evolve in live conditions rather than remaining frozen in time.
Best for: Quantitative traders, data scientists, institutional research teams.
CryptoHopper — AI Strategy Builder & Exchange Backtesting
CryptoHopper provides an accessible entry point into AI-assisted backtesting for crypto traders. Its strategy designer allows users to combine technical indicators, signal providers, and AI-generated logic, then test those strategies across historical exchange data.
The platform models real-world constraints like fees, slippage, and order execution delays — an often-overlooked detail that significantly impacts crypto strategies. CryptoHopper’s team has written about how AI helps reduce emotional bias by evaluating strategies statistically before capital is deployed, rather than relying on intuition alone.
Best for: Retail traders and semi-systematic strategy builders.
TensorTrade is an open-source framework designed specifically for training reinforcement learning agents in financial markets. Instead of backtesting predefined rules, TensorTrade allows AI agents to learn trading behavior by interacting with historical crypto environments.
TensorTrade’s reinforcement learning backtests are closer to simulations than traditional tests — the agent adapts position sizing, timing, and execution dynamically. This makes TensorTrade especially useful for exploring adaptive crypto strategies that respond to volatility spikes, liquidity shifts, or changing correlations.
Best for: AI researchers, Python developers, experimental quant traders.
Wyden — Institutional AI Strategy Simulation
Wyden is an enterprise-grade trading platform used by hedge funds, banks, and professional crypto desks. Its backtesting engine incorporates AI-driven execution modeling, advanced risk analytics, and portfolio-level simulations across spot, futures, and options.
The key is the importance of modeling how trades would execute — not just whether a signal was correct. By simulating latency, liquidity depth, and smart order routing, AlgoTrader’s AI backtests help avoid strategies that look profitable on paper but fail in live markets.
Best for: Funds, proprietary trading firms, institutional desks.
Backtrader + AI Libraries — Custom ML Backtesting in Python
Backtrader is a widely used Python backtesting framework that becomes AI-powered when paired with machine learning libraries like TensorFlow, PyTorch, or scikit-learn. Traders can embed predictive models directly into strategy logic and test how those models behave across historical crypto datasets.
A major point is Backtrader’s flexibility: users can test neural-network-based signals, probabilistic position sizing, or volatility-adaptive risk models within a single backtest. This makes it ideal for traders who want complete control over how AI interacts with market data.
Best for: Python developers and DIY quant traders.
Numerai Signals offers a unique take on backtesting by crowdsourcing predictions from data scientists and evaluating them through live and historical performance metrics. While best known for equities, the platform increasingly incorporates crypto-related signals and validation techniques.
Numerai’s founder has spoken publicly about the importance of generalization — ensuring that models perform well on unseen data rather than memorizing historical noise. This philosophy translates directly to crypto backtesting, where regime shifts punish over-optimized strategies.
Best for: Data scientists focused on model robustness and validation.
Shrimpy — AI Portfolio Backtesting & Rebalancing
Shrimpy focuses on portfolio-level backtesting rather than individual trade signals. Its AI-assisted tools allow users to simulate different allocation strategies, rebalance frequencies, and diversification models across historical crypto cycles.
Long-term returns in crypto are driven more by allocation and risk management than by perfect entry timing. Shrimpy’s backtesting tools reflect this insight by evaluating how strategies perform across bull, bear, and sideways markets.
Best for: Long-term investors and portfolio strategists.
MetaTrader 5 — AI Expert Advisors for Crypto Backtests
MetaTrader 5 remains one of the most widely used backtesting engines in global trading. With the addition of AI-powered Expert Advisors (EAs), traders can test neural-network-driven strategies on crypto pairs offered by supported brokers.
MetaTrader emphasizes walk-forward optimization and parameter sensitivity testing — techniques that help ensure AI strategies don’t collapse when market conditions change. The massive EA ecosystem also means traders can experiment with pre-built AI logic or build their own.
Best for: Algorithmic traders familiar with MT5 and EA development.
TradeStation — AI Optimization & Strategy Stress Testing
TradeStation offers robust backtesting with machine-learning-based optimization tools, including walk-forward analysis and parameter stability testing. For crypto traders, this means strategies can be tested not just for peak performance, but for consistency across different market phases.
TradeStation often emphasizes that the goal of AI backtesting is to eliminate fragile strategies, not to find perfect ones. By stress-testing strategies under varying assumptions, traders gain a clearer picture of what might survive real-world trading.
Best for: Advanced retail traders and systematic strategy designers.
Disclaimer
In line with the Trust Project guidelines, please note that the information provided on this page is not intended to be and should not be interpreted as legal, tax, investment, financial, or any other form of advice. It is important to only invest what you can afford to lose and to seek independent financial advice if you have any doubts. For further information, we suggest referring to the terms and conditions as well as the help and support pages provided by the issuer or advertiser. MetaversePost is committed to accurate, unbiased reporting, but market conditions are subject to change without notice.
About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
More articles
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
Published: January 02, 2026 at 7:00 am Updated: January 02, 2026 at 6:03 am
by Ana
Edited and fact-checked:
January 02, 2026 at 7:00 am
To improve your local-language experience, sometimes we employ an auto-translation plugin. Please note auto-translation may not be accurate, so read original article for precise information.
In Brief
Non-technical users are constantly exposed to smart contract risks, and modern no-code auditing and monitoring tools help them assess safety, detect vulnerabilities, and make informed decisions without reading or understanding Solidity code.
If you’re not writing Solidity, the assumption goes, auditing isn’t your problem. In practice, the opposite is true. Non-technical users interact with smart contracts constantly — staking, swapping, bridging, farming, minting NFTs — and they’re often the ones absorbing the losses when something goes wrong.
The challenge is that traditional audits aren’t designed for everyday users. PDF reports are technical, static, and quickly outdated. They tell you what a contract looked like at one moment in time, not how it’s behaving now or whether it has dangerous upgrade paths. For non-coders, “audited” can become a false sense of security.
Modern auditing and security tools increasingly recognize this gap. Instead of asking users to read code, they surface signals: verification status, ownership privileges, upgradeability, exploit patterns, abnormal behavior, and ecosystem-wide alerts. These tools don’t replace professional audits, but they let non-technical users ask smarter questions before interacting with a contract.
Below are real smart contract auditing and risk-assessment tools that non-coders can use today to reduce exposure.
Alt text: Etherscan is one of the most widely used smart contract auditing tools you can use without coding in 2026.
Etherscan is often seen as a block explorer, but for non-technical users it’s also the most important first audit layer. Its contract pages immediately show whether a contract is verified, whether it’s a proxy, and how many related contracts are involved.
Without reading code, users can still identify red flags: unverified contracts, complex proxy setups, or contracts with frequent administrative changes. The “Read Contract” and “Read as Proxy” sections expose parameters like owner addresses and upgrade controls in plain terms.
For non-coders, Etherscan doesn’t answer “Is this safe?” — but it does answer “Is this opaque?” And opacity alone is often reason enough to pause.
TokenSniffer: Automated Contract Risk Scoring
Alt text: TokenSniffer is a no-code smart contract auditing tool that helps users identify risky crypto contracts in 2026.
TokenSniffer abstracts smart contract analysis into a simple scoring system. It scans token contracts for known malicious patterns — honeypots, hidden mint functions, blacklist logic, and dangerous owner privileges — and surfaces results as a numeric score with clear explanations.
Non-technical users benefit from TokenSniffer’s binary framing: specific checks pass or fail, and the reasons are spelled out without jargon. You don’t need to understand how a blacklist works to understand that one exists.
While it’s not a substitute for deep audits, TokenSniffer is effective for quick screening — especially in fast-moving DeFi environments where scams rely on users acting before checking basics.
CertiK Skynet: Continuous, Not Static, Auditing
Alt text: CertiK Skynet is a continuous smart contract auditing platform designed for non-technical users in 2026.
CertiK Skynet moves beyond one-time audits by continuously monitoring deployed contracts. Instead of relying on an old report, users can see real-time security scores, detected anomalies, and alerts tied to live contract behavior.
For non-technical users, the value lies in change detection. If ownership changes, if a risky function is triggered, or if behavior deviates from norms, Skynet reflects that dynamically.
CertiK’s researchers have repeatedly emphasized that most exploits don’t involve novel bugs — they exploit known patterns combined with timing and user inattention. Continuous monitoring addresses that reality better than static audits.
GoPlus Security: Pre-Transaction Risk Warnings
Alt text: GoPlus Security is a smart contract auditing solution that lets users assess contract risk without coding in 2026.
GoPlus Security focuses on preventing bad interactions before they happen. Its tools aggregate contract risk signals — honeypot behavior, approval abuse, upgrade risks — and present them as clear warnings.
Many wallets and dApps already integrate GoPlus behind the scenes, meaning non-technical users benefit from its analysis without even realizing it. When surfaced directly, its dashboards allow users to check contract safety without inspecting code.
GoPlus effectively translates low-level security analysis into yes/no questions: Can you sell this token? Does this contract have abnormal permissions? For everyday users, that translation is what matters.
DeFiSafety: Operational Risk Without Code Review
Alt text: DeFiSafety is one of the best non-technical smart contract auditing tools for evaluating DeFi protocols in 2026.
DeFiSafety approaches auditing from a different angle. Instead of focusing on code vulnerabilities, it evaluates protocol maturity: documentation quality, testing practices, admin controls, and operational transparency.
For non-technical users, this answers a different but equally important question: Is this protocol run responsibly? Many losses come not from exploits, but from rushed deployments, poor controls, or opaque governance.
DeFiSafety’s structured scorecards help users compare protocols based on process rather than promises — a useful lens when evaluating long-term DeFi exposure.
RugDoc: Rapid Risk Reviews for DeFi Contracts
Alt text: RugDoc is a popular no-coding smart contract auditing tool for DeFi users in 2026.
RugDoc specializes in fast-moving DeFi ecosystems where users often don’t have time for deep analysis. It reviews yield farms, staking contracts, and new deployments for common risk factors.
Rather than issuing formal audits, RugDoc flags issues like upgradeable contracts, owner-controlled parameters, and missing safeguards. The language is intentionally simple and blunt.
For non-technical users, RugDoc’s value lies in speed and clarity. It’s designed to answer “Is this obviously dangerous?” — not “Is this theoretically perfect?” That distinction aligns well with real user behavior.
Alt text: OpenZeppelin Defender is a smart contract monitoring and auditing tool accessible without coding in 2026.
OpenZeppelin Defender is often associated with developers, but its dashboards and alerts are accessible to non-coders as well. It monitors contract activity, admin actions, and system changes in real time.
Users can track whether privileged functions are being called, whether upgrades occur, and whether governance actions align with expectations. This is particularly useful for protocols where admin misuse is a bigger risk than bugs.
The underlying idea, frequently emphasized by OpenZeppelin’s security team, is that most damage happens after deployment. Monitoring matters as much as auditing.
Alt text: PeckShield Alerts is a smart contract security and auditing tool that helps users track DeFi risks in 2026.
PeckShield provides exploit monitoring and security alerts across the DeFi ecosystem. Instead of analyzing individual contracts in isolation, it tracks patterns: similar vulnerabilities, active attack campaigns, and newly exploited mechanisms.
Non-technical users can use PeckShield as an early warning system. If a class of contracts or a specific mechanism is under attack, that information alone may justify avoiding interaction — even if your target contract hasn’t been exploited yet.
Security researchers often stress that contagion effects are real in DeFi; PeckShield helps users see those risks before they propagate.
Alt text: PeckShield Alerts is a smart contract security and auditing tool that helps users track DeFi risks in 2026.
CoinGecko’s Trust Score isn’t a smart contract audit, but it’s useful for initial screening. It combines liquidity quality, transparency, and basic security signals into an accessible metric.
For non-technical users, this helps narrow the universe of protocols worth deeper inspection. Low trust scores often correlate with poor disclosure, thin liquidity, or unreliable infrastructure — all of which increase contract risk indirectly.
Used carefully, Trust Scores function as a filter, not a verdict.
Alt text: SolidityScan is one of the best no-code smart contract auditing tools for detecting vulnerabilities in 2026.
SolidityScan applies AI to scan contracts for known vulnerability patterns. While its underlying analysis is technical, the results are presented in simplified dashboards that highlight issue severity and categories.
Non-technical users don’t need to interpret reentrancy details or overflow logic. They can compare risk profiles across contracts and see whether major red flags exist.
AI-based scanning isn’t perfect, but it’s effective at identifying known bad patterns — which still account for the majority of real-world exploits.
Disclaimer
In line with the Trust Project guidelines, please note that the information provided on this page is not intended to be and should not be interpreted as legal, tax, investment, financial, or any other form of advice. It is important to only invest what you can afford to lose and to seek independent financial advice if you have any doubts. For further information, we suggest referring to the terms and conditions as well as the help and support pages provided by the issuer or advertiser. MetaversePost is committed to accurate, unbiased reporting, but market conditions are subject to change without notice.
About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
More articles
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
Bitcoin ends 2025 well below predictions, showing volatility and cautious traders dominate despite early-year optimism.
High-profile BTC forecasts missed by 60%, highlighting the challenge of timing the market even for experts.
Institutional interest grows as banks may adopt Bitcoin services in 2026, potentially shaping the next price phase.
As the year 2025 ended, Bitcoin’s rally ended far below high-profile predictions, leaving bulls disappointed and analysts questioning market sentiment. While speculations from leaders sparked optimism within the market, none of the potential marks have been hit as expected.
As noted by Common Sense Investor (CSI) on X, leading personalities such as Eric Trump, Michael Saylor, Robert Kiyosaki, Tom Lee, Chamath Palihapitiya, and Tim Draper anticipated that Bitcoin would break above $150,000 by the end of 2025. But it closed at only around $87,000 for the year – a drastic deviation from the expected forecast.
THEY WERE ALL WRONG ABOUT BITCOIN ! $BTC
– Eric Trump: “I think we’re going to be over $175,000 this year… You go out a couple of years and there’s no question Bitcoin goes over $1 million.”
– Michael Saylor: “$150,000 by the end of 2025.”
– Robert Kiyosaki:… pic.twitter.com/N0yR43bZP7
— Common Sense Investor (CSI) (@commonsenseplay) January 1, 2026
Since the beginning of 2025, various leaders praised Bitcoin continually and predicted unbelievable highs. Eric Trump was optimistic about Bitcoin reaching over $175,000 in 2025, then hitting $1 million in a couple of years.
One of the most prominent Bitcoin advocates and Strategy’s founder, Michael Saylor had more conservative estimates at $150,000, while the prognosis by Robert Kiyosaki was $180,000–$200,000. Leaders like Tom Lee and Tim Draper have voiced their forecast of $250,000, while entrepreneur Chamath Palihapitiya stated it will jump as high as $500,000 this October.
In reality, all these forecasts drastically overshot the actual. Even Young Hoon Kim, hailed as the “World’s Highest IQ Record Holder,” had Bitcoin reaching $220,000 in 45 days. The result has BTC reaching $88,000, off by approximately 60%.
Technical Trends and Market Sentiment
As per TradingView data, the price of Bitcoin continues to exhibit high volatility since the second half of 2025. The range of the fluctuations is between $88,000 and $125,000, peaking during October and then falling below $90k in late-November.
The technical analysis indicators portray a mixed position. The MACD is moderately positive, as the indicator line is crossing above the signal line. The RSI is at 50, suggesting a neutral market. The volumes were stable, indicating a cautious entry by buyers and sellers.
Data from CryptoQuant paints a picture of sharp movements in Bitcoin open interest on various exchanges. From 2023 to 2025, as Bitcoin increased in price, open interest also increased and reached from $5 billion to as high as $45 billion.
After October, many leveraged traders exited the market, dropping open interest below $30 billion, even as BTC stayed near $89,000. This divergence shows caution among leveraged participants despite strong price action.
According to CoinMarketCap, as of writing, Bitcoin trades near $89,500, gaining nearly 2.2% in the last 24 hours. Its trading volumes currently sits at $25.94 billion, declined by 15% over the last day.
Institutional Developments on the Horizon
While his Bitcoin price prediction falling short, Michael Saylor also expressed rumors that U.S. banks may enter the Bitcoin services industry by mid-2026. This would include buying, storing, and lending against Bitcoin assets.
If it comes to action, the move will position Bitcoin and other digital financial assets to be integrated into conventional banking systems and possibly offer a basis for wider adoption. Institutions may also impact Bitcoin’s subsequent price-discovery levels.
Bitcoin’s 2025 journey shows how hard it is to predict its price in the short term. Big-name forecasts missed the mark, and market ups and downs kept traders cautious. Going forward, banks and institutions getting involved could influence Bitcoin, but price swings are still likely.
Also Read: Polymarket Traders Remain Skeptical About BTC’s Performance in 2026
Published: January 02, 2026 at 6:00 am Updated: December 30, 2025 at 7:35 am
by Ana
Edited and fact-checked:
January 02, 2026 at 6:00 am
To improve your local-language experience, sometimes we employ an auto-translation plugin. Please note auto-translation may not be accurate, so read original article for precise information.
In Brief
Deepfake scams surged in 2025 as AI-generated video, voice, and phishing attacks target individuals and businesses.
With the advent of deepfakes in 2025, the concept of online digital content and its reliability to individuals has completely changed. Even more sophisticated artificial intelligence can now be used to create highly realistic audio, video, and multimodal content with minimal effort, and sometimes it would be hard to distinguish it from real media.
There have been concerns about the growing application of these synthetic media tools in scams, fraud schemes, and misinformation campaigns, bringing concern to the people, businesses as well and governments.
By 2025, the increase in the number of deepfake content on the Internet will have never been so high. And as this has come along with it, the technology has become so easy to access and consumer-friendly tools that anyone can become a source of credible fake media.
The advances have posed significant problems in confirming the authenticity of digital content, which has eroded confidence in communications, media, and business.
Advancements in Deepfake Technology
Video generation models advanced noticeably in 2025 and now produce content with stable motion and consistent identity. These systems separate a person’s identity from movement data, allowing accurate reproduction across many different situations.
This progress has reduced flicker, warping, and facial distortions that once made deepfakes easier to spot. Video output now remains clear even during low-resolution video calls or compressed social media uploads. Cybersecurity researchers report that this level of realism frequently misleads viewers without technical training.
Voice cloning technology also progressed rapidly during the year. Just a few seconds of audio can now recreate a person’s voice with convincing accuracy. The models capture natural intonation, pacing, pauses, and emotional expression.
Many businesses report receiving thousands of AI-generated scam calls each day using cloned voices. The US Federal Trade Commission has warned about a sharp rise in these calls, especially those imitating relatives. Voice synthesis now challenges trained professionals and older identity verification systems.
Consumer-focused AI tools have also reduced the technical skill needed to create deepfakes. Platforms such as OpenAI Sora 2 and Google Veo 3 enable rapid script writing and media generation.
AI agents can automate the full production of narrative-driven videos at scale. The growing mix of high volume and realism has made detection slower and more complex. Security experts warn that synthetic media often spreads widely before verification can take place.
AI-Driven Scams and Fraud
The new generation of scams against people and organizations has become possible thanks to the use of AI technology. Fraudsters are now using deepfake video, voice cloning, and text generated by AI to make a realistic fraud. According to the reporting of Europol and cybersecurity agencies, these attacks are directed toward trust, urgency, and authority. The prey of AI frauds usually suffers monetary damages or a tarnished image.
One of the most widespread threats is identity impersonation. Thieves imitate the voices of relatives or managers to coerce their victims to hand over money. A grandmother may get a call that her grandchild is being held captive in a foreign country. The staff can be commanded by a deepfake CEO voice, and an immediate wire transfer ordered. The manipulation of emotions rather than technical aptitude is the driving force behind these scams, according to experts.
There are now more deepfake video deceptions and fake advertisements. There are videos of celebrities or people in a position to market investments or products that are spreading widely. Such videos tend to be trusted by the viewers because of authority bias, which causes financial losses. AI can also enable scammers to recycle videos into various languages to reach a larger audience.
Hyper-realistic phishing messages are now generated in seconds using AI. Scammers create emails and websites that mirror real companies, often including personalized references. Victims may enter sensitive information into cloned portals, giving attackers access to accounts. AI enables these campaigns to scale quickly and reach hundreds of thousands of targets.
E-commerce fraud has also grown through cloned online stores and fake shopping ads. Criminals produce realistic websites with false reviews and promotional content. Shoppers may pay for products that never arrive while exposing personal information. Cybersecurity firms report that such attacks peak during holiday or shopping seasons.
Sextortion frauds play on AI-created sexual pictures. The criminals intimidate by publishing falsified materials unless payments are made by the victims. They are very emotional scams and hard to ignore, even in the case of fabricated pictures. Regulators such as Ofcom in the UK have reported increasing amounts of harmful deepfakes for blackmail.
Corporate and Market Impacts
Businesses face increasing pressure to adopt AI security measures. Executive impersonation and business email compromise scams are more sophisticated than traditional attacks. Employees are often manipulated into bypassing internal protocols. Companies need multi-factor verification and monitoring tools to prevent financial loss.
Consolidation of digital identity and biometric solutions has been done as a solution to the problem of fraud. In 2025, there were more mergers and acquisitions that grew as there was a high demand for secure identity systems. Fraud- prevention and verification technologies are being bought by companies to enhance offerings. Market analysts note the ongoing activity in 2026, with organizations looking at solutions that are integrated.
Corporate cybersecurity strategy has also been affected by AI. Probabilistic, behavioral, and device signals have been added to the fraud detection systems. The standardized methods used in traditional identity verification, in terms of documents or single-factor verification, are not adequate.
Due to threats that are changing threats, organizations are integrating several levels of security. AI has also increased the rate and magnitude of attacks, necessitating dynamic protection.
Detection and Verification Challenges
It is not an easy task to detect quality deepfakes. Even seasoned viewers will find it hard to tell when there is manipulated material. The existing detection technologies examine micro-expressions, blinking patterns, sound anomalies, and other less obvious signals. Platforms are testing cryptographic watermarks, digital signatures, and forensic pipelines to determine authenticity.
Nevertheless, detection is not enough any longer. The Deepfake generation has been made more accessible, faster, and cheaper than the effective detection mechanisms. The experts of cybersecurity stress the significance of infrastructure-level security measures, such as secure provenance, verified metadata, and probabilistic risk scoring.
Deepfake-o-Meter is one of the tools that can offer an evaluation of probability, although there is hardly a guarantee of accuracy. To restrict exposure, organizations will need to implement multi-layered strategies, which will entail the integration of technical, procedural, and human control.
Credibility has changed its meaning in the digital era. It is no longer sufficient to see or hear anything so as to create trust. Decisions that concern high-stakes should not be based on one kind of evidence.
Specialists observe that it should not be the burden of human beings to determine deception. Rather, the authenticity standards should be imposed by technology, organizational practice,s as well as societal systems.
Preventive Measures for Individuals
People are advised to be even more cautious when communicating online and communicating with strangers. When faced with urgent or emotional demands, validation by a secondary trusted channel should be made.
Secure communication channels, family code words, and predetermined security phrases might be used to identify identities before acting. Individual and financial details must never be distributed over unknown, unsolicited, or suspicious sources.
Suspect messages, calls, or emails should be verified using official websites and contacting the organization directly or calling it through channels that are confirmed to be verified. Unsolicited messages using links, QR codes, and attachments should be handled, and URLs should be typed in rather than clicking on potentially harmful websites.
Multi-factor authentication (MFA) will ensure an added security layer that will minimize the risk even in the case of the loss of login credentials. Accounts can also be monitored regularly, suspicious activity reported in time, and proactive passwords maintained to reduce the damage.
The knowledge of AI-based scams is also essential since these frauds become more and more plausible. Fraudsters can use credibility, urgency, and perceived authority to influence victims into making quick decisions.
People are encouraged to take time, have doubts about ordering, and find out more information with reliable sources before transferring money or providing personal information. Fraud attempts should be reported to law enforcement and digital platforms, as well as to disrupt criminal networks.
The Road Ahead for Deepfakes
Deepfakes have been progressing towards real-time synthesis and interaction. AI systems are being built so that responsive avatars can be developed to adapt voice, appearance, and behaviour in real-time. According to cybersecurity experts, video call participants may be created in a few seconds.
An identity model forms converging systems that follow appearance, voice, and behavioral patterns. This development enables deepfakes to imitate human interactions more convincingly. Internet companies and citizens should embrace mechanisms and processes that ensure trust in online communication.
Provenance-based verification is developing as a defensive tool. Assurances on content authenticity are achieved using cryptographic signing, secure metadata, and verifiable audit trails. Authentic content enables decision-makers to identify authentic material as opposed to possibly manipulated media content. Organizations are modernizing workflows by minimizing the use of perceived authenticity.
The high occurrence of AI-based frauds demonstrates the significance of digital literacy. Training on how to understand manipulative behavior, information checks, and how to report suspicious behavior is needed. The collaboration between the citizens, institutions, and technology providers is needed to keep online trust. The collective role of all the stakeholders enhances resilience to synthetic deception.
The deepfake technology in 2025 is a point of interest due to the accelerated achievement of technical advances and the growth of threats to security. With the improvement of video, audio, and multimodal synthesis, more and more realistic scams, phishing, and identity fraud have become possible. Businesses and individual users are particularly having increased difficulties in testing the authenticity of digital content.
To reduce exposure to AI-driven deception, multi-layered verification systems, systems of content provenance, and enhanced digital literacy are needed. With such technologies ever-changing, the coordination of institutions, platforms, and individuals will play a critical role in maintaining trust in media and communication.
Although AI is opening up creative and technological opportunities, the misuse of the technology, particularly in the form of scams, raises concerns and underscores the need for people to be educated and have solid defense measures in place to protect individuals, companies, and the community at large.
Disclaimer
In line with the Trust Project guidelines, please note that the information provided on this page is not intended to be and should not be interpreted as legal, tax, investment, financial, or any other form of advice. It is important to only invest what you can afford to lose and to seek independent financial advice if you have any doubts. For further information, we suggest referring to the terms and conditions as well as the help and support pages provided by the issuer or advertiser. MetaversePost is committed to accurate, unbiased reporting, but market conditions are subject to change without notice.
About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
More articles
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
Published: January 02, 2026 at 6:00 am Updated: December 30, 2025 at 6:32 am
by Ana
Edited and fact-checked:
January 02, 2026 at 6:00 am
To improve your local-language experience, sometimes we employ an auto-translation plugin. Please note auto-translation may not be accurate, so read original article for precise information.
In Brief
Crypto is exiting the 2025 shakeout with real usage holding up, and the likely “shining stars” of 2026 are projects providing essential cash rails, AI compute, modular infrastructure, and information systems rather than pure speculation.
The end of 2025 has been a rough one for crypto. Prices cooled after a monster run, leverage got flushed, but on-chain activity, stablecoin volumes and DeFi usage quietly kept performing decently. A new cohort of projects is setting up for a much bigger role in 2026, not just as speculative bets, but as core infrastructure for money, AI, and the open web.
What Makes a “Shining Star” in 2026?
In 2017, “new and popular” mostly meant ERC-20 tokens that could tell a good story. In 2021, it meant DeFi TVL and NFT floor prices. For 2026, the bar is different. Across reports from VC giants like a16z, a few common traits keep showing up:
Real usage: transaction volume, active users, or integration into other protocols
Clear role in the stack: money, data, compute, identity, or security
Credible teams + funding: not anon devs vanishing after launch
Staying power through drawdowns: projects that kept shipping in 2024-25
With those traits in mind, here are the sub industries to watch, including a few projects for each.
The New Cash Layer
Ethena (USDe / ENA)
Ethena is trying to answer a simple question: can you build a “dollar” that lives entirely on crypto rails, without parking cash in banks? Ethena’s USDe is a synthetic dollar backed by crypto collateral (BTC, ETH, etc.) plus delta-hedged futures positions. Instead of T-bills in a bank account, it balances spot and derivatives to stay near $1.
Why it’s a star:
USDe has already grown into one of the largest dollar assets on-chain.
Staked USDe (sUSDe) turns those funding rates + staking rewards into yield, making it a kind of “on-chain dollar savings account”.
It’s crypto-native, composable across DeFi, not dependent on a single bank.
Why it’s risky:
The October 2025 depeg. USDe briefly traded as low as $0.65 on one major CEX during a market crash, showed that synthetic dollars inherit derivatives risk and liquidity risk, not just smart-contract risk.
The system is complex. You’re effectively betting that Ethena can manage basis spreads, exchange risk, and counterparty risk better than the market fears.
Ondo Finance (ONDO)
On the opposite side of the spectrum you have tokenized Treasuries like Ondo’s OUSG and USDY: boring in a good way.
This is the “safe yield” side of the 2026 narrative. Staid, regulated assets wrapped in crypto form factors and used as collateral across DeFi and CeFi.
AI x DePIN
If 2020-21 was about DeFi, 2025-26 is very much about DePIN + AI, which are decentralized physical infrastructure powering machine learning workloads.
Aethir (ATH)
Aethir runs a distributed GPU cloud aimed at enterprise-grade AI and gaming clients. In the first half of 2025 alone, it delivered 955M+ compute hours across more than 430k GPU containers, and by Q4 it reported infrastructure in 90+ countries, 200+ locations and 435k+ containers, with ARR above $140M. So this isn’t just a whitepaper network. It’s selling real compute to paying customers.
Why it’s interesting:
It turns idle or distributed GPUs into a globally accessible compute pool.
It sits at the intersection of AI demand shocks, GPU shortages, and crypto incentives.
It’s already integrated with other infra projects (including restaking and rollup ecosystems).
Render (RENDER)
Render started as a decentralized rendering network for 3D graphics and VFX. It’s now leaning hard into AI and real-time compute:
The Render Network connects creators needing GPU power with node operators who rent out their hardware, backed by the RENDER token.
The 2026 roadmap focuses on scaling compute subnets for AI/ML workloads and expanding enterprise integrations.
Together, Aethir and Render sit at the sweet spot where GPU supply meets AI demand, but with crypto-native incentives. If the “internet becomes the bank,” these networks are the ones turning it into the world’s shared supercomputer as well.
Modular & Restaked
The story of L1s used to be “who has the fastest chain?” In 2026, the more interesting question is: who offers the best shared infrastructure for thousands of chains and apps?
Celestia (TIA)
Celestia is the flagship modular data availability (DA) network: instead of doing execution + consensus + DA in one monolithic chain, it focuses on consensus + DA and lets rollups / appchains handle execution. Developers can:
Launch custom rollups or sovereign chains that publish their data to Celestia.
Get high-throughput “blobspace” without bootstrapping their own validator set.
Scale beyond the constraints of single-chain gas markets.
If a16z is right and blockspace starts to look like commodity infrastructure, Celestia is one of the networks that can aggregate demand from many chains instead of competing as yet another monolith.
EigenLayer (EIGEN)
EigenLayer, built on Ethereum, introduces restaking, letting stakers and LST holders reuse their staked ETH to secure additional services (AVSs) in exchange for extra yield. Think of it as a marketplace for trust:
Celestia + EigenLayer are a good snapshot of where infra is going:
DA networks turning into neutral “data utilities”
Restaking markets turning ETH security into something composable and reusable
If you want to understand the 2026 infra meta, watching TIA and the restaking ecosystem is a solid starting point.
Identity and Prediction Markets
Beyond finance and infra, some of the most controversial and popular projects sit at the intersection of identity, information, and incentives.
Worldcoin (WLD)
World (formerly Worldcoin) is attempting a global proof-of-human network: biometric verification via the Orb, a privacy-preserving World ID, and a WLD token that ties it all together. In 2025 it expanded its eye-scanning hardware and launched more ID pilots with governments and institutions, pitching itself as a defense against bots, deepfakes, and AI spam. Whatever you think of the trade-offs, it’s clearly one of the most ambitious “identity + crypto” experiments live today.
Polymarket
Prediction markets went from niche to mainstream in 2024-25. Polymarket, one of the most prominent platforms, has processed tens of billions in cumulative volume, with 2025 alone estimated around $18B according to several analyses. Why this is important:
For big elections, macro events, and even pop culture, markets are now often more accurate than pundits, because they aggregate information into prices.
a16z’s 2026 piece explicitly calls out prediction markets as a key frontier, especially when combined with AI oracles and better governance.
We’re also seeing a push on the regulatory side: new coalitions and licensed markets (e.g., Kalshi, Gemini’s CFTC-approved platform) are lobbying for clearer rules as volumes explode.
How to Build a 2026 Watchlist
You don’t need to buy everything here (in fact you probably shouldn’t). But if you want to track where the puck is going, you can organize your watchlist around themes instead of tickers:
Cash lLayer & RWAs
Ethena (USDe / ENA), Ondo (ONDO), major stablecoins and tokenized T-bill platforms.
Watch metrics like supply growth, depeg events, on/off-ramp integrations, and yields relative to TradFi.
AI Compute & DePIN
Aethir (ATH), Render (RENDER), and competing GPU networks.
Track active clients, compute hours, and revenue growth, not just token price.
Modular Infra & Restaking
Celestia (TIA), EigenLayer and the restaking ecosystem.
Follow rollup launches on Celestia, DA usage, and the number/value of AVSs secured via restaking.
Identity & Information Markets
World / WLD, Polymarket and other prediction markets.
Keep an eye on regulatory developments, volume growth, and real-world integrations (media, research, governance).
Then, Apply a Few Simple Rules
Start from use case, not hype. If you can’t explain what a project actually does in one sentence, skip it.
Check who uses it when prices are down. Survivors of bear phases usually have something real going on.
Size positions by risk. Core infra and cash-layer plays belong in a different bucket from presale memes.
Always assume you can lose 100% on any single token. Plan accordingly.
Disclaimer
In line with the Trust Project guidelines, please note that the information provided on this page is not intended to be and should not be interpreted as legal, tax, investment, financial, or any other form of advice. It is important to only invest what you can afford to lose and to seek independent financial advice if you have any doubts. For further information, we suggest referring to the terms and conditions as well as the help and support pages provided by the issuer or advertiser. MetaversePost is committed to accurate, unbiased reporting, but market conditions are subject to change without notice.
About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
More articles
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
Honestly, the footage I watched from that Chinese factory kept me up at night.
We have always joked that “robots would take over the world” one day. We laughed at the videos of robots falling over or walking clumsily. But looking at these new humanoid robots working 24/7 without food, sleep, or a salary… the joke isn’t funny anymore.
In this article, I am not just talking about a cool new piece of technology. I am talking about our survival in the job market. This isn’t just an update; it is a reality check.
What Are “Dark Factories”?
There is a term in the manufacturing world that sounds like something out of a sci-fi horror movie: The Dark Factory.
Why “dark”? Because inside these facilities, the lights are turned off. There are no humans working there, so there is no need for light.
When I saw the footage of the “Iron” robot and others like it in China, I realized we are entering a new phase. These aren’t just mechanical arms bolting a door onto a car. These are humanoid agents moving freely, making decisions, and handling delicate tasks with precision.
They don’t need breaks.They don’t form unions.They don’t get sick.They don’t stop.
The Brutal Math: $3,000 vs. $0
Let’s put feelings aside for a second and look at the cold, hard math. If you were a business owner, you would be staring at a very difficult spreadsheet right now.
Option A: The Human
Cost: Average $3,000/month (salary, insurance, benefits).Limitations: Works 8 hours a day, needs sleep, has emotional days, takes vacations.Output: Variable.
Option B: The Humanoid Robot
Cost: Near $0 in labor wages (after the initial purchase/lease).Advantages: Works 24 hours a day, 7 days a week. Consistent quality.Output: Constant and scalable.
I love technology. I live for this stuff. But when I look at those numbers, I don’t see a “choice.” For a capitalist economy, this is a mathematical inevitability.
Is This Another Industrial Revolution?
Whenever I bring this up, someone always says: “Don’t worry, Ugu. The Industrial Revolution destroyed farming jobs but created factory jobs. AI will create new jobs too.”
I really want to believe that. But this feels different.
In previous revolutions, machines replaced muscle. They helped us lift heavier things and move faster. But these new humanoid robots, powered by advanced AI, are starting to replace cognitive function and dexterity.
They can fold laundry, sort logistics, assemble electronics, and even perform quality control. If a robot can do the physical work and the basic thinking required for the task, where does that leave the average worker?
My Honest Take
I am not a pessimist. I believe human creativity is irreplaceable. But let’s be real: most jobs in the world right now aren’t about “creativity.” They are about repetition and labor. And those are exactly the jobs these robots are coming for.
We need to stop looking at this as “cool tech news” and start looking at it as a signal to adapt. The era of getting paid just for showing up and doing a routine task is ending.
What Do You Think?
I want to end this with a serious question. I want you to be absolutely honest with me.
If you owned a manufacturing company today, would you hire a human for $3,000 a month, or deploy a robot that works for free?
Drop your answer in the comments below. I’m genuinely curious if ethics can beat economics.
Iran has begun accepting cryptocurrency for the sale of advanced drones, missiles, and warships.
The move aims to bypass Western sanctions and stabilize a failing national economy.
Tehran is using blockchain to build a trade network independent of global banking controls.
Iran’s Ministry of Defense export agency, Mindex, announced today that it is now accepting cryptocurrency as payment for advanced weaponry. This move targets foreign governments and aims to use digital assets to get around Western financial sanctions and banking restrictions that have severely affected the nation’s trade routes.
According to reports, Tehran is incorporating blockchain technology and keeping its defense industry running despite being mostly cut off from the global SWIFT banking network.
Advanced military hardware
The initiative includes the sale of Iran’s advanced military equipment, which includes Shahed drones, Emad ballistic missiles, Soleimani-class warships, and short-range air defense systems. The official website FAQ of Mindex states that the agency is ready to negotiate contracts that use cryptocurrency, barter systems, or the Iranian rial.
The agency stated that it has set up specific protocols to ensure delivery of these products and fulfillment of contracts, regardless of international maritime or financial restrictions. This shift to digital currency occurs during a time of severe domestic economic hardship, as the Iranian rial reached record lows against the dollar in 2025, with national inflation rising to about 42.5%.
Economic and geopolitical hardships
The decision follows years of mounting economic pressure and recent geopolitical tensions. Iran has dealt with decades of sanctions that have limited its access to the global financial system. This situation has contributed to high inflation and public unrest.
The pressure ramped up this week, as the United States imposed new sanctions on weapons trade networks between Iran and Venezuela. The geopolitical climate has become more unstable, with Donald Trump warning of possible military action if Tehran expands its weapons programs.
These developments have urged the Iranian government to find “independent” financial channels that do not depend on U.S. dollar-clearing systems. Iranian officials have mentioned plans to use these digital payment methods for trade with BRICS nations, including India.
The role of blockchain technology
At the deBlock Summit, Iran’s first government-backed international blockchain conference, Parliament Speaker Mohammad Bagher Ghalibaf said cryptocurrencies could offer new ways to conduct business and facilitate trade.
He emphasized that such technologies can assist independent nations and expressed a desire for Iran to become a regional and global center for blockchain technology and digital trade.
Recently, Bitwise CEO Hunter Horsley also spoke about how Iran needs Bitcoin. He stated that as national currencies fail and access to global banking is restricted, decentralized assets become essential tools for financial survival.
Iran has also urged members of the Shanghai Cooperation Organisation (SCO) to develop a shared cryptocurrency to facilitate easier cross-border transactions. This proposal aligns with Tehran’s broader strategy to build a unified financial front with regional partners. It reduces their collective dependence on the U.S. dollar and Western banking infrastructure.
Looking ahead, the success of this initiative will largely depend on whether foreign buyers are willing to engage in high-value crypto transactions while under the watch of international regulators.
Major global powers, such as the US, are likely to keep a close eye on these blockchain transactions as Iran works to stabilize its economy through 2026. This situation could pave the way for how sanctioned states use new technology to maintain their military-industrial capabilities.
Also Read: Crypto Tax Crackdown: UK Starts New Exchange Reporting Rules
In brief
TRON founder Justin Sun is suing Bloomberg to block publication of his detailed crypto holdings.
Sun warns that disclosing specific crypto amounts would...
