D E X O R A

I'm going to be straight with you. The first time I tried copy trading on Binance, I picked the leader with the highest ROI. Guy had something like 800% in two weeks. I thought I found a goldmine. Three days later, half my money was gone. He took one massive leveraged bet, it went wrong, and everyone who copied him got wrecked.
That was a cheap lesson compared to what some people pay. And it taught me something important — copy trading and trading bots are real tools that can actually make you money. But only if you understand how they work under the hood. Most people don't. They see the big green numbers on the leaderboard and throw money at the first name they see. That's gambling, not trading.
So I'm going to walk you through everything I've learned. Not the marketing version. The real version. How it works, how to pick the right people to follow, which bots actually make sense, and the mistakes that drain accounts every single day.
How Copy Trading Works on Binance

The idea is simple. You find a trader on Binance who has a good track record. You click copy. From that moment, every trade they make gets copied into your account automatically. They buy ETH, you buy ETH. They close the position, yours closes too. You don't have to sit in front of a screen. You don't need to know how to read charts. The system handles everything.
But here's where people get confused. There are two modes. Fixed amount means you put in a set dollar amount for each trade regardless of what the leader does. Fixed ratio means your trade size matches the leader's as a percentage. So if they put 20% of their portfolio into a trade, you put 20% of your copy budget into it too.
Fixed ratio is closer to actually copying what they do. Fixed amount gives you more control. Most beginners should start with fixed amount and keep it small until they understand the rhythm of the person they're following.
The leader gets paid through profit sharing. On spot copy trading, they take 10% of whatever profit they make for you. On futures, it can go up to 30%. So if a leader makes you $1,000, they keep $100-$300. That's the deal. If they lose you money, they don't pay you back. That's important to remember.
The Part Nobody Talks About — Picking the Right Leader

This is where most people mess up. And I mean most. The Binance leaderboard shows you traders ranked by profit. And your brain immediately goes to the person at the top with the biggest number. That's a trap.
Here's why. A trader can show 1000% ROI by taking one massive bet with 125x leverage and getting lucky. One trade. That's not skill. That's a coin flip. And the next coin flip might wipe out your entire copy balance. What you want is someone boring. Someone who makes 5-15% a month consistently. Month after month. For at least 90 days. That's the kind of person who actually knows what they're doing.
The max drawdown number is your best friend. It tells you the worst peak-to-bottom drop that leader has ever had. If it's over 50%, walk away. That means at some point, their followers lost half their money before things recovered. Can you stomach that? Most people can't.
Check how many followers they have and how long those followers stay. If a leader has 500 people copy them this week and 200 leave next week, that tells you something. People who tried it and left weren't happy with the results. But if a leader has steady followers who stick around for months, that's trust earned over time.
Look at what pairs they trade. A leader who only trades one pair is putting all eggs in one basket. Someone who spreads across BTC, ETH, SOL, and a few altcoins shows they think about risk and don't rely on one market going their way.
And check their Sharpe ratio if it's shown. Above 1.0 is good. It means they're getting decent returns for the amount of risk they take. Below 0.5 means they're taking huge risks for small rewards. Not worth your money.
Spot vs Futures Copy Trading — Know the Difference
This one catches a lot of beginners off guard. Spot copy trading means the leader buys actual coins. If they buy BTC, you own BTC. If the market drops 10%, you lose 10%. Simple. Your downside is limited to what you put in. You can't lose more than your copy budget.
Futures copy trading is a completely different animal. It uses leverage. Right now, Binance caps futures copy leverage at 10x. That means a 10% move against you wipes out your entire position. Not 10% of it. All of it. Gone. And it happens fast. One bad candle at 3 AM and you wake up to zero.
My honest advice? Start with spot. Get comfortable. Learn how the system works. Watch your P&L move. Feel what it's like to trust someone else with your money. After a few months, if you want more action, try futures with a small amount and low leverage. Don't jump into 10x futures copy trading on day one. I've seen that story end badly too many times.
Trading Bots — Your 24/7 Worker

Copy trading follows people. Bots follow rules. You set the rules, the bot runs them day and night. No emotions, no hesitation, no sleeping. Binance offers seven different bot types, and each one does something different.
The Spot Grid Bot is the most popular one, and for good reason. You set a price range — say BTC between $60K and $70K. The bot places buy orders at the bottom of the range and sell orders at the top. Every time the price bounces between those levels, it skims a small profit. In sideways markets, this thing prints money. The catch? If the price breaks above your range, you miss the rally. If it drops below, you're holding bags at a loss.
The Spot DCA Bot is perfect if you don't want to think at all. You tell it to buy $50 of BTC every Monday. It does exactly that. No matter if the price is up or down. Over time, this averages out your entry price. It's the simplest and safest bot on the platform. Not exciting. But it works.
The Arbitrage Bot is interesting. It makes money from the tiny price gap between spot and futures markets. The returns are small — think 2-5% a year in calm markets — but the risk is also very low because you're hedged on both sides. It's basically the savings account of crypto bots.
The Rebalancing Bot keeps your portfolio in check. Say you want 50% BTC and 50% ETH. If BTC pumps and becomes 70% of your portfolio, the bot automatically sells some BTC and buys ETH to bring it back to 50/50. It forces you to sell high and buy low without you having to do anything.
TWAP and VP bots are for people moving serious money. If you need to buy or sell a large amount without moving the market, these bots spread your order across time or match it to real-time volume. Most regular traders won't need these, but it's good to know they exist.
The 7 Mistakes That Drain Accounts

I've made some of these myself. Talked to plenty of others who made the rest. Let me save you the tuition.
Picking leaders by ROI alone is mistake number one. We already covered this but it's worth repeating because it's the most common trap. A huge ROI in a short time almost always means huge risk. Look at the timeframe. Look at the drawdown. Look at the consistency. If the ROI only came from one or two trades, that's luck, not skill.
Going all-in on one leader is mistake number two. If that leader has a bad week, you have a bad week. Split your copy budget across 3-5 leaders with different styles. Maybe one trades BTC only. Another trades altcoins. A third uses conservative leverage. That way, if one blows up, the others keep your portfolio alive.
Not setting your own stop-loss is a big one. The leader might not have a stop-loss on their position. Or their risk tolerance might be way higher than yours. They might be fine losing 40% because their overall strategy recovers. But you might not sleep at night with that kind of drawdown. Set your own limits. Protect yourself.
Using high leverage on futures copy trading without understanding it is how people go to zero. Start at 2-3x if you must use leverage. Feel what it's like. A 5% move at 3x is a 15% swing in your account. That's already a lot. Don't go 10x until you really know what you're doing.
And forgetting about fees. Profit share plus trading fees plus funding rates on futures — it adds up. A trade that made 3% profit on paper might only net you 1% after the leader takes their cut and Binance takes the trading fee. Run the math before you celebrate.
My Personal Setup Right Now
I'll share what I'm currently doing. Not as advice. Just as a real example of how one person puts this together.
I have three copy leaders running on spot. One focuses on BTC and ETH majors with very low drawdown. Super boring. Makes maybe 4-6% a month. Second one trades mid-cap altcoins with slightly more risk but has a 120-day track record of steady growth. Third one is more aggressive — smaller altcoins, higher potential, but I only put 15% of my copy budget with them.
On the bot side, I run a Spot Grid on BTC with a range that I adjust every two weeks based on where the price is sitting. And I have a DCA bot stacking ETH weekly regardless of what happens. The grid makes me money in sideways markets. The DCA builds my long-term position.
Total time I spend on this each week? Maybe 30 minutes checking the dashboard. That's it. The rest runs on autopilot.
Bottom Line
Copy trading and bots aren't magic money machines. They're tools. Good tools in the right hands, dangerous ones in the wrong hands. The difference between the two is knowledge. And now you have more of it than most people who start.
Start small. Learn the system. Pick boring leaders over flashy ones. Set your own stop-losses. Don't trust anyone else to care about your money as much as you do. And give it time. The best results come from weeks and months of steady compounding, not overnight moonshots.
The crypto market doesn't sleep. With the right setup on Binance, you don't have to either.

NFA

#Binancecopytrading #MarketRebound #TradingCommunity #Write2Earn #Crypto_Jobs🎯

I spent last Tuesday watching a robotics company demo their latest autonomous delivery system at a tech conference in San Francisco. The presentation was impressive with videos showing robots navigating sidewalks, avoiding obstacles, and making deliveries without human intervention. The CEO confidently predicted they’d have 10,000 robots deployed across major cities within eighteen months. Then during the Q&A session, someone asked a question the company clearly wasn’t expecting and the whole narrative started unraveling.
“How many of those demo robots are actually making autonomous decisions versus being remotely piloted?” The CEO paused uncomfortably before admitting that their “autonomous” system currently has human operators monitoring video feeds and taking control during what he called “edge cases and complex scenarios.” Pressed further on what percentage of operation time involves human control, he eventually acknowledged it’s “north of 40 percent” during actual deployments.

This matters enormously for understanding what Fabric Protocol is actually building with $ROBO and whether the robot coordination infrastructure they’re developing serves a market that exists or might exist anytime soon. The entire thesis assumes millions of truly autonomous robots making independent decisions about coordination and navigation in shared spaces. But what’s actually deploying in cities right now are expensive remote-controlled systems with increasing automation assistance, which is completely different from true autonomy and requires fundamentally different infrastructure.
What Autonomous Actually Means When Marketing Meets Reality
The robotics industry has a definition problem that everyone acknowledges privately but nobody wants to address publicly because it affects funding and valuations. Companies market their systems as autonomous when what they really mean is “automated with human supervision and frequent intervention.” The distinction seems minor until you understand how dramatically it changes infrastructure requirements.
True autonomy means robots perceive environments, make decisions, and execute actions without human involvement even in novel or complex situations. This requires AI systems that can handle the infinite variety of real world scenarios reliably and safely. Current technology does this reasonably well in controlled environments but struggles badly in unstructured spaces where unexpected situations happen constantly.
I talked to an engineer who worked on one of the major delivery robot projects before leaving for a traditional robotics company. He described the actual operation as “like playing a video game where you’re controlling twenty robots simultaneously and constantly taking over when they get confused, which is often.” The robots handle straightforward navigation on empty sidewalks decently but need human intervention for anything mildly complicated like construction zones, large crowds, or unexpected obstacles.
The company’s marketing emphasized autonomous operation and breakthrough AI, but the operational reality was a warehouse full of remote pilots managing robot fleets and intervening constantly. The engineer estimated they’d need AI capabilities that are maybe five to ten years away before the robots could actually operate without regular human intervention. In the meantime, the business model depends on human operators being cheaper than the alternative of developing true autonomy.
This economic reality is what destroys Fabric’s timeline assumptions. True autonomy requires massive additional AI development that’s expensive and uncertain. Hybrid human-robot systems work reasonably well right now and are improving gradually through better automation assistance. Companies have strong financial incentives to stick with hybrid approaches rather than pursuing full autonomy that costs more and might not work reliably enough for deployment.
Why The Coordination Problem Doesn’t Exist Yet
Fabric’s infrastructure is designed to solve coordination challenges that emerge when you have large numbers of truly autonomous robots operating in shared spaces making independent decisions that need coordination to prevent conflicts and inefficiencies. This is a genuine problem that would definitely need solving if those conditions existed. The catch is those conditions don’t exist and probably won’t exist within any reasonable funding timeline based on current deployment patterns.
Current robot deployments involve small numbers of units in controlled areas with heavy human oversight. The robots aren’t making independent coordination decisions because humans are managing coordination through normal communication channels. When multiple delivery robots from different companies encounter each other, remote operators communicate and coordinate just like human delivery drivers would.
I watched this happen during field research last month. Two delivery robots from competing companies approached the same narrow sidewalk section simultaneously. Rather than some elegant autonomous coordination protocol, what actually happened was their remote operators communicated via radio and one operator had their robot wait while the other passed through. The coordination happened through human communication, not robot-to-robot protocols.
The infrastructure Fabric is building becomes necessary only when robots are numerous enough and autonomous enough that human coordination becomes impractical. We’re nowhere close to that state and the path to getting there isn’t clearly defined or scheduled. Companies are deploying more robots gradually, but they’re also keeping human oversight because it works better economically than developing full autonomy.
The market timing bet requires deployment accelerating dramatically beyond historical patterns while autonomy improves dramatically beyond current capabilities. Both of these things need to happen simultaneously within the next few years for coordination infrastructure to matter. That’s optimistic given how slowly both deployment and autonomy have actually progressed compared to repeated predictions about imminent breakthroughs.
The Pattern That Keeps Repeating in Robotics Funding
What’s happening with Fabric fits a pattern I’ve seen play out multiple times in robotics infrastructure investments over the past decade. Smart people identify genuine problems that would definitely need solving if certain conditions existed. They build quality solutions anticipating those conditions emerging soon. Then the conditions take far longer to materialize than funding timelines allow, and the infrastructure sits underutilized while burning capital.
I talked to an investor who funded a warehouse robotics coordination platform back in 2018. The thesis was solid. Warehouses were adopting robots from multiple vendors and needed better coordination systems. The infrastructure they built worked well technically. The problem was warehouses mostly bought robots from single vendors who provided proprietary coordination, or they deployed so few robots that coordination wasn’t really a bottleneck worth solving with external infrastructure.

The company existed for about four years, never achieved meaningful revenue despite impressive technical demonstrations, and eventually got acquired by a larger robotics company who shelved the technology. The infrastructure was ahead of market needs by several years. Being right eventually doesn’t help if you run out of money before eventually arrives.
Fabric faces similar dynamics but potentially worse because they’re betting on general-purpose robots in public spaces rather than industrial robots in controlled environments. Industrial robotics is further along in deployment and still didn’t generate enough demand for coordination infrastructure quickly enough. General-purpose robotics in uncontrolled public spaces is much earlier in development with less clear timelines.
The funding committed to Fabric gives them maybe three to five years of runway depending on burn rate. The question is whether autonomous robot deployment in shared spaces reaches coordination-requiring scale within that timeline. Historical patterns of robotics deployment being consistently slower than predictions suggest this is unlikely, but infrastructure investors are fundamentally making timing bets that could prove correct even if the odds seem long.
What The Governance Complexity Reveals About Readiness
Beyond the deployment timeline questions, there’s a governance challenge that Fabric needs to solve which reveals how far we are from the autonomous coordination future. Getting competing robot manufacturers, different city governments, varied stakeholder groups, and regulatory bodies to coordinate on standards and behavior rules through decentralized protocol is extraordinarily difficult even if the robots existed at scale requiring it.
I’ve attended two different city council meetings in the past year where autonomous robots were discussed. Both involved delivery robot pilots operating with heavy human supervision in limited areas. The regulatory discussions were contentious despite the limited scale and heavy oversight. Cities want robots following traffic laws, respecting pedestrian right-of-way, not blocking sidewalks, and maintaining certain safety standards.
But cities also have completely different priorities and preferences about acceptable robot behavior. Some want to encourage innovation and are permissive about pilot programs. Others are restrictive and want proof of safety before allowing any operations. Some care primarily about not interfering with pedestrians. Others worry more about not blocking business access or interfering with street maintenance.
Achieving global coordination on robot behavior standards when each city has different priorities is genuinely hard. Doing it through decentralized protocol governance without formal authority is even harder. The governance framework Fabric is building might simply not be adequate for the coordination challenge even if everything else works perfectly.
There’s also the question of what happens when robot behavior causes problems that need rapid responses. If an autonomous robot injures someone or creates a dangerous situation, cities want immediate ability to restrict or ban operations until issues are resolved. Decentralized protocol governance is probably too slow for this kind of emergency response, which might push regulatory oversight toward centralized control that makes Fabric’s infrastructure less relevant.
Where This Actually Goes
The honest assessment is that Fabric built quality infrastructure for a robot future that probably arrives eventually but timing is genuinely uncertain and might extend beyond funding capacity. The deployment patterns and autonomy capabilities aren’t advancing as fast as their thesis requires. The economic incentives favor hybrid human-robot systems over pure autonomy. The governance challenges are substantial even if deployment accelerates.
For anyone evaluating $ROBO , the fundamental question isn’t whether the infrastructure is good or whether robots eventually coordinate autonomously at scale. The question is whether those conditions materialize within three to five years or whether it takes ten to fifteen years based on historical deployment patterns. Infrastructure investments are pure timing bets, and robotics has a twenty-year history of deployment predictions being wrong by large margins.
The company might pivot to adjacent markets if autonomous coordination doesn’t develop fast enough. They might find unexpected use cases that generate earlier revenue. They might get acquired by larger robotics players who can use the technology internally. Or they might run out of funding before the market matures to where their infrastructure becomes necessary.
What seems unlikely is the original thesis playing out where millions of autonomous robots coordinate through their protocol within reasonable timeframes. The robots aren’t autonomous enough and won’t be for years. The deployment isn’t scaling fast enough to create coordination needs. The governance is too complex for decentralized protocol to handle effectively. That doesn’t mean Fabric definitely fails, but success probably requires something very different from the original vision materializing, and those alternative paths aren’t obvious based on what I can observe about current robotics reality versus the PowerPoint version.​​​​​​​​​​​​​​​​

#Robo $ROBO @FabricFND

Something weird happened last month that nobody in crypto seemed to notice. A major gaming publisher quietly shelved their blockchain integration project after spending two years and roughly $40 million on development. The official reason was “strategic realignment” but the leaked internal memo told a completely different story. Their executive team concluded that connecting their gaming economy to external financial systems would fundamentally break everything that made their business model work.
This isn’t an isolated incident and it reveals something critical about what @Mira - Trust Layer of AI is actually trying to solve. The infrastructure they’ve built is technically sophisticated and addresses real cross-chain challenges around moving value between gaming ecosystems and traditional finance rails. But there’s a fundamental misunderstanding at the core of their thesis that becomes obvious when you look at how gaming companies actually think about their economies versus how blockchain builders assume they think about them.
Gaming companies aren’t sitting around frustrated that they can’t access institutional capital. They’re actively designing their systems to prevent exactly the kind of external financial integration that Mira enables. And the reasons why tell you everything about whether this infrastructure will ever find meaningful adoption beyond a handful of experimental cases that never scale.
The Economics Gaming Companies Won’t Talk About Publicly
I’ve had conversations with monetization teams at three different major publishers over the past six months while researching how they think about blockchain integration. None of them wanted attribution because speaking honestly about their business models creates PR problems. But the pattern across all three conversations was remarkably consistent.
Gaming economy design is built around controlled inflation and deflation cycles that would be completely incompatible with external investors expecting stable asset values. A typical free-to-play game might introduce powerful new items that make previous items less valuable, not because the old items are broken but because creating desire for new content drives spending. This planned obsolescence generates billions in revenue annually across the industry.
When I asked how they’d handle this with institutional investors holding positions in their economies, one monetization director put it bluntly. “We’d get sued constantly. Every balance change becomes potential litigation when someone’s retirement fund is invested in our virtual swords.” The legal exposure alone would transform their entire approach to content updates and economy management in ways that would probably reduce both player satisfaction and revenue.
The more interesting insight came from understanding their retention mechanics. Games deliberately create FOMO through limited-time offers and seasonal exclusivity that drives engagement through psychological triggers that border on manipulative. These mechanics work because the company controls scarcity completely. Players trust that rare items are actually rare because the company says so and maintains that scarcity through their control.
Blockchain verification of scarcity sounds like it should make this better. But from the company perspective, it removes a valuable tool. They can no longer adjust digital scarcity based on business needs without it being transparently obvious to everyone. The ability to quietly inflate supply when they need to drive engagement or deflate it when they want to create premium value disappears. Transparency around scarcity mechanics would expose psychological manipulation that works better when it’s less visible.
What Institutional Investors Actually Said When Given the Opportunity

The other side of Mira’s market hypothesis involves institutional investors supposedly eager for gaming economy exposure once proper infrastructure exists. I managed to sit in on two separate institutional investment committee meetings where gaming assets were evaluated seriously over the past year. The discussions revealed assumptions about gaming that are fundamentally incompatible with how institutions think about portfolio construction.
The first meeting involved a mid-size pension fund managing around $8 billion. The pitch came from an external advisor who’d done impressive work showing gaming economy scale and blockchain enabling access. The committee listened politely for about twenty minutes before the questions started dismantling every premise.
The portfolio manager handling alternatives asked about correlation with existing assets. Gaming tokens correlate almost perfectly with individual game success, which means they’re essentially venture bets on specific entertainment properties rather than diversifiable market exposure. You can’t hedge this risk effectively because there’s no broader gaming economy that succeeds or fails together. Each game is its own isolated bet.
When the advisor tried arguing that a basket of gaming tokens would provide diversification, the chief risk officer shut it down immediately. She pulled up data showing that gaming hits are power law distributed with a few massive successes and hundreds of failures. Creating a diversified basket means you’re guaranteed to own mostly failures with maybe one or two hits if you’re lucky. That’s not portfolio construction, it’s expensive lottery tickets.
The meeting ended when the legal counsel asked about fiduciary duty around investing in assets where value depends entirely on continued support from game developers who can withdraw that support unilaterally. The silence that followed pretty much killed any chance of the proposal moving forward. You can’t explain to pension beneficiaries that their retirement funds got invested in digital swords that became worthless because a game developer decided to shut down servers.
The second meeting I observed was at a university endowment managing about $3 billion. They’re generally more open to alternative investments than pension funds, but the gaming pitch died even faster. Their investment policy explicitly prohibits assets without clear regulatory classification, and gaming tokens exist in this weird undefined space where different jurisdictions treat them completely differently. The compliance overhead of trying to invest across multiple jurisdictions with conflicting rules made it a non-starter before they even evaluated the economic merits.
Why The Infrastructure Quality Doesn’t Actually Matter
Mira built genuinely impressive cross-chain infrastructure that solves real technical challenges around moving assets between gaming ecosystems and traditional finance. The custody solutions meet institutional security standards. The compliance modules address KYC and AML requirements properly. The liquidity mechanisms work as designed. None of this matters if both sides of the market actively prefer not being connected.
The assumption underlying huge infrastructure investments was that connection was desired but technically difficult. Building better pipes would enable flow that both sides wanted but couldn’t achieve. Reality appears to be the opposite. The technical challenges aren’t preventing adoption. The fundamental economic incentives make both parties prefer disconnection regardless of how good the infrastructure becomes.
Gaming companies prefer total economic control over outside capital that constrains their operational freedom. Institutional investors prefer assets with characteristics that gaming tokens fundamentally don’t have and probably can’t have while remaining useful for gaming. Better infrastructure connecting these preferences doesn’t change the preferences themselves.

This creates an uncomfortable situation where you’ve built sophisticated solutions to problems that your target customers are actively avoiding having. The engineering execution might be flawless but the market development thesis appears wrong based on what both customer groups actually want versus what infrastructure builders assumed they wanted.
What Actually Happens to Projects Like This
I’ve watched three similar infrastructure plays in crypto over the past four years. All of them identified genuine inefficiencies, built quality solutions, and struggled to achieve meaningful adoption because their market hypotheses were based on what seemed logical rather than what actual customers were asking for. The pattern that typically emerges is instructive for anyone evaluating $MIRA as potential investment.
The first phase involves impressive partnership announcements that don’t translate to actual usage. Company X integrates the infrastructure and everyone celebrates the validation. Then you discover the integration serves maybe a hundred users doing experimental pilots that never scale to production. The partnerships create appearance of traction without the underlying economics that would make them sustainable.
The second phase involves burning capital while hoping usage eventually materializes. Engineering costs continue as systems need maintenance and updates. Business development expenses remain high as the team chases partnerships. Marketing spending tries to drive awareness and adoption. The revenue from actual usage stays minimal because the fundamental demand doesn’t exist at scale justifying the infrastructure investment.
The third phase usually involves either pivoting to adjacent markets that show real demand signals, getting acquired by larger players who can absorb the technology into existing product lines, or gradually winding down as funding exhausts. The infrastructure might work perfectly but market timing or market existence questions make commercial success impossible regardless of technical quality.
Mira is currently in the early part of phase one based on what I can observe publicly. They’ve got the infrastructure built and are pursuing partnerships and integration. The critical question is whether this leads to actual sustained usage at scale that generates revenue justifying the ongoing costs or whether it follows the pattern where partnership announcements don’t translate to meaningful economic activity.
The challenge for anyone evaluating this is distinguishing between normal early-stage adoption curves where usage grows over time and fundamental market hypothesis problems where the usage never materializes because customers don’t actually want what’s being offered. The evidence I’ve seen from actual gaming companies and institutional investors suggests this might be the latter, but that assessment could obviously be wrong if there are customer segments I haven’t observed who do want this connection.
The honest evaluation requires looking at whether gaming companies are asking for this capability and whether institutions are demanding gaming exposure. From what I can tell through conversations and observations, both answers appear to be no. That doesn’t mean Mira will definitely fail, but it suggests the path to success requires either changing what both customer groups want or finding completely different customer segments than originally envisioned. Neither of those outcomes seems particularly likely based on the fundamental economics involved, but infrastructure plays have surprised people before when markets developed in unexpected ways.​​​​​​​​​​​​​​​​

#Mira $MIRA @mira_network