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Smart societies shape their future by understanding the potential of each new technological revolution and designing how it will be deployed. But the in order to establish itself it needs to replace the . And that’s not easy because a lot of people LOVE the old. They made their success, their wealth, their lives based on what used to be the new, but it’s already old. new old Now it’s obsolete. This natural from the old market and the government, plus the difficulty in assimilating these changes makes each great revolution go through bubbles and crashes. resistance How to identify a revolution Revolutions are created by new infrastructures that allow Everything from canals to railways, to steamships, to highways and electricity, to the internet. wider and deeper market penetration at decreasing costs. The rich, educated and young tend to be the pioneering adopters, with increasing layers of society copying their example. Phase 1 — The excitement At its birth, technological revolution attract investments from . These investors fund the technological transformation. They back the new startups, the , the excitement. They encourage experimentation with new business models until it decouples from the real but obsolete economy. private investors crazy ones As mentioned above, during this period there is intense polarization between the new and the old. The old represents a mature, conservative market while the new adapts a free market ideology, in order to encourage the abandonment of the old way of doing things. Phase 2 — Wild west 🌵 Now, imagine being in a “wild west” like investment environment. An enviroment with tons of funds and flexibility to speculate; where you are and government involvement. free from regulations Eventually, everything moves closer and closer to financial instruments, huge returns that lead to more speculation. All creating a “paper” economy. casino-like Besides that, this investment-frenzy phase ends in over-investment in a relatively . small market Both the casino-like character and the over investment in a small market end up in a major market . And eventually, every bubble bursts… bubble A few examples include the that led to the , the , the . “Roaring Twenties” “Wall Street Crash of 1929" dot com bubble of 2000 subprime mortgage crisis in 2007 In a much smaller scale the that we just experienced in 2018. bitcoin and ICO bubble Phase 3 — Recession, the good and the bad news The good news is that after the frenzy and thanks to private investments, the basic infrastructure of the new technology has been , ready for full growth potential across the entire economy. installed The bad news is that immediately following the , private investors have become and are not ready to fund the expansion. crash risk averse Also, a recession period always follows where hopelessness, inequality and unemployment is observed. Phase 4 — Government steps in As a result, after the major collapses, the state has historically stepped in to play an active role in favour of investment and growth. This is where were see regulations and (the old money) coming in. institutional money In this phase, the government gives a that spreads the new economy across the globe. direction Repeat. You can read more about technological revolutions and bubbles here: by Carlota Perez. The advance of technology and major bubble collapses: Historical regularities and lessons for today

Bubbles and crashes. The pattern.

A Fundamental Valuation of Bitcoin and a Diagnostic for Bitcoin Bubbles Visualization of the bitcoin price — Spencer Wheatley, Didier Sornette, Tobias A. Huber, Max Reppen, Robert N. Gantner based on our recently published paper . Since its release in 2008 by Satoshi Nakamoto, has grown tremendously, and cryptocurrencies have become an emerging asset class. At the end of 2017, the price of bitcoin peaked at almost 20’000 USD, but now sits at around 8'500 USD. The explosive growth and volatility of bitcoin has intensified debates about the cryptocurrency’s intrinsic or fundamental value. While many have claimed that bitcoin is a scam and its value will eventually fall to zero, others believe that further enormous growth and adoption await, often comparing it to the market capitalization of stores of value, such as gold. By comparing bitcoin to gold — an analogy that is based on the digital scarcity that is built into the Bitcoin protocol — some market analysts predicted bitcoin prices as a high as . Given bitcoin’s wild trajectory, many are wondering where it will go next. Bitcoin 10 million USD per bitcoin While there is an emerging academic literature on cryptocurrency valuations, which, for example, attributes some technical feature of the Bitcoin protocol, such as the “proof-of-work” system, as bitcoin’s source of value, an alternative valuation can be based on its network of users — the more users/nodes it has, the more valuable the network becomes. In the 1980s, Metcalfe proposed that the value of a network is proportional to the square of the number of nodes. Now, if Metcalfe’s law holds here, fundamental valuation of bitcoin may in fact be easier than valuation of equities — which relies on various multiples, such as price-to-earnings, price-to-book, or price-to-cash-flow ratios — and might, therefore, be indicative of bubbles. Here, we develop a diagnostic for bubbles and crashes in bitcoin that combines Metcalfe’s law — which will provide a fundamental value for bitcoin — and the Log-Periodic Power Law Singularity (LPPLS) model, which has been developed to detect bubbles. When both measures coincide, this provides a convincing indication of a and impending correction. For more details, see our . bubble paper A Fundamental Valuation of Bitcoin Metcalfe’s law states that the value, in this case market capitalization (cap), of a network is proportional to the number of users squared — i.e., relating to the number of connections when all users are connected to each other. To visualize this, Figure 1 shows the bitcoin market cap versus the number of users in logarithmic scales, where a linear relationship with slope 2 would qualify Metcalfe’s Law. Fitting by linear regression provides an estimate of 1.7 — being significantly smaller than Metcalfe’s value of 2. This would mean that, for instance, for 1 million users, a typical user would be connected to “only’’ 10'000 other users, rather than 1 million. Figure 1: Scatterplot of the bitcoin market cap versus the number of active users, with logarithmic scales It is however more interesting to directly compare the market cap predicted by Metcalfe’s Law with the true market cap, as visualized in Figure 2. In particular, we interpret the blue and orange dashed lines as fundamental support levels, whereas the rough red and green lines, with parameters given by the regression in Figure 1, fall between the fundamental level and bubble levels. Figure 2: Comparing bitcoin market cap (black line) with predicted market cap based on various generalized Metcalfe regressions of active users. In any case, the predicted values for the market cap indicate a current (as of the first week of March, 2018) over-valuation of at least four times. Further, assuming continued user growth (whose growth rate is in fact decreasing), the Metcalfe-based predictions for the market cap at the end of 2018 are 77, 39, and 64 billion USD respectively, still well below the current level. On this basis alone, the current market looks similar to that of early 2014, which was followed by a year of sideways and downward movement. In other words, some separate fundamental development would need to exist to justify such high valuation, which we are unaware of. Bitcoin Bubbles: Universal Unsustainable Growth? As is well known, bitcoin’s history has been punctuated by spectacular bubbles and crashes. We were able to identify four main bubbles, corresponding to massive upward deviations of the market cap from its estimated fundamental value. These four bubbles are highlighted in Figures 3 and 4 — in some cases exhibiting a 20 fold increase in less than 6 months! In all cases, the burst of the bubble is attributed to fundamental events: In 2011, for example, the bitcoin exchange Mt. Gox was hacked, which resulted in a 88% decrease in the cryptocurrency’s price. In 2013, China banned financial institutions from using bitcoin, which caused bitcoin’s market cap to drop by 50%, and two weeks later Mt. Gox shut down. Similarly, in the end of 2017, South Korean regulators threatened to close local cryptocurrency exchanges, which triggered a steep decline in prices. However, the fourth and most recent bubble was much longer, and it is plausible that the triggering factor, which resulted in the bubble’s bursting, was bitcoin’s all-time high price of 20’000 USD. In other words, bitcoin collapsed under its own weight. Figure 3: The upper triangle represents the market cap of bitcoin with four major bubbles indicated by bold colored lines, numbered, and with bursting dates given. The lower triangle shows the four bubbles scaled to have same log-height and length with the same color coding, and with pure hyperbolic power law and LPPLS models fitted to the average of the four bubbles, given in dashed and solid black, respectively. What is interesting is that, although the height and length of these bubbles vary considerably, when scaled to the same log-height, a near-universal super-exponential growth is evident. And in this sense, like a sandpile, once the scaled bubble becomes steep enough (the so-called angle of repose), it will avalanche. In other words, what causes the collapse is the instability of the system itself; the instantaneous cause of collapse is secondary. This key insight is built into the Log-Periodic Power Law Singularity (LPPLS) model, which has been developed by Didier Sornette and collaborators. In sharp contrast to the deeply entrenched view in finance and economics that financial bubbles can be characterized as unpredictable phenomena, as asset prices are assumed to follow random walks, the LPPLS model captures the radically different insight that . Based on Sornette’s hypothesis that the underlying causes of crashes should be identified in the preceding period, the LPPLS model captures the unsustainable super-exponential price acceleration, which means that the growth rate of the price grows itself. As the speculative frenzy intensifies and the bubble matures, the market approaches a critical point, being driven by positive feedbacks in herding and imitation behavior, at which time any small disturbance can trigger a crash. financial markets have predictable components Formally, the model looks like this: where is the price of the asset, corresponds to the bubble, and is noise. When applied to bitcoin, the LPPLS allows one to detect the signatures of bubbles, which are represented in Figure 3. p_i t_c ε(t) Now, given the proposed fundamental value of bitcoin based on the generalized Metcalfe regression presented above, we define the Market-to-Metcalfe value (MMV) ratio as the actual market cap divided by the market cap predicted by the Metcalfe support. As shown in Figure 4, bubbles are persistent deviations of the MMV above support level 1. In our paper, we show that these bubbles are not only well modeled by the LPPLS model, but that the model offered useful advance warning information for the 2017 correction, producing a confidence interval bracketing the true crash time, when back-tested. Figure 4: Market-to-Metcalfe value ratio (MMV) over time. The apparent bubbles, which radically depart from the fundamental level 1, are colored. To sum up, by combining a generalized Metcalfe’s law, which provides a fundamental value based on network characteristics, with the Log-Periodic Power law Singularity (LPPLS) model, we were able to develop a rich diagnostic of bubbles and their crashes that have punctuated the cryptocurrency’s history. In doing so, we were able to diagnose four distinct bubbles, being periods of high overvaluation and LPPLS-like trajectories, which were followed by crashes or strong corrections. This is in radical contrast to the view that crypto-markets follow a random walk and are essentially unpredictable. Further, in addition to being able to identify bubbles in hindsight, given the consistent LPPLS bubble characteristics and demonstrated advance warning potential, the LPPLS can be used to provide ex-ante predictions. Our Metcalfe-based analysis indicates current support levels for the bitcoin market in the range of 22–44 billion USD, at least three times less than the current level. Given the high correlation of cryptocurrencies, the short-term movements of other cryptocurrencies are likely to be affected by corrections in bitcoin (and vice-versa), regardless of their own relative valuations. For more, see the paper “Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe’s Law and the LPPLS Model” by Spencer Wheatley, Didier Sornette, Tobias Huber, Max Reppen, and Robert Gantner on which this blog post is based: https://arxiv.org/abs/1803.05663

Are Bitcoin Bubbles Predictable?

In mid-October, Bitcoin’s value hovered a bit under $6,000. Two months later, it hit the attention-getting peak price of over $19,000. And now, almost two months after this peak, its value is about $8,000. Bitcoin’s price increased 200 percent in two months, then lost almost all of that gain during the next two months. Is that normal? Not really. “Bubble” is a fun-sounding term for a not-so-fun event. defines it as “an economic cycle characterized by rapid escalation of asset prices followed by a contraction.” After exuberant, irrational speculation drives prices for an asset far higher than any sane person would say the asset really warrants, investors stop buying and a massive sell-off occurs very quickly. Investopedia Bubbles have been around as long as large trade systems have existed — a nearly crashed the whole Dutch economy in the early 1600’s. More recently, we can look at the “dotcom” bubble of the late 90s, when investors scrambled to buy into absolutely any internet company they came across, and the housing bubble that popped in 2008, an economic crisis still affecting our world today. tulip bubble The Bitcoin market’s recent behavior sure makes it look like a bubble, and it’s not the only cryptocurrency that might be mid-pop. of $830 billion in mid-January, the total value of all cryptocurrencies fell in early February to $276 billion, a bar it hadn’t sunk to since November. The impending (or already-upon-us) pop of the cryptocurrency bubble has recently been proclaimed in legions of news sources, and cryptocurrency folks are either plugging their ears or hunkering down in anticipation of feeling some pain. After following Bitcoin to a market cap high But is the bubble popping necessarily a bad thing? Ivan Goldensohn, CMO of blockchain company , recently argued in that it’s not. ICOs have been singled out as especially bad actors in creating and popping the bubble; an astonishing number have popped up in the past year — recorded 235 new ones in 2017. But Goldensohn claims that “In the ICO world, there is a massive gap between the speculative value of the cryptocurrencies and the actual usage of the services or networks those cryptocurrencies represent.” Dispatch Labs TechBullion CoinSchedule He’s not alone in claiming this. reports that Ethereum co-founder Vitalik Buterin has stated 90 percent of ICOs launched on his own protocol will fail. reports only 1 in 10 ICOs have use cases and an actual product platform even months after their release. All others were held and traded as speculative products that someday be traded in the ecosystem they were meant to be used in. CoinJournal Bloomberg might Of course, even if an ICO product gets off the ground, that doesn’t guarantee the ecosystem it operates in will be a healthy one. Goldensohn points out that Dentacoin, a cryptocurrency designed for dental offices, is technically “in use” in the dental world, but in such small numbers that its peak price seemed ludicrous (and then crashed). ICOs have been lauded as the answer to all fundraising problems, but the fact is that a lot of technological issues won’t necessarily be solved by incorporating cryptocurrency. Launching an ICO, in short, is not synonymous with building a feasible product that generates real value and incorporates token circulation into its platform. This doesn’t mean ICOs have to be tied to a product that’s already generating value by the time they’re put on the market. One of the joys of supporting a new business is joining something that’s great before the rest of the world has realized it’s great. A good idea is worth a lot, regardless of how far along it is in the implementation process, and ICOs are one way of helping those ideas on their journey towards reality. If the crypto bubble pops, those promising products that can really benefit from ICO usage in their platforms won’t have to compete quite so much in a market swimming in speculation. Goldensohn writes: “More restrictions on blockchain technology could hamper the abilities of the real companies out there to succeed. These new technologies are already struggling to rise above the sheer volume of bandwagon- jumpers and scam artists who are popping into the world of cryptocurrency and ICOs.” Bubbles might need to pop for society to move forward. recently pointed out that while the dotcom bubble entailed a lot of frivolous companies and lost fortunes, it also genuinely and permanently changed our technological landscape, putting in place internet infrastructure that allowed the rise of many of today’s genuinely valuable online companies. Marc Hochstein Jon Evans at recently wrote that the crypto bubble is “strangling innovation,” as valuable ideas get lost in splashy news about surging bitcoin prices. And blockchain is a truly valuable idea, set to foster many more. The New York Times also recently wrote about that potential for deep change in how our society stores and uses data in “ .” TechCrunch Beyond the Bitcoin Bubble After the bubble pops and the cryptocurrency world becomes one where money and valuable ideas are closely tied concepts rather than vague associates, blockchain users will need a feasible platform to exchange innovative ICOs, which is where companies like Dispatch come in. Another reason the Bitcoin bubble has popped is the massive transaction scaling issues it has faced. A leaner, stronger Bitcoin world will use a leaner, stronger protocol, promoting innovation even further. How are you preparing for the cryptocurrency bubble pop? Disclosure: The author has had a working or personal relationship with one or more companies mentioned in this article in the past. Access to mentioned company’s management and information was made through the author’s personal network. All information was vetted prior to posting. Disclaimer: This essay is not intended to be a source of investment, financial, technical, tax, or legal advice. All of this content is for informational purposes only.

What You Need to Know About the Bitcoin Bubble

These days, as always, the mainstream news is full of opinion pieces, argued from the outside looking in. The rationale usually comes down to Bitcoin having no intrinsic value ( , fiat doesn’t) or a contrived interpretation of whatever Bitcoin’s price has done lately. Bitcoin is a bubble it does For example: BTC has risen -> this is just like tulip mania, it’s a bubble BTC has fallen -> see, we told you it’s a bubble BTC has neither risen nor fallen -> nobody cares about bitcoin anymore, it’s eventually going to zero “Bitcoin is a bubble” has been the consensus view of the mainstream news since the beginning. And in my opinion it holds little weight. But just because the rationale doesn’t hold up, that doesn’t mean Bitcoin is definitely a bubble. not The sharp rally and sell off of the last few months do seem at least plausibly bubble-like. There is no way to know definitively whether or not Bitcoin is a bubble, but here are a few metrics we are looking at that may help inform your intuition as well. Bitcoin really does look a lot like other asset bubbles The other day, we comparing the price history of bitcoin to NASDAQ during the rise and fall of the dotcom bubble. tweeted out this graphic Credit: Elementus The time scale is not the same. NASDAQ took years to reach its peak, while Bitcoin rose 20x in less than a year. But the patterns look eerily similar. The charts both begin with a steady rate of ascent, followed by a kink where the slope shifts upward and the growth takes off. At the top, they both show the same double peak pattern, the first one taller than the second. And then they begin their fall back to Earth. By the time NASDAQ finally reached the end of its decline, nearly all of the gains it had accumulated during its dramatic rise had been lost. Does a similar fate await Bitcoin? The historical similarities don’t end there This next chart is from Dr. Jean-Paul Rodrigue’s research on . He explains the pattern by breaking the lifespan of a bubble into four stages. market booms and busts Stages in a bubble Compare this chart with the ones above. It’s nearly an exact match. The timespan and amplitude of market bubbles have varied over time and across different asset classes, but this same pattern has repeated over and over again. For example, here is the S&P 500 index before and after the crash of 1929. The S&P 500, 1920–1933 You can also see the same pattern in the , ’80s, the , and many other asset bubbles. gold bubble of 1980 Japan’s stock market in the late Shanghai Composite Index in 2007 If you squint, you can see the same “bull trap” double-peak pattern in U.S. home prices in 2006–2007. Case-Shiller U.S. National Home Price Index, 2001–2013 U.S. home prices in 2007 were every bit as much of a bubble as NASDAQ was in 2001. But the price trend looks very different… What’s the difference between a bubble and a normal market fluctuation? Most would agree that the U.S. housing market in 2007 was most certainly a bubble. But in terms of the price decline, it was not nearly as severe as the other bubbles shown previously. From its highest point to where it bottomed out, the U.S. housing market lost about of its value. 25% Compare that to the dotcom bubble, where NASDAQ lost of its value, or the stock market crash of 1929, where by the late 1930’s, the S&P 500 had fallen by . 78% 85% Even more extreme, compare the U.S. housing market to Bitcoin, which can move up or down 25% or more in a single . And it does so regularly — 8 times since 2013 to be precise. day For Bitcoin, a 25% fall would not indicate a bursting bubble, but a normal price fluctuation that you would expect to see a few times a year. For U.S. home prices, which are historically very stable, a 25% fall is a once in a generation event. Bitcoin is and always has been a highly volatile asset. The chart below shows how its daily fluctuations over the last five years compare with NASDAQ, the S&P 500, and the U.S. housing market over the same period. The bars are histograms of daily price moves. The blue curves are smoothed estimates of the distributions ( ). kernel density estimations The of each market: daily volatility Bitcoin: 4.80% NASDAQ: 0.89% S&P: 0.76% Case-Schiller: 0.04% On average, Bitcoin’s price fluctuates 5x as much as NASDAQ, 6x as much as the S&P 500, and over 100x as much as the U.S. housing market. So, is Bitcoin a bubble? Who knows? Bitcoin looks and feels a lot like other market bubbles from the past. And it has all the hallmarks of a bubble — speculative buying, fear of missing out, a sudden frenzy of interest from the general public. The recent price decline is big, even by Bitcoin standards, but not out of the range of “normal” given its past fluctuations. Bitcoin is a highly volatile asset, far more than other financial markets we are accustomed to following. And we should be careful applying the same intuition to make predictions about its future price. Elementus is a semantic inference engine and data layer for crypto asset intelligence. Want to contribute or have an idea to discuss? We’d love to hear from you . Originally published at elementus.io on February 12, 2018.

How Bitcoin compares to historical asset bubbles

Don’t feel like reading? Listen instead! Charlie Joe, Dominique, Tiffany, John, Carlos, Alex. I’m very bad with names. Bad as in, I already forgot your name after you just told me, especially after meeting a lot of people in a short period of time. But when I spoke with each of these lovely human beings that are living in the streets of the Tenderloin, I was humbled, touched, and overwhelmed enough to have their names etched in my memory. I felt a strong desire to somehow, someway, help them. I’ve been meaning to volunteer my time for-e-va. But I figured that as long as I donated to a charity, I was being charitable enough with my money instead of time, and this covered my volunteering duties. But the day finally arrived when had a volunteering program going on, and I decided to take the opportunity right away. my church “You will be walking in groups, you will have a team leader, and you will always have a partner with you when approaching someone. We all know that these streets are dangerous, so if your team leader tells everyone to go, you go. You don’t ask questions. If you feel you are getting into a dangerous situation, give your team leader and everyone will leave right away. Don’t get too close, because you will be entering their personal space, this is their homes and you need to respect that. If they start hate talking, you politely say that you have a different point of view and leave. Also, do not talk to anyone who is shooting up drugs.” It was 7pm, a couple of people in the group were admittedly scared (and secretly myself). And we were all “God is with us!” and I thought….“and please save us God”! the look My partner was one of the admittedly scared ones, so I had to get the courage to start talking with the first person. - What’s your name? - Charlie Joe, and this is my friend Dominick.- You both have beautiful names. Would you like a muffin? - Sure. - Would you like, hmmmmm, a prayer? I’m sorry, it’s the first time I do this… She calmly looked at me, as in to say don’t be nervous, put the muffin away, and said yes.I held her gentle hands, dirtied with black street marks, and surprisingly had absolutely no nervousness of holding them (I have barely changed one diaper in my life, might as well held someone’s hands who is likely on drugs and hasn’t showered in a very long time). Charlie Joe was probably in her early 20’s, a beautiful person, and her eyes showed that she was under the influence of something. I wanted to know what got her there, and if anything could have stopped it from happening. But I didn’t know how to ask. After I was done praying for her, she surprisingly took the stage and prayed for me, asking God to make all my dreams come true. Wow, Charlie Joe, this homeless person, who is part of a group of people that I have purposefully ignored even looking at, cared about dreams coming true when she knows I already have so much more than she does. My heart froze, cracked into a million pieces and sank to the deepest part of the ocean. my We had a box of small snacks like bananas, muffins, and some flowers for the ladies. Each person we encountered humbly took only 1 item, clearly leaving some for others that needed it as well. One of them just stopped, looked at the flowers in the box with a focus that nobody that owns a phone has left, picked a single stem, raised it up and looked at it like it was a piece of art, smelled it, carefully put it next to his bag, and said thank you as he walked away. No snacks, just one flower. I was watching that with the same focus he was looking at the flower (a focus I haven’t had in a while) as if nothing else around me mattered, thinking of the abundance of flowers I buy for myself, put in a vase and leave them there, not really stopping to appreciate the bouquet. The next person we approached was Alex, a young man probably in his early 30’s. I asked him if he wanted a snack and he promptly accepted. Where are you from Alex? “San Francisco”. Unsure of what to respond (as I typically respond with “It’s such a beautiful and awesome city, isn’t it?!”) I quickly asked him if he wanted a prayer. He said yes. Alex was a bit quieter and I felt that maybe Alex was ashamed of being there as a man, having a woman around his age offering him food and a prayer. We quickly left so Alex wouldn’t feel uncomfortable. By now my partner tells me to “Keep it up Steffany, you’re doing great” as she is still a bit nervous about the situation, and the night settles in. We then approached Tiffany, another young girl probably in her mid twenties. Tiffany told us that she had some emotional challenges, as her mother was using drugs when she was pregnant with her. Tiffany had problems holding jobs because, as soon as a challenge was presented, she couldn’t deal with the problems and would quit. She went on to say that she has a son who is going to be six next week. Her son was taken away from her after he was 1 year old. She said that she wishes her son was with her, as tears started streaming from her eyes, followed by “But I know that it’s very selfish of me, because, if I can’t even give myself one meal a day, how can I take care of my son?”. Needless to say, I was bawling too. What an amazing mom to say that it was selfish of her to wish that the human that she loves and longs for the most in this world was with her, because she can’t even take care of herself. Riding back home in my Uber had a very different feeling this time around, as we passed through the Westfield Mall on Market Street, with my iPhone in hand, looking outside of the window and seeing all of the frivolous things we buy being heavily marketed to us as “Falls Must Have’s” on storefront after storefront. What has our world come to, that these sweaters had homes with a roof, when so many other human beings slept just outside of it, in the cold? As I arrived in my warm apartment building, I remembered I had to pickup an Amazon box in the “Amazon-boxes-overflowing-mail-room”, knowing that all of the box owners will get strangely excited to open their newly arrived box, taking what is inside, getting a kick from their new “toy” followed by immediately loosing that kick, and throwing away the box — that very discarded box will become someone’s mattress, and blanket… Silicon Valley was put in the spotlight when we woke up completely baffled after America decided who should be our president. This volunteering experience showed me that we have a long way to go. If we don’t acknowledge the situation in our own backyard, and choose to turn our heads, how could we even begin to fix the disparity between us and the middle of America? How can we even attempt to fix the problem? I know that most of us would also be shaken by an experience like this. And we can make an impact in these human being’s lives by creating and supporting programs in our organizations that can help train and employ them, give them a chance, a chance that we were all just handed. If every organization makes it a point to help even just one human being per, say, 500 employees, imagine the exponential effect this can have in our own backyard. Empathy also makes us more emotionally intelligent and informed leaders, and it is very important for every organization to have a team with a broader perspective on life. — Otherwise, we work in silos, inside our echo chamber, out of touch with the reality around us, and we make mistakes that seem like basic common sense in hindsight. Can I get a clap clap or a comment for every person who will propose this at their next All Hands meeting? Shall we call it “Meet the Homeless”? Here are a few organizations that you can get started to #MeetTheHomeless: Lava Mae Miracle Messages SF Food Bank Glide Defy Ventures Because Justice Matters My lovely __ Canvas Church Thank you and for reading drafts of this article. Chandini Ammineni Lecole Cole

Stepping Out of My Bubble: Meeting the Homeless in Silicon Valley

Hey Hackers! I’m Ritika Mehta, and I currently run & building a new tool for creators. Ritika’s newsletter Marked First of all, a huge thank you to the HackerNoon community and staff for nominating me for the 2021 Noonies awards! I’ve been nominated in the following categories, please do check out these award pages and vote: HackerNoon Contributor of the Year - Marketing Strategies HackerNoon Contributor of the Year - Founder Advice HackerNoon Contributor of the Year - Founder Stories HackerNoon Contributor of the Year - Startup Advice As someone who loves building products, I believe no-code & low-code has given endless opportunities to non-tech founders like me and the way it’s emerging into web3. Learn more about my thoughts and opinions on the same and my journey in the industry via the interview below. 1. What do you do and why do you do it? (tell us your story) I’m a curious mind that keeps experimenting and building new products. I’ve been building since 17. My first one was a marketing business which I sold after 3 years of working on it. Then I created a campaign management marketing tool that is on hiatus. Currently, I’m building a platform for creators with no-code tools, and started for founders on decoding startups from building to scaling, growth, community building & somewhere into Web3. I have developed this keen interest to build a community through my newsletter which has unexpectedly opened numerous doors for me. Marked, a newsletter 2. Tell us more about the things you create/write/build! As I’m building Marked, I’m using tools like & the landing page with . These tools are very dynamic in nature which is giving me the possibility of validating the idea and testing MVP at a fast pace with no-code. bubble Webflow Regarding the newsletter, I write, and I’m significantly part of the creator economy, I’m very much excited to turn it into a paid product. In the end, both the things I’m working on are leading towards the creator economy. 3. How did you end up on your current career path? Do you like it? I will definitely say, till now, I was running with a flow. I didn’t have a clear picture of what I wanted to do. But I believe this is the right thing I want to do now. Along with my two projects, I’m also a product advisor, so this is a new thing I’m exploring. I’m still curious to see what it brings for me next. 4. What are you most excited or passionate about right now and why? . As a person who doesn’t know how to code, I had to hire developers for the previous projects, which cost a lot to me, and the product didn’t work out well. No-code tools have lowered the barrier of creation and cost, enabling millions of people like me to build everything from websites to complex web apps with little to no capital or outside help. No-code & low-code 5. What are you most worried about right now and why? Everything has its pros and cons, and at some point, one of them will worry you. Same with the no-code, it’s still in the early stage, limiting super functional development. Nevertheless, in the end, it’s still something that’s developed on code. 6. If we gave you 10 million dollars to invest in something today, what would you invest in and why? I was recently approached by a VC firm to mentor founders they invest in. This got me thinking anyway I can invest in other founders & which also helped me better understand the investment system. So, there are two things I’ll do: Spend some on developing Marked. Rest spend on investing in other founders. My niche would be community-led products and Web3 creators. 7. What are you currently learning? Community growth, I’ve learned this, if one knows . And the other is NFT. I wasn’t much interested in NFT before, but as I started coming over some very cool NFT projects on Twitter, it’s something that excites me now. how to build a community, it will never run out of business 8. What’s the best advice you’ve ever given someone? Unexpected opportunities can fall from anywhere only if you show up every day and do it consistently. I have experienced it myself. The way things have changed for me after I become active on Twitter & writing on my newsletter, it’s just unbelievable at times - whether it be speaking at a huge event, mentoring at a cohort, or this award nomination. 9. What’s the best advice you’ve ever received? There’s someone who said, which makes me want to keep working (sometimes even when I have a burnout) & less procrastinating, says: Waiting for inspiration is just fancy procrastination. When you wait for inspiration to strike, you’ll end with 0 things or words. When you tell yourself to start, you’ll end with an article or something. Do it first, then make it better. If you wait for things to happen, you will never begin. About HackerNoon’s 2021 Noonie Awards The annual Noonie Awards celebrate the best and brightest of the tech industry, bringing together all who are making the Internet and the world of tech what it is today. Please be sure to , nominate, and vote for the people and companies who you think are making the biggest impact on the tech industry today. check out our award categories The 2021 Noonies are sponsored by: , , and . Thank you so much to these sponsors who are helping us celebrate the accomplishments of all our nominees. bybit Dottech Domains Avast

Rooting for No-Code and Community-Led Startups: Noonies Nominee Ritika

Building Bubble. In this series of posts, our engineering team talks about the inner workings of Bubble, the cloud-based visual programming language that’s making programming accessible to everyone. If you’ve ever tried to build a compiler or an interpreter, you are probably familiar with Abstract Syntax Trees, generally abbreviated ASTs. An AST represents what code looks like to a computer. It’s the naked structure of the program, stripped of all the punctuation and spaces, organized hierarchically in a tree. For instance, in Javascript, the code if (x === 3) {alert('hi!');} Might turn into a graph like Whenever a computer compiles or runs a computer program, it first converts the code that the programmer types into an AST, and then navigates the AST to actually execute it. , unlike a traditional programming language, doesn’t have code. It’s an AST, without any syntax. Instead of storing Bubble apps as code, we just store the tree itself. Our web-based visual editor manipulates the AST directly. Bubble just Because Bubble apps are stored as ASTs instead of code, it is much easier for us to evolve the Bubble language over time than it is in a traditional programming language. We can make radical changes to the user interface that Bubble programmers work with without breaking compatibility with existing user applications, because we can write code that interprets the existing ASTs and translates them into the new format. For this reason, Bubble is interpreted, not compiled: when running a Bubble app, we feed the raw AST to an interpreter that knows how to execute it, which means that when we change how Bubble works, there’s no need to re-compile existing applications. So, because ASTs are so core to the Bubble language — they are, basically, the Bubble language — one of the very first decisions we faced building Bubble was what technology to use to store and transmit them. Spoiler alert: we picked JSON. JSON is an obvious choice for representing data trees in a web-based environment, for a couple of reasons. First, it’s a standard format that can easily be sent over the internet as plain text. Second, it’s very flexible and easy to build trees with: you just nest objects, using any combination of keys and values. Finally, Bubble is written in Javascript (well, CoffeeScript, to be precise, but we’ll discuss that in another post), so a JSON document translates directly into a Javascript object graph, without any weird impedance mismatches. When we first started working on Bubble, we imagined that each Bubble app would be a single JSON document that we would send around the web as needed. We figured we could just store the whole thing in memory, either in the user’s browser or on the server. Ha! In retrospect, that was blindingly naive. The first time we saw an application with a 5 MB document, we had an “oh shit” moment. Our code we loading the entire 5 MBs into memory to do , and we started watching the Bubble platform crawl to a grinding halt (Today, there are Bubble applications that are over a 100 MBs). anything Obviously, we had to break applications into chunks somehow, and only send the chunks we needed to perform any given operation. In some cases, this was pretty easy. For instance, when running a Bubble workflow on the server, all the code that represents that workflow is in a single branch of the AST. In other cases, though, it gets harder. There are a lot of things in Bubble that reference other parts of the app: elements reference styles, actions reference elements, and expressions reference user-defined types, for instance. As a result, it can get complicated to figure out what part of the app we need to load in order to do something. Often we need to load data in one part of the app, and that data will contain a reference to another part of the app, which we’ll also need to load, but we won’t know that til we load the first part. In some cases, the efficient thing to do is to just load a big chunk of the app upfront. When a user displays a Bubble page in the web browser, we know we need the AST for rendering all the elements on the page, so it makes sense to bundle that together and just send it. In other cases, it is more efficient to lazy-load data as we discover we need it: when running a workflow on the server, if a server action references something else in the app, we want to be able to pull just the node that it references — often only a few bytes of data — temporarily into memory. Having a messy problem like this so close to the core mechanic of our product is no good. We want to be able to develop Bubble without having to constantly juggle the question of what data is available when and where. So, we did what we always do when we encounter a messy problem we don’t want to deal with: we invented an abstraction layer. We call the layer — a little uncreatively — JSONBase. It’s an abstraction over a node in a JSON document. Each object that inherits from JSONBase has an application name and application version that identifies which document it belongs to, and it has a path that indicates which node in the document it points to. For instance, path ‘a.b.c’ refers to {a: {b: {c: ← THIS}}} JSONBase (or JSONs, for short), are entirely abstract. The key, or even the entire sub-tree, that a JSON points to may or may not actually exist. This means we can work with them without worrying about errors on null references, or whether or not the data is loaded. We’ve abstracted the position in a JSON document from the actual JSON object itself. JSONs expose methods for navigating the tree and accessing the data. For instance, calling on the json that represent ‘a.b.c’ yields the json ‘a.b.c.d’; calling yields ‘a.b’. returns a javascript object representing the data stored at this location in the tree; returns a boolean indicating if there is any data stored there at all. json.child('d') json.parent() json.raw() json.exists() JSONBase is an abstract base class; it defines an interface and some functionality, but it doesn’t say anything about how the data is stored or loaded. We actually have three separate implementations of JSONBase, corresponding to the three main environments that Bubble code runs in: in the Bubble visual editor, in the user’s web browser when visiting a page built on Bubble, and on our web servers. The simplest is RuntimeJSON: this is what we use in the browser when rendering a Bubble app. RuntimeJSON is all about : when we are rendering a page, we want it to be as fast as humanly possible. RuntimeJSON doesn’t have the ability to fetch data at all. We pre-compute on the server what parts of the application are needed to render a page, and send them over: RuntimeJSON is a thin wrapper around the actual data loaded in memory. speed In contrast, EditorJSON, which we use in the web browser as part of the Bubble editor, is all about flexibility. The subset of application data we need when someone is using the Bubble editor changes on the fly as they navigate to different parts of the app, so EditorJSON is capable of dynamically querying the server for more data as needed. EditorJSON also has the ability to handle changes to the application object. Each EditorJSON object has a method that overwrites that part of the application tree. When is called, EditorJSON both saves it in memory in the web-browser, and sends a request to the server to permanently save it to our database. .set(data) set Finally, EditorJSON has a notification mechanism to let other parts of the editor code that rely on that data know that there’s been an update, so that they can re-draw the UI as needed. (We use data-binding similar to the way React does… that’s a topic for another post!) Because multiple users might be editing the same app at the same time, EditorJSON periodically polls the server for any updates made by other users, so that we can display those edits in real time. As you might imagine, the code for EditorJSON is significantly more complicated than the code for RuntimeJSON, and it’s not quite as performant. But because they share the same JSONBase interface, most of our code doesn’t need to know which of the two it is dealing with: we can write code compatible with either, and then gain the performance of RuntimeJSON when running an app (we call this “run mode”), or the flexibility of real-time data updates in the Bubble visual editor (“edit mode”). ServerJSON, which is used in run mode on the Bubble servers, is somewhere in between RuntimeJSON and EditorJSON. We load data on the servers in order to build the HTML for pages, to calculate what chunks of the application tree we need to send to the web browser, and to execute workflows that run on the server. So, like EditorJSON, we need to load data on the fly as we need it, since at any time our server could need to execute any part of any user’s app. In contrast to EditorJSON, though, performance is a top priority, since when running an app, we want to make things as fast as possible. Luckily, we can make it simpler and faster than EditorJSON by not worrying about the app owner making changes. While executing a single page or workflow, we want to present a consistent view of the application, so we neither need nor want to take changes into account. JSONBase is a testament to the power of abstraction. By wrapping a layer around the raw JSON documents that represent the AST of a Bubble application, we’re able to write code that manages multi-megabyte documents, loading only the parts into memory that we actually need, without the complexity of that enterprise leaking its way into the rest of our codebase. JSONs aren’t the only level of abstraction in the Bubble language interpreter. A raw AST is all fine and good, but by itself, we don’t know what all those JSON trees . You can read about that in our next post in the series, . mean Trees in the Clouds, Part II Found this interesting? ! We’re always looking for great engineers to join us Originally published at blog.bubble.is on May 2, 2018.

How we built a cloud-based programming language: ASTs in the Cloud

TL;DR This article is about how we bootstrapped in an absolutely new niche of No-Code web development. Using DIYs, wide community support, super efficient and affordable marketing we built our development shop from 3 people into a company of 20. The article tells how we are running 2 parallel brands of No-Code Services and No-Code Products. How we became global leaders in the no-code web development niche in less than 2 years, and what we learned along the way. Zeroqode Background From the age of 21 until 32 I was running multiple businesses, most of which were offline. At 32, I decided to take one year off, which in fact stretched over a few years. During this time I spent most of my energy on self growth and oriental practices, after which I decided it was time for me to do something real again :) . In 2015, I had an idea for a niche social but went on building what seemed to me a simpler product — a meditation app. I’ve spent about $50K building it with very little payback (read more about this ). Because of this bad experience, I never wanted to hire developers again in order to implement a idea. network here startup How the Idea was Born One day, I received a newsletter from Product Hunt which featured — an easy to use no-code app building platform. I was intrigued and decided to give Bubble a try. At first, I was put off by their high price. At the time, Bubble was charging per number of visitors. I thought to myself, if the app I intend to build becomes very popular, my monthly overhead will grow far too high. When I checked back a few month later they changed the pricing strategy. Their charging model was now per workflow and it became much more affordable. (Recently, Bubble changed their pricing again and now their monthly plans start only at $14/month.) Bubble This is when I really started to learn the platform. I quickly went through all their basic tutorials and found the builder to be very easy to use. I completed my app in only one week, and most of the time I was polishing the UI rather than building the core functionality. In doing this, I realised I was more excited about the process of building rather than the application itself. This is where the idea crossed my mind — it would be cool to build applications without code for others! I was surprised to find out there were no other companies specializing on building apps without code. I reached out to (co-founder of ) and asked if they would be interested for us to become a Bubble partner and offer no-code web development services based on their platform. It turned out they were, in fact, going to launch their partner program shortly and it was the right time for me to approach Emmanuel. Emmanuel Straschnov Bubble I decided to build a landing page and, because my design skills were lousy (still are to be honest), I found a freelance designer who produced the drafts at the cost of $200. I consider this to be the only investment I put into this business (well, plus the domain name). I called the company because I wanted to reiterate that we focus on web development using Bubble — I figured this would give a “newbie” company more credibility in the eyes of potential clients. Bubblewits I started Bubblewits alone, but about 7 months later Vlad Larin joined me. We met at a coworking space we were residents of. Vlad and I were sharing a desk. In my previous businesses I never had co-founders, preferring the one-man shows. This time, I learned it is great to have a partner you can rely on and share the responsibilities with. At the beginning, we were focused on custom development services and only a few months later we built and published our first no-code app template, which became the foundation for . Zeroqode Getting customers was a whole process within itself and we had to come up with innovative and inexpensive ways to find them. Here is where the official partnership with Bubble came in handy. A significant amount of our clients were referred to us from Bubble’s partner page. Also, we were always very active on Bubble Forum. We were helping other users by posting hacks and solutions for things that were complicated to build on Bubble and we were publishing updates about new products we built, like template and plugins. We branched Zeroqode from Bubblewits out of the need for a better way to promote our products. Before Zeroqode, Bubble Template Marketplace page was the only place to show our products. As more templates were published by other users, the page became too crowded and therefore not structured to our benefit. We were looking for a better way to reach out to our customers and explain how the product works and why it is useful. We launched Zeroqode by featuring our templates, then step by step more products (like , and ) were added to our portfolio. Later on, we started featuring that teach how to build on Bubble. plugins no-code backends web-to-native solutions curated resources All our products are built to help our customers develop their applications up to 10X times faster. No-code app Templates, for instance, are easy to set-up and affordable, they come with built-in functionality and reliable updates.These are not simply design mockups but finished, fully functional, responsive web applications, mostly recreating the functionality of well-known and proven websites or apps. The templates can be modified without code to launch any product faster and with way less costs involved. We have already published dozens of templates and keep building more — from templates that are similar to AirBNB, Linkedin, Instagram, Trello to Backend and Telegram Bots. The goal is to make Zeroqode for complex web applications what Themeforest/Wordpress have become for simpler websites. Bootstrapping Effect on Zeroqode Bootstrapping was never a planned approach. In our case, it was more like a circumstance — it happened naturally. When I thought about resources needed to start the company (landing page and domain name), I realised I only needed a couple hundred dollars and I knew I could afford the costs myself. I wasn’t sure the whole idea would work, so it was just an experiment which turned out to be very successful. None of my previous business were bootstrapped. My first startup was an IT company selling computers and providing IT services. In order to finance the operations, I got a loan from my parents and later a bigger one from a local bank. My other two businesses were related to long distance calling provider through callback and clothing retail operations. For both, I went through the same process. So bootstrapping is new to me. Bootstrapping allowed us more flexibility and freedom in the decision making process. We were able to focus on taking the company toward the goals we set together as a team. When raising funds, often investors will blurr your goal with their own vision for business. your Bootstrapping was an effective way to satisfy our hunger for doing business right. It pushed us to come up with quick and innovative ways for efficiency. It motivated us to get good at what we are doing in a minimal amount of time. We had no extra resources to waste on mistakes. When we failed, we learned so much more out of it just because it was way more painful. For instance, when you receive investment, you have enough funds to hire a team almost immediately. We hired the first employee only 3 months after launching, in May 2016. Our first employee was Anna — a great designer who never did any programming or coding. She learned the platform quickly and is now one of our most proficient Bubble designers/developers. We were the first company in the world (and still are) who train designers to build functional apps without code. In this way, one person can build everything — design, database structure, workflows, APIs, etc.. We hire very carefully and only when we can afford it, but as our business has grown, we’ve been able to grow from a team of 2 to a team of 20. Another relevant example is marketing. The main challenge of any startup nowadays is marketing and promotion. Startups that get financing can allocate significant marketing budgets. Since we couldn’t afford it, we had to look into guerilla marketing alternatives that wouldn’t cost us much. Our Guerilla Marketing Strategy They say you need to spend money to make money, and in most cases this is true. But when you make a decision to bootstrap your startup, there is often a very limited budget to spend or spending money on marketing might not be an option at all. With this being our case we were pushed to be creative and come up with efficient and on budget marketing solutions. When I was working on building Zenify — the meditation application, I learned launching on was a very significant source of traffic and new users. So, we launched Zeroqode on Product Hunt as well. The launch was successful and we got the #3 Product of the Day badge, which gave us significant traffic boost (read more about ). We also launched our new templates and side projects on Product Hunt to gather feedback and let the community know about our news. One of the most successful side project we’ve built was which is the biggest library of API collections for Postman tool. This project was built on our in 2 weeks only. We also launched our new products on and for extra traction. Product Hunt How we got 800+ upvotes on Product Hunt Postman Collections Product Hunt clone template Designer News Hacker News To focus our audience’s attention even more, we launched two “Best Startup Idea” Competitions, where we gave away templates for first, second and third place. We announced the competition on platforms like , , and . Indie Hackers Reddit HackerNews our forum Showcasing your startup in an international conference is also a great way to let the world know about yourself. We were able to get sponsored by a local support organisation to exhibit at TechCrunch Disrupt Berlin. At the conference we won an award called “Wild Card” which gave us the opportunity to get interviewed for TechCrunch’s news portal and get worldwide exposure (read what and learn more about ). TechCrunch wrote about us How We Won Wild Card at TechCrunch Disrupt in Berlin Another conference we attended is STEP in Dubai, which allowed us to enlarge our audience even more, and connect with potential clients and partners from a totally new and unique region. Future of Zeroqode Development When analyzing where the company should go moving forward, we decided to stay committed to bootstrapping. We do not require big budgets, unless we choose to spend a lot of money on marketing. Fortunately, our guerilla strategy is working well for us. At the moment, raising investment would distract us from running the business, forcing us to deal with all the formalities and procedures required to get funded. Also, we don’t want to release control of the company and decision making process. Yet, we will be revising our business strategy when we reach the next level, and make new decisions based on the situation at that moment in time. Lessons Learned Bootstrapping is not applicable for every startup out there. The decision to bootstrap does not have to be the only way and there is no ideal capitalization structure for growing a company. Some companies start off bootstrapping and later fundraise. Some never raise funds, while others don’t stand a chance without investment. If you have a huge opportunity of disrupting the industry and fast execution and capital are core to your possibility of success, then seeking investment is probably the only way. Bootstrapping fits the opposite. While determining whether to bootstrap or seek investment, consider your priorities, opportunities, and your market dynamics. Then, make an educated choice that fits your scenario. We made ours, and it has been working great for us so far. Join our newsletter to receive occasional tips about building products and startups without code.

How We Bootstrapped Our No-Code Web Development Startup to a Team of 20 in 2 Years

Every now and then there’s an article written claiming that Bitcoin is a pyramid scheme. But is it really? Sure the system rewards the early bird but is this really unfair? The earlier you hopped on the train the bigger the risk you took. Every now and then a bank CEO or some other relic plays the tulip bubble card. Bitcoin’s price curve looks exactly like what you’d expect from a global fear of missing out. They all fail to recognize that Bitcoin is a completely different beast. If there is a bubble it may pop but, as Bitcoin has proven many times, only to come back again even stronger and more resilient. Why is this? What makes Bitcoin so special? First of all “first mover advantage” is everything when it comes to cryptocurrencies. Not only due to the network effect but, more importantly, due to the 21 million coin limit. This is the scarce number that no human can ever tamper with and this is by far the most important feature of any currency. Thus far it is also a Bitcoin exclusive feature. With this in mind, let’s crunch some numbers. Right now, 1800 Bitcoins are created each day. In three years and a couple of months that number will be cut in half. The network does this every fourth year. Meanwhile, more than a hundred thousand new users are welcomed to the Bitcoin ecosystem every day. That’s 0,018 new Bitcoin available for each of those new users if they’re split equally which they’re not. Not a lot. In addition to this articles are written, t-shirts are sold and news segments are broadcasted all over the planet. Maybe a 1350% growth rate per year isn’t that far fetched after all? Do you know anyone that gets paid their monthly salary in Bitcoin? Do you even know of someone who does? I know a few. Namely some Bitcoin pioneers and their henchmen. Not a lot. This is as good an indicator as any of the fact that we’ve barely even scratched the surface of this new ocean of riches. Your Bitcoin are not going away as long as you control your keys. The price of your Bitcoin is not going down unless you sell. This is probably not a fad. The dollar probably was. More recently, the debate has been around the Bitcoin’s high fees. The gaming platform Steam just cancelled their Bitcoin payment option for instance. Some see this as a major flaw of the network and its greatest hurdle towards mainstream adoption. Those who do seem to be the same type of people who think that a better, more technically sophisticated cryptocurrency will someday dethrone Bitcoin from its high seat and take over the mantle as the the king of cryptos. We’ve been hearing about this usurper coin for quite a while now. Where is it? Which one is it? What everyone fails to understand is that Bitcoin does not derive its value from its usability but from its . No entity on earth can tamper with the codebase unless virtually everyone agrees that something needs to be done. This governance model has proven time and time again to be extremely valuable. Bitcoin hardly needs to function as a payment network to gain value. immutability at all A couple of days ago (at the time of writing this) the Lightning Protocol was successfully tested on the Bitcoin main-net. This is the first so called layer 2 scaling solution to be implemented for Bitcoin. It will change the game dramatically again. Users will once again be able to send transactions for a minimal fee, instantly, if they know what they’re doing. In a not so very distant future even those who don’t know what they’re doing will too. When that day comes even the usability argument will have no meaning. Bitcoin is not a pyramid scheme. It’s a globe scheme.

Bitcoin is NOT a pyramid scheme

Why do people still listen to economists? Why does anyone care what they say? Did any successful entrepreneur or inventor ever do anything original. Do a YouTube search for Bitcoin. Almost all the the top search results are about how to trade and how to make a profit. They call it “technical analysis” and there’s some random self proclaimed expert saying he knows how to see Fibonacci series in the patterns of some random price chart. Do they even know what a Fibonacci sequence is and why it’s important mathematically? Fibonacci never cared about commodity prices. He cared about numbers and sequences. Economists try to predict the behavior of investors by predicting what other investors would do if they had the same information available as all the other investors. This puts the economist in a unique position. The economist knows that what he says or doesn’t say can have an impact on how the market behaves. This in turn gives the economist an opportunity to invest in something else entirely, giving him a head start compared to any other investor. He is incentivized to give away partial information at all times. This is the main reason to why you hear the word “bubble” so often when it comes to Bitcoin. not Bitcoin has had a truly remarkable journey price-wise. It has gone from 10 000 Bitcoin for a couple of pizzas to 10 000 dollars for a single Bitcoin in less than ten years. To your average economist this looks like the mother of all bubbles. What most of them fail to see however, is that this is a totally different beast compared to ever asset class that preceded it. There may be as many altcoins and Bitcoin-forks as you can dream of but there will only ever be 21 million Bitcoin. They call Bitcoin a tulip bubble. Is there a limit to how many tulips the world can sustain? Altcoins are more akin to tulips since anyone can make their own breed and start growing them anywhere but the best target for the tulip metaphor is fiat currency. Central banks are tulip farmers and we’ve all fallen for their scam. Until now. At a faster and faster pace, the world is starting to realize that tulips are worthless and that had any clothes worth noticing. why the price of Bitcoin is skyrocketing. Before you can even start to grasp the concept of Bitcoin you have to learn how money works. Here’s a hint, it doesn’t. The price goes up because of curiosity. Bitcoin makes people question the very nature of money. It makes them ask questions that our so called leaders might not be wanting them to ask. If they did there would probably be a whole school subject about such questions. What is money? Where does money come from? What gives it value and, maybe most importantly, why does the value decrease over time? If people knew these things we’d all be Bitcoiners and we wouldn’t trust our “leaders” half as much as we do today. no emperor ever That’s Metcalfe’s law states that the value of a network is equal to its number of users, squared. With this in mind, the price of Bitcoin skyrocket if amount of users are added to the network each day. But the number of users that are added is so the price curve should probably be Renowned physics professor Albert Allen Bartlett once said that “The greatest shortcoming of the human race is our inability to understand the exponential function”. There’s evidence of this everywhere. Inflation, for instance, simply wouldn’t exist if people understood the exponential function. The true magic in Bitcoin lies in its ability to make people question what they think they know. There’s simply no way of understanding Bitcoin without first noticing the great shortcomings of our current paradigm. The more that do the more fragile our systems become and what most of us haven’t realized yet is that we this. Things may be comfortably predictable in the matrix but the red pill is being prescribed en masse and when the effect of the remedy kicks in we’ll all see that our emperors are naked and that economists are hopelessly stuck in their loops, failing to see the true nature of things. So swallow it whole and say goodbye to mr Smith and his lackeys once and for all. There’s no turning back and this is a good thing. should the same increasing even steeper. should want

Why is economics still a thing?

Greetings from ! Today we’re announcing a new, simpler . Bubble pricing We explain the changes in detail below. In a nutshell, our new pricing offers unlimited private apps, SSL certificates for all apps and the ability to buy additional server capacity. We do not limit on workflow runs. The new system is per-app based, instead of being per-account. For our existing paying customers, nothing changes until you want to switch! You’ll be able to keep your current plan as long as you want, but we hope you’ll see more value in the new system. New plans Plans are now on a per-app basis. That lets you get the features you need for the apps that need it. Therefore, the plans are now controlled in the Application Editor, under the Settings Tab. Payment methods and invoices can be accessed in the page as usual. Account You can find the details of our pricing in our . Here are the key changes: Pricing page Private apps are now unlimited on any account. This will let you add an additional layer of safety on all apps. Applications on a free plan will have a Bubble-branded banner in run-mode. All paid plans offer SSL on a custom domain. We do not limit workflow runs. Applications on a Professional plan and higher can buy more capacity for better performance. You can save money with a yearly membership or choose a monthly plan for more flexibility. Our existing paying customers retain current settings and features until migration (more details under ‘Switching to the new system’). Dedicated customers are not impacted by this change. Adding capacity a-la-carte Bubble lets you run advanced programming operations on our servers. On the Professional plan and above, we reserve server capacity for your app to ensure that your app runs quickly. Each app with reserved capacity starts with 2 units, which determines the amount of server and database CPU time that your app can consume in a given interval. You can scale your app to match your app growth by buying additional units for just $20/month (up to 10 additional units). If your app temporarily exceeds its capacity, we rate-limit the speed at which it runs to keep it under the threshold. You can monitor your usage in real time from the Application Editor. Switching to the new system While we think our existing paying users will love the value our new system provides, we are giving them the flexibility to choose when they want to switch. You can stay on a legacy plan as long as you want. Until you migrate, you’ll have access to the same feature set as your existing plan. If you are on a legacy paid plan, you’ll have a reserved server capacity, but won’t be able to add capacity until you migrate to the new system. As each plans comes with a certain number of private apps, all your private apps that are included in your previous plan app limit will be under the same plan, including newly created apps (if you’re under the maximum number of private apps). You can change plans under the Plan section of the Settings Tab and click ‘Migrate to new system’. This will show you a popup with the list of apps you own, where you can customize the plan for each of them. Once this is done, you’ll be able to enjoy the new plans! Other news Beyond our main update regarding the new plans, we also want to share some product updates that were pushed over the last 4 weeks. If you follow the forum you’ll see the whole list of and you can see our since our last email. Here is a highlight of some interesting stuff we’ve added: announcements release notes A of API workflow request data when you trigger them with webhook (including handling nested objects). smart detection The ability to define ; colors can now be dynamic expressions, which enable your users to customize their design themselves. dynamic colors And we grew the team a little more with , who will be working with our users to help them get the most out of Bubble! Neerja Thanks for building on Bubble! It’s great to see our community grow (more than !) and help each other on the . More and more apps are launched every day, we’re grateful to have your trust and are excited to see what you can do with Bubble. 80,000 Bubblers Forum Cheers,The Bubble Team

Announcing a New Pricing

This is the first in a series of posts identifying the sources of value in cryptocurrencies Image: Nikita Andreev Shell: 100000+ yrs Gold wearable: 3800 Gold & silver coin: 2400 Paper IOUs for gold & silver: 250 Pure fiat: 47 Cryptocurrency: avail. now- Nick Szabo The recent trend in coin offerings, including Tezos’ massive raise, has put a spotlight on . The valuation of the world’s leading crypto currency, exceeded US$ 100 billion recently. Are cryptocurrencies overvalued and due for a correction? Perhaps. cryptocurrencies Bitcoin In this series (1/3), I will attempt to identify sources of demand of cryptocurrencies and to develop a framework for looking at their value. The source of demand for cryptocurrencies is derived from their attributes as a ‘medium of exchange’ and that as a ‘store of value’. They are not yet useful as a unit of account but that could change. In this post, I will look at the value of cryptocurrencies as a medium of exchange, particularly, as an enabler of the and the implications of this on their value. internet of money The rise of the ‘internet of money’ The most fundamental use-case of cryptocurrencies is the role they are beginning to play as a medium of exchange on the internet. a. Peer to peer payments (VOIP:Talk :: Cryptocurrencies:Payments) Cryptocurrencies are creating opportunities for a massive scale-up in peer-to-peer payments. Firstly, they can reduce the cost of transactions. They can do this by removing intermediaries and developing a lower-cost consensus system to validate transactions. This is opposed to the centralized ‘trusted’ payment infrastructure which has always been necessary for transferring value. This layered interaction involving multiple intermediaries adds costs to the transfer of value. Source: Balaji Srinivasan/a16z Secondly, they can remove friction from transactions — creating opportunities for seam-less transfer of value across peers. Come to think of it, the original Paypal innovation of being able to was at its heart about making payments frictionless (and secure). email money In fact, the first ‘killer app’ for the mainstreaming of cryptocurrencies has already been international money transfers. It came to play in China when imposition of capital controls forced users to be creative and utilize crypto mediums to move money across borders. Napster? — the use of bitcoin to move money Dejavu Valuing Crypto’s value as a medium of exchange Given the above context, we can now try to put a number on the total value of cryptocurrency coming from its attribute as a medium of exchange on the internet. For this purpose, let’s look at the global money transfer volume and try to see where cryptocurrencies can come in to capture value. We see that just cross-border remittances were close to US$ 600 billion globally in 2014. Even if we assume no growth in the global remittances market and look at a fifth of global remittances being processed by a large cryptocurrency network in steady state, US$ 120 billion of value would be transmitted by the network on an annual basis. Today, the commission charged by the lowest priced intermediary is about 1%. If the cryptocurrency network is able to pocket even 0.1%-0.2%, the annual commission captured by the network would be US$ 120–240 million annually. This is just the global remittances market. Now, let’s look at the volume of global digital payments. According to Euromonitor, the total card transactions expected in 2017 are likely to exceed US$ 30 trillion. Even if 10% of these transactions can be processed on one crypto network, that would mean US$ 3 trillion of value transfer. Again, assuming 0.1%-0.2% of commission on this volume, this could easily mean US$ 3–6 billion of commission for the largest crypto-network annually. Source: Euromonitor Therefore, just looking at 20% of global remittance volume and 10% of global card transaction volume in 2017, a single cryptocurrency can reach ~ US$ 3–6 billion in commissions. Even if one believes that the cash-flow associated with this crypto-network needs to be subject to a very high discount rate (~25%) and a growth of 15%, one can arrive at a network valuation of US$ 45 billion just for a single use-case (assuming US$ 4.5 billion commission). No wonder that there is excitement about cryptocurrencies. Below, a chart by Chris Burniske, an expert in crypto-valuations, looks at the correlation between transactions on the Ethereum network and the corresponding network value. Very logically, there is a strong alignment in the growth of transactions and value. Source: Chris Burniske‏ Specialized crypto-currencies like zCash similarly might focus on money transfers for specific user groups or specific use cases and capture a portion of the value. : b. The evolving monetization model of the internet or the Machine Payable Web Cryptocurrencies as a remittance rail was the first “killer app”. However, in a , Balaji S. Srinivasan describes how the nature of monetization in the web is evolving. Today, the dominant monetization business model in the current consumer web is advertising. 88% of Google’s revenue and 97% of facebook’s revenue in 2016 came from advertising sales. Many of their APIs are public and monetization happens by exposing users (humans) to ads. recent talk However, the ad-based monetization model is not suited for application programming interfaces ( ). APIs can’t be compensated for “viewing ads”. Additionally, the current model disincentivizes commerce by adding friction (think of the onboarding and sign-up process) to(API) interactions. Cryptocurrencies have the potential to streamline commerce between APIs or indeed any agents. In a machine payable web, cryptocurrencies replace ads (or human attention) as the dominant currency of the web and allow API-to-API or indeed machine-to-machine transactions without human intervention based on contracts at a low cost. APIs What’s the volume of transactions which will happen due to API-to-API or machine-to-machine transactions? Estimating a defensible number requires another post but it will certainly involve many billions of transactions. Structurally, the architecture of such a dominant crypto-network in the space might be split with lower ticket-size transactions facilitated through a less computationally intensive process to ensure that even very small ticket transactions are encouraged. c. The Uberization of connected computational and storage assets The third use-case for cryptocurrencies as a medium of exchange is that which comes from monetization of computational and storage assets. While ‘uberization’ of assets is not a new phenomenon anymore, it is largely unexplored in the world of personal and even professional computing. Our computational and storage resources are quite underutilized. Currencies based on cryptographic proof offer a fascinating opportunity for unlocking under-utilized resources to work and earn an income out of it. This works on the ‘supply side’ and keeps a check on price by encouraging more and more users to jump on the supply side to provide mining and/or transaction processing. In the long run, one would expect that mining would be provided by a mix of super-specialized nodes and under-utilized assets. This helps build the supply side case for cryptocurrencies. A typical PC is using only a fraction of its computational resources while active Think of connected assets and specialized mining as roof-top solar and utility scale solar respectively. Utility scale solar is efficient and profitable on an ROI basis and continues to scale but roof-top solar is attractive for many users. The significance of this can’t be understated. We may be on the verge of a solar-type revolution in computing. As cryptocurrencies become more mainstream, many more truly decentralized computational and storage resources are likely to come online and compete with conventional cloud services. Hash rate of the Bitcoin network (source: blockchain.info) Given the excitement about bitcoins, it is not surprising that the hashrate (a measure of computational power in terms of calculations per second) of all cryptocurrency miners already exceeds the equivalent computational power of internet giants like Google. According to Bitcoinist, as of Jan ’17, the total computational capacity of the bitcoin network was over 43 thousand times than every supercomputer on the TOP500 list combined. Hence, we see that the use case for demand for cryptocurrencies as a medium of exchange itself is quite high and arguably explains why people are betting that individual cryptocurrencies could be worth as much as US$ 100 billion. Does that mean that the current values of all cryptocurrencies are justified? Not really. Instead, my point is to argue that the winners of the cryptocurrency play— the first currency (or currencies) to actually find product-market fit in some of the use-cases I mentioned (and others which I will cover in subsequent posts) would be big enough to be worth a lot. Maybe even GAFA big! This post was on cryptocurrencies as mediums of exchange. I will cover cryptocurrencies as foundations of decentralized applications and contracts in the next post. Thanks to Edward Russell and Shaina Trotta for reviewing the manuscript of this post. References 1. https://github.com/ethereum/wiki/wiki/White-Paper 2. https://bitcoin.org/bitcoin.pdf 3. https://stratechery.com/2017/tulips-myths-and-cryptocurrencies/ 4. http://offers.worldpayglobal.com/rs/850-JOA-856/images/GlobalPaymentsReportNov2015.pdf 5. https://medium.com/@cburniske/cryptoasset-valuations-ac83479ffca7

What’s the true value of cryptocurrencies?

Introduction The metaverse has been one of the most hyped technologies of recent years, and it is generally believed that it will be the future of digital interaction, virtual economies, and decentralized ownership. The tech  giants such as Meta, Microsoft, Apple, and Google have already invested more than 60 billion U.S. dollars in the development of this virtual world and Web3 enthusiasts consider the metaverse as the  further development of the Internet. Nevertheless, despite all the hype, the metaverse has its drawbacks, for instance, low rates of user engagement, vague potentials, and underpinning value proposition. Is the metaverse really the next big thing or has it turned out to be just another technological bubble that is going to burst at one point? The Hype: What We Were Promised A Persistent and Fully Immersive
Virtual World The metaverse was conceived as a permanent interconnected and immersive 3D world in which people could work, socialize, and shop in virtual reality (VR) and augmented reality (AR).  Meta (previously Facebook) visualized the metaverse as the further evolution of the social network, where people would meet as avatars in digital environments instead of using traditional social media platforms.


Digital ownership in the form of NFTs and Blockchain
The metaverse was promoted by the advocates of Web3 as a decentralized network of virtual worlds developed on blockchain technology. It was expected that NFTs would act as proof of ownership of the virtual land, digital assets, and in-game items and give the users real ownership of their digital assets. Platforms like Decentraland and The Sandbox hit the roof with NFT land prices with investors buying virtual real estate hoping that it would become the next digital economy. The future of work and collaboration
Companies like Microsoft, Meta, and Nvidia argued that remote work and digital collaboration would occur in the metaverse. Thus,  instead of video conferencing applications like Zoom, people would meet in virtual reality-based workspaces. Meta’s Horizon Workrooms and Microsoft’s Mesh for Teams were supposed to change the way that businesses conduct their meetings, allowing employees to meet from anywhere in the world. A multi-trillion-dollar economy
Investment firms and analysts predicted that the metaverse could generate up to trillions of dollars by 2030. Many companies developed metaverse strategies; lest they be left behind in the next gold rush. Major brands like Nike, Adidas, Gucci, and Balenciaga also joined in, selling virtual sneakers and fashion items within digital worlds. The Reality: What Happened This Time Around As it turns out, the metaverse has not been able to meet the expectations that were set  for it. Here’s what’s actually happening: Low Rates of User Engagement and Participation.
Most metaverse platforms have not been able to attract the general population. Even well-funded projects such as Decentraland and The Sandbox have less than 1,000 daily active users, even though the projects have raised millions of dollars. Meta’s Horizon Worlds has also done poorly, and the company has  had to change its strategy and focus more on the enterprise. The decline of NFTs and virtual land prices.
The 2022 NFT market crash revealed the speculative nature of digital assets. Many metaverse projects based on NFTs also experienced a crash in the value of their virtual land as investors realized that there was no demand for such digital properties. Walmart, Disney, and Microsoft have quietly pulled back their metaverse plans to focus on AI and other technology that is coming up. VR and AR are still niche technologies.
For the metaverse to be fully immersive, there must be a widespread use of VR and AR. However, high costs, cluttered hardware, and sparse content have limited mass adoption. Meta’s Quest headsets and Apple’s Vision Pro are very good but they are not yet proximity devices. Unless the AR/VR technology improves on the aspects of price, weight, and accessibility, the metaverse will not see much progress.


Interoperability is Missing.
A real metaverse has to be interoperable, which means that the user should be able to take their assets and identity across platforms. Today, metaverse projects are not connected; there is no unified standard. Your NFT avatar from The Sandbox will not be usable in Horizon  Worlds, and your Decentraland items will not be usable in Fortnite or Roblox. Without cooperation, the idea of a single interconnected metaverse cannot be achieved. What’s Next? The Future of the Metaverse Despite the failures, the metaverse is still developing. The following are the likely  changes to be seen in the future: Virtual Worlds Powered by AI.
AI and machine learning will be an essential component in the development of the metaverse in the future. Companies are now using  AI-powered avatars, generative AI for world-building, and real-time AI assistants to create more realistic and engaging virtual environments.


The enterprise metaverse will drive growth.
Consumers may have abandoned the metaverse, but businesses are embracing it. For training, remote collaboration, and simulations in healthcare, engineering, and military, VR is being used by companies. Example: Walmart is using VR training simulations for its employees. BMW and Ford are using the digital twin of the factory to enhance production. Surgeons are preparing for intricate surgeries in VR simulation. 3. The role of Web3 and Blockchain in the metaverse. Although the NFT mania has slowed down, blockchain still has a place in the metaverse especially for digital identity and seamless ownership of assets. But, centralized platforms (Meta, Apple, Microsoft) are going to be much more prominent than decentralized blockchain-based metaverses. Conclusion: The Metaverse is Evolving, Not Dead The metaverse has lost its hype, but its core ideas are still under development. The consumer metaverse (gaming and social VR) is still small and needs better hardware. The enterprise metaverse (VR training, remote collaboration) is finally seeing real adoption. It is most likely that the future will be a combination of AI, Mixed Reality (MR), and Web3, but in a more realistic and less revolutionary way. The metaverse is not dead; it is just growing up. The real question is: Are we ready for what comes next? Introduction The metaverse has been one of the most hyped technologies of recent years, and it is generally believed that it will be the future of digital interaction, virtual economies, and decentralized ownership. The tech  giants such as Meta, Microsoft, Apple, and Google have already invested more than 60 billion U.S. dollars in the development of this virtual world and Web3 enthusiasts consider the metaverse as the  further development of the Internet. Nevertheless, despite all the hype, the metaverse has its drawbacks, for instance, low rates of user engagement, vague potentials, and underpinning value proposition. Is the metaverse really the next big thing or has it turned out to be just another technological bubble that is going to burst at one point? The Hype: What We Were Promised A Persistent and Fully Immersive
Virtual World The metaverse was conceived as a permanent interconnected and immersive 3D world in which people could work, socialize, and shop in virtual reality (VR) and augmented reality (AR).  Meta (previously Facebook) visualized the metaverse as the further evolution of the social network, where people would meet as avatars in digital environments instead of using traditional social media platforms. Digital ownership in the form of NFTs and Blockchain
The metaverse was promoted by the advocates of Web3 as a decentralized network of virtual worlds developed on blockchain technology. It was expected that NFTs would act as proof of ownership of the virtual land, digital assets, and in-game items and give the users real ownership of their digital assets. A Persistent and Fully Immersive Virtual World The metaverse was conceived as a permanent interconnected and immersive 3D world in which people could work, socialize, and shop in virtual reality (VR) and augmented reality (AR).  Meta (previously Facebook) visualized the metaverse as the further evolution of the social network, where people would meet as avatars in digital environments instead of using traditional social media platforms. A Persistent and Fully Immersive A Persistent and Fully Immersive Virtual World The metaverse was conceived as a permanent interconnected and immersive 3D world in which people could work, socialize, and shop in virtual reality (VR) and augmented reality (AR).  Meta (previously Facebook) visualized the metaverse as the further evolution of the social network, where people would meet as avatars in digital environments instead of using traditional social media platforms. Digital ownership in the form of NFTs and Blockchain The metaverse was promoted by the advocates of Web3 as a decentralized network of virtual worlds developed on blockchain technology. It was expected that NFTs would act as proof of ownership of the virtual land, digital assets, and in-game items and give the users real ownership of their digital assets. Digital ownership in the form of NFTs and Blockchain Digital ownership in the form of NFTs and Blockchain The metaverse was promoted by the advocates of Web3 as a decentralized network of virtual worlds developed on blockchain technology. It was expected that NFTs would act as proof of ownership of the virtual land, digital assets, and in-game items and give the users real ownership of their digital assets. Platforms like Decentraland and The Sandbox hit the roof with NFT land prices with investors buying virtual real estate hoping that it would become the next digital economy. The future of work and collaboration
Companies like Microsoft, Meta, and Nvidia argued that remote work and digital collaboration would occur in the metaverse. Thus,  instead of video conferencing applications like Zoom, people would meet in virtual reality-based workspaces. The future of work and collaboration Companies like Microsoft, Meta, and Nvidia argued that remote work and digital collaboration would occur in the metaverse. Thus,  instead of video conferencing applications like Zoom, people would meet in virtual reality-based workspaces. The future of work and collaboration The future of work and collaboration Companies like Microsoft, Meta, and Nvidia argued that remote work and digital collaboration would occur in the metaverse. Thus,  instead of video conferencing applications like Zoom, people would meet in virtual reality-based workspaces. Meta’s Horizon Workrooms and Microsoft’s Mesh for Teams were supposed to change the way that businesses conduct their meetings, allowing employees to meet from anywhere in the world. A multi-trillion-dollar economy
Investment firms and analysts predicted that the metaverse could generate up to trillions of dollars by 2030. Many companies developed metaverse strategies; lest they be left behind in the next gold rush. A multi-trillion-dollar economy Investment firms and analysts predicted that the metaverse could generate up to trillions of dollars by 2030. Many companies developed metaverse strategies; lest they be left behind in the next gold rush. A multi-trillion-dollar economy A multi-trillion-dollar economy Investment firms and analysts predicted that the metaverse could generate up to trillions of dollars by 2030. Many companies developed metaverse strategies; lest they be left behind in the next gold rush. Major brands like Nike, Adidas, Gucci, and Balenciaga also joined in, selling virtual sneakers and fashion items within digital worlds. The Reality: What Happened This Time Around As it turns out, the metaverse has not been able to meet the expectations that were set  for it. Here’s what’s actually happening: Low Rates of User Engagement and Participation.
Most metaverse platforms have not been able to attract the general population. Even well-funded projects such as Decentraland and The Sandbox have less than 1,000 daily active users, even though the projects have raised millions of dollars. Low Rates of User Engagement and Participation. Most metaverse platforms have not been able to attract the general population. Even well-funded projects such as Decentraland and The Sandbox have less than 1,000 daily active users, even though the projects have raised millions of dollars. Low Rates of User Engagement and Participation. Low Rates of User Engagement and Participation. Most metaverse platforms have not been able to attract the general population. Even well-funded projects such as Decentraland and The Sandbox have less than 1,000 daily active users, even though the projects have raised millions of dollars. Meta’s Horizon Worlds has also done poorly, and the company has  had to change its strategy and focus more on the enterprise. The decline of NFTs and virtual land prices.
The 2022 NFT market crash revealed the speculative nature of digital assets. Many metaverse projects based on NFTs also experienced a crash in the value of their virtual land as investors realized that there was no demand for such digital properties. The decline of NFTs and virtual land prices. The 2022 NFT market crash revealed the speculative nature of digital assets. Many metaverse projects based on NFTs also experienced a crash in the value of their virtual land as investors realized that there was no demand for such digital properties. The decline of NFTs and virtual land prices. The decline of NFTs and virtual land prices. The 2022 NFT market crash revealed the speculative nature of digital assets. Many metaverse projects based on NFTs also experienced a crash in the value of their virtual land as investors realized that there was no demand for such digital properties. Walmart, Disney, and Microsoft have quietly pulled back their metaverse plans to focus on AI and other technology that is coming up. VR and AR are still niche technologies.
For the metaverse to be fully immersive, there must be a widespread use of VR and AR. However, high costs, cluttered hardware, and sparse content have limited mass adoption. Meta’s Quest headsets and Apple’s Vision Pro are very good but they are not yet proximity devices. Unless the AR/VR technology improves on the aspects of price, weight, and accessibility, the metaverse will not see much progress. Interoperability is Missing.
A real metaverse has to be interoperable, which means that the user should be able to take their assets and identity across platforms. VR and AR are still niche technologies. For the metaverse to be fully immersive, there must be a widespread use of VR and AR. However, high costs, cluttered hardware, and sparse content have limited mass adoption. Meta’s Quest headsets and Apple’s Vision Pro are very good but they are not yet proximity devices. Unless the AR/VR technology improves on the aspects of price, weight, and accessibility, the metaverse will not see much progress. VR and AR are still niche technologies. VR and AR are still niche technologies. For the metaverse to be fully immersive, there must be a widespread use of VR and AR. However, high costs, cluttered hardware, and sparse content have limited mass adoption. Meta’s Quest headsets and Apple’s Vision Pro are very good but they are not yet proximity devices. Unless the AR/VR technology improves on the aspects of price, weight, and accessibility, the metaverse will not see much progress. Interoperability is Missing. A real metaverse has to be interoperable, which means that the user should be able to take their assets and identity across platforms. Interoperability is Missing. Interoperability is Missing. A real metaverse has to be interoperable, which means that the user should be able to take their assets and identity across platforms. Today, metaverse projects are not connected; there is no unified standard. Your NFT avatar from The Sandbox will not be usable in Horizon  Worlds, and your Decentraland items will not be usable in Fortnite or Roblox. Without cooperation, the idea of a single interconnected metaverse cannot be achieved. What’s Next? The Future of the Metaverse Despite the failures, the metaverse is still developing. The following are the likely  changes to be seen in the future: Virtual Worlds Powered by AI.
AI and machine learning will be an essential component in the development of the metaverse in the future. Companies are now using  AI-powered avatars, generative AI for world-building, and real-time AI assistants to create more realistic and engaging virtual environments. The enterprise metaverse will drive growth.
Consumers may have abandoned the metaverse, but businesses are embracing it. For training, remote collaboration, and simulations in healthcare, engineering, and military, VR is being used by companies. Virtual Worlds Powered by AI. AI and machine learning will be an essential component in the development of the metaverse in the future. Companies are now using  AI-powered avatars, generative AI for world-building, and real-time AI assistants to create more realistic and engaging virtual environments. Virtual Worlds Powered by AI. Virtual Worlds Powered by AI. AI and machine learning will be an essential component in the development of the metaverse in the future. Companies are now using  AI-powered avatars, generative AI for world-building, and real-time AI assistants to create more realistic and engaging virtual environments. The enterprise metaverse will drive growth. Consumers may have abandoned the metaverse, but businesses are embracing it. For training, remote collaboration, and simulations in healthcare, engineering, and military, VR is being used by companies. The enterprise metaverse will drive growth. The enterprise metaverse will drive growth. Consumers may have abandoned the metaverse, but businesses are embracing it. For training, remote collaboration, and simulations in healthcare, engineering, and military, VR is being used by companies. Example: Walmart is using VR training simulations for its employees. BMW and Ford are using the digital twin of the factory to enhance production. Surgeons are preparing for intricate surgeries in VR simulation. 3. The role of Web3 and Blockchain in the metaverse. The role of Web3 and Blockchain in the metaverse. Although the NFT mania has slowed down, blockchain still has a place in the metaverse especially for digital identity and seamless ownership of assets. But, centralized platforms (Meta, Apple, Microsoft) are going to be much more prominent than decentralized blockchain-based metaverses. Conclusion: The Metaverse is Evolving, Not Dead The metaverse has lost its hype, but its core ideas are still under development. The consumer metaverse (gaming and social VR) is still small and needs better hardware. The enterprise metaverse (VR training, remote collaboration) is finally seeing real adoption. It is most likely that the future will be a combination of AI, Mixed Reality (MR), and Web3, but in a more realistic and less revolutionary way. The metaverse is not dead; it is just growing up. The real question is: Are we ready for what comes next?

Is the Metaverse a Technological Revolution or Just Another Bubble?

Author:
(1) Andrew J. Peterson, University of Poitiers (andrew.peterson@univ-poitiers.fr). Table of Links Abstract and Introduction Related Work The media, filter bubbles and echo chambers Network effects and Information Cascades Model collapse Known biases in LLMs A Model of Knowledge Collapse Results Discussion and References Appendix Comparing width of the tails Defining knowledge collapse Related Work Technology has long affected how we access knowledge, raising concerns about its impact on the transmission and creation of knowledge. Yeh Meng-te, for example, argued in the twelfth century that the rise of books led to a decline in the practice of memorizing and collating texts that contributed to a decline of scholarship and the repetition of errors (Cherniack, 1994, p.48-49). Even earlier, a discussion in Plato’s Phaedrus considers whether the transition from oral tradition to reading texts was harmful to memory, reflection and wisdom (Hackforth, 1972). We focus on recent work on the role of digital platforms and social interactions, and mention only in passing the literature on historical innovations and knowledge (e.g. Ong, 2013; Mokyr, 2011; Havelock, 2019), and the vast literature on the printing press (e.g. Dittmar, 2011; Eisenstein, 1980). Like other media transitions before it (Wu, 2011), the rise of internet search algorithms and of social media raised concerns about the nature and distribution of information people are exposed to, and the downstream effects on attitudes and political polarization (Cinelli et al., 2021; Barbera´, 2020). The following section considers research on the impact of recommendation algorithms and self-selection on social media, and how this might generate distorted and polarizing opinions, as an analogy for understanding the transformation brought about by reliance on AI. We consider game theoretic models of information cascades as an alternative model for failure in social learning, in which the public to fails to update rationally on individuals’ private signals. Next, we review the main findings of network analysis on the flow of information in social media, which also identify mechanisms which distort knowledge formation. We then examine the specific nature of generative AI algorithms, focusing on the problem of model collapse and known biases in AI outputs. The media, filter bubbles and echo chambers A common critique of social media is that they allow users to select in to “echo chambers” (specific communities or communication practices) in which they are exposed to only a narrow range of topics or perspectives. For example, instead of consulting the “mainstream” news where a centrist and relatively balanced perspective is provided, users are exposed to selective content that echoes pre-existing beliefs. In the ideological version of the echo-chamber hypothesis, individuals within a latent ideological space (for example a one-dimensional left-right spectrum), are exposed to peers and content with ideologically-similar views. If so, their beliefs are reinforced socially and by a generalization from their bounded observations, leading to political polarization (Cinus et al., 2022; Jamieson and Cappella, 2008; Pariser, 2011). A simple model for this assumes homophily within in a network growth model, in which similar individuals chose to interact. Implicitly the approach presumes that this is common on social media but not common within traditional media, which for technological reasons were constrained to provide the same content across a broad population with possibly heterogeneous preferences.[1] This general dynamic may hold even if traditional media and newspapers were themselves dynamic systems interacting with their consumers, markets and advertisers, and themselves adapting their message to specific communities and preferences (e.g. Angelucci, Cage, and ´ Sinkinson, forthcoming; Cage´, 2020; Boone, Carroll, and van Witteloostuijn, 2002). The second main line of analysis focuses on “filter bubbles,” whereby the content to which users are exposed is selected based on a recommendation system. Jiang et al. (2019) model this as a dynamic process between a user’s evolving interests and behavior (such as clicking a link, video, or text) and a recommender system which aims to maximize expected utility for the user. In their reinforcement learning-inspired framework, the aim is for the user to explore the space of items or topics without the algorithm assigning degenerate (extremely high or zero) probabilities to these items. As above, a key concern is the political or ideological content of recommendations their relation to polarization (Keijzer and Mas¨ , 2022). In a more recent twist, Sharma, Liao, and Xiao (2024) find that LLM-powered search may generate more selective exposure bias and polarization by reinforcing pre-existing opinions based on finer-grained clues in the user’s queries. Particularly relevant for our context is the issue of “popularity bias” in recommender systems, in which a small subset of content receives wide exposure while users (distributed based on some long-tailed distribution, like the topics) from smaller groups or with rare preferences are marginalized. On the one hand, users may desire to be exposed to popular content, for example to understand trending ideas or fashions. But overly favoring popular items can lead to user disengagement because it neglects their unique interests, lacks variety, etc. (e.g. Klug et al., 2021). Recommendation systems are often biased in the sense that even when a subset of users wants to get access to non-popular items, they receive few or no such recommendations (Abdollahpouri et al., 2021). A number of approaches have been suggested to counteract this tendency (e.g. Lin et al., 2022; Gao et al., 2023). The problem of popularity bias is ironic given that one of the unique contributions of the internet was its ability to provide access to long-tailed products and services that were previously ignored or inaccessible (Brynjolfsson et al., 2006; Brynjolfsson, Hu, and Smith, 2003). By extension, we would expect social media and the internet to make possible a more diverse and rich informational environment. The role of self-selection into communities and recommendation algorithms provides a explanation for why this might not be the case. In the next section we consider a more general set of models that examine information flow within networks and the idea of information cascades. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. [1] The reality is as usual more complex. For example, in the post-war era, the concern was almost the inverse- the fear that the few channels that were possible with television led to ‘homogenization.’ There are also other dynamics at play than technological constraints. For example, in contrast to TV, the 1950s and 1960s saw a proliferation of more diverse and local radio stations, some catering to ethnic minorities and musical tastes outside the mainstream. The ‘payola’ scandals, however, led to regulations that shifted content decisions from diverse DJs to centralized music directors (Douglas, 2002). Author: (1) Andrew J. Peterson, University of Poitiers (andrew.peterson@univ-poitiers.fr). Author: Author: (1) Andrew J. Peterson, University of Poitiers (andrew.peterson@univ-poitiers.fr). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work The media, filter bubbles and echo chambers The media, filter bubbles and echo chambers Network effects and Information Cascades Network effects and Information Cascades Model collapse Model collapse Known biases in LLMs Known biases in LLMs A Model of Knowledge Collapse A Model of Knowledge Collapse Results Results Discussion and References Discussion and References Appendix Appendix Appendix Comparing width of the tails Comparing width of the tails Defining knowledge collapse Defining knowledge collapse Related Work Technology has long affected how we access knowledge, raising concerns about its impact on the transmission and creation of knowledge. Yeh Meng-te, for example, argued in the twelfth century that the rise of books led to a decline in the practice of memorizing and collating texts that contributed to a decline of scholarship and the repetition of errors (Cherniack, 1994, p.48-49). Even earlier, a discussion in Plato’s Phaedrus considers whether the transition from oral tradition to reading texts was harmful to memory, reflection and wisdom (Hackforth, 1972). We focus on recent work on the role of digital platforms and social interactions, and mention only in passing the literature on historical innovations and knowledge (e.g. Ong, 2013; Mokyr, 2011; Havelock, 2019), and the vast literature on the printing press (e.g. Dittmar, 2011; Eisenstein, 1980). Like other media transitions before it (Wu, 2011), the rise of internet search algorithms and of social media raised concerns about the nature and distribution of information people are exposed to, and the downstream effects on attitudes and political polarization (Cinelli et al., 2021; Barbera´, 2020). The following section considers research on the impact of recommendation algorithms and self-selection on social media, and how this might generate distorted and polarizing opinions, as an analogy for understanding the transformation brought about by reliance on AI. We consider game theoretic models of information cascades as an alternative model for failure in social learning, in which the public to fails to update rationally on individuals’ private signals. Next, we review the main findings of network analysis on the flow of information in social media, which also identify mechanisms which distort knowledge formation. We then examine the specific nature of generative AI algorithms, focusing on the problem of model collapse and known biases in AI outputs. The media, filter bubbles and echo chambers A common critique of social media is that they allow users to select in to “echo chambers” (specific communities or communication practices) in which they are exposed to only a narrow range of topics or perspectives. For example, instead of consulting the “mainstream” news where a centrist and relatively balanced perspective is provided, users are exposed to selective content that echoes pre-existing beliefs. In the ideological version of the echo-chamber hypothesis, individuals within a latent ideological space (for example a one-dimensional left-right spectrum), are exposed to peers and content with ideologically-similar views. If so, their beliefs are reinforced socially and by a generalization from their bounded observations, leading to political polarization (Cinus et al., 2022; Jamieson and Cappella, 2008; Pariser, 2011). A simple model for this assumes homophily within in a network growth model, in which similar individuals chose to interact. Implicitly the approach presumes that this is common on social media but not common within traditional media, which for technological reasons were constrained to provide the same content across a broad population with possibly heterogeneous preferences.[1] This general dynamic may hold even if traditional media and newspapers were themselves dynamic systems interacting with their consumers, markets and advertisers, and themselves adapting their message to specific communities and preferences (e.g. Angelucci, Cage, and ´ Sinkinson, forthcoming; Cage´, 2020; Boone, Carroll, and van Witteloostuijn, 2002). The second main line of analysis focuses on “filter bubbles,” whereby the content to which users are exposed is selected based on a recommendation system. Jiang et al. (2019) model this as a dynamic process between a user’s evolving interests and behavior (such as clicking a link, video, or text) and a recommender system which aims to maximize expected utility for the user. In their reinforcement learning-inspired framework, the aim is for the user to explore the space of items or topics without the algorithm assigning degenerate (extremely high or zero) probabilities to these items. As above, a key concern is the political or ideological content of recommendations their relation to polarization (Keijzer and Mas¨ , 2022). In a more recent twist, Sharma, Liao, and Xiao (2024) find that LLM-powered search may generate more selective exposure bias and polarization by reinforcing pre-existing opinions based on finer-grained clues in the user’s queries. Particularly relevant for our context is the issue of “popularity bias” in recommender systems, in which a small subset of content receives wide exposure while users (distributed based on some long-tailed distribution, like the topics) from smaller groups or with rare preferences are marginalized. On the one hand, users may desire to be exposed to popular content, for example to understand trending ideas or fashions. But overly favoring popular items can lead to user disengagement because it neglects their unique interests, lacks variety, etc. (e.g. Klug et al., 2021). Recommendation systems are often biased in the sense that even when a subset of users wants to get access to non-popular items, they receive few or no such recommendations (Abdollahpouri et al., 2021). A number of approaches have been suggested to counteract this tendency (e.g. Lin et al., 2022; Gao et al., 2023). The problem of popularity bias is ironic given that one of the unique contributions of the internet was its ability to provide access to long-tailed products and services that were previously ignored or inaccessible (Brynjolfsson et al., 2006; Brynjolfsson, Hu, and Smith, 2003). By extension, we would expect social media and the internet to make possible a more diverse and rich informational environment. The role of self-selection into communities and recommendation algorithms provides a explanation for why this might not be the case. In the next section we consider a more general set of models that examine information flow within networks and the idea of information cascades. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv available on arxiv [1] The reality is as usual more complex. For example, in the post-war era, the concern was almost the inverse- the fear that the few channels that were possible with television led to ‘homogenization.’ There are also other dynamics at play than technological constraints. For example, in contrast to TV, the 1950s and 1960s saw a proliferation of more diverse and local radio stations, some catering to ethnic minorities and musical tastes outside the mainstream. The ‘payola’ scandals, however, led to regulations that shifted content decisions from diverse DJs to centralized music directors (Douglas, 2002).

How AI and Social Media Shape Knowledge Through Echo Chambers and Filter Bubbles

Author:
(1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Table of Links Abstract and 1 Introduction 2 Related Works 3 Datasets and Experiment Setup 4 Methodology 4.1 NFTs Transaction Network 4.2 NFTs Bubble Prediction 5 Discussion and Conclusions, and References 4.2 NFTs Bubble Prediction In recent years, the LPPL model[38] has been studied in different research for bubble detection, here, the model uses time-series price data. This model assumes the presence of two types of agents in the market: a group of traders with rational expectations and the second group of so-called “noise” traders, that is, irrational agents with herding behavior. The algorithm approximates the log price of the data at a given period t. as where A, B and C are the linear parameters and other are the nonlinear parameters Tc, m, w, φ. In the above equation, Tc denotes the singularity at which the previous bubble ends and transitions to another set. A denotes the log price, B denotes the magnitude of the power law acceleration and ranges from B>0 for the increasing price to B<0 for decreasing price. The LPPL model derives these positive and negative bubble indicators to all t2, thereby visualizing its own predictions. Similarly, C denotes the magnitude of the logperiodic oscillations, where if m=1, C=0, then LPPL reduces to an exponential fit of the price time series. If m<1, then LPPL grows super-exponentially until the critical time T. If C, w>0, then LppL exhibits oscillation that becomes progressively more frequent as x approaches the critical time. Here for the model, we need to calibrate the model. Model calibration can be defined as finding a unique set of model parameters that provide a good description of the system behavior and can be achieved by confronting model predictions with actual measurements performed on the system. For our test the model is calibrated with the Ordinary Least square method, providing the estimations of all parameters mention in the above equation. Similarly, the equation also contains non-linear parameters. we set the following calibration for a given time [t1, t2]. We then make bubble predictions at each period of the data, by letting this calibration iterate for the shrinking time window [t1, t2]. According to the daily data, [t1, t2] is in daily units. t2 denotes a fictitious today corresponding to t; t1 denotes an earlier day. For a given t2, the iterative calibration sets the initial range of the time window as 120 days and the shrinking Interval of t1 as 5 days. That is, we need to estimate parameters 24 times for each t2 (e.g., [1, 120], [5, 120], …, [115, 120]). This process emphasizes the prediction and here we use only historical data as input. The outcome of t2 depends only on data from t2 to the last 120 days. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Author: Author: (1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works 2 Related Works 3 Datasets and Experiment Setup 3 Datasets and Experiment Setup 4 Methodology 4 Methodology 4.1 NFTs Transaction Network 4.1 NFTs Transaction Network 4.2 NFTs Bubble Prediction 4.2 NFTs Bubble Prediction 5 Discussion and Conclusions, and References 5 Discussion and Conclusions, and References 4.2 NFTs Bubble Prediction In recent years, the LPPL model[38] has been studied in different research for bubble detection, here, the model uses time-series price data. This model assumes the presence of two types of agents in the market: a group of traders with rational expectations and the second group of so-called “noise” traders, that is, irrational agents with herding behavior. The algorithm approximates the log price of the data at a given period t. as where A, B and C are the linear parameters and other are the nonlinear parameters Tc, m, w, φ. In the above equation, Tc denotes the singularity at which the previous bubble ends and transitions to another set. A denotes the log price, B denotes the magnitude of the power law acceleration and ranges from B>0 for the increasing price to B<0 for decreasing price. The LPPL model derives these positive and negative bubble indicators to all t2, thereby visualizing its own predictions. Similarly, C denotes the magnitude of the logperiodic oscillations, where if m=1, C=0, then LPPL reduces to an exponential fit of the price time series. If m<1, then LPPL grows super-exponentially until the critical time T. If C, w>0, then LppL exhibits oscillation that becomes progressively more frequent as x approaches the critical time. Here for the model, we need to calibrate the model. Model calibration can be defined as finding a unique set of model parameters that provide a good description of the system behavior and can be achieved by confronting model predictions with actual measurements performed on the system. For our test the model is calibrated with the Ordinary Least square method, providing the estimations of all parameters mention in the above equation. Similarly, the equation also contains non-linear parameters. we set the following calibration for a given time [t1, t2]. We then make bubble predictions at each period of the data, by letting this calibration iterate for the shrinking time window [t1, t2]. According to the daily data, [t1, t2] is in daily units. t2 denotes a fictitious today corresponding to t; t1 denotes an earlier day. For a given t2, the iterative calibration sets the initial range of the time window as 120 days and the shrinking Interval of t1 as 5 days. That is, we need to estimate parameters 24 times for each t2 (e.g., [1, 120], [5, 120], …, [115, 120]). This process emphasizes the prediction and here we use only historical data as input. The outcome of t2 depends only on data from t2 to the last 120 days. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

LPPL (Logarithmic Periodic Power Law) Model Effectively Predicts NFT Market Bubbles

Author:
(1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Table of Links Abstract and 1 Introduction 2 Related Works 3 Datasets and Experiment Setup 4 Methodology 4.1 NFTs Transaction Network 4.2 NFTs Bubble Prediction 5 Discussion and Conclusions, and References 2. RELATED WORKS Graph analysis of Blockchain networks. Several works explored cryptocurrency networks based on graph theory and network analysis. Among them, BitCoinView[17] was developed to visualize the flow of the bitcoin transaction graph. This can be used to understand what essential things are needed and to be considered to visualize a transaction. Similarly, numerous algorithms have been developed to discover the relationship between smart contracts and tokens[18] and understand the ecosystem of Ethereum using graph analysis. Very recently, S. Casale-Brunet[19] analyzed the NFTs communities; the author selected the top 8 NFT collection projects and claimed that the NFT network graph follows power-law in their degree distribution; they also compared the topological values with social network and web networks, ERC-20 token networks. Our approach is quite similar, but we have added Temporal analysis of the NFT network and predicted the network bubble. Similarly, to understand a node’s influence in the blockchain network. Maeng, Soohoon Essaid, and Meryam [20]have worked on the visualization of Ethereum p2p network topology and analyzed the attributes of the network such as node degree, path length, diameter, and clustering coefficient. They then explore the node properties and provide analytical results regarding the relationship between nodes, heavily connected nodes, node geo-distribution, security issues, and possible attacks over the influential nodes. Network properties of transaction graphs. Bernhard Haslhofer, Roman karl and Erwin Filtz[21]studied the large-scale network properties of Bitcoin transaction graphs. Similar work was done by Ron D and Shamir A[22]. D’angelo studies only the transaction of the Ethereum network[23]. There has been numerous network analysis of the ERC-20 token. Somin et al[24] investigated the entire address graph spanned by ERC20 token trade-in the Ethereum blockchain and studied the social signal in the Ethereum ERC20 token trading network.[25]. Victor and Luders recently measured Ethereum based ERC-20 token network. [26]. Much less work has been done in the NFT ERC-721 trading network. S. Casale Brunet, P. Ribeca, P. Doyle, and M/. Mattavelli was among the first to propose a systematic analysis of the transaction network. [11]. All the transaction carried on the blockchain is publicly available; because of this, empirical study of NFT markets and ideas for implementation in different sectors can be seen. Recently Nadini, et al is the first empirical study on NFTs. They have analyzed a massive 6.1 million NFT trade transaction between 2017 and 2021[27]. They also have mentioned ways to detect communities and analysis them. Ante l[28]studied the relationship between Bitcoin and Ethereum and NFT sales. Bubble prediction on Cryptocurrencies. Bubble prediction on big cryptocurrencies, such as Bitcoin, Ethereum, and Solana. Basically, having high market capital has been a popular topic. One of the early from 2014, Macdonell A[15] is the first paper to study this topic of bubble prediction. Here the author used the LPPL model as well. Since then, there have been numerous research on bubble prediction. Buanchetti M and ItoK, shibanoK [29], [30] studied different ways of using the LPPL model in other cryptocurrencies. In most of the papers, the commonly used model is the LPPL model. That is the reason behind the chosen LPPL model for the NFT bubble prediction. Some of the papers have tried adding new terms to the model and tried fusing models to observe a new finding. [31]Though the commonly used model is the LPPL model. Some of the research attempted to use the machine learning approach [32] to predict the price of the cryptocurrency price bubble[33]. There is not much work done on bubble prediction of the NFT bubble. The NFTs marketplace, such as Open Sea, shows a strong growing market, but the evaluation of the NFTs collection market is lacking. Very recently and almost concurrent to ours [34] analyzed the top 5 NFTs and predicted the bubble for each NFTs collection as per their size. Our approach is quite similar, but we are using the LPPL model only for a single NFTs collection. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Author: Author: (1) Prakhyat Khati, Computer Science, University of Saskatchewa, Saskatoon, Canada (khati.prakhyat@usask.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works 2 Related Works 3 Datasets and Experiment Setup 3 Datasets and Experiment Setup 4 Methodology 4 Methodology 4.1 NFTs Transaction Network 4.1 NFTs Transaction Network 4.2 NFTs Bubble Prediction 4.2 NFTs Bubble Prediction 5 Discussion and Conclusions, and References 5 Discussion and Conclusions, and References 2. RELATED WORKS Graph analysis of Blockchain networks. Several works explored cryptocurrency networks based on graph theory and network analysis. Among them, BitCoinView[17] was developed to visualize the flow of the bitcoin transaction graph. This can be used to understand what essential things are needed and to be considered to visualize a transaction. Similarly, numerous algorithms have been developed to discover the relationship between smart contracts and tokens[18] and understand the ecosystem of Ethereum using graph analysis. Very recently, S. Casale-Brunet[19] analyzed the NFTs communities; the author selected the top 8 NFT collection projects and claimed that the NFT network graph follows power-law in their degree distribution; they also compared the topological values with social network and web networks, ERC-20 token networks. Our approach is quite similar, but we have added Temporal analysis of the NFT network and predicted the network bubble. Similarly, to understand a node’s influence in the blockchain network. Maeng, Soohoon Essaid, and Meryam [20]have worked on the visualization of Ethereum p2p network topology and analyzed the attributes of the network such as node degree, path length, diameter, and clustering coefficient. They then explore the node properties and provide analytical results regarding the relationship between nodes, heavily connected nodes, node geo-distribution, security issues, and possible attacks over the influential nodes. Network properties of transaction graphs. Bernhard Haslhofer, Roman karl and Erwin Filtz[21]studied the large-scale network properties of Bitcoin transaction graphs. Similar work was done by Ron D and Shamir A[22]. D’angelo studies only the transaction of the Ethereum network[23]. There has been numerous network analysis of the ERC-20 token. Somin et al[24] investigated the entire address graph spanned by ERC20 token trade-in the Ethereum blockchain and studied the social signal in the Ethereum ERC20 token trading network.[25]. Victor and Luders recently measured Ethereum based ERC-20 token network. [26]. Much less work has been done in the NFT ERC-721 trading network. S. Casale Brunet, P. Ribeca, P. Doyle, and M/. Mattavelli was among the first to propose a systematic analysis of the transaction network. [11]. All the transaction carried on the blockchain is publicly available; because of this, empirical study of NFT markets and ideas for implementation in different sectors can be seen. Recently Nadini, et al is the first empirical study on NFTs. They have analyzed a massive 6.1 million NFT trade transaction between 2017 and 2021[27]. They also have mentioned ways to detect communities and analysis them. Ante l[28]studied the relationship between Bitcoin and Ethereum and NFT sales. Bubble prediction on Cryptocurrencies. Bubble prediction on big cryptocurrencies, such as Bitcoin, Ethereum, and Solana. Basically, having high market capital has been a popular topic. One of the early from 2014, Macdonell A[15] is the first paper to study this topic of bubble prediction. Here the author used the LPPL model as well. Since then, there have been numerous research on bubble prediction. Buanchetti M and ItoK, shibanoK [29], [30] studied different ways of using the LPPL model in other cryptocurrencies. In most of the papers, the commonly used model is the LPPL model. That is the reason behind the chosen LPPL model for the NFT bubble prediction. Some of the papers have tried adding new terms to the model and tried fusing models to observe a new finding. [31]Though the commonly used model is the LPPL model. Some of the research attempted to use the machine learning approach [32] to predict the price of the cryptocurrency price bubble[33]. There is not much work done on bubble prediction of the NFT bubble. The NFTs marketplace, such as Open Sea, shows a strong growing market, but the evaluation of the NFTs collection market is lacking. Very recently and almost concurrent to ours [34] analyzed the top 5 NFTs and predicted the bubble for each NFTs collection as per their size. Our approach is quite similar, but we are using the LPPL model only for a single NFTs collection. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Graph Analysis and Bubble Prediction Are Key to Understanding NFT Networks

Let’s start by saying that determining the “real” value of an asset isn’t easy, and the free market, made up of people with quite subjective views, isn’t always helpful —sometimes, quite the opposite. If enough people believe, for any reason (imaginary or not), that a certain asset’s price will go to the moon and start buying massively, then it could happen. The problem comes when those same people start to panic because it’s never enough. That’s the moment when we have economic bubbles. This type of “bubble” occurs when the price of something, like houses, stocks, or any other asset, rises much higher than its ‘actual’ value because of too much buying, often leading to a sudden crash. Just like inflating a balloon until, logically, it bursts. Now, even if it’s difficult to determine the ‘actual’ value of something, there are some common-sense pointers. For instance, do you believe that a single tulip bulb (the flower, yes) could buy 12 acres of land? Because it’s happened. (In)Famous Financial Bubbles Economic bubbles often lead to fascinating yet devastating stories of market collapse. One of the earliest examples is the Dutch Tulip Mania of 1637. In the Netherlands, tulips became highly coveted, and prices for their bulbs soared due to speculation. Some bulbs were sold for the price of lands and houses, but in February 1637, the market collapsed almost overnight, leaving many investors bankrupt. This event is a classic example of speculative mania and its consequences, though its impact on the broader Dutch economy remains debated. A closer example to all of us is the dot-com bubble of the late 1990s. This one centered around the rapid rise of Internet-based companies. Investors poured money into businesses simply because they were labeled as ".com," leading to wildly overvalued stock prices. By 2000, the bubble burst when these companies failed to deliver profits, and the Nasdaq index (the stock market) fell by over 78%. Many firms, like Pets.com, went bankrupt, though others like Amazon and Google emerged stronger. There are more severe bubbles, though. The U.S. housing bubble in the 2000s was a major factor that led to the 2008 economic crisis. As property values rose, banks offered risky subprime mortgages (loans to borrowers with poor credit). When home prices began to decline in 2007, many borrowers defaulted, triggering a financial crisis that led to the Great Recession. This collapse caused widespread economic fallout, with millions losing their homes and jobs. It also spurred stricter financial regulations to prevent a similar disaster. Cryptocurrency Bubbles Someone will try to tell you that cryptocurrencies are economic bubbles, but that’s not true. They’re a new type of asset, and, as such, determining their ‘real’ or ‘intrinsic’ value has been a wild ride. Cryptocurrencies aren’t financial bubbles themselves, but they can suffer from financial bubbles. And they have, indeed, suffered from some big bubbles over the years. For instance, 2017 is very well remembered by all crypto users. In December of that year, cryptocurrency values, led by Bitcoin, skyrocketed to unprecedented highs, with Bitcoin nearing $20,000. By early 2018, panic selling, fraud, and hacks caused prices to plummet. By year’s end, Bitcoin lost 80% of its value, sinking below $4,000, and most other cryptocurrencies followed, marking one of the largest market collapses in history. The crypto market is quite resilient, though, and it recovered eventually —when the panic and pessimism faded and with the arrival of other good news. Six years later, Bitcoin has crossed $100,000 per unit, and the whole crypto market capitalization is over $3 trillion. Of course, we can’t say that all coins have survived their own bubbles. Terra (LUNA) and the FTX Token (FTT) are examples that never recovered from big crashes. In the end, it all depends on the valuable features (or not) offered by a certain crypto ecosystem and the support of its community. For its part, Obyte, founded in 2016, has been resilient to economic bubbles due to its focus on decentralization and utility over speculative hype. Its DAG (Directed Acyclic Graph) structure eliminates the need for middlemen and offers an efficient, low-cost network. Obyte doesn’t rely on energy-intensive mining or validator rewards, minimizing dependency on volatile token economics. Its real-world use cases, such as data verification and decentralized finance, further establish its value in utility rather than speculation. This way, Obyte is building a truly decentralized future! Featured Vector Image by WangXiNa / Freepik Let’s start by saying that determining the “real” value of an asset isn’t easy, and the free market, made up of people with quite subjective views, isn’t always helpful —sometimes, quite the opposite. If enough people believe, for any reason (imaginary or not), that a certain asset’s price will go to the moon and start buying massively, then it could happen. The problem comes when those same people start to panic because it’s never enough. That’s the moment when we have economic bubbles. This type of “bubble” occurs when the price of something, like houses, stocks, or any other asset, rises much higher than its ‘actual’ value because of too much buying, often leading to a sudden crash . Just like inflating a balloon until, logically, it bursts. Now, even if it’s difficult to determine the ‘actual’ value of something, there are some common-sense pointers. For instance, do you believe that a single tulip bulb (the flower, yes) could buy 12 acres of land? Because it’s happened. This type of “bubble” occurs when the price of something, like houses, stocks, or any other asset, rises much higher than its ‘actual’ value because of too much buying, often leading to a sudden crash (In)Famous Financial Bubbles Economic bubbles often lead to fascinating yet devastating stories of market collapse. One of the earliest examples is the Dutch Tulip Mania of 1637. In the Netherlands, tulips became highly coveted, and prices for their bulbs soared due to speculation. Some bulbs were sold for the price of lands and houses, but in February 1637, the market collapsed almost overnight, leaving many investors bankrupt. This event is a classic example of speculative mania and its consequences, though its impact on the broader Dutch economy remains debated. Economic bubbles Economic bubbles A closer example to all of us is the dot-com bubble of the late 1990s. This one centered around the rapid rise of Internet-based companies . Investors poured money into businesses simply because they were labeled as ".com," leading to wildly overvalued stock prices. By 2000, the bubble burst when these companies failed to deliver profits, and the Nasdaq index (the stock market) fell by over 78%. Many firms, like Pets.com, went bankrupt, though others like Amazon and Google emerged stronger. A closer example to all of us is the dot-com bubble of the late 1990s. This one centered around the rapid rise of Internet-based companies There are more severe bubbles, though. The U.S. housing bubble in the 2000s was a major factor that led to the 2008 economic crisis. As property values rose, banks offered risky subprime mortgages (loans to borrowers with poor credit). When home prices began to decline in 2007, many borrowers defaulted, triggering a financial crisis that led to the Great Recession. This collapse caused widespread economic fallout, with millions losing their homes and jobs. It also spurred stricter financial regulations to prevent a similar disaster. Cryptocurrency Bubbles Someone will try to tell you that cryptocurrencies are economic bubbles, but that’s not true. They’re a new type of asset, and, as such, determining their ‘real’ or ‘intrinsic’ value has been a wild ride. Cryptocurrencies aren’t financial bubbles themselves, but they can suffer from financial bubbles. And they have, indeed, suffered from some big bubbles over the years. Cryptocurrencies aren’t financial bubbles themselves, but they can suffer from financial bubbles. And they have, indeed, suffered from some big bubbles over the years. For instance, 2017 is very well remembered by all crypto users. In December of that year, cryptocurrency values, led by Bitcoin, skyrocketed to unprecedented highs, with Bitcoin nearing $20,000. By early 2018, panic selling, fraud, and hacks caused prices to plummet. By year’s end, Bitcoin lost 80% of its value, sinking below $4,000, and most other cryptocurrencies followed, marking one of the largest market collapses in history. The crypto market is quite resilient, though, and it recovered eventually —when the panic and pessimism faded and with the arrival of other good news . Six years later, Bitcoin has crossed $100,000 per unit, and the whole crypto market capitalization is over $3 trillion. Of course, we can’t say that all coins have survived their own bubbles. The crypto market is quite resilient, though, and it recovered eventually —when the panic and pessimism faded and with the arrival of other good news all Terra (LUNA) and the FTX Token (FTT) are examples that never recovered from big crashes . In the end, it all depends on the valuable features (or not) offered by a certain crypto ecosystem and the support of its community. from big crashes from big crashes For its part, Obyte , founded in 2016, has been resilient to economic bubbles due to its focus on decentralization and utility over speculative hype. Its DAG (Directed Acyclic Graph) structure eliminates the need for middlemen and offers an efficient, low-cost network. Obyte doesn’t rely on energy-intensive mining or validator rewards, minimizing dependency on volatile token economics. Obyte Obyte Its real-world use cases, such as data verification and decentralized finance, further establish its value in utility rather than speculation. This way, Obyte is building a truly decentralized future! Featured Vector Image by WangXiNa / Freepik Featured Vector Image by WangXiNa / Freepik Freepik Freepik

Educational Byte: What Is an Economic Bubble and How Does It Affect Crypto?

Byte Educativo: ¿Kunas Burbuja Económica ukat Kunjamas Cripto ukarux jan walt’ayi?

Təhsil Baytı: İqtisadi Bubble nədir və Kriptoya necə təsir edir?

Obrazovni bajt: Šta je ekonomski balon i kako utiče na kripto?

Byte educativo: ¿Qué es una burbuja económica y cómo afecta a las criptomonedas?

Byte educativo: que é unha burbulla económica e como afecta a criptografía?

Educational Byte: Apa itu Gelembung Ekonomi dan Bagaimana Pengaruhnya terhadap Kripto?

教育バイト: 経済バブルとは何か、そしてそれは暗号通貨にどのような影響を与えるのか?

საგანმანათლებლო ბაიტი: რა არის ეკონომიკური ბუშტი და როგორ მოქმედებს ის კრიპტოზე?

교육 바이트: 경제적 거품이란 무엇이고 암호화폐에 어떤 영향을 미치는가?

Vzdelávací bajt: Čo je to ekonomická bublina a ako to ovplyvňuje krypto?

I-Byte yeMfundo: Yintoni iBubble yezoQoqosho kwaye iyichaphazela njani i-Crypto?

I-Byte Yezemfundo: Iyini Ibhamuza Lezomnotho Futhi Liyithinta Kanjani I-Crypto?

If history is a teacher, technology grows in booms and busts. This seems particularly true of network technologies. It seems even more true when you combine financial assets with network technologies. Bubbly talk It would be hard not to talk about the price of bitcoin this week. As of this writing, BTC hit an all-time high of $99,655.50, which puts it within striking distance of the major psychological milestone of $100,000. Hitting $100k will be a big deal for a few reasons, but mostly because, like it has done in previous cycles, hitting that milestone is likely to trigger the interest of more investors and more capital. In other words, by hitting $100,000, bitcoin will likely trigger a bubble or bubble-like behavior. Bitcoin is notoriously bubbly, so much so that it's becoming a scientific discipline. This is pulled from a recent study that was published in the journal Nature, for example: "The starting hypothesis is that there must indeed be parallels between Bitcoin prices and those of other speculative assets. After the empirical study, the hypothesis was confirmed. This research shows that there are significant coincidences between the behavior of Bitcoin prices and that of previous well-known bubbles. Therefore, thanks to this study, the idea that Bitcoin is an asset that tends to behave in the form of a bubble is reinforced." But even without an academic study, looking at the peaks and valleys of bitcoin’s charts over the past decade is enough to confirm that there are notable boom and bust periods. In the case of bitcoin, the boom times, or bubbles happened in 2013, 2017, 2021, and now, well… Financial bubbles have happened for as long as markets have existed. Bitcoin’s bubbles are compared with one of the earliest and most famous financial bubbles — the tulip mania that happened in 1636. Understanding the mechanics of a bubble is pretty straightforward — it’s basically when FOMO or fear of missing out takes over — and new investors and more capital flood the market not wanting to miss the top. We can all understand that FOMO feeling because it’s part of how humans work. It’s basic psychology. It’s also why we see this kind of behavior form around other non-financial trends, like Pokemon cards, for example. This is all to say that the bubble dynamics are not limited just to crypto. Why bitcoin’s bubble dynamics exist In the context of bitcoin, the market bubble dynamics are a feature, not a bug. The hype cycle or the boom/bust cycle serves the purpose of bringing more people and more capital to the market. Sure, for the investors that time the market wrong or enter at the top, and then get quickly wrecked as the bubble pops and the price recedes, the bubble dynamics can lead to negative sentiment. But bitcoin bubbles are part of its underlying design. This is a point missed by a lot of people, including new entrants to the market looking to catch the top. Bitcoin’s bubbles are fueled, on a regular schedule, by its halving dynamics. Each halving tights the supply side and creates issues related to mining activities and other conditions that drive the price up following basic supply and demand theory. The last bitcoin halving was just this past April. According to a study published in the June issue of the journal Sustainable Futures: "However, despite numerous empirical studies on the determinants of the Bitcoin speculative bubble, this event is often overlooked. This omission could have significant consequences for institutional investors who make decisions based on scientific research results. It may lead to a partial understanding of price dynamics and expose their portfolios to increased investment risks. This is because they will not be prepared for potential price volatility leading up to the halving.” It’s also helpful to think that each market cycle is a series of bubbles or a chain of bubbles, that all connect at regular intervals to drive market adoption, development, and innovation. Since bitcoin’s bubbles, so far, happen in relation to halving events, which occur every four years, it’s kind of like bitcoin bubbles are the financial market equivalent to the Olympics or the World Cup. At regular intervals, the world gathers around and finds itself captivated by new storylines and new unbelievable feats, but eventually, it all comes to an end and we all go back to our normal lives until the next go-around. And that’s where we find ourselves right now. If the past few weeks of bitcoin’s price climb are any indication, then it feels like we are entering a new phase and potentially the next bubble. The role of bitcoin’s market dominance in the overall crypto market It’s important to understand the role of bitcoin as a crypto market leader. When the dynamics described above play out, the impacts will trickle down to the rest of the crypto market, essentially driving bigger bubble behavior. This is when understanding why market capitalization and market dominance come into play. Historically, bitcoin has always been a market leader. Its current market dominance is 57.68%. Understanding bitcoin’s dynamics, and its bubble-by-design architecture helps to understand how related assets perform, and what to expect from Open Money markets. As a parting word, bubble markets and the overall hype cycle can be fun to watch and participate in. But they can also cause financial distress and ruin for people who get too caught up. Be sure to keep in mind that all bubbles pop, eventually. This post is part of an ongoing newsletter about bitcoin and other digital assets called Open Money. New issues go out every Sunday. If history is a teacher, technology grows in booms and busts. This seems particularly true of network technologies. It seems even more true when you combine financial assets with network technologies. If history is a teacher, technology grows in booms and busts. This seems particularly true of network technologies. It seems even more true when you combine financial assets with network technologies. Bubbly talk It would be hard not to talk about the price of bitcoin this week. As of this writing, BTC hit an all-time high of $99,655.50, which puts it within striking distance of the major psychological milestone of $100,000. BTC Hitting $100k will be a big deal for a few reasons, but mostly because, like it has done in previous cycles, hitting that milestone is likely to trigger the interest of more investors and more capital. In other words, by hitting $100,000, bitcoin will likely trigger a bubble or bubble-like behavior. Bitcoin is notoriously bubbly, so much so that it's becoming a scientific discipline. This is pulled from a recent study that was published in the journal Nature, for example: was published in the journal Nature, "The starting hypothesis is that there must indeed be parallels between Bitcoin prices and those of other speculative assets. After the empirical study, the hypothesis was confirmed. This research shows that there are significant coincidences between the behavior of Bitcoin prices and that of previous well-known bubbles. Therefore, thanks to this study, the idea that Bitcoin is an asset that tends to behave in the form of a bubble is reinforced." "The starting hypothesis is that there must indeed be parallels between Bitcoin prices and those of other speculative assets. After the empirical study, the hypothesis was confirmed. This research shows that there are significant coincidences between the behavior of Bitcoin prices and that of previous well-known bubbles. Therefore, thanks to this study, the idea that Bitcoin is an asset that tends to behave in the form of a bubble is reinforced." But even without an academic study, looking at the peaks and valleys of bitcoin’s charts over the past decade is enough to confirm that there are notable boom and bust periods. In the case of bitcoin, the boom times, or bubbles happened in 2013, 2017, 2021, and now, well… Financial bubbles have happened for as long as markets have existed. Bitcoin’s bubbles are compared with one of the earliest and most famous financial bubbles — the tulip mania that happened in 1636 . Bitcoin’s bubbles are compared with one of the earliest and most famous financial bubbles — the tulip mania that happened in 1636 Understanding the mechanics of a bubble is pretty straightforward — it’s basically when FOMO or fear of missing out takes over — and new investors and more capital flood the market not wanting to miss the top. We can all understand that FOMO feeling because it’s part of how humans work. It’s basic psychology. It’s also why we see this kind of behavior form around other non-financial trends, like Pokemon cards, for example. This is all to say that the bubble dynamics are not limited just to crypto. Why bitcoin’s bubble dynamics exist In the context of bitcoin, the market bubble dynamics are a feature, not a bug. The hype cycle or the boom/bust cycle serves the purpose of bringing more people and more capital to the market. Sure, for the investors that time the market wrong or enter at the top, and then get quickly wrecked as the bubble pops and the price recedes, the bubble dynamics can lead to negative sentiment. But bitcoin bubbles are part of its underlying design. This is a point missed by a lot of people, including new entrants to the market looking to catch the top. Bitcoin’s bubbles are fueled, on a regular schedule, by its halving dynamics. Each halving tights the supply side and creates issues related to mining activities and other conditions that drive the price up following basic supply and demand theory. The last bitcoin halving was just this past April. According to a study published in the June issue of the journal Sustainable Futures: Sustainable Futures: "However, despite numerous empirical studies on the determinants of the Bitcoin speculative bubble, this event is often overlooked. This omission could have significant consequences for institutional investors who make decisions based on scientific research results. It may lead to a partial understanding of price dynamics and expose their portfolios to increased investment risks. This is because they will not be prepared for potential price volatility leading up to the halving.” "However, despite numerous empirical studies on the determinants of the Bitcoin speculative bubble, this event is often overlooked. This omission could have significant consequences for institutional investors who make decisions based on scientific research results. It may lead to a partial understanding of price dynamics and expose their portfolios to increased investment risks. This is because they will not be prepared for potential price volatility leading up to the halving.” It’s also helpful to think that each market cycle is a series of bubbles or a chain of bubbles, that all connect at regular intervals to drive market adoption, development, and innovation. Since bitcoin’s bubbles, so far, happen in relation to halving events, which occur every four years, it’s kind of like bitcoin bubbles are the financial market equivalent to the Olympics or the World Cup. At regular intervals, the world gathers around and finds itself captivated by new storylines and new unbelievable feats, but eventually, it all comes to an end and we all go back to our normal lives until the next go-around. And that’s where we find ourselves right now. If the past few weeks of bitcoin’s price climb are any indication, then it feels like we are entering a new phase and potentially the next bubble. The role of bitcoin’s market dominance in the overall crypto market It’s important to understand the role of bitcoin as a crypto market leader. When the dynamics described above play out, the impacts will trickle down to the rest of the crypto market, essentially driving bigger bubble behavior. This is when understanding why market capitalization and market dominance come into play. Historically, bitcoin has always been a market leader. Its current market dominance is 57.68%. market capitalization Understanding bitcoin’s dynamics, and its bubble-by-design architecture helps to understand how related assets perform, and what to expect from Open Money markets. As a parting word, bubble markets and the overall hype cycle can be fun to watch and participate in. But they can also cause financial distress and ruin for people who get too caught up. Be sure to keep in mind that all bubbles pop, eventually. This post is part of an ongoing newsletter about bitcoin and other digital assets called Open Money. New issues go out every Sunday. This post is part of an o ngoing newsletter about bitcoin and other digital assets called Open Money . New issues go out every Sunday. ngoing newsletter about bitcoin and other digital assets called Open Money

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