Bitcoin Wasn't Invented in College - Bitcoin Magazine

Bitcoin Wasn’t Invented in College – Bitcoin Magazine

Bitcoin has reached a market valuation of over one trillion dollars since the announcement of its launch in October of 2008. As a result of its rapid expansion, it has attracted investments from ordinary investors as well as institutional investors, who are beginning to recognize it as a real store of value and an alternative to traditional assets such as gold. The use of bitcoin as a medium of trade is becoming an increasingly viable option as a result of developments in second-layer settlement protocols such as the Lightning Network.

However, Bitcoin’s history in the academic world might be described as unstable and rather chaotic. The cryptocurrency known as Bitcoin is almost never mentioned in any of the courses offered at universities. In their place, the teachings are frequently delegated to student organizations and nonprofit organizations. This could change in the future as Bitcoin and the cryptocurrency sector as a whole continue to expand, capturing the attention of highly skilled professionals in the fields of both engineering and business. It is not Bitcoin’s fault that it is not taught in universities; rather, the problem lies within academia, with its inadequate embrace of innovation, its emphasis on backward-looking data analysis, and its excessive preoccupation with individual disciplines rather than collective knowledge. Bitcoin’s absence from universities is not a problem with Bitcoin in and of itself. Bitcoin has the potential to serve as a model for how academic research should and should not be conducted. In point of fact, it lays out a plan for improving the quality of higher education in the future.

Comparable Elements To Those Of The Academy

One might reasonably question why anyone would even consider the possibility that Bitcoin and educational institutions are connected. While academic members focus on developing fundamental sciences that (may) have applications in the distant future, technologists are in daily contact with the actual needs of customers in the present. In the end, successful businesses such as Facebook, Microsoft, Apple, and even Ethereum were all founded by young men who did not complete their degrees at an accredited college. Silicon Valley and Route 128 both developed in close proximity to some of the most prestigious coastal colleges in the United States. This was no accident. Therefore, there is most likely a connection to be made between academic institutions and the technological industry. However, Bitcoin is not like other currencies. Even more so than before, Bitcoin is inextricably linked to the intellectual and scholarly underpinnings of its origins. Examining the past of Bitcoin is necessary if we are to comprehend this.

Cypherpunks were a loose-knit group that included cryptographers, computer scientists, economists, and libertarians. They communicated with one another using an internet mailing list around the turn of the century. This was an unusual online gathering of a broad group of scientists, technologists, and computer science enthusiasts who were working on inventing new ideas for cryptography and computer science breakthroughs and exchanging those ideas with one another. This is the place where several of the early pioneers of applied cryptography, such as Hal Finney, one of the early pioneers of Pretty Good Privacy, spent time (PGP).

This is the mailing list where the pseudonymous developer of Bitcoin, Satoshi Nakamoto, revealed his idea for an online payment network to the general public for the first time. After making that announcement, he started answering questions from the community forum about the concept as well as the way it will be implemented. In a short time after that, Nakamoto provided the final version of Bitcoin’s implementation. Participants in the forum were able to download the software, use it on their own computers, and perform their own tests thanks to this feature.

There are many parallels to be drawn between academic study and the Bitcoin white paper. It is formatted like an academic paper, has citations, and has an appearance that is comparable to that of a work published in the field of computer science in the present day. The “proof-of-work” method is one of the fundamental components of Bitcoin, and both the “white paper” and the discussions that surround it make reference to earlier attempts to implement the algorithm. As an illustration, the white paper refers to HashCash from 2002, which was also a component of the body of knowledge that existed before to Bitcoin. While working on a solution to the challenge of removing spam from email, Adam Back came up with the proof-of-work concept for the HashCash cryptocurrency.

Therefore, Bitcoin did not originate from thin air but rather arose from a long lineage of concepts that were developed over the course of decades, rather than days or weeks. We have a tendency to conceive of technology as moving at warp speed, changing quickly, and being pushed by ambitious young people who dropped out of college, but Bitcoin was not founded on the principle of “move fast and break things.” It was and still is the opposite: a deliberate, thorough consideration based on decades of actual research conducted not by children but rather more like their parents in place of children. The forum on cryptography had a format that was comparable to that of an academic research seminar, which is a meeting at which trained professionals in the field of research strive to respectfully but insistently dismantle concepts in order to get closer to the truth. In spite of the fact that the idea of a white paper is currently all the rage among alternative cryptocurrency coins and tokens, it is still the standard means of expressing ideas within the community of professional researchers.

The mining process involves the creation of a competition that awards newly mined bitcoins to miners who successfully solve a riddle. This is an example of a mechanism used in microeconomics; more specifically, it is a game economy design in which individual agents compete against one another for a payoff. Bitcoin’s issuance schedule is an important aspect of its macroeconomics because it changes in a way that can be forecasted over time. For example, every four years, the block reward will decrease by one-half. This results in a limit of 21 million bitcoins being in place. This essentially places a cap on the expansion of the currency’s inflationary potential and establishes a restriction that no fiat currency in use today is required to comply with. Regardless of the total computing power of the network, the difficulty of the underlying puzzle is adjusted approximately once every two weeks. This ensures that the implementation remains stable in spite of the exponential growth in computing power that has occurred in the years since Bitcoin was first introduced.

This inter-disciplinary aspect of Bitcoin is not a supplementary feature; rather, it is fundamental. Bitcoin would not be able to function properly if any of its three components—public key cryptography, a backward-linked blockchain, and a mining competition employing proof-of-work—were missing. Each of the three components, when taken by itself, constituted a coherent body of information and ideas. It was the two of them working together that made Nakamoto such a genius. In the same manner, significant inventions of the future will need to link together numerous disciplines in existential ways. Otherwise, the combination of these fields would not be able to survive.

Why Shouldn’t We Join the Academy?

Why was it not possible for Bitcoin to originate in a school setting? To begin, Bitcoin is naturally interdisciplinary, although academics are typically recognized for their achievements in certain fields of study. Although concepts from computer science, mathematics, and economics are combined in Bitcoin, it is highly unlikely that a single university faculty would have the depth of knowledge required for multidisciplinary consilience.

Second, there is a tendency toward incrementalism in the academic world. Academic journals require its writers to provide an explanation of the incremental contribution that their work makes to the body of existing knowledge. Step by step, this is how the body of humankind expands. But Bitcoin, like other radical inventions in history such as the airplane and the transistor, made great leaps forward that are highly unlikely to have survived the peer review process of the academy. Bitcoin, like other innovative products in history, like the airplane and the transistor.

Third, Bitcoin is based on libertarian political principles, which are currently unpopular within the mainstream academic community, particularly among professional economists. The Bitcoin protocol will release an unpredictable number of new bitcoins on a schedule, and these computational representations of sound money are already baked into the program. This is in stark contrast to the society in which we currently live, in which the Federal Open Market Committee has complete discretionary authority over the amount of money in circulation. The cypherpunks who were responsible for vetting Bitcoin v0.1 had a common distrust of collective power. They believed that technology and cryptography might offer individuals with privacy, shielding them from the prying eyes of the government or any other huge institution.

The vast majority of economists do not have this pessimistic view towards central authority. At least the community of social scientists has never taken Bitcoin seriously. In addition, the Federal Reserve plays a significant part in both the funding and promotion of standard academic economic research. It selects bank presidents and governors who have previous experience as economics professors, and it encourages its staff to write in the same academic publications as the academy does. Additionally, it recruits from the most prestigious Ph.D. schools. Because the culture of the Fed has such a strong effect, it is not surprising that the faculty at universities would be resistant to adopting technologies that could fundamentally replace their role.

I invited all living recipients of the Nobel Prize in Economics to speak at the Texas A&M Bitcoin Conference, but all but one of them denied my invitation. Some of them stated that they did not know sufficient information about Bitcoin to merit delivering a lecture; at least they were honest about the limitations of the disciplinary model in which they have so successfully thrived. Others, such as the economist Paul Krugman, see cryptocurrency as the next subprime mortgages (he also once predicted that the internet would have the same impact on the economy as the fax machine). Academic economists paid almost no attention to the rise of Bitcoin and even now remain clueless of how the Bitcoin blockchain works, despite the fact that it is the only major breakthrough in finance to have occurred in the past decade.

A significant intellectual contribution, first and foremost, lies behind Bitcoin. It is not necessary for you to have in-depth knowledge of the industry, a unique insight into the present practices of firms, or knowledge of the specific idiosyncrasies of the labor and capital markets. It did not build on previously established practices but rather on previously established theories. Because of these factors, Bitcoin sprang unequivocally from the land of ideas and, in some sense, should have originated in the academic world. The mining competition might have been conceived of by an academic economist, the blockchain might have been created by a computer scientist, and public key cryptography might have been conceived of by a mathematician. To merge these three breakthroughs into a single product, you need an unusual collaborator (or team). Universities cultivate faculties that have extensive knowledge in their respective fields of study, but they don’t do anything to connect the fields of study together like Bitcoin does. Bitcoin could not have originated within a university for this same reason, despite the fact that it is based on academic subjects that are very well developed within universities. The organization of the knowledge is what needs improvement, not the knowledge itself. And precisely therein lays the possibility.

How Did We Get Here?

The academy, in its current configuration, is not suitable for new developments such as Bitcoin. Once students enter graduate school, they begin to learn the methods that are specific to their chosen field. They then put these methods to use by publishing their work in specialized journals, which ultimately leads to tenure and future academic recognition among a select group of peers who work in that field. Since the inception of higher education, these compartmentalized intellectual pathways have become increasingly rigid over the course of several centuries. What led to this occurrence?

Since the end of World War II, the academic community has mostly been influenced by two distinct movements. The digital revolution is by far the most crucial of all the revolutions. The goal of scientific research switched from the formulation of theories to the gathering of empirical evidence when computing power became available to the general public. Researchers, no matter where they were located in the world, could now access a plethora of data in the fields of social and natural sciences on their laptops. The proliferation of data sharing and data availability was made possible by the advent of the internet, and developments in the power of microprocessors made it easy and affordable to analyze enormous amounts of data. The academic world as a whole gravitated towards data analysis, and on cycles of ten to fifteen years, they went from one trend to the next. The first cycle focused on summary statistics and variance analysis, the second cycle concentrated on linear regression, and the third cycle was dedicated to machine learning. Rarely did academics go back to the theory that underpinned their field in order to make adjustments when issues developed in the specific area of each study. Instead, they just pushed additional data into the system in the hopes that some measurement error or missing variable was to blame.

The proliferation of large amounts of data and statistics, in conjunction with advances in machine learning, have brought us to a point in time when artificial intelligence (AI) is a mystery. No researcher has yet come close to completely explaining what it is that AI is accomplishing. Concurrently, the questions themselves have become more condensed. Before, the question “Why is Africa so poor?” would be asked in the subject of development economics. Now, research being conducted in this sector investigates the question of whether or not posting a sign on the left or right side of a restroom door is more likely to result in people using the facility. This fixation with causality is cognitively desirable, but it comes at a great price, as it frequently requires the researcher to restrict his domain to actions that are easily observable and measurable. Because the massive, complicated, and mathematical theories that were established after World War II were mostly untestable, empirical researchers abandoned the theoretical foundations upon which those ideas were based. It used to be that academics held the intellectual high ground by asking the most important issues of the day, but these days empirical research is more likely to be published in academic publications. Both experimental physicists and empirical economists cite other data-driven works the vast majority of the time.

Students were introduced to the concept of computation at an earlier age as a result of the proliferation of computers in our society. The fundamentals of data manipulation and analysis were already second nature to them by the time they started their studies at an undergraduate or graduate institution. Why bother with mathematics when some straightforward experiments and linear regressions can yield tables of results that can be written up and distributed in a short amount of time? Students gradually began to focus more on data work as the academic profession gradually moved away from mathematics over the course of time.

Journal editors now have a much simpler time when it comes to accepting publications that contain some minor experimental or empirical truth about the world. As a result of the fact that editors and referees make decisions regarding academic research on a paper-by-paper basis, there is no comprehensive evaluation to determine whether or not the collection of empirical and experimental work actually increases human understanding. As a consequence of this, data analysis has become completely out of control, with teams of academics making ever more incremental breakthroughs, mining the same basic data sets, and asking questions that are both more specific and more meaningless. Does the weather, specifically the sun or rain, have an impact on how traders feel and, as a result, the stocks they choose to invest in? Is it possible to gauge a chief financial officer’s level of narcissism and determine whether or not he will commit fraud by looking at the size of his signature on an annual report? (I’m not making any of this up in my head.)

It could have been expected that developments in computing would have prompted study that verified some of the hypotheses proposed after World War II, but this has not been the case. In scientific parlance, a good number of these intricate models are endogenous, which means that they simultaneously determine the equilibrium values of numerous variables. As a result of this, it might be difficult for empirical researchers to pinpoint exactly what is going on, such as determining whether or not raising the minimum wage will lead to an increase in unemployment, as the Economics 101 textbook implies. This has resulted in a shift in focus to causation. However, in order to make a causal inference, certain requirements need to be met, and frequently these circumstances do not hold true for the economy as a whole but rather for a small number of specific cases, such as the states in the United States that approved anti-abortion laws at different times. The economics revolution brought about by Freakonomics may not have dominated the Nobel Prizes, but it has unquestionably had an impact on the vast bulk of the social science research that has been published.

The fact that this strategy is ultimately gazing in the wrong direction is the primary issue with the data-driven approach. Data is a representation of the world at a particular point in time, according to its very definition. The entire domains of research in business and economics are now almost entirely empirical, and researchers are competing with one another to either collect new datasets or apply innovative empirical approaches to datasets that already exist. In either case, we are continually looking back into the past through the rearview mirror, attempting to comprehend what did or did not occur in the past. Is it possible that historically low interest rates contributed to the Global Financial Crisis? Do abortions have an effect on crime rates? Does increasing the minimum wage make it harder to find work? Instead than focusing on developing novel approaches to problems that will arise in the future, these concerns primarily center on the past.

The second pattern that has been observed is a reduction in the size of the theoretical community, both within and outside of academic institutions. The number of theorists has drastically decreased, and in addition, they have turned down opportunities to work together with their empirical and experimental colleagues, who are in much bigger numbers. Because of this tribalism, theorists wrote mathematical models that were ever more complicated, detailed, and self-referential. These models had very no basis in reality, and there was no prospect that they could be empirically validated. A significant portion of game theory cannot be verified or tested, and string theory is possibly the most severe illustration of a self-referential reality that can never be proved or tested in its entirety.

In conclusion, academic theory lags far behind technological advancement. Ex-post rationalizations are typically provided by mathematicians, physicists, and economists for technological advancements that have already been implemented successfully in industry. These ideas don’t make any novel predictions; rather, they confirm what everyone already knows to be true. Even among theorists themselves, the number of people who read increasingly complicated theories is decreasing. The tribalism that exists in theory causes the group to behave like a club, excluding members who do not accept its esoteric language and procedures. This is similar to how everything else in life works.

As a result, we have reached a point where there is something resembling a civil war; the theory community is diminishing year by year and losing its relevance to reality, while the empirical/experimental data community is growing over time and asking more specific questions without any conceptual guidance. Academics and engineers alike are in the dark regarding the nature of the problems that need to be solved and the best way to tackle them. It also contributes to a pervasive unpredictability in our collective consciousness, which causes us to blow in whatever direction the winds of the time take us. As a result of this, we are more likely to act irrationally. In economics, there are numerous well-established theories of markets and how they operate; nevertheless, technology businesses are vast marketplaces that are unmoored in a significant portion of these same economic theories. The field of computer science is built on a solid foundation of algorithms and data structures; yet, the theory community is consumed with discussions on computational complexity, even while trillion-dollar tech businesses use straightforward A/B tests to make their most important judgments.

A tipping point has been achieved in the size of human knowledge, at which time academics are refining their theories to ever more exact levels, speaking to communities of academics that are getting smaller and smaller. The segmentation of knowledge has resulted in hyperspecialization, a phenomenon in which academic journals and fields of study continue to subdivide and segment their content into increasingly more specific subfields. This hyperspecialization is demonstrated by the abundance of journals that are published.

From Scientific Research To Engineering Design

In light of the fact that a large deal of information has already been unearthed inside pre-existing fields of study, most of the future innovation will take place at the boundaries of the disciplines; nonetheless, there is a greater need for transformation. The scientific method is still widely used in today’s universities, which seeks to establish knowledge for its own purpose while also attempting to get an understanding of the natural, physical, and social world. However, we shouldn’t stop there. Given the breadth and depth of their expertise, scientists are in the best position to design and develop more effective solutions for our collective future. Adopting the mindset of an engineer will need members of the academic community to devise and implement answers to the most important issues we face. Additionally, throughout the course of time, it will help bridge the gap between academic institutions and the business world. Because the needs of the market are not being met by the academic curriculum, students are put under a lot of pressure to look for jobs and start their own businesses, which has a negative impact on the students’ ability to focus on their academic assignments. This cognitive dissonance would be alleviated if the difference between the two groups were to shrink, and if students spent their time in college working on developing better solutions for the future.

This transition has already started to take place in several fields, such as economics. One of the most fruitful applied areas of economics is market design, which has unmistakably taken on the mindset of engineering and has produced three Nobel Prize winners in only the past ten years. These academics came from the field of engineering and adopted game theory to design better marketplaces that are able to function in the real world. For example, they developed more effective ways to match kidney donors and recipients, students with schools, and medical residents and hospitals. They also developed several of the largest auctions that are used today, such as the auction for the spectrum that the government held and the ad auction that Google does internally. There is no reason why the remainder of the economics profession, or even the remainder of higher education and the academic community, cannot similarly position themselves towards adopting more of an engineering attitude.

Over time, narrowing this gap between academic institutions and commercial enterprises will relieve a significant portion of the

public outrage against rising tuition costs and rising levels of student debt Once students and professors begin to direct their research toward the development of better solutions for society, their pupils as well as the firms that employ them will begin to do the same. If the results of academic research immediately lead to the development of technologies that are to the students’, as well as their future employers’ and society at large’s, advantage, then students will no longer resent their teachers for devoting time to research rather than teaching. This, in time, will naturally close the skills gap that the United States of America is currently facing. It will no longer be necessary for universities to place an explicit emphasis on STEM abilities; instead, they should concentrate on developing technology solutions, which will, in the end, draw significantly from STEM fields.

An appeal to take action

What kinds of changes should be made to higher education in order to foster innovation like Bitcoin? Naturally, the next Bitcoin won’t be Bitcoin per se; rather, it will be an innovation based on basic principles that conceives of an old problem in an altogether new way. I would want to make three specific suggestions on the organizational structure, priorities, and culture of the university.

In the first place, the academy needs to embrace engineering in a more overt manner than science, even on the periphery. Because of the Renaissance and the Age of Reason, higher education in the United States has come to appreciate science and knowledge just for their own sake. The University of Chicago has as its motto “Crescat scientia, vita excolatur,” which translates to “Let knowledge grow from more to more, and so that human life may be enriched.” The motto for Harvard University is “Veritas,” which means “truth.” However, the last half century has been the age of engineering universities, with Stanford and MIT competing to build solutions for the world, not just to understand it. These universities, which are based on the scientific and liberal arts traditions, have done much to establish the corpus of knowledge necessary for human progress. This philosophy of engineering ought to be applied beyond the confines of engineering departments, and especially to the realm of social science. For instance, all first-year students should be required to take a class in fundamental engineering to acquire the knowledge necessary to construct mental frameworks for finding answers to issues. Since the dawn of time, economists have debated the merits of a financially stable currency, but it is only through a technologically advanced system such as Bitcoin that these discussions may become a tangible reality.

This transition in engineering is somewhat occurring within the social sciences at this point. For instance, the recent awarding of the Nobel Prize in Economics to Paul Milgrom and Bob Wilson was in recognition of the work that they did to develop new markets and auctions to address genuine issues regarding the distribution of resources that are faced by governments and society. This community of microeconomic theorists is still a small minority within the economic profession; nonetheless, their work blends theory with practice like no other discipline and should have a stronger representation among working scholars because of the unique way it combines the two. Universities should move away from the forced equity that treats all fields of study as equivalent, assigning an equal proportion of faculty lines and research resources to each field of study regardless of the influence that each field has on society. Instead, place an emphasis on developing disciples who are ready and able to create solutions for the future. This culture needs to originate at the very top of the organization and penetrate all the way down to the level where teachers and students are recruited.

Second, recognize and commend work that crosses disciplines. The conventional, centuries-old model of in-depth work within a discipline is beginning to show its age, whilst the majority of the fascinating inventions of our day may be found at the edges of the disciplines. Universities give interdisciplinary work lip service as a new phrase that is sweeping across college campuses, but unless there is a change in the incentives for academics, nothing will change. Publications that go outside of an academic’s primary field should be rewarded by promotion and tenure committees, particularly those that involve collaboration with faculty from other schools or departments. Although large government agencies such as the National Science Foundation have increased the amount of funding that is allocated toward cross-disciplinary teams, academic promotion and tenure committees continue to reward scholars working within their own disciplines. This is despite the fact that large government agencies such as the National Science Foundation have increased the amount of funding that is allocated toward cross-disciplinary teams. It is my expectation that this will shift over time as baby boomers enter retirement; but, the most important issues facing society cannot wait, and colleges ought to make quicker adjustments right now. Nothing else is relevant until the bodies in charge of promotions and tenure make an explicit announcement about recognizing multidisciplinary work.

Third, the academy needs to have lofty goals. In far too many cases, academic publications are content with seeking contributions of a more incremental nature to the body of knowledge. Because of our fixation on making incremental changes and citing previous work, we can only make incremental progress. Academic societies have an innate tendency to be insular and tribal in their thinking and behavior. Therefore, academics enjoy attending smaller conferences with other colleagues who share their interests. Outside of the accepted paradigm of scientific thought, enormous leaps of comprehension have been responsible for some of the most significant advances in scientific knowledge over the course of human history. Bitcoin is merely one example among many others; it is not the only one. Take, for example, the finding of the double helix, the development of the airplane, the establishment of the internet, and, more recently, the identification of the mRNA sequence necessary for the COVID-19 vaccine. The only way to make genuine headway is to unreservedly challenge the prevalent intellectual dogma and open oneself up to a whole new perspective at all times. Our teaching staff and students are held to a standard of excellence, and that standard requires them to work toward finding solutions to the most pressing issues facing humanity. This kind of discussion is stifled on campuses much too frequently, and as a result, the morale of our young people suffers over time. To do this, the funding for research should be distributed according to its impact, and the conditions should be stringent.

The tremendous increase in wealth that has resulted from the technological industry has put numerous strains on the university. To begin, it encourages young students to forgo their education in favor of launching new businesses, so mimicking the behavior of other young entrepreneurs who currently dominate the technology and financial press. This is something that can only occur because there is a disconnect between the operations of universities and the benefits offered by the market. Keep in mind that the Bitcoin protocol was developed by a tight-knit group of academics who were looking to solve an age-old problem by leveraging cutting-edge technology. This simply could have happened within the school, and in some ways, it ought to have happened there.

The natural setting for incremental innovation is the business firm, whether it is just starting out or has been there for a while. Because of the consistent cacophony of customer needs, investor demands, and industry expertise, it is a perfect venue for incremental shifts in the manufacturing possibilities available to society. It is up to the academic community to demonstrate that it is capable of rising to the challenge posed by radical innovation, which is uniquely suited to the academic setting due to its longer and more deliberate timetable, access to deep science, and isolation from the noise of the market. Let Bitcoin serve as a source of inspiration for us, so that the university might become the quarterback of the next profound invention of our time rather than just a spectator to it.

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  • dashDash (DASH) $ 42.03 0.96%
  • arweaveArweave (AR) $ 9.12 4.42%
  • basic-attention-tokenBasic Attention (BAT) $ 0.302593 1.55%
  • enjincoinEnjin Coin (ENJ) $ 0.451177 0.92%
  • kavaKava (KAVA) $ 1.52 1.8%
  • ethereum-name-serviceEthereum Name Service (ENS) $ 17.01 2.27%
  • xdce-crowd-saleXDC Network (XDC) $ 0.031449 3.02%
  • immutable-xImmutable X (IMX) $ 0.757112 2.88%
  • blockstackStacks (STX) $ 0.322852 1.28%
  • ravencoinRavencoin (RVN) $ 0.036370 2.52%
  • tether-goldTether Gold (XAUT) $ 1,697.44 0.18%
  • terra-luna-2Terra (LUNA) $ 2.50 0.09%
  • defichainDeFiChain (DFI) $ 0.690434 1.55%
  • mina-protocolMina Protocol (MINA) $ 0.586169 0.77%
  • compound-governance-tokenCompound (COMP) $ 59.17 1.42%
  • stepnSTEPN (GMT) $ 0.664835 5.64%
  • trust-wallet-tokenTrust Wallet (TWT) $ 0.955083 4.63%
  • kusamaKusama (KSM) $ 43.39 0.06%
  • amp-tokenAmp (AMP) $ 0.005048 1.12%
  • reserve-rights-tokenReserve Rights (RSR) $ 0.009042 1.77%
  • convex-financeConvex Finance (CVX) $ 5.50 0.79%
  • wavesWaves (WAVES) $ 3.79 1.38%
  • decredDecred (DCR) $ 26.28 1%
  • compound-usdtcUSDT (CUSDT) $ 0.022052 0.11%
  • nemNEM (XEM) $ 0.040840 2.01%
  • loopringLoopring (LRC) $ 0.290451 2.32%
  • celoCelo (CELO) $ 0.769433 1.13%
  • bitcoin-goldBitcoin Gold (BTG) $ 20.48 1.04%
  • holotokenHolo (HOT) $ 0.002020 0.62%
  • gmxGMX (GMX) $ 44.14 6.41%
  • ecomiECOMI (OMI) $ 0.001249 0.51%
  • curve-dao-tokenCurve DAO (CRV) $ 0.889613 3.25%
  • gemini-dollarGemini Dollar (GUSD) $ 0.997226 0.24%
  • maiar-dexMaiar DEX (MEX) $ 0.000055 1.09%
  • 1inch1inch (1INCH) $ 0.580481 0.97%
  • frax-shareFrax Share (FXS) $ 4.77 0.25%
  • theta-fuelTheta Fuel (TFUEL) $ 0.052783 0.74%
  • galaGala (GALA) $ 0.041079 0.89%
  • nxmNexus Mutual (NXM) $ 44.38 0.9%
  • oasis-networkOasis Network (ROSE) $ 0.059562 1.5%
  • gnosisGnosis (GNO) $ 115.08 1.03%
  • qtumQtum (QTUM) $ 2.84 1.97%
  • olympusOlympus (OHM) $ 10.20 0.13%
  • terrausdTerraClassicUSD (USTC) $ 0.029405 0.55%
  • serumSerum (SRM) $ 0.770766 2.92%
  • kadenaKadena (KDA) $ 1.42 1.67%
  • safemoonSafeMoon [OLD] (SAFEMOON) $ 0.00000001 3.42%
  • iostokenIOST (IOST) $ 0.011837 0.84%
  • iotexIoTeX (IOTX) $ 0.029201 0.76%
  • golemGolem (GLM) $ 0.265398 1.11%
  • huobi-btcHuobi BTC (HBTC) $ 20,141.00 1.02%
  • sushiSushi (SUSHI) $ 1.34 5.67%
  • (YFI) $ 8,197.67 2.02%
  • tensetTenset (10SET) $ 1.35 3.92%
  • msolMarinade staked SOL (MSOL) $ 35.52 2.34%
  • ankrAnkr (ANKR) $ 0.030317 2.44%
  • convex-crvConvex CRV (CVXCRV) $ 0.865276 2.34%
  • oec-tokenOKC (OKT) $ 13.91 0.15%
  • livepeerLivepeer (LPT) $ 9.32 0.75%
  • omisegoOMG Network (OMG) $ 1.69 1.65%
  • harmonyHarmony (ONE) $ 0.019299 1.88%
  • synapse-2Synapse (SYN) $ 1.26 5.19%
  • juno-networkJUNO (JUNO) $ 3.79 1.68%
  • escoin-tokenEscoin (ELG) $ 3.15 0.24%
  • zelcashFlux (FLUX) $ 0.820756 0.24%
  • justJUST (JST) $ 0.025579 0.65%
  • 0x0x (ZRX) $ 0.266784 1.16%
  • iconICON (ICX) $ 0.236986 1.09%
  • magic-internet-moneyMagic Internet Money (MIM) $ 0.995432 0.11%
  • tether-eurtEuro Tether (EURT) $ 0.977926 0.79%
  • ontologyOntology (ONT) $ 0.225625 0.85%
  • constellation-labsConstellation (DAG) $ 0.075255 9.38%
  • baby-doge-coinBaby Doge Coin (BABYDOGE) $ 0.00000000 2.74%
  • merit-circleMerit Circle (MC) $ 0.668381 1.06%
  • moonbeamMoonbeam (GLMR) $ 0.480706 3.36%
  • balancerBalancer (BAL) $ 5.21 0.04%
  • nucypherNuCypher (NU) $ 0.153473 0.31%
  • linkLINK (LN) $ 29.60 0.85%
  • waxWAX (WAXP) $ 0.085153 0.95%
  • hiveHive (HIVE) $ 0.501648 0.16%
  • optimismOptimism (OP) $ 0.843576 2.52%
  • siacoinSiacoin (SC) $ 0.003487 1.14%
  • chiaChia (XCH) $ 33.89 0.61%
  • alchemix-usdAlchemix USD (ALUSD) $ 0.998918 0.06%
  • zencashHorizen (ZEN) $ 13.70 2.97%
  • audiusAudius (AUDIO) $ 0.209632 1.94%
  • ergoErgo (ERG) $ 2.86 1.64%
  • secretSecret (SCRT) $ 0.947368 0.72%
  • liquity-usdLiquity USD (LUSD) $ 1.03 0.14%
  • songbirdSongbird (SGB) $ 0.019564 0.34%
  • safemoon-2SafeMoon (SFM) $ 0.000288 1.02%
  • dydxdYdX (DYDX) $ 1.23 1.78%
  • umaUMA (UMA) $ 2.30 0.55%
  • dao-makerDAO Maker (DAO) $ 1.34 3.45%
  • ethosVoyager VGX (VGX) $ 0.508415 4.28%
  • neutrinoNeutrino USD (USDN) $ 0.978872 0.56%