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The concept of ideas being trumped by execution in the startup world is so prevalent that there is no single adage to capture it. It is such a common idea that I could have opened with quotes from any one of: , , , Goethe, (Pony), Luckey - this eclectic list could go on indefinitely. Jobs Stravinsky Neistat Ma While the sentiment of all these great thinkers is entirely correct, it also remains true that for anything to be executed on top of. ideas are a prerequisite In an age where increasingly intelligent machines are reducing the cost and pace of execution at a rate of knots, the demand for big ideas to solve both objective (i.e scientific) and subjective (i.e humanistic) functions in our world becomes ever more apparent. In 2018, USV’s Nick Grossman and Dani Grant wrote a now canonical article titled This article broke down the cycles by which new apps tend to appear en masse more or less right off the back of the development of the infrastructural foundations necessary for those apps to exist. ‘The Myth of the Infrastructure Phase’. To use their words: “First, apps inspire infrastructure. Then that infrastructure enables new apps.” We live at a critical juncture in human history where our species is fast becoming the second best of ideas. For this reason, it is worth taking a step back from Grossman and Grant’s theory of the infrastructure stack and stripping it all the way back to ‘ideafrastructure’. producers Where the infrastructure-app cycle visualises incremental improvements in technology over time, the ideafrastructure cycle appears incremental but in practice delivers complete transformations in human output and wellbeing as each new inflection point is reached. The reason that the ideafrastructure cycle can appear incremental as in the diagram above is because of the . New idea generation is by-and-large a function of the cross-pollination of two or more pre-existing ideas. composability of ideas In reality, composability =! incrementalism. The product of two cross-pollinating ideas in our case can have entirely transformative impacts on productive capacity. As our powers of execution expand, the ambitiousness and size of the ideas needed to level up collective surplus grows to fill these new capabilities and push them further. This is a weird kind of intellectual . As we improve the efficiency by which we create new things to improve our lives, our satisfaction seems to diminish. More creative and ambitious ways to increase our expected wellbeing pop up to fill the new capabilities enabled by new technology. Jevon’s Paradox Once the capacity of the latest generation of productive assets to improve wellbeing has been exhausted, a new generation of idea takes its place and inspires new productive assets altogether (or vice versa). Intentionally or not, the curves in USV’s infrastructure framework provide an interesting way of observing this. Ideas lay the groundwork for foundational changes in innovation cycles by enhancing our productive possibilities. In turn, improved production possibilities expand the ambition and scope of our ideas to drive even more advances in production and wellbeing. Now that I have had the chance to try and explain , the practical question: how do we make more? why we want/need more ideas What constitutes an Idea? Many people have tried asserting different answers to the above question. Some of them are more assertive than others (ironically, the most assertive ideas about ideas are from some of history’s most prominent idea-benders - the ). ancient philosophers For the sake of this piece, I will follow two similar definitions that I think best fit the context of ideas as they apply to entrepreneurship. Those explanations are as follows: “An idea is a or used to or something.” concept, thought representation describe explain “An idea involves the in a novel or innovative way, leading to new insights or perspectives. synthesis of existing knowledge or concepts It can be a , a , a for a product or system, a philosophical , an artistic , or a for the future.” solution to a problem hypothesis to be tested design concept expression vision The Market for Ideas Today The would-be founder in the market for ideas is fortunate and unfortunate in equal measure. Fortunate, because they have the pleasure of competing against some of the most rigid, unprogressive institutions on the face of the earth. Unfortunately, these institutions hold significant power in the public domain and an entrenched position in the marketplace of ideas. If having a marketplace for ideas dominated by a small number of institutions sounds paradoxical, that’s because it is. IBISWorld values the market for scientific research & development (both academic and institutional) at $227.5bn annually. This covers the functions of research & discovery, peer review & validation, IP transfers, contributions to policy formation and much more. Because of the foundational role that research plays in any market (being the basis for technological development and all), its disproportionately large problems have disproportionately large flow-on effects. To name but a few: Multibillion dollar institutions like Elsevier, Harvard, thrive on a price-making ability driven through manufactured scarcity. As mentioned above, ideas are composable. By close-sourcing this knowledge, the reputed gatekeepers of today’s knowledge are kneecapping the potential growth in production possibilities cited above. Inaccessibility. The incentives for academics are largely geared such that they are remembered for their discoveries. This creates a bias for publishing positive results. Often, these positive results are actually not so positive. But who has time for verifying these when you need to be publishing your own positive results? Incentives are needed to ensure that 1) research is validated and 2) academics are rewarded for producing productive research. Publication Bias and the Replication Crisis. actually To be an expert in any field requires niching down to an extreme degree. The corpus of existing research has been so large for a long enough time that in order to ‘make it’ in academia requires devoting one’s self to a field and taking the time to learn that field inside out. Siloes. First, the time is spent learning informational precedents inside out. Only after the decades that this step takes can researchers strive to create original knowledge. By the time they’re creating this original knowledge, their existing knowledge base is often so esoteric that it is hard to apply at scale to real-world problems that tend to have problem drivers from multiple disciplines. This creates a culture of academic introspection and incrementalism. More mechanisms need to be in place to break up interdisciplinary boundaries and incentivise cross-pollination of research. Anyone who has read a research paper from an unfamiliar field knows this one too well. New knowledge, almost by definition, is esoteric. This is not helped by the silo effect described above. Without the ability to sell research through stories that people can relate to and understand, it is hard to drum up the reach needed for the idea to leave a footprint on the world. Dissemination & Storytelling. If there are so many problems, why are the incumbents still winning? . The most obvious example of economies of scale in the market for new knowledge creation is the ability of major institutions to gain access to advanced technology that is difficult to produce or distribute at scale (e.g. large Hadron colliders). Beyond being symbols of progressiveness, these are key resources for driving new discoveries and ideas as outlined in the Ideafrastructure diagram above. A second, derivative version of economies of scale comes through the exclusivity effect that gatekeepers like Elsevier or Taylor & Francis possess. Economies of Scale . Research institutions, particularly universities, have accumulated immense brand value over time. Harvard is not just a research institution. Like a few other old universities, It has evolved into a universal symbol for intelligence and achievement. This provides institutions like Harvard with both incredible reach and credence - two points that will be expanded upon below. Legacy Effects Which Ideas win? Once again, a major philosophical question is posed that is very open to interpretation. For the sake of any founders reading this Substack, I will again oversimplify it. To bastardise Justin Kan’s theory of product above: The ideas that win are those that achieve the most effective distribution with regards to 1) reach, 2) credence and 3) validity. refers to the size of the audience that engages with the idea. This is best achieved through the media platforms and KOLs that promote any given idea. For example, the idea of killer AGI. The idea of red wine being an aid for {insert most human problems} appeals to the masses and achieves reach through many a weekend magazine and lifestyle article. Reach Beyond getting the research into the right hands (i.e magazines, newspapers, Twitter KOLs, non-fiction books), a key part of achieving reach through research is via storytelling. Research around red wine works because it’s easy to digest and gets hits. Quantum physics less so. is concerned with the reputation of the institutions and KOLs that originate or promote a given idea. This most obviously applies to the reputations of research institutions and professors that originate new discoveries in research papers, but also appears in less obvious contexts. To borrow the example of red wine extending longevity used above - this argument gained a lot of credence through the empirical evidence lended to it by . Credence centenarians who attribute their drinking habits to their longevity shares a lot of crossover with credence. Validity here refers to the replicability of ideas to ensure their truth in practice. This is a key area where . The incentives for scientists today revolve around producing ideas to achieve acclaim. Validity science suffers today If protocols were to come online that take their place as the pre-eminent producers of knowledge, it is my belief that a larger portion of scientific practice can be devoted to validating the ideas of others. In designing generative knowledge creators, some of the major long-term questions that need to be asked will be around how to balance and optimise this triumvirate in order to ensure that new ideas can be distributed effectively at scale whilst also remaining true. The more replicable ideas are distributed at scale, the more validity will be optimised and the more trust will be instilled in synthetic new knowledge. The Future of Ideas Different Forms of Ideas Beofre going deeper, a breakdown of the essential ingredients for idea-generation protocols. Taking the bolded wording from the definitions used above, we can divide the idea of the idea into varying levels of actionability from a product development perspective. For the sake of the theme of ‘Requests for Startups’ as a newsletter, I intend to focus on the leftmost items on the venn diagram above to help inspire would-be ideamakers. That is not to discount the forms of ideas covered elsewhere as many of them are also necessary ingredients to the next generation of idea-creation protocols. The aim of condensing the components of what makes an idea are ultimately to help define the objective functions of future products & protocols who specialise in synthesising, judging, distributing and creating competitions for ideas. What is important in each of these potential functions of new idea-centric products? Synthesis The current methods of knowledge creation practiced in academia and corporate R&D facilities follow very straightforward processes. Hypotheses are made around potential outcomes of an experiment based on previous knowledge, tested in a laboratory setting and then validated again against the pre-existing knowledge base. This is essentially how new ideas are rubber-stamped today. The dependence of this system on previous citation, often within a single field, fosters incrementalism. What if methods for new knowledge creation occurred in a manner similar to which a neural network operates? Two fields of knowledge are thrown in as inputs, with all pre-existing research and ideas comingling with one another to develop a massive array of potential hypotheses. Humans or agents can enter a marketplace to vote on the potential impacts of any given hypothesis, with those ‘winners’ being submitted for validation and testing in laboratories. The function of ideation is outsourced to the machines, whilst humans (for the time being) are still required in the loop for the physical validation of these ideas. Competition How should a marketplace be designed to evaluate the potential future impacts of new ideas - whether human or machine generated? The most common idea to date has been the idea of prediction markets, first credited to . This is how Hanson views the application of prediction markets to science: Robin Hanson Imagine , with the going odds available to the popular media, and treated socially as the current academic consensus. a betting pool or market on most disputed science questions Imagine that academics are expected to "put up or shut up" and accompany claims with at least token bets, and that statistics are collected on how well people do. Imagine that funding agencies subsidize pools on questions of interest to them, and that research labs pay for much of their research with winnings from previous pools. And imagine that anyone could play, either to take a stand on an important issue, or to insure against technological risk. This would be an market, which I offer as an alternative to existing academic social institutions. "idea futures" Somewhat like a corn futures market, where one can bet on the future price of corn, here one bets on the future settlement of a present scientific controversy. This is admittedly an unusual suggestion. But consider what might happen. Inspired by Hanson, the protocol (disclaimer: incubated by my employer) aims to create a market for knowledge whereby researchers upload papers and vote on the possible future effects of it. This creates an incentive for replication as other researchers/financial professionals can gain a financial advantage by proving or disproving the current validity of the research. Rosetta The rewards from future research are distributed 1) to those who staked a vote on the winning side of the argument, 2) to the researchers who posited the argument and 3) to the precedent researchers cited as contributors to the researcher’s idea. Storytelling Many a non-fiction writer (see: Gladwell, Harari, Taleb) has made a killing by interweaving academic knowledge with experiences in the real-world to create digestible ideas for the layman. These are prominent literary careers built off the back of arbitraging the obnoxious tendency of research papers to be virtually incomprehensible. With the expanding context windows available in today’s LLMs, the scope for summarising and reframing research in a real-world context is becoming infinitely easier. Finding broadly applicable examples and affects to apply to once esoteric research becomes infinitely easier. We can also have markets for how research stories are told in order to elicit the greatest public interest whilst not compromising the integrity of the actual ideas themselves. Twitter operates as the largest story marketplace today - what if there were new media platforms that replaced social’s currency of likes and follows with monetary incentives for understanding and communicating new knowledge. Besides the obvious educational impacts this may have, better stories also serve the function of condensing complex research into easy-to-digest logic that can be used as inspiration for product development. Commercialisation has a strong claim to being the godfather of commercialisation theory in the startup world. His claim (reflected by Marc Andreesen ) is that any startup innovation can be traced to basic (often federal) research funding from as long as 50 years prior waiting to be productised. This means that today, there is a full and growing repository of insights waiting to be converted into entrepreneurial opportunity. Bill Janeway here With the proliferation of autonomous agents that are taking on large portions of roles in (admittedly simple) startups, how far away could we be from near instant commercialisation or commercial testing of novel ideas? already Picture this. A researcher or neural network-based hypothesis creator posits a novel idea. A storytelling protocol summarises the research and turns it into a digestible story that is matched to existing customer needs - these needs can be determined by media sentiment analysis or from some other form of intuition. From here, a human (or autonomous) manager can segment the key tasks or functions to be completed in order to find the best method for this research to be productised based on the market needs identified above. This can trigger a task execution loop spanning years or even decades to ensure the consistent growth of the product/business/software/protocol. These loops can be monitored by a single human manager overseeing its operation. Rewards can be distributed to all of those who contributed research in the training data used by the agent to breakdown the product strategy at the beginning. Requests for Startups: This concerns both knowledge made ‘organically’ by today’s scientists or hypothesised ‘synthetically’ via neural networks trained on the existing corpus of research. How can we a) allow researchers to have to vote on the potential impacts of their research. Genuine, No BS Markets for new knowledge creation. skin in the game There are volumes upon volumes of research in existence that are i) hard for entrepreneurs to understand and ii) difficult to commercialise by turning into product & marketing logic. The capabilities of LLMs even today to summarise and reframe arguments means that this unnecessary complexity can be a thing of the past. Can anyone build a living protocol that scrapes & trains on popular recent research on arXiv or other open-source research platforms, combines ideas and retells them as stories that would-be founders can turn into solutions to problems? Autonomous Storytellers. Marco Mascorro has already made some inroads with the paper summariser . What does this look like if it goes a step further and finds stories that interweave between different fields and identifies the problems they may be able to solve? arXiv GPT . What if methods for new knowledge creation occurred in a manner similar to which a neural network operates? Two fields of knowledge are thrown in as inputs, with all pre-existing research and ideas comingling with one another to develop a massive array of potential hypotheses. Humans or agents can enter a marketplace to vote on the potential impacts of any given hypothesis, with those ‘winners’ being submitted for validation and testing in laboratories. Pseudo-Laboratories Also published . here

Idea Factories — How Do we Create More and Better Ideas for Humanity

Why this “Technological Revolution” is Different Are you a “Techno-Optimist” or a “Techno-Pessimist’”? This question kept popping up in the discussion on digitization, productivity and regulation at the in Berlin on September 15, 2017. It seemed to frame much of the discussion and “divide” participants. OECD Global Forum on Productivity On the one hand, “Techno-optimists” love the current technological revolution. For “optimists”, the pace and range of innovations, as well as the shorter innovation cycles, are a source of wonder and excitement. They believe that, on balance, technology will solve many societal problems and improve our lives. Governments could and should be doing much more to promote innovation, particularly in fields such as artificial intelligence and robotics. On the other hand, are the “Techno-pessimists”. For them, the current age is not experienced with excitement, but rather trepidation and fear. They believe that the current pace of automation is a serious problem and technological developments in the field of AI and robotics could, without proper vigilance, threaten mankind. Government needs to do more to control new technologies in order to reduce our technological dependency and mitigate unforeseen risks and dangers. “Optimist or Pessimist” “Wonder or Fear” “Encourage or Control” These are the distinctions that we often use to make sense of what is happening when thinking about new technologies. Certainly, these are the distinctions that have been used in trying to come to terms with previous technological revolutions. But, speaking at the event in Berlin, made me realize that something else is going on. The difference between “techno-optimist” and “techno-pessimist” is actually irrelevant now. At least, it obscures the really important question that we all should be asking ourselves: Am I willing to participate in co-creating an automated future and how can I develop the capacities and skills to contribute to this “project”? To understand what this means and why it matters, it helps to think about how the current technological revolution is different from previous periods of technology-driven economic and social change. Technological “Revolutions” Post-1770 Consider the major technology-driven “revolutions” of the last 250 years: The , which began in the 1770s in England with the invention of a water-wheel powered spinning machine (the “spinning mule”) and led to the creation of the first textile factories. industrial revolution The invention of the in the 1820s, also in England, and the rapid growth of a railway system that facilitated rapid and cheap transportation of raw materials and finished products, as well as people. steam engine The emergence in the late 19th century of the in Germany and the US. Steel ships, transcontinental railways, preserved foods, electrical lighting and a global telegraph were all made possible by large-scale enterprises like . steel, electricity & heavy engineering General Electric The in the early 20th century was driven by the US and companies like and . Improvements in refining technology and cheap mass-produced cars meant that the car was affordable for the emerging middle class. automobile, oil & mass production Standard Oil Ford emerged in Silicon Valley in the early 1970s with the discovery of the microprocessor by and related networked technologies. Telecommunications & information technologies Intel Commentators on these technological revolutions — check out , for instance — give us a powerful framework for making sense of what happens during periods of technological-driven social and economic change. Carlota Perez In particular, there seem to be three “phases” that characterize such “revolutions”: First, there is the “Installation phase”. A new technology is discovered, developed and scaled. During this phase, the core applications of the technology are established and introduced. And , there is the “Deployment phase” when there is a large-scale implementation and integration of that technology, throughout the economy and society. third But, what is really interesting is what happens “Installation” and “Deployment”, i.e., the second phase. between All previous technological revolutions seem to have experienced a “Crisis phase” around 20–30 years after “Installation”. Expectations quickly outstrip the capacities of a new technology creating a “bubble” that, at some point “bursts”, promoting a painful process of “collapse and re-adjustment”. Carlota Perez Technological Surge Cycle Venture capitalist, Chris Dixon, summarizes this process as follows: “Each revolution begins with a financial bubble that propels the (irrationally) rapid installation of the new technology. Then there’s a crash, followed by a recovery and then a long period of productive growth as the new technology is “deployed” throughout other industries as well as society more broadly. Eventually the revolution runs its course and a new technological revolution begins.” _Technological revolutions happen in two main phases: the installation phase and the deployment phase. Here's a chart…_cdixon.org The computing deployment phase In all previous technological revolutions, “government and business leaders working together” played a crucial role in helping society move from “Crisis” to “Deployment”. i.e., market failure triggered government “regulation”. State-driven intervention involved a reactive process of “fact-finding” (gathering all relevant information/evidence regarding a new technology), “understanding” (identifying and evaluating the risks) and, finally, “regulation” (the imposition of a new legal framework that would control/regulate how a new technology would be deployed). In this way, a “Crisis” provided a vital opportunity for state-managed learning about, and understanding of, new technology. Technologies could then be disseminated in a controlled and responsible way. If we apply this framework to the present technological revolution, we will soon enter the deployment phase of the telecommunication and IT revolution. _History doesn't exactly repeat itself, but it does run in cycles. One of the most robust theories of such cycles was…_www.strategy-business.com Are We on the Verge of a New Golden Age? Think about it. The installation phase of the latest revolution started in 1971, followed by the crash of 2000 and the 2008 Financial Crisis. These crises could be seen as the turning point. dot.com They should be followed by a process of state-managed “readjustment”, which in turn would result in deployment and “new ways of living”, “new ways of producing”, “new ways of communicating” and “new ways of working”. But . . . What if Things are Different this Time? However, there are good reasons to think that things might be different this time. We live in an age of multiple, overlapping technological revolutions With the exponential growth of new technologies, such as artificial intelligence, blockchain, Big Data, robotics and synthetic biology, it seems counter-intuitive to argue that we are still in the computer and Internet era. The age of the computer and the Internet is already over. At least, in the sense that deployment has already occurred. Instead, we have entered the “installation phase” of the next revolution, which could be called the “automation era”, as it involves a combination of new technologies. We now live in an age of simultaneous and highly complex innovations across diverse fields. “Understanding” complex new technologies is impossible The speed, range and persistence of technological innovation means that any kind of reactive learning and management is extremely difficult, if not impossible. A paradox of recent new technologies is that they make our lives easier, but they make the world harder — perhaps even, impossible — to understand. We live in a world of radical uncertainty, in which all we know is that there are many things that we do not know. As soon as we believe that we have a clear understanding in one context, a new development has already occurred that renders that understanding obsolete. But more than that, “understanding” of complex man-made systems is now beyond human comprehension and it is a nostalgic fantasy to believe that we can still achieve that kind of understanding. For the first time in history, we live in a world where human technologies appear to be beyond human understanding. National governments and business can no longer deliver controlled “deployment” The companies that were driving the information and telecommunication revolution are already being replaced by “younger” companies that are taking the technological evolution to the next level. Government can no longer rely on partnering with established corporations when those very incumbents are facing constant challenge from innovators. Moreover, new and upcoming technologies are less centred around a specific place (innovations are emerging everywhere), which makes it much more difficult for business leaders and governments to develop a coordinated and controlled deployment. national The result? The “deployment” of new technology is much less controlled than in previous technological revolutions. Consumers are more and more in the driver’s seat Consumers also play a much more important role in the acceptance and dissemination of the technologies. Business leaders and governments are no longer in the driver’s seat. They no longer have the capacities to lead the process of readjustment and deployment that has previously occurred. Deployment is no longer (mainly) a “top down” process, but “bottom up”. Listening to the discussion in Berlin made me realize that the “technological revolution theory” needs to be revisited. At least, as it applies to the present / future. How Should We Deal with Constant Technological Change? Well, for a start, we should not wait for a crisis and then try to “figure out” what’s happening. “Reacting” is no longer an option. Governments Governments need to shift from a focus on “understanding” the risks and then designing a framework for the controlled deployment of technology to active participation in the project of co-creating the future in partnership with other stakeholders. In order to do this, it becomes necessary to have “touch points” (sensors) society, that facilitate constant dialogue and collaboration with innovator-entrepreneurs. in The emergence of new regulatory models around “regulatory sandboxes” and “regulatory testbeds” shows that (at least some) governments understand this need for a more proactive and dynamic role. Clearly, these regulators believe that pre-emptive dialogue and collaboration can enable / facilitate the process of designing better regulatory solutions. Business Leaders The business leaders of today’s most successful companies also seem to get it. They are spending more on innovation, setting up innovation labs and focusing on becoming more agile. There is still a lot of theatre and window-dressing, but the “winners” have been able to transform their companies into “open and inclusive ecosystems”. In particular, they have created more engaged and fluid relationships with start-ups (often acquiring them, but letting them keep their own identity and culture). The most successful companies also have a more open relationship with consumers. Even B2B businesses understand the need for much greater engagement with the end-consumers. And, For the Rest of Us? Well, everybody needs to ask themselves: Am I willing to participate in this project of co-creating the future together? What can I contribute and how can I build the skills to make a more meaningful contribution that will provide me with greater personal satisfaction? The really important distinction is not between the “optimists” and “pessimists”, but between those who chose to ignore what is happening, those who still try to understand and those who seek to actively participate. But, it still amazes me how few people are actually engaged in, or even thinking about, this project of “co-creating the future”. When you engage with the writers and readers on it appears that we are all part of the discussion. But it surprises me how few colleagues, students (including millennials), etc. are aware of what’s happening (particularly the challenges) and the possibilities that are now available. Medium Education plays a crucial role. Everybody needs to be better equipped to somehow participate in the “process of co-creating the future”. The focus should be on creativity, design, speed, agility, choice, freedom and responsibility. These are the values of the present and the future, and more needs to be done to give people the skills to operate in this flatter and more open world. Back to Berlin . . . In this new increasingly automated world, it doesn’t matter whether you are a “techno-optimist” or a “techno-pessimist”. Neither option seems to make much sense when we can no longer understand or control technological developments in the way that was possible in the nineteenth and twentieth centuries. In this new world — where the only real choice is whether to participate in the project of co-creating the future or not — everyone needs to be asking themselves how they want to respond to this challenge, and what they might do to improve their skills and maximize opportunities for personal fulfilment. These seem like much better and more important questions. 👏 Thank you for reading! Please click the (which can be clicked as many times as you want) below, or leave a comment. There is a new story every week. So if you follow me you won’t miss my latest insights about how the digital age is changing the way we live and work.

“Co-Creating” an Automated Future Together

How to Prepare the Next Generation for the Uncertain Things to Come As a university professor and director of an international program, the summer has always been the best time to reflect on the content of the program and my courses. I regularly update my teaching materials to keep them relevant for students. Teaching new issues also keeps me fresh and helps with my research and other writing. As a teacher/educator, I have an obligation to ensure that students are given the latest insights and trends. But, this year, something feels different. I really believe that a more fundamental change is necessary. “Updates” are no longer enough. Instead, what is needed is a whole new approach to education. Why? My sense of “dissatisfaction” with the current state of university education has everything to do with the exponential growth of technology. As I’ve written about before, we are experiencing a “ ”. digitization of reality This is the result of the global proliferation of new technologies. We all now live in a “digital world” that is characterized by fast-paced, technology-driven social change. The future will be full of tremendous opportunities, but it will also be a world of tremendous uncertainty. Such uncertainty creates a huge challenge for educators. With the current pace of innovation and shorter innovation cycles, it seems obvious that new technologies are going to continue to transform every aspect of how we live and work. Constant technological disruption is the new normal. “Old world” concepts, models, paradigms and ideas will no longer be relevant. So, should we be teaching our students today? what What? Teaching has always tended to be “backward-looking”. Transmitting the settled knowledge of the past has been the starting point for our whole approach to education. For instance, in the field of law, students have traditionally analyzed existing laws, regulations and cases. The idea has been that if you understand historical developments, you would be able to solve future problems by applying old doctrines and precedents to the new situation. A similar logic can be seen in other fields. MBA programs, for example, employ the same approach in a business context. The responsibility of the educator was to transmit this information / content. In a world of information asymmetries, the educator-student relationship was, by necessity, a hierarchical one. After all, the teacher had all the knowledge. But, this model seems ill-suited in a world of constant change. If the future is radically different from the present, it doesn’t make much sense to focus on content that seems likely to be irrelevant. Moreover, the ready availability of information means that that the informational advantage of the teacher is of much less significance. The result? Education needs to become much more “forward-looking” and skills-based. How then do we prepare the next generation for dealing with unknown future problems? This is the question that we need to be asking. “Updating” the content of our programs or courses is really no longer enough. Technology For a start, everyone is going to need a much better theoretical understanding of the technologies surrounding computers, communication networks, artificial intelligence and big data. For many of us, the underlying technologies that are driving social change remain a mystery and that is a problem. Practical technical knowledge also needs to be integrated into many fields of education. Coding and data analytics seem a good starting point. But, we also need to think about other skills and capacities that are important in a world of unprecedented change. The focus should be on building skills that will assist the next generation in making the right decisions under conditions of extreme uncertainty. Here are some suggestions of the kind of issues that I believe are important: #1 — Creative Thinking The next generation has to be able to think fast and “out of the box”. Dynamic analysis of complex situations and the ability to communicate solutions, in presentations or in video form, will be key. #2 — Entrepreneurship In the future, we will see more open and looser organizations and social platforms. It is therefore important that the next generation finds ways to become more productive and self-motivating, i.e., how to operate without a “boss”/supervisor telling them what to do. As traditional concepts of a “career” become much less relevant, it will become increasingly important to build a personal brand by telling the right kind of story. #3 — Teamwork More open organizations mean having to work in teams of strangers, often from diverse national or disciplinary backgrounds. The ability to work in a team, constantly adapting to new situations and working patterns, becomes crucial. #4 — Ethics Many of the problems of the future will be ethically complex. This seems particularly true in the context of robotics and artificial intelligence. _The robot revolution is gaining pace, but is it running in line with our values? Here are some of the main ethical…_www.weforum.org Top 9 ethical issues in artificial intelligence Yet, all new technologies raise difficult ethical issues. Building the capacity of students to think about ethics seems another way that teachers can add value. #5 — Interdisciplinary learning Finally, we need to be open to interdisciplinary and multidisciplinary study, however strange it might initially seem. For example — and this is just a personal opinion — I think that a greater knowledge of biology can help prepare the younger generation for the challenges of the future. Partly, this reflects my own preference for biology metaphors for understanding recent changes in the business world. I have written elsewhere on . I believe that metaphors involving the “environment” and “evolution” are similarly helpful. as an open and inclusive “ecosystem” Tesla But, this also reflects my belief that the next big wave of innovation is likely to be in the field of biology, and that knowledge of the field will be at a premium. Of course, I could be wrong about this. But the basic thought that exposure to multiple perspectives can only help in preparing the younger generation for an uncertain future, is surely correct. How? This brings us to the final questions: should we teach the next generation? How What teaching methods do we need to employ to be more effective as educators in the digital world? Of course, this is not a new question. Educators have always reflected on how to improve their performance. There is a lot of discussion on this issue right now. But too much of the current debate seems overly simplistic. In particular, the current focus on “distance learning” or “online teaching” seems particularly problematic. The idea that putting everything online makes everything OK seems a little naïve. Of course, it might give certain groups access to information that they otherwise wouldn’t have and that is obviously a good thing. But, we also need to be aware of the risks of such an approach. In particular, it preserves the traditional teacher-student hierarchy and focus on content. Instead, we need to be creating flatter, more inclusive learning environments — “Labs” — in which students are challenged to be more creative and entrepreneurial. Students must be forced to work in teams and think about possible scenarios with the associated challenges and solutions. In this way, the capacities relevant for a digital age can be cultivated. Student Expectations But, perhaps the strongest argument for changing our approach to education is the expectations and demands of young people today. It seems obvious to me that the “next” generation expects something different from education. The traditional approach simply bores them. They just switch off. The temptation to play with their phone — or simply to skip class altogether — is just too strong. “Why should I go to class if I can get the same (or even better) information online?” After all, the university entrants of today were born into a digital world. They belong to a culture that has no memory of a pre-Internet age. They are totally immersed in a digital culture and all its relatively “effortless” possibilities. The risk for universities is that if they don’t adapt to this new reality, they will go the way of the “dinosaurs”. Lumbering giants ill-suited to a different and fast-changing environment. ❤️ 👏 Thank you for reading! Please click the or below, or leave a comment. There is a new story every week. So if you follow me you won’t miss my latest insights about how the digital age is changing the way we live and work.

Education in a Digital Age

97 Stories To Learn About Quality Assurance

The contemporary discourse on generative Artificial Intelligence (AI) presents a nuanced and dualistic narrative of technological development. I will delve into contrasting perspectives surrounding the current state and future of generative AI, juxtaposing the pervasive sentiments of disillusionment against the sustained advancements in the field. Notably, despite apparent stagnation in some sectors and declining public interest, significant strides in AI technology and applications continue to emerge. This analysis provides a nuanced examination of the cycle of hype, stabilization, and potential revolutionary impact of generative AI, comparing it with historical technological disruptions such as the printing press, electricity, and the internet. Through a balanced review of both setbacks and breakthroughs in the AI landscape, I aim to discern whether generative AI is merely experiencing a temporary lull or if it signifies a broader technological revolution. By critically assessing the evidence and forecasting future trends, the article also explores the implications of this technology on societal norms and industry practices, ultimately questioning the revolutionary potential of generative AI amidst ongoing challenges and evolving expectations. Economic Performance and Market Valuations Recent data suggest that the economic outcomes related to generative AI have not aligned with prior expectations. For instance, despite significant investments in hardware, such as the $50 billion spent on Nvidia equipment, the return, in terms of revenue, has been relatively modest, amounting to only $3 billion (The Wall Street Journal). Additionally, there is a notable stagnation in visits to AI-focused websites (Exponential View), and venture capital firm Sequoia has highlighted the overvaluation of AI startups, suggesting a disconnect between market valuations and underlying economic realities (The Information). Industry Challenges and Strategic Adjustments Several high-profile AI startups have experienced difficulties, signaling potential instability within the sector. For example, InflectionAI has been effectively disbanded, with Microsoft acquiring its assets, including the expertise of former CEO Mustafa Suleyman (TechCrunch). Moreover, StabilityAI faces uncertain prospects following leadership controversies (Forbes). Additionally, the broader enterprise adoption of AI technologies is hampered by concerns over security and ethical implications (Diginomica). Public Perception and Technological Engagement While the initial public enthusiasm for AI technologies like ChatGPT has waned, a subset of users, particularly software developers, continue to find value in these tools (Pew Research Center). This contrasts with the broader perception that AI tools serve more as novelties rather than practical aids in productivity (The Register). Innovative Developments and Future Prospects Despite these challenges, the development of AI technologies continues at a rapid pace. New models like Claude 3 from Anthropic are setting benchmarks in AI capabilities (Anthropic). The field also anticipates the arrival of advanced models such as OpenAI's GPT-5, signaling ongoing innovation and potential market recovery (Business Insider). Historical Perspective on Generative AI Development Tyler Cowen, in his analysis, proposes that the current state of AI reflects a natural progression seen in other revolutionary technologies. Drawing parallels to the adoption of the printing press, electricity, and the internet, Cowen suggests that the lifecycle of transformative technologies typically transitions through phases of hype, stabilization, and eventual widespread utility. This pattern, he argues, is evident in the current trajectory of generative AI technologies (Bloomberg). Cowen articulates that while the initial excitement surrounding generative AI has diminished—a phase characterized by inflated expectations and speculative investments—the underlying advancements in technology continue to progress significantly. This 'silent lull' is not a regression but a recalibration of expectations as the technology matures and integrates more deeply into various sectors. Generative AI, despite facing skepticism regarding its immediate impacts, mirrors the developmental paths of its historical counterparts. Notably, the internet, post-dot-com bubble, and the gradual integration of electricity into industrial processes both experienced periods of underwhelming performance followed by significant, long-term contributions to economic and social structures. Recent developments in AI technology further support Cowen's thesis. OpenAI's enhancement of enterprise services and the advancement of models like Google's GPT-4 competitor illustrate ongoing innovation within the field (ZDNet, Google). Moreover, the rapid progression of open-source AI models, although less visible to the casual user, underscores a robust, underlying growth in AI capabilities that continues to push the boundaries of what these technologies can achieve (Archive). The Path Forward Despite the optimism expressed by some industry leaders, there are notable challenges and setbacks that temper the enthusiastic projections about AI. Issues such as the slower-than-anticipated adoption rates in enterprises and the mixed results of new AI products like Google Gemini Advanced highlight the complex reality facing AI development (Diginomica, The Algorithmic Bridge). The question remains whether generative AI will indeed follow the transformative paths of its predecessors or diverge due to unique challenges and societal dynamics. The industry's ability to navigate these challenges, coupled with ongoing technical advancements, will likely determine the trajectory of AI's impact on society. Concluding Thoughts Generative AI's journey is marked by cycles of hype and recalibration, similar to earlier technologies. While immediate outcomes may seem underwhelming, the continued development and integration of AI into various sectors may very well underpin the next wave of significant technological and societal transformations. As with historical innovations, the true measure of AI's impact will likely be observed in hindsight, shaped by a complex interplay of technological capabilities, market forces, societal acceptance, and regulatory environments. References "A Peter Thiel-backed AI startup seeks $2 billion valuation" - The Wall Street Journal. Available at: https://www.wsj.com/tech/ai/a-peter-thiel-backed-ai-startup-cognition-labs-seeks-2-billion-valuation-998fa39d
"Growth and investment in AI technology" - Exponential View. Available at: https://substack.com/@exponentialview/note/c-52677620
"AI startups overvalued" - The Information. Available at: https://www.theinformation.com/articles/ai-agenda-openai-is-a-relative-steal
"Inflection AI dismantled" - TechCrunch. Available at: https://techcrunch.com/2023/06/29/inflection-ai-lands-1-3b-investment-to-build-more-personal-ai/
"Stability AI's uncertain future" - Forbes. Available at: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/
Tyler Cowen on AI's ongoing revolution despite subdued hype - Bloomberg
OpenAI introduces business version of ChatGPT - ZDNet
Challenges in AI adoption within enterprises - Diginomica
Google's GPT-4 and its marketplace challenges - The Algorithmic Bridge The contemporary discourse on generative Artificial Intelligence (AI) presents a nuanced and dualistic narrative of technological development. I will delve into contrasting perspectives surrounding the current state and future of generative AI, juxtaposing the pervasive sentiments of disillusionment against the sustained advancements in the field. Notably, despite apparent stagnation in some sectors and declining public interest, significant strides in AI technology and applications continue to emerge. This analysis provides a nuanced examination of the cycle of hype, stabilization, and potential revolutionary impact of generative AI, comparing it with historical technological disruptions such as the printing press, electricity, and the internet. Through a balanced review of both setbacks and breakthroughs in the AI landscape, I aim to discern whether generative AI is merely experiencing a temporary lull or if it signifies a broader technological revolution. By critically assessing the evidence and forecasting future trends, the article also explores the implications of this technology on societal norms and industry practices, ultimately questioning the revolutionary potential of generative AI amidst ongoing challenges and evolving expectations. Economic Performance and Market Valuations Economic Performance and Market Valuations Recent data suggest that the economic outcomes related to generative AI have not aligned with prior expectations. For instance, despite significant investments in hardware, such as the $50 billion spent on Nvidia equipment, the return, in terms of revenue, has been relatively modest, amounting to only $3 billion ( The Wall Street Journal ). Additionally, there is a notable stagnation in visits to AI-focused websites ( Exponential View ), and venture capital firm Sequoia has highlighted the overvaluation of AI startups, suggesting a disconnect between market valuations and underlying economic realities ( The Information ). The Wall Street Journal Exponential View The Information Industry Challenges and Strategic Adjustments Industry Challenges and Strategic Adjustments Several high-profile AI startups have experienced difficulties, signaling potential instability within the sector. For example, InflectionAI has been effectively disbanded, with Microsoft acquiring its assets, including the expertise of former CEO Mustafa Suleyman ( TechCrunch ). Moreover, StabilityAI faces uncertain prospects following leadership controversies ( Forbes ). Additionally, the broader enterprise adoption of AI technologies is hampered by concerns over security and ethical implications ( Diginomica ). TechCrunch Forbes Diginomica Public Perception and Technological Engagement Public Perception and Technological Engagement While the initial public enthusiasm for AI technologies like ChatGPT has waned, a subset of users, particularly software developers, continue to find value in these tools ( Pew Research Center ). This contrasts with the broader perception that AI tools serve more as novelties rather than practical aids in productivity ( The Register ). Pew Research Center The Register Innovative Developments and Future Prospects Innovative Developments and Future Prospects Despite these challenges, the development of AI technologies continues at a rapid pace. New models like Claude 3 from Anthropic are setting benchmarks in AI capabilities ( Anthropic ). The field also anticipates the arrival of advanced models such as OpenAI's GPT-5, signaling ongoing innovation and potential market recovery ( Business Insider ). Anthropic Business Insider Historical Perspective on Generative AI Development Tyler Cowen, in his analysis, proposes that the current state of AI reflects a natural progression seen in other revolutionary technologies. Drawing parallels to the adoption of the printing press, electricity, and the internet, Cowen suggests that the lifecycle of transformative technologies typically transitions through phases of hype, stabilization, and eventual widespread utility. This pattern, he argues, is evident in the current trajectory of generative AI technologies ( Bloomberg ). Bloomberg Cowen articulates that while the initial excitement surrounding generative AI has diminished—a phase characterized by inflated expectations and speculative investments—the underlying advancements in technology continue to progress significantly. This 'silent lull' is not a regression but a recalibration of expectations as the technology matures and integrates more deeply into various sectors. Generative AI, despite facing skepticism regarding its immediate impacts, mirrors the developmental paths of its historical counterparts. Notably, the internet, post-dot-com bubble, and the gradual integration of electricity into industrial processes both experienced periods of underwhelming performance followed by significant, long-term contributions to economic and social structures. Recent developments in AI technology further support Cowen's thesis. OpenAI's enhancement of enterprise services and the advancement of models like Google's GPT-4 competitor illustrate ongoing innovation within the field ( ZDNet , Google ). Moreover, the rapid progression of open-source AI models, although less visible to the casual user, underscores a robust, underlying growth in AI capabilities that continues to push the boundaries of what these technologies can achieve ( Archive ). ZDNet Google Archive The Path Forward The Path Forward Despite the optimism expressed by some industry leaders, there are notable challenges and setbacks that temper the enthusiastic projections about AI. Issues such as the slower-than-anticipated adoption rates in enterprises and the mixed results of new AI products like Google Gemini Advanced highlight the complex reality facing AI development ( Diginomica , The Algorithmic Bridge ). Diginomica The Algorithmic Bridge The question remains whether generative AI will indeed follow the transformative paths of its predecessors or diverge due to unique challenges and societal dynamics. The industry's ability to navigate these challenges, coupled with ongoing technical advancements, will likely determine the trajectory of AI's impact on society. Concluding Thoughts Concluding Thoughts Generative AI's journey is marked by cycles of hype and recalibration, similar to earlier technologies. While immediate outcomes may seem underwhelming, the continued development and integration of AI into various sectors may very well underpin the next wave of significant technological and societal transformations. As with historical innovations, the true measure of AI's impact will likely be observed in hindsight, shaped by a complex interplay of technological capabilities, market forces, societal acceptance, and regulatory environments. References "A Peter Thiel-backed AI startup seeks $2 billion valuation" - The Wall Street Journal. Available at: https://www.wsj.com/tech/ai/a-peter-thiel-backed-ai-startup-cognition-labs-seeks-2-billion-valuation-998fa39d "Growth and investment in AI technology" - Exponential View. Available at: https://substack.com/@exponentialview/note/c-52677620 "AI startups overvalued" - The Information. Available at: https://www.theinformation.com/articles/ai-agenda-openai-is-a-relative-steal "Inflection AI dismantled" - TechCrunch. Available at: https://techcrunch.com/2023/06/29/inflection-ai-lands-1-3b-investment-to-build-more-personal-ai/ "Stability AI's uncertain future" - Forbes. Available at: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ Tyler Cowen on AI's ongoing revolution despite subdued hype - Bloomberg OpenAI introduces business version of ChatGPT - ZDNet Challenges in AI adoption within enterprises - Diginomica Google's GPT-4 and its marketplace challenges - The Algorithmic Bridge "A Peter Thiel-backed AI startup seeks $2 billion valuation" - The Wall Street Journal. Available at: https://www.wsj.com/tech/ai/a-peter-thiel-backed-ai-startup-cognition-labs-seeks-2-billion-valuation-998fa39d https://www.wsj.com/tech/ai/a-peter-thiel-backed-ai-startup-cognition-labs-seeks-2-billion-valuation-998fa39d "Growth and investment in AI technology" - Exponential View. Available at: https://substack.com/@exponentialview/note/c-52677620 https://substack.com/@exponentialview/note/c-52677620 "AI startups overvalued" - The Information. Available at: https://www.theinformation.com/articles/ai-agenda-openai-is-a-relative-steal https://www.theinformation.com/articles/ai-agenda-openai-is-a-relative-steal "Inflection AI dismantled" - TechCrunch. Available at: https://techcrunch.com/2023/06/29/inflection-ai-lands-1-3b-investment-to-build-more-personal-ai/ https://techcrunch.com/2023/06/29/inflection-ai-lands-1-3b-investment-to-build-more-personal-ai/ "Stability AI's uncertain future" - Forbes. Available at: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ Tyler Cowen on AI's ongoing revolution despite subdued hype - Bloomberg Bloomberg OpenAI introduces business version of ChatGPT - ZDNet ZDNet Challenges in AI adoption within enterprises - Diginomica Diginomica Google's GPT-4 and its marketplace challenges - The Algorithmic Bridge The Algorithmic Bridge

Generative AI in 2024: Navigating the Evolution from Hype to Transformation

2024 সালে জেনারেটিভ এআই: হাইপ থেকে রূপান্তরে বিবর্তন নেভিগেট করা

Generative KI im Jahr 2024: Vom Hype zur Transformation

IA generativa en 2024: navegando por la evolución del entusiasmo a la transformación

L'IA générative en 2024 : naviguer dans l'évolution du battage médiatique à la transformation

2024 में जनरेटिव एआई: प्रचार से परिवर्तन तक विकास की यात्रा

2024 年の生成 AI: 誇大宣伝から変革への進化をナビゲート

2024년의 생성적 AI: 과대 광고에서 혁신으로의 진화 탐색

IA generativa em 2024: navegando na evolução do hype à transformação

Генеративный ИИ в 2024 году: путь эволюции от хайпа к трансформации

2024'te Üretken Yapay Zeka: Heyecandan Dönüşüme Evrimi Yönlendirmek

AI sáng tạo vào năm 2024: Điều hướng quá trình phát triển từ cường điệu đến chuyển đổi

2024 年的生成式人工智能：从炒作到转型

While the United States continued its reactionary posture toward the crypto space throughout 2023, last year saw many countries take more hardline approaches toward industry oversight and enforcement. The European Union formally its long-awaited Markets in Crypto-Assets legislation, or MiCA, which set in motion the implementation of a legal framework for key dimensions of the crypto ecosystem. Much like the workplace safety standards that helped correct cruelties of the , MiCA’s standardized language establishes ecosystem conduct with the aim of increasing stability and participant trust. passed Industrial Revolution In similar moves, Hong Kong, South Korea, and Singapore all passed sweeping policies to define how cryptocurrency companies can conduct business in their regions. In Hong Kong, a went live in June 2023 that imposed financial and regulatory restrictions on exchanges, while greenlighting a limited menu of services for retail customers. While some critics bemoaned the high application costs as a for aspiring companies, the new process would officially legalize basic retail trading. In turn, greater due diligence was implemented for builders, business, and tokens alike, requiring background checks and clean bills of financial health to qualify. Prior to this decision, Hong Kong-based participants relied on unregulated platforms to access the digital economy, often at their own . new licensing regime barrier to entry peril Also in June, South Korea accelerated its oversight of the crypto space by passing that combined 19 pending bills aimed at ramping up participant protections. The Act on the Protection of Virtual Asset Users tightened restrictions for virtual asset service providers’ (VASPs) handling of customer funds and information and mandated oversight through regular reporting. Rules for “self-issued virtual assets” appear in response to the shell game accounting practices that caused countless participants hardship. Perhaps more importantly, the law confers power on the Financial Services Commission (FSC) to enforce these regulations through imprisonment, sanctions, and fines for bad actors. After successful passage, these policy changes are now set to take effect in July 2024. omnibus legislation Later in August, the Monetary Authority of Singapore (MAS) announced of its legal framework for stablecoins, and how they can interact with the traditional economy. In addition to clearly defining stablecoins, the body outlined its regulatory reach as pertaining to “single-currency stablecoins (SCS) pegged to the Singapore Dollar or any G10 currency, that are issued in Singapore.” This allowed the MAS to set for SCS transactions and redemption that placed the onus of compliance on token issuers while establishing clear mechanisms for enforcement. By dispelling another legal gray area, retail and institutional participants alike can transact with greater certainty and peace of mind after . a finalized version broader rules high-profile collapses Back in London, the Financial Conduct Authority (FCA) is cracking down on crypto companies in an effort to root out scams and potential fraud. In addition to protecting consumers by maintaining the integrity of financial markets, the FCA holds service providers accountable for adhering to regional standards. While the regulator imposed a host of requirements to ensure crypto companies are operating above-board, many of the latest policy updates have concerned messaging. Centered primarily around the of crypto products and services, the FCA’s intersectional guidelines propose a unique topographical environment that appears to disqualify the bulk of common industry practice. This includes the use of promotional incentives, affiliate program activities, and “refer a friend” bonuses, all of which must pass review by an . marketing approved third-party It’s amidst this climate that the U.S. opened the floodgates to Bitcoin ETFs, as outlined above, despite maintaining an overall standoffish posture toward the rest of the ecosystem. The institutional of SEC precedent, after causing uncertainty and litigation, now appears to be setting the stage for greater cross-pollination between digital assets and financial behemoths. Those playing close attention to the of asset managers and regulators leading up to the approval saw how raw financial will can influence the wheels of bureaucracy. However, it’s crucial to note that the embrace of specific financial instruments, in the case of Bitcoin ETFs, does little to resolve the absence of legal foundations necessary to adequately regulate the crypto ecosystem. flip-flopping choreography From a bird’s eye view, this realignment toward greater regulatory policy is a dominant, worldwide trend. According to a by TRM Labs examining “21 jurisdictions representing approximately 70% of global crypto exposure,” governments are beginning to turn a critical eye toward the digital economy. The crypto analytics firm discovered that “[i]n 2023, 80% of these jurisdictions moved to tighten crypto regulations, and almost half specifically progressed consumer protection measures.” If these percentages denoted the chance of precipitation, it would be foolish to not carry an umbrella. In light of such a seismic change in climate, industry leaders are being left with little choice but to start dressing for the weather. Looking ahead in 2024, TRM Labs drew up key dates and landmark decisions to illustrate how the regulatory landscape will continue to evolve throughout the year. While an element of uncertainty remains in the air due numerous election cycles, it should be clear that crypto is becoming more regulated across major jurisdictions. report While this timeline highlights the electoral vulnerabilities that could serve to undermine efforts toward regulation, industry leaders must embrace and support these measures. To that end, it’s also crucial to question those who see any interference by the government as a detriment to innovation. There’s a growing movement in tech circles for that encourage developers to reach further in their renderings of products and systems. This attitude will be essential going forward as crypto platforms decide how to operate in the increasingly defined world of digital asset regulation. As more markets have clear rules and penalties in place, companies will be forced to tailor their services by region or comply with the steepest guidelines platform-wide. Barring a step backward to legal gray zones and black swan events that could (and should) have been prevented, these are the paths forward for ethical crypto providers. generative regulations Better yet, these provisions should be celebrated as concrete solutions to the rampant mismanagement of customer funds and outright fraud that’s occurred in recent years. Most jurisdictions currently exploring heightened regulations have experienced the implosion of a homespun crypto hero, and are not in any rush to re-live such trauma. That’s why many of these legislative packages set their sights on common sense measures that demarcate official oversight and lean heavily on consumer protections. It’s at this crossroads that crypto leaders could choose to send a strong message by working with officials to ensure a smooth transition. Rather than just asserting user-centric values in mission statements, the industry should embrace and be receptive to regulation if it means protecting and growing the global crypto community. As we’ve seen, words can only go so far to remedy actions. Therefore, leaders must refrain from pushing empty platitudes, and instead focus on concrete ways to boost participant confidence. These changes are inevitable, and the crypto ecosystem would be wise to anticipate and work to accommodate their implementation. Undoubtedly, there will be some that choose to leapfrog expectations, forever seeking the next regulatory lillipad amenable to their unique situation. Where fringe services may continue to operate outside official oversight, retail exchange is gaining legal architecture at a rate that will outpace even the nimblest of entities. With each legislative victory portending greater control in an official capacity, eventually, there’ll be nowhere left to hop. But then again, it’s a tough sell to lobby for customer trust after fleeing regulation to the point of running out of road. To avoid such a humbling and exhaustive process, companies should strive to ensure their services meet all jurisdictional requirements, or risk the consequences.

In 2024, Crypto Regulation is Key to Boosting Participant Confidence

In an era where the blockchain revolution is reshaping the digital landscape, it takes a visionary to not only grasp its complexities but also to harness its potential. Enter Stelian Balta, the billionaire investor and strategic mind behind Hyperchain Capital, known for his profound insights and successful ventures in the blockchain domain. In this exclusive Hackernoon "Behind the Startup" interview, we dive deep into the psyche of a man who has become synonymous with innovation in blockchain technology. Get ready to embark on a thought-provoking journey with one of the leading minds in the blockchain investment industry. Stelian Balta Reveals the Keys to Success in Blockchain Investing: Insights from the Mastermind Behind HyperChain Capital Ishan Pandey: Hi Stelian, great to have you here for our "Behind the Startup" series. What inspired you to enter the blockchain industry, and how has your journey evolved over the years? As the founder of HyperChain Capital, can you share the vision behind its creation and the specific goals the fund aims to achieve in the blockchain investment landscape? Further, can you tell us more about your investment thesis for the web3 industry? Hi, it's fantastic to join your "Behind the Startup" series. My journey into the blockchain industry was motivated by my early entrepreneurial ventures and a deep fascination with digital assets. At 16, I began my entrepreneurial path by designing online games, leading to the creation of my first company during my 1st year in college. This early exposure to virtual goods and digital assets in gaming laid the foundation for my interest in blockchain technology. Stelian Balta: My formal entry into the blockchain space began around 2013 when I started developing trade engines for crypto exchanges. This step not only allowed me to invest in Bitcoin but also gave me a unique insight into the world of digital currencies. The decision to transition from the gaming industry to blockchain felt natural, especially with my background in creating and monetizing virtual goods. Throughout the years, I've focused on investing in people and innovative ideas, particularly in the blockchain and crypto space. Our investments have not just been financial but also strategic, providing guidance and support to entrepreneurs from the idea stage to execution. This approach has led me to be involved in numerous projects, including significant investments in Ethereum, Fantom, Cosmos, block.one, Polkadot, among others. Ishan Pandey: Given your extensive experience, what trends do you currently observe in the web3 industry, and how do these trends shape your investment strategies? There is a notable trend in both Layer 1 and Layer 2 networks, with a growing emphasis on building developer-centric ecosystems to increase activity and fee generation. Stelian Balta: Among these, Fantom differentiates itself with novel methods of support such as their recent Sonic Labs accelerator program, which offers mentorships from DeFi legends such as Andre Cronje, capital funding and more. The other drawing factor of Fantom is their GasM program, which shares network revenue with its developers and provides regular bonuses for the top performing dApps on the network, just recently allocating over $150,000 in rewards. These approaches align with our investment philosophy, as they nurture innovation and foster a more robust and dynamic blockchain ecosystem. Ishan Pandey: How does HyperChain Capital differentiate itself in the highly competitive blockchain investment space, and what principles guide your investment decisions? Our investment decisions are guided by a philosophy of doing good. We have a deep commitment to supporting innovation and technological advancement. Our focus has always been on long-term and high-potential projects. For example, our early investments in Ethereum, Cosmos, Fantom, and many more, starting from 2016, 2017, and 2018 respectively, prove that we’re dedicated to sticking with this long-term strategy. Stelian Balta: Our investment strategy is not just about financial gains; it's about contributing positively to the development of the blockchain industry. We focus on a range of verticals such as Layer 0-3 blockchain technologies, DeFi, Metaverse, and gaming, with a particular interest in the Fantom ecosystem. Our involvement with Fantom, for instance, is driven by a shared vision and philosophy, and we're excited about its potential having recently launched the Sonic Labs accelerator program and is anticipated to introduce the Sonic tech stack upgrade this year. Ishan Pandey: What factors do you consider most crucial when evaluating blockchain projects for potential investment, and how has this philosophy evolved over time? At HyperChain Capital, our aim is to be at the forefront of blockchain innovation, supporting projects and technologies that drive the industry forward. Our investment decisions are guided by a philosophy of doing good, a commitment to long-term growth, and a focus on innovation. Stelian Balta: Ishan Pandey: What problems do you see early-stage web3 founders committing and how can they avoid them? In the rapidly evolving world of Web3, early-stage founders often encounter a range of challenges. One of the key issues is the tendency to focus on short-term goals, or the current "cycle," rather than establishing a vision for the next 10 years. This short-term perspective can limit the potential of their projects, making it difficult to achieve lasting impact and success. I think founders should develop a broader vision, setting ambitious but achievable long-term goals that drive their projects forward. Stelian Balta: Another common problem for early-stage Web3 founders is inadequate financial planning, particularly in terms of securing a sufficient runway. Many founders underestimate the amount of funding required to sustain their projects over a longer period. Having a financial runway for at least 5-10 years is crucial. This extended runway allows projects to navigate through market cycles and continue building and growing without the constant pressure of fundraising. An example of effective financial planning is the Fantom Foundation, which reportedly has a runway for up to 40 years. This kind of long-term financial stability is exemplary and should be a goal for other Web3 projects, as it ensures that they have the resources to continue their development and achieve their long-term objectives. Moreover, alignment between founders and investors is crucial. Long term and serious investors are typically looking for commitments that align with their vision and long-term goals. They prefer to invest in projects where the founders are committed full-time and share a common vision for the future. This alignment ensures that both parties are working towards the same objectives. Ishan Pandey: What advice would you give to aspiring entrepreneurs looking to build a lasting business in the web3 economy? Web3 economy it's a highly dynamic space influenced by many factors. I believe the key to navigating this is continuous innovation. There's a lot of potential for new and exciting products, and fresh minds are constantly bringing in new ideas and technologies. For entrepreneurs, this means there's always room for creativity and innovation. Stelian Balta: I think it’s important to get comfortable with failures and to be persistent. The blockchain industry is at the early stage of a decentralized global financial system, and there's much to build. Reading extensively, working hard, and having a long-term perspective are crucial for success in this space. In my experience, with all its ups and downs, I've come to understand how important it is to work really hard, always be keen to learn new things, and have a clear vision in mind. This way of doing things sounds easy, but it actually needs a lot of effort and a humble attitude. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR

Cyces Innovation Labs

cyces.co

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Since 2018

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Cyces Innovation Labs

cyces.co

11-50 employees

Since 2018

COMPANY RANKING

CYCES INNOVATION LABS

EVERGREEN INDEX #3481

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