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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

The continued fallout of the Covid-19 pandemic and widespread economic downturns may have punctuated much of the early 2020s, but thanks to innovations in the world of , we may soon be set for the unlikely return of the ‘Roaring 20s’. generative AI At least, these are the thoughts of leading economist Ed Yardeni, of Yardeni Research, who to subscribers that generative AI "may be the event that re-launches the Roaring 2020s." wrote in a May newsletter It may be hard to anticipate the emergence of a Wall Street boom period given the economic backdrop that investors have been contending with. As inflation rates continue to cause issues in the cost of living for global populations, the prospect of a recession still lingers. Despite this, it’s important to note that indexes like the S&P 500 have been remarkably quick to recapture their significant losses in the wake of 2022’s tech stock sell-offs. In 2023 alone, we’ve seen the S&P 500 grow more than 18% off the back of innovations in generative AI and their ability to capture the imagination of investors. Could the cautious optimism delivered by generative AI so far in 2023 be sustainable? And is it possible for investors to buy into the market despite significant trading volumes entering the market? Let’s take a deeper look at the AI phenomenon and how it may develop: Generative AI Gears up for a Strong Second-Quarter The S&P 500 isn’t the only tech-heavy index that’s experienced a strong resurgence amidst 2023’s generative AI boom. The Nasdaq composite has grown since the beginning of the year and is showing little signs of abating. almost 35% “Heading into the second half of 2023, we see a much broader tech rally ahead as investors further digest the ramifications of this $800bn AI spending wave on the horizon and what this means for the software, chip, hardware, and tech ecosystem over the next year,” , managing director at Wedbush Securities. explained Dan Ives “We view this as a ‘1995 internet moment’... not a ‘1999 dot bubble moment’. We estimate for 2024 that AI could comprise up to 8% to 10% of overall IT budgets v 1% in 2023,” Ives added. This strongly indicates that the generative AI boom that we witnessed in early 2023 is just the beginning, and that seismic price rallies are sustainable enough to transcend the perception that they exist in a bubble. Crucially, leading tech firms like , , and are working to realize the potential of this surge in investor interest in AI. Microsoft, for instance, has introduced a $30 subscription service focused on uniting generative AI with Microsoft 365 services. Microsoft Alphabet Adobe The general feeling among financial experts is that generative AI could also be the tide that raises all boats. Maxim Manturov, head of investment advice at Freedom Finance Europe, suggests that may even be capable of reviving interest in ailing metaverse projects. generative AI “Coupled with AI, the metaverse could become a major force in the coming years. It could revolutionize the way we learn, work, and play,” Manturov notes. “However, it's important to remember that the meta is still in the early stages of development. It is too early to say for sure what the future holds for the metaverse.” So, how can investors embrace the age of generative AI? Are leading AI-facing stocks overvalued in comparison to small-cap opportunities? While it’s impossible to anticipate which firms will come out of the generative AI boom thriving, it’s certainly worth exploring small-cap options with the potential to grow exponentially should the ‘Roaring 20s’ truly come to fruition. Notable small-cap AI stocks include: 1. UiPath (NYSE: PATH) When looking at UiPath’s 2023 performance, you’d be forgiven for thinking that it’s another example of a wildly successful generative AI stock. However, zooming out a little further shows that the stock is still over 78% underwater and steadily recapturing ground based on the intelligent adoption of new AI technologies. Recently, UiPath announced an and tools offering more support for Amazon’s Falcon Large Language Model (LLM), OpenAI, Azure OpenAI, and Google’s Vertex connector. expanded suite of generative AI technologies These prospects, coupled with UiPath’s relatively modest market cap of just under $10 billion illustrate a forward-focused firm with plenty of room for growth. 2. Domo Inc. (NASDAQ: DOMO) With a market capitalization of around $600 million, Domo is certainly a stock that’s yet to be fully swept away by the generative AI force. Operating as a cloud software company delivering business intelligence tools and data visualization solutions, Domo’s AI Service Layer helps to simplify the process of managing, deploying, and enhancing AI and machine learning models, as well as Large Language Models (LLMs) for data experiences. Offering generative AI and natural language processing for text generation and SQL queries to audio transcribing and text translating, Domo may prove to be a leading resource in B2B generative AI services. 3. Lemonade Inc. (NYSE: LMND) Lemonade is a digital insurance company that utilizes AI and behavioral economics to offer a vast range of insurance services for customers in the US. In a May shareholder letter, the firm announced that it anticipates leveraging generative AI to enhance . With strong roots in artificial intelligence services, Lemonade’s $1.5 billion market cap may have room for growth as economic headwinds weaken. as many as 100 of its business processes For investors looking for small-cap market opportunities in generative AI, sufficient research must be conducted before adding a new firm as a long hold. The market is still in its fledgling phase, and new challengers are likely to emerge at a rapid pace. With this in mind, it’s fair to expect volatility throughout generative AI stocks over the coming years. However, likely, the stocks that emerge as market leaders in their respective sectors will see significant growth as the AI boom continues to win new supporters.

Could Wall Street’s ‘Roaring 20s’ be Powered by Undervalued Generative AI Stocks?

ওয়াল স্ট্রিটের 'ররিং 20' কি কম মূল্যহীন জেনারেটিভ এআই স্টক দ্বারা চালিত হতে পারে?

Könnten die „Roaring 20s“ der Wall Street durch unterbewertete generative KI-Aktien angetrieben werden?

¿Podrían los 'Roaring 20s' de Wall Street estar impulsados por acciones generativas de IA infravaloradas?

Les "Roaring 20s" de Wall Street pourraient-elles être alimentées par des actions d'IA générative sous-évaluées ?

क्या वॉल स्ट्रीट का 'रोअरिंग 20' अंडरवैल्यूड जेनरेटिव एआई स्टॉक्स द्वारा संचालित हो सकता है?

ウォール街の「激動の20年代」は、過小評価されている生成型AI株によって支えられる可能性があるだろうか？

월스트리트의 '광란의 20대'는 저평가된 생성 AI 주식에 의해 구동될 수 있습니까?

Os '20 anos loucos' de Wall Street poderiam ser impulsionados por ações geradoras de IA subvalorizadas?

Wall Street'in 'Kükreyen 20'leri', Değeri Düşük Üretken Yapay Zeka Hisse Senetlerinden Güç Alabilir mi?

'Những năm 20 bùng nổ' của Phố Wall có thể được hỗ trợ bởi các cổ phiếu AI sáng tạo bị định giá thấp không?

华尔街的“咆哮的20年代”能否由被低估的生成型人工智能股票推动？

The beginning of the 21st century brought about an exciting change in the internet. It was the freedom of blogging to express opinions and views and share information. The whole process was interesting as it was a step in a different direction from the traditional legacy media. For once, anyone with access to the internet could write something and share—Facebook, Twitter, MySpace, and similar platforms became a voice for the general public. A decade later, it turns out that the new media wasn't any different, as users still had no control. Most of the platforms were still plagued, belonging to specific corporations with a centralized structure, and control of social media platforms like Twitter, Facebook over user data (Cough* Cambridge Analytica), and more… The condition of the legacy and new media is pitiful, and people have started to distrust them looking for an alternative that gives freedom and is decentralized. So, how can decentralized media build a fairer stage? Before you find the answer, let's look at current media problems and the potential technologies that solve them. Problems Censorship The usual definition of censorship is suppressing information in the form of words, images, or ideas that are offensive, obscene, or a threat. One might think it's necessary and keeps unsocial elements in check. And you wouldn't be wrong. However, stifling dissent and diversity through censorship is a problem. In 2021, that were critical of the Indian government and questioned their handling of the pandemic in the country. It wasn't the first time something like this had happened. Twitter deleted more than 50 tweets In 2020, on coronavirus reported by media outlets like USA Today, Buzzfeed, NY Post, The Independent, and more, and defended itself saying the actions were taken as precautionary measures to combat misinformation. Facebook deleted posts Incidents like these show how vulnerable and powerless people are with someone else in control and that any fact you state is just a few clicks away from being censored. Bias Bias is another largely prevalent problem in today's media. We have seen both online and electronic media leaning towards one side. Fox News, for example, has been observed to run news in favor of the Republicans. The media often overlooks high-quality content to favor shallow sensationalism to drive engagement. Filtering is vital to weed out irrelevant and harmful content, but it is too narrow and results in a Filter Bubble. Facebook, Twitter, and Google's algorithm selectively predict a user's interest based on their previous clicks, search history, and location. This tracking leads to users being presented with content that agrees with their viewpoints, separating them from critical thinking on topics they might agree or disagree with. Controlled According to the , media freedom is on a downward trend throughout the globe, and this decline has been directly linked to a global decline in democracy. Freedomhouse report In its report, Freedomhouse says. " While the threats to global media freedom are real and concerning in their own right, their impact on the state of democracy is what makes them truly dangerous." The Internet media has been dramatically affected by the authoritarian regime and big corporations that support the authorities. We have previously seen social media succumb to the demand of the governments. This trend has resulted in the rise of extremism that does the authorities' job for them, be it mob justice in one place and vandalism in others. Getting unbiased information is now more complicated, and freedom of expression has become a threat, leading to persecution. Almost every information that comes our way is controlled. Surveillance Have you ever felt like your smartphone is listening to you? You talk about sneakers, and the next moment you open Facebook, Instagram, or a website, you see sneaker ads. Big companies often carry out this constant surveillance that uses specific algorithms for targeted advertisement. By extracting data from users, they incentivize their clients and themselves. This data extraction allows these companies to gather as much information on users as possible by whatever means they find. Cookies on websites, click and search history, posts you interact with, all these actions you make online get surveilled, and the collected data get sent to these companies for personalized targeting. For example, a common person can be influenced by targeted ads on repeat, changing their narratives and perception. In that case, the technology can very well be used to manipulate various decisive events, for example, probable rigging of elections somewhere. If it is true, then this puts the integrity of the whole democratic process in question. So, how do we take back control? How can we make media free and decentralized? Well, looks like the solution is out there. Solution Blockchain Blockchain is a revolutionary technology that distributes power among people, and no single entity can control the whole network alone. Blockchain can promote community/user participation in decision-making, promoting transparency and fairness equally. The media as a project can be governed by the community members or supervisors chosen by the members through a democratic voting process. One can also bring a DAO (Decentralized Autonomous Organization) like organizational framework into play. As it uses a smart contract, maintaining an influence-free operation of the project/magazine/news outlet becomes easier and more trustworthy. A community of members/users can be assigned content-producing tasks and give them rewards in the form of project tokens or anything of monetary value. AI Artificial Intelligence is one of the greatest and probably the only tools to fight misinformation and information bias. AI can help reduce subjective interpretation of data collected from humans using machine learning algorithms trained to consider variables that enhance AIs' predictive ability and accuracy. Unlike humans, an AI's decision can be further explored, investigated, and even overlooked. AI can also help in filtering and tagging content based on the selected personal preferences of the users, minimizing the possibility of a filter bubble. AI can also detect fake news faster and with more accuracy. One can argue that with AI hype at its peak and numerous AI-driven tools like Deep Fake, content generators, image generators, and more, it is increasingly becoming harder and harder to detect true from false. The fake content creation systems keep evolving as the detection system improves, making detecting fakes much more difficult. This is the reason why we haven't reached 100% accuracy yet. But we will discuss some potential ways around this later in this article. Monetization/Incentivization/Compensating Contributors A reward is the biggest motivation, and most media miss out on it. A handful of platforms give content creators and contributors freedom to monetize their content; however, most of them feed on user content to make a big profit. Incentivizing and allowing content creators to monetize their content encourages them to produce quality content. Since the community/users will be the king, the best content will have more interaction, leading to better popularity and better income opportunities through collaboration or earning tokens and more. How Can Media Utilize Blockchain and AI to Maintain Fairness? No Censorship Big corporations often control media, and many people create and impose rules. This control leads to censorship, suppressing freedom of speech. Although censorship helps balance what's good and damaging to society, the concept stands against free speech. Freedom of speech can also be debated. For example, hate speech on social media. Someone has the absolute freedom to say whatever they want, which is unacceptable because it is wrong. But what's also wrong is when the whole power of censoring lies within a body or an institution; how do we know the process is fair or even just? That is where blockchain can contribute. By allowing the community to participate, elect moderators, and rate content based on a rating or merit system, you can keep the good of censorship and avoid the bad. Decentralized A decentralized media means no central entity can control it. Free media often grows better when there's no one controlling it. The whole point of new media was to give people a voice without anyone having any say, but that seems long gone. Undoubtedly, decentralized media gives users complete control over their content, personal data, and privacy. No single individual can create arbitrary rules. Here the community votes on the rules, and the whole process is democratic. Merit/Reputation System Reputation point is one of the ways media can be made fair. Reputation systems are algorithms that allow AI to rate users based on their activity or interaction on the platform to build credibility. It can be understood with a Reddit reference where users upvote or downvote posts based on the quality. An open to all platform is sure to attract a lot of subpar contributions, and monitoring and moderating tons of contributions can be extremely difficult for a team. By trusting the users and allowing them to rate content, the media finds an extra helping hand in filtering the content. Good content will indeed receive better reputation points, while lousy content will either be downvoted or receive no upvotes. The reputation system can further be tied with the tokenization of the project, rewarding content creators monetarily. The higher a user's reputation point, the higher the reward. This combination can encourage users to rate content critically, creating a moderation wall of filters. AI-Based Recommendation Systems One of the biggest problems and criticism the current media faces is tracking user activity and recommending content based on the collected data. You may eliminate a few ads using ad blockers, but there's no going around cookies; this creates a filter bubble and limits users from gaining a new point of view that might change their perception. An AI-driven recommendation system can be helpful that not just recommends what users like but also much more. Quality Control With Rewards Rolling out good quality content and maintaining the flow is a tough job. Usually, it takes a lot of time and work, which can sometimes be demotivating. That's where the community of users comes in. When users interact with content, they rate it, and their ratings help them figure out which content is good and which is not. So, creating a reputation and reward system creates a circle of healthy contributions. Users will rate good content, which will reward content creators. It will push more content creators to produce quality content since they'll know producing good stuff is the only way to earn more reputation points. Users interacting with content also get rewarded for filtering, encouraging them to keep up the interaction for more rewards. The whole process soon creates a quality standard for content, weeding out the bad contributions. Projects That Are Redefining How Traditional Media Works Mindplex Mindplex is an incubated project by SingularityNET. It is a complete multimedia platform that uses AI tools like a Reputation system and recommendation engine to keep the platform fair and open. The reputation system helps Mindplex maintain high-quality interactions with the content, awarding users with reputation points for spending time-consuming content and interacting with content in Likes, Comments, and Shares. This project has three different aspects: Mindplex Magazine, Mindplex Podcast, and Mindplex Content Factory. The magazine is where Mindplex editors and other esteemed writers write and contribute content. Mindplex Podcast explores different verticals with experts and hosts Pubcasts where users can ask questions to hosts or guests. Mindplex Content Factory is a decentralized structure running on blockchain where users can interact with other users, request content from them in exchange for tokens or some monetary payment, post content themselves, and do much more. SteemIt SteemIt is a decentralized blogging and social media platform application (dApp) based on SteemIt blockchain technology. It is an open to all platform that rewards users in its native token STEEM for creating content. SteemIt is also popular in China as Medium, the blogging platform, is blocked in the country; hence, more users flock to SteemIt to publish their content. Steemit is unlike your traditional social media platform, where you post about anything. Since there is a monetary reward system on Steemit, valuable content is given more importance. Users are selective and usually interact with good-quality content, rewarding content creators. Another crucial factor is token holders. Users holding more STEEM Power have more impact on upvotes or downvotes. Mastodon Mastodon is a free and open-source media platform unlike any other. It lets users create self-hosted social networking servers. These servers are independent and are often based on different topics, interests, or themes. Anyone can create or join an existing server to engage with posts such as likes, comments, share, and more, follow other users, create posts on things, and do whatever other social media platform lets them do. Many people called Mastodon a rip-off of Twitter, but it is far from being a copy. First thing, it is decentralized compared to the centralized structure of Twitter. On Mastodon, you won't get your account deleted by Mastodon officials for any reason; on Twitter, you might. Mastodon is made up of thousands of servers independent of any official body. Users can get their accounts moderated, silenced, or frozen by the server's admin. Conclusion: Can AI and Blockchain Help Shape a Better Internet Media Landscape? AI and blockchain have pros and cons, and we've only started seeing their prowess in many fields, including medic/social media. More AI and blockchain-based platforms are popping up, each better than the competitors. We can only expect to see improved versions as these technologies improve. One day, we may have one or more platforms free of censorship, institutional control, surveillance, and covetousness. Till then, we will ride the wave, testing and enjoying what comes our way.

The Promising Future of Internet Media With AI and Blockchain

The initial launch of ChatGPT just one year ago paved the way for a generative AI boom that’s continued to captivate a vast array of industries around the world. With more firms seeking to embrace this next generation of , some market commentators are gaining a sense of . AI déjà vu This widespread clamoring to draws parallels to the dotcom boom at the turn of the 21st Century. With ChatGPT users recently dropping for the , should we be wary of a bubble that could be destined to burst? embrace generative AI third consecutive month "I think this will be the biggest bubble of all time," , CEO of Stability AI. "I call it the 'dot AI' bubble, and it hasn't even started yet." warned Emad Mostaque \Mostaque’s views were echoed by James Penny, Chief Investment Officer of TAM Asset Management, in a recent Bloomberg interview. " " . Companies that even mention the word AI in their earnings are seeing boosts to their share price, and that smells very much like the dot-com era, Penny explained Generative AI has certainly become the word of the year in terms of emerging technology, and stocks that possess varying forms of AI exposure have experienced significant growth throughout 2023. One of the biggest beneficiaries of the rise of generative AI on Wall Street has been NVIDIA, which recently attained a market capitalization of , recording almost 200% growth throughout the first three quarters of 2023 alone. over $1 trillion “The company is growing rapidly and is well-positioned to benefit from the development of artificial intelligence (AI), metaverse, and other new technologies,” explains Maxim Manturov, head of investment research at . Freedom Finance Europe “ ” Manturov added. Factors such as generative artificial intelligence and the metaverse may provide NVIDIA with a FAANG level of popularity. Generative artificial intelligence is a rapidly growing field that has the potential to revolutionize many industries, As well as its position as a market leader in providing the hardware that can power generative AI services, NVIDIA also moved quickly in embracing the technology. In March 2023, the company providing businesses with the resources to ‘build refine and operate custom large language models and generative AI models.’ announced a set of cloud services So, should NVIDIA and Wall Street’s other key players fear an AI bubble that’s destined to burst? Evidence suggests that the rise of generative AI could be powerful enough to transcend the hype of the dot-com bubble. Tangible Signs of Growth While the dotcom boom saw hype swell to the point where stock values became no longer sustainable, there’s much about the excitement for AI that appears to be far more tangible. Crucially, circumstances surrounding tech markets are fundamentally different from the turn of the century, and many Wall Street firms have been reacting to widespread investor sell-offs throughout 2022, punctuated by high inflation and rising interest rates. As Financial Times columnist , “ ” John Plender notes applying a higher discount rate to distant future cash flows in the tech sector shrank the present value of those cash flows. This year’s bounce, far from being driven by central banks, reflects something real. The potential of generative AI could transform how the economy works on a fundamental level, which can see significant and positive change throughout a vast range of industries that transcends the potential of the dotcom boom some 25 years ago, with more . people investing in it According to investment advisory firm , comparisons between the current market and the dotcom boom ignore the underlying data points that can indicate that a bubble is emerging. Roundhill Investments The firm’s analysis of key factors that can indicate a bubble, such as the emergence of irrational exuberance, high valuation, weak stock fundamentals, growing IPO volumes, and the rise of baseless market narratives suggests that a bubble for generative AI is not currently in play. Although this doesn’t necessarily mean that generative AI is immune to becoming a bubble in the future, we’re seeing many signs of tangible growth and industry adoption that point to a technology that has the power to disrupt the vast majority of sectors. The Rise and Rise of Generative AI Use Cases The clearest sign that isn’t in a bubble can be found in its rapidly growing set of use cases across different industries. generative AI Recently, system software firm Workday announced that it will be launching its own AI marketplace in the first half of 2024, with partners like BetterUp, Paradox, Hiredscore, Techwolf, Accenture, and Amazon already signed up. “ ” , Workday’s co-founder and co-CEO. The Workday AI Marketplace will enable our customers to harness the incredible amount of AI innovation happening today while at the same time give them the peace of mind in knowing that each solution is proven, trusted, and meets Workday’s stringent standards for responsible AI, said Aneel Bhusri “ ” Combining the power of the Workday platform with some of the world’s most innovative AI native companies will give our customers a running start in building for the future. Just two days prior to Workday’s announcement came a similar news story from another software firm, SAP. Rather than an AI marketplace, SAP opted to utilize generative AI to develop a Joule, natural-language co-pilot that’s designed to provide rich contextualized insights for businesses. “ ” . “Joule delivers on SAP’s proven track record of revolutionary technology that drives real results.” By quickly sorting through and contextualizing data from multiple systems to surface smarter insights, Joule helps people get work done faster and drive better business outcomes in a secure, compliant way, explained a SAP press release Generative AI has even provided fresh opportunities in the world of social media, offering and generative AI stickers on Meta’s messaging apps that have the power to . intelligent image editing on Instagram create images based on textual cues These generative AI use cases illustrate that the technology isn’t just helping businesses modernize their processes; it’s showing that AI is adaptable enough to provide bespoke solutions to companies regardless of their sector or USP. It’s this flexibility and adaptability that makes generative AI a technological proposition without precedence. Finding Sustainability in an Ageing Workforce In a into the emergence of AI technology, it’s suggested that the adoption of AI has come at an opportune time in nations like the United Kingdom, which has recorded an increasingly older workforce since 2020, with 10-year projections pointing to a growing trend. recent Barclays report Although there is plenty of fear among workers that generative AI may soon become intelligent enough to replace them entirely, it’s worth considering that artificial intelligence can be a sustainable solution in maintaining productivity for businesses that are seeing more employees reach retirement age without a large enough talent pool to recruit a replacement. This makes AI’s rising use cases not only transformative but essential for businesses worldwide seeking productivity enhancements. AI’s Big Bang While falling numbers of ChatGPT users are worth monitoring, it’s more likely a sign of a maturing AI market. For many months, ChatGPT was the star of the generative AI boom, and its ability to generate richly detailed and accurate content from simple prompts provided a crucial novelty factor among curious users. Now, is beginning to permeate the world around us less than a year on from ChatGPT’s initial launch, this new technological frontier is less of a novelty and more of a necessity for growth. as generative AI With more varied and bespoke use cases emerging on quite literally a daily basis, the generative AI boom is shaping up to be more of a big bang and is more likely to complement future technological developments like 5G and the metaverse rather than be replaced by them. While the danger of market hype can always lead to inflated prices, it’s clear that generative AI is a rare transformative technology that’s likely to change the world. With this in mind, the available evidence points to more of a boom than a bust.

Backing the Boom: Why the Rise of Generative AI is Nothing Like the Dotcom Bubble

Respaldando el auge: por qué el auge de la IA generativa no se parece en nada a la burbuja de las puntocom

Soutenir le boom : pourquoi l’essor de l’IA générative n’a rien à voir avec la bulle Internet

बूम का समर्थन: क्यों जनरेटिव एआई का उदय डॉटकॉम बबल जैसा कुछ नहीं है

ブームを後押しする: 生成 AI の台頭がドットコム バブルのようなものではない理由

Apoiando o boom: por que a ascensão da IA generativa não se parece em nada com a bolha pontocom

Ủng hộ sự bùng nổ: Tại sao sự trỗi dậy của AI sáng tạo không giống bong bóng Dotcom

支持繁荣：为什么生成式人工智能的兴起与互联网泡沫完全不同

A healthy imagination has always been a trait sported by human civilization. This potential for imagination is ever more magnified when it's projected toward conceptualizing an advanced future. Popular media of the Sci-Fi orientation only adds to this effect by making most of us imagine a world full of flying cars, android doctors, and interstellar travel. While a lot of these imagined scenarios invariably play out to become starkly different realities, a small proportion are rooted in relatively tangible developments in the present. Among these is the innate human fear of replacement by machines and automated algorithms. While the anthropological origins of these anxieties might lay in humanity’s tribalistic past, it has come to embody economic and identitarian conflicts in the post-information age era of human society. Among these, special emphasis is given to professions and skills that are considered innately human—either because of the element of critical thought, empathy, or intuitive decision-making approaches. Teaching and education are but some of the most identifiable with these exact attributes. However, with the arrival of the prodigal ChatGPT and a slew of other language model chatbots, the uniquely human claim to education has been put into question. Although there has been a palpable increase in the investment to create newer, better, and coherent AI systems, the development of education-centric AIs has not quite picked up as much as one would have thought. So, are experts and academicians jumping the gun on this one? Maybe. While on one end doomsday predictions surrounding a complete AI takeover of the classroom abound, the other extreme seems far too dismissive and complacent about AI in education. The truth, like always, is somewhere in the middle. Is AI in Education Even Practical? The arrival of easily accessible generative AI opened up a Pandora's box. AI’s effects on the general populace—especially school and university students—took the world by storm in the late months of 2022 and in the opening weeks of 2023. Most news forums were busy discussing the rising usage of chatbots and other generative AI platforms to complete assignments and automate essential course-oriented exercises. This even prompted in nations like France to ban LLM-based AI chatbots in early 2023. The ban was especially targeted at student misuse of and other generative algorithms for supplanting their efforts in academics. major universities ChatGPT Though this is evidently an ethical issue—one that’s been brought up rather frequently since AI presence began growing—it also presents a segue into the deeper aspects of integrating AI into an academic framework. While the ethical conundrums formed the bulwark of the pushback against the initial advances of the AI bubble, there also exist other nuanced concerns with the possibility of fully integrating AI with current academic infrastructures. According to a 2022 report published by , premade AI solutions can cost anywhere between $5,000 to $50,000, if not more. Now, that’s a steep monthly bill for most schools and universities. LinkedIn Most analyses often leave out the financial component of running, maintaining, and upgrading AI systems in an organizational format. Considering AI becomes important enough to academics that educational institutions actively begin considering dedicated AI deployment and support for their students, the financials become an inalienable part of the process. Since most educational institutions deploy anywhere between basic to intermediate tech frameworks and support systems, it’s safe to assume that these establishments might prefer subscribing to ready-made AI solutions. According to a 2022 report published by , premade AI solutions can cost anywhere between $5,000 to $50,000, if not more. Now, that’s a steep monthly bill for most schools and universities. According to another , the upkeep for maintaining a dedicated team catering to artificial intelligence needs might exceed $320,000 in just technology development, without taking into account overheads and other costs. Affordability, at least for the time being, is not a part of the recent AI boom and subsequent popularization. AI seems like a rather expensive affair even if academics actively consider it for their students. LinkedIn cost breakdown for AI Comparing Traditional Education and Artificial Intelligence Apart from the depressing calculations from accounting, it’s equally important to assess whether AI is even a good fit for extant education systems. While the trend of associating AI’s adaptability with students’ idiosyncratic needs has become popular, it must be stated that most systemic changes are brought about by large groups and not individuals or smaller clusters of students. Traditional education approaches are by no means dynamic and humans have gone from using slate tiles to write on and learn during the dawn of civilization to submitting their assignments with a click on their laptops. Although the methods and practices of traditional education systems might have transformed throughout human history, the central elements of human interaction, creativity, critical thinking, and independent reasoning have remained. have shown that a creative learning environment can greatly enhance student outcomes and help build an overall innovative thought process in students. This innate characteristic of traditional education despite the percolation of modern digital technologies over the last three decades has remained more or less intact. Studies Regardless of speculative predictions, it has become increasingly evident that AI, too, is prone to its set of pitfalls and biases, and is limited by its determining factors like datasets and training protocols. However, that might not necessarily be the case with the entry of artificial intelligence in the classroom and the larger academic landscape. While it may be true that AI can bring a refreshing departure from the one-size-fits-all approach of most curriculums with , any reduction in human interaction and a social environment might result in impacted learning outcomes. Now that AI has garnered a sort of critical mass with just about everyone talking about a looming AI takeover, better senses must prevail and assess compatibility before any major revamps to curriculums and educational frameworks are undertaken. adaptive learning Regardless of speculative predictions, it has become increasingly evident that AI, too, is prone to its set of pitfalls and biases, and is limited by its determining factors like datasets and training protocols. Clearly, humans are still in the early stages of AI development, even by their own yardstick of tangible predictions about the domain. Any hasty integration of such mechanisms into the current academic syllabi might just kick up more pandemonium. Will AI Take Over the Classroom? Artificial intelligence in education is no new phenomenon. Its prevalence in rather mundane aspects such as administration and planning has already existed to a rudimentary capacity for some time now. However, technologies like AI-influenced student support services and learning solutions are still clearly a pipedream in the long and arduous process of developing capable artificial intelligence algorithms. Despite the uptick in the generalized usage of generative AI by students across different educational levels, this indulgence with a new piece of technology is more of an offshoot than the core purpose in and of itself. Since these chatbots and their underlying language models are technological demonstrations, there clearly remains a greater scope that might take longer to actualize. Bearing in mind the negative of learning sans human touch, socializing will remain central to the educational process regardless of whether or not we achieve superior AI capabilities. There’s no denying that AI will percolate into extant systems to cover more than the mundane, but the replacement of a human teacher might just remain a figment locked away in the many speculations of our collective human consciousness. short-term outcomes

Will Teachers Dream of Electric Sheep?

Marvin Steinberg is a renowned German serial entrepreneur who, after dominating the German energy startup sector, has made an entry into blockchain technology, with a goal at mainstreaming STO.  Today, Marvin Steinberg will be sharing his thoughts on blockchain technology, startups, and security tokens, and offering advice to entrepreneurs interested in venturing into security tokens and STOs. The success story of gives us a true definition of “resilience” and the “die-hard” attitude of an ardent entrepreneur. Once primed to be a Ping Pong champion, Marvin suffered a knee injury that pushed an abrupt stop to his sports career. Unwilling to let fate limit his rise, Marvin ventured into the German energy sector and pulled the industry into the modern age of digital sales. He eventually built his own energy startup db swpro, which was sold to an American energy conglomerate in a multi-million-dollar deal. Marvin Steinberg Now, has his eyes on the beginning of a new journey involving the pioneering footsteps of security tokens and STOs. Marvin Steinberg is the founder of CPI Technologies. Marvin Steingberg is a trend-setter that blends rock-solid software solutions with data-driven marketing to create highly profitable financial products, crypto exchanges, and security token (STOs) offerings. The company is on the verge of launching a $700M STO, comprised of asset-backed tokens that will give investors across the world exposure to some of the most prime real estate at Times Square. CPI Technologies Mr. Steinberg, the energy sector has a very high entry barrier. How did you manage to break in such a competitive sector with just a startup? Innovation drives opportunity. My startup entered the energy sector with a key differentiation: while every other German energy company ignored the rising tide of the internet and kept their customer engagement largely offline, I led market penetration and customer support through online services, making my company’s supply more accessible. Now, the larger energy companies have finally adopt the web, but there was plenty of time for smaller ventures to break in to this otherwise highly competitive sector by simply offering their energy distribution services through online channels. Mr. Steinberg, how could it be that other entrepreneurs did not seize the grasp to offer energy distribution through online channels? Others certainly tried to enter the market too. Most sectors are large enough to easily support many enterprises, let alone several startups. Even at a startup level, though, it is important to maintain reasonable and useful differentiation from the competition. The key to my marketing engine was my own “affiliate army,” numbering thousands of people. While traditional businesses relied on field salesforce going door to door, I noticed the web was making way for a new field needing a new form of salesforces. I created a digital affiliate army, enabling users to benefit from sourcing online conversion. I knew as a startup, my own capacity for sales would be limited if all of them were sourced directly from my own efforts. Thus, I let anyone be a part of the sales growth.  Of course, before I came along, there were affiliate opportunities, but I was the first to combine affiliate opportunities with online conversions, at least in the German energy sector. This created enormous leverage for me and my team. The key is to constantly look for an edge, no matter how small it may seem, it could be the factor tipping you ahead of the competition. It is not shocking that this quickly broke every sales record, producing more than 5,000 new contracts per month and millions of dollars of extra revenue compared to the traditional sales tactics that were prominent in the German energy sector. highly efficient sales force Your time in the German energy sector involved many successes. Why sell your company and then create CPI Technologies? Well, I was given a very nice offer my company which allowed me to hold some stake in its future success without me having to directly manage it. So not only was the sale very lucrative, it also enabled me to explore new opportunities. As an innovator and entrepreneur, I was hungry for new challenges. I was one of the first German entrepreneurs to take advantage of the internet so I looked into new technologies. AI, IoT, and blockchain floated across my mind was and I felt that blockchain technology seemed as the most disruptive of the three. I wanted to be part of the revolution of how we handle data and carry out businesses. You have become a very successful businessman, but do you ever look back and wish you were able to pursue the life of a sportsman, as had been your previous dream? Sometimes, things don't work out for various reasons and most times these reasons remains a mystery to us. The best we can do is look ahead with optimism, search new opportunities, and pursue a new dream with dedication and diligence. At least that’s what I did and I have no regrets. I still enjoy watching and playing sports in my leisure time, however, I believe I am living my best life right now. What are your comments on the current failing state of the ICO market? 2017 and 2018 turned blockchain technology into buzzwords and everyone wanted a piece of the action. Far too much money flew in the sector without sufficient rational to back up the investments. A handful of project seized people’s irrationality as an opportunity to make astonishing figures. Key examples are token sales like Filecoin, which held an ICO in January 2018, raising more than $250 million. Sadly, there were many risks associated with the lack of regulations around ICOs. Most ICO-funded projects have either gone defunct or were simply scams. It is my belief that the ICO model is broken and that STOs are the future as they mandate greater investor security and more transparency from the issuers. Current regulations are tough on and . Most startups looking to hold one usually have to ban many countries from participating, especially the US. To be honest, respectable companies with real products seeking to fundraise their blockchain project ought to seek out better alternatives, like STOs. ICOs IEOs Mr. Steinberg, IEOs seem to be catching the hype currently. Do you think this new funding model will be sustainable? It's just another bubble waiting to explode. While the fundraising model claims to be better than ICOs, its underlying features are the same. Many Crypto exchanges’ data is actually falsified as they use wash trading to construct a liquidity illusion. If the IEO offered is a fraud, most exchanges could not care less as their main focus is to promote their platform, and even if a scammy IEO is willing to offer funds for marketing, exchanges are unbothered. initial exchange offering Regulations and laws for IEOs are even more unclear and ambiguous. Market manipulation is a high risk for IEOs as most of the tokens offered are pre-minted. It is possible for the exchange to keep a large number of tokens for itself and manipulate the price later. Legitimate companies looking out for a sustainable long-term success should steer away from IEOs. Being a STO advocate gives you a lot of controlling knowledge around this funding model. Mr. Steinberg, why do you think STOs will be sustainable in the long-run? A STO is a token sale that offers security tokens. These tokens are backed by a real asset and grant investors additional benefits over ICOs, like the ability to profit from dividends and equity shares. STOs are far safer because they are fully regulated. STOs can be more complicated but they can also attract bigger investors, the so-called “institutional investors.” This means that STOs are able to raise more funds in record time. My belief in the success of STOs is the reason why I founded CPI Technologies, a company offering white label solutions for STOs. We have a proven record in helping more than 40 projects through CPI Tech. CPI has completed over 40 projects and its upcoming flagship venture is a $700M tokenization of a part of Times Square itself. These figures speak for themselves and demonstrate the massive potential for security tokens, which are far newer than utility tokens but are still making massive strides to over take even the best times seen by utility tokens. STOs are going to be the future, this model is  only in its infancy stage with a lot of prospects for growth. It is absolutely worth it to launch a STO if you are looking for a safe and reliable method of fundraising. While of an IPO, they come without the cons. STOs have all the pros What steps guarantee a successful launch of a STO? The whole method of initiating a STO is and complex if practiced with necessary compliance, but the extra is worth it since regulatory compliance brings legitimacy to the venture. I have been able to put together a that will help you get going with your STO journey, irrespective of whether you are new to the subject or acquainted with it. concise 7-step guide Marvin Steinberg, thank you for taking the time for this interview. On a closing note, what are your final words to startups looking to leverage the power of blockchain technology? The price of Bitcoin should not have an impact on a legitimate product. If you’re building a viable product, the market conditions should not phase you. Focus on your work, build, and keep pushing. Talk less and let your product speak for itself. This market is very new and being a pioneer requires a steadfast mindset so remain loyal to your cause.

Interview with a German Serial Entrepreneur: Marvin Steinberg

Artificial intelligence is transforming industries across the board. From to finance, companies are racing to incorporate AI into their products and services. AI promises immense benefits like heightened efficiency, deeper insights, and automation of tedious tasks. However, amidst the AI gold rush, a troubling trend has emerged: AI washing. healthcare What Is AI Washing? AI washing refers to the deceptive practice of misrepresenting a product or service as utilizing more advanced AI than it truly does. Companies may exaggerate their AI capabilities and benefits to appear more technologically sophisticated. With AI dominating tech headlines, it has become a powerful marketing tool. But as tempting as it is to jump on the AI bandwagon, AI washing can have serious consequences for businesses and consumers alike. AI washing takes its name from the term , which refers to companies making dubious or misleading claims about having a positive environmental impact. Just as some brands exaggerate their eco-credentials through greenwashing, AI washing involves companies exaggerating their use of artificial intelligence for marketing purposes without substance behind the claims. Understanding this parallel helps explain why the practice of inflating AI capabilities became known as "AI washing." greenwashing A recent example of AI washing is Equinix, an American multinational company that claims to be a global leader in the data center market. published a report accusing Equinix of riding the AI bandwagon by making false claims about its power capacity to meet AI demands. This led to a 30% increase in Equinix's valuation to $80 billion in the past year. Hindenburg Research The Rise of AI Washing Vendors may claim a product uses AI when it actually relies on simple rules-based programming and algorithms. While rules and algorithms do play a role in AI, true AI systems involve technologies like machine learning and neural networks that enable more advanced capabilities like natural language processing, computer vision, and predictive analytics. AI washing takes advantage of the hype and ambiguity surrounding true AI. By making dubious AI claims, companies aim to appear more innovative, get more media coverage, attract investors, and charge higher prices for their products and services. Several factors have contributed to the proliferation of AI washing in recent years: Excitement and optimism around AI have led to intense media coverage and exaggerated claims. This hype motivates companies to jump on the AI bandwagon. AI hype: There is no single definition of what constitutes true AI. The lack of clarity enables companies to call any type of automation or rules-based software "artificial intelligence." AI ambiguity: As interest in AI grows, companies feel pressure to showcase AI capabilities even if their technology is not that advanced. AI washing fills this demand. Demand for AI: Currently, there are no laws or standards governing AI claims in marketing. This regulatory gap enables companies to freely engage in AI washing without legal consequences. Lack of regulation: As AI becomes more prevalent, consumers and businesses should be aware of these motivations and strategies for AI washing. Evaluating AI claims critically is key to avoiding deception. The US Securities and Exchange Commission (SEC) recently penalized two investment advisory companies, Delphia (USA) Inc. and Global Predictions Inc., with a $400,000 fine for AI washing. Delphia claimed to use AI to predict successful companies and trends for early investment, while Global Predictions boasted of being the first AI-regulated financial advisor. However, the and the public about their AI usage. SEC found that both companies misled clients Examples of AI Washing Many industries are now rife with AI washing. Here are some common examples of exaggerated or misleading claims about artificial intelligence: Customer service chatbots are often touted as using AI when they actually rely on simple rules and scripts. They may have limited abilities to understand natural language or handle new queries. True AI chatbots utilize machine learning to train on diverse conversations and improve their capabilities over time. Chatbots: Many companies promote product recommendation features as AI-driven. In reality, they frequently rely on simplistic rules like identifying commonly purchased items together. Genuine AI recommendation systems apply complex algorithms to large datasets to uncover non-obvious patterns and predictively model customer preferences. Recommendation engines: Basic image analysis software is sometimes positioned as artificial intelligence for marketing appeal. Most can only identify objects, people, or text that were explicitly programmed for recognition. Advanced AI image recognition leverages deep learning neural networks to understand images contextually and generalize to novel inputs. Image recognition: Software that forecasts outcomes like future sales by extrapolating historical data is not necessarily AI-enabled. True AI predictive analytics systems can process more variables, recognize complex patterns, and continuously learn from new data to improve predictions. Predictive analytics: While big data platforms enable the collection and storage of vast datasets, they do not automatically include AI capabilities. AI-powered big data analytics applies machine learning algorithms to large datasets to uncover insights that would not be visible to humans or standard analytics. Big data analytics: These examples reveal how the lines between conventional software and true artificial intelligence are often blurred. As AI washing becomes more common, it's essential for businesses to evaluate vendor claims critically before making purchase decisions. Looking past the AI hype to understand underlying technologies is key. How to Spot AI Washing Since there are no official auditing standards for AI, identifying exaggerated claims comes down to asking the right questions and investigating vague assertions about "AI" or "machine learning." Here are some tips for seeing through AI washing: If a company claims their product uses AI, ask for specifics on the techniques used and how they enable intelligent, adaptive behavior. The lack of technical details is a red flag. Ask how it works: Don't take capabilities like natural language processing at face value. Test the product extensively to verify whether supposed AI features actually work as advertised. Validate capabilities: Ask vendors if their AI claims have been validated by independent testing agencies like AI laboratories and universities. The lack of external validation is suspicious. Request third-party evaluation: Examine the qualifications of the vendor's AI team. Do they have recognized experts in AI and advanced degrees in the field? Insufficient in-house talent makes lofty AI assertions doubtful. Check qualifications: Ask how the AI model was trained. Thorough training on extensive, high-quality data is essential for developing systems that work well. Review training approach: Research competitor products making similar AI claims. If others have more modest claims for similar offerings, it may indicate exaggeration. Compare competitors: Ask for specific case studies and examples demonstrating the claimed AI capabilities in real-world applications over time. The lack of solid examples exposes weak claims. Get examples: By probing vendors with these questions and scrutiny, buyers can push past the hype and determine if true AI or mere AI washing is at play. What AI Really Looks Like Artificial intelligence is a continually advancing field with many techniques and applications. There is no single formula, but authentic AI solutions exhibit some key characteristics: The system’s algorithms have the ability to learn from data without explicit programming. Machine learning techniques enable adaptation and improvement. Learning ability: The AI demonstrates an understanding of context and meaning, not just data patterns. For example, comprehending language or analyzing images situationally. Contextual comprehension: The AI can extend its training on particular datasets to unfamiliar data and scenarios it has not directly experienced before. Generalizability: The system can store what it learns over time and apply that learning in the future, rather than just responding based on immediate inputs. Memory: The AI acts proactively to analyze ambiguous, complex problems and delivers solutions based on reasoning and judgment, not just preset rules. Problem-solving: The system can make reasonable predictions and suggestions even with uncertain, incomplete data. AI handles ambiguity well. Uncertainty handling: In many applications, the AI can explain its processes, rationale, and decisions in understandable terms to human users when needed for trust and accountability. Explanation: While the techniques behind AI solutions may differ, these hallmarks indicate that a system incorporates true artificial intelligence that evolves and adapts over time, rather than just simple automation. Understanding these AI standards helps separate legitimate applications from AI washing. The Hype and History of AI Washing AI washing is not a new phenomenon. It follows a similar pattern to previous technological hype, such as the metaverse mania of recent years, the dot-com craze of the late 1990s, and even the airplane hype of the 1920s. During the metaverse mania, companies like Facebook (now Meta) heavily promoted the concept, leading to a surge in mentions of the term by various companies, many of which may not have had serious metaverse ambitions. Similarly, during the dot-com bubble, companies added ".com" or "Internet" to their names to attract investors, resulting in significant stock price increases. In the 1920s, investors were drawn to companies associated with airplanes, even if their actual business had little to do with aviation. One example is Seaboard Airlines, which attracted investor interest despite being a railroad company. These historical examples demonstrate that companies often try to capitalize on the hype surrounding new technologies, even if their actual capabilities or involvement in those areas are limited. AI washing is the latest iteration of this trend, fueled by the excitement and potential of artificial intelligence. Moving Forward With True AI As artificial intelligence advances, AI washing is likely to remain an issue plaguing many industries. To avoid deception and move forward with authentic AI, consumers and businesses should: on the true nature of AI and its techniques like machine learning so they can scrutinize vendor claims knowledgeably. Educate themselves that force vendors to back up AI assertions and not rely on buzzwords. Ask tough questions to evaluate vendor offerings and determine if they constitute true AI versus exaggeration. Consult AI experts within industries to certify legitimate AI and combat false claims. Advocate for auditing standards in research and talent development to drive continued AI innovations that live up to the technology’s true potential. Support investments Ultimately, focusing on the reality of artificial intelligence capabilities, not the hype, will lead to smart adoption of AI that delivers genuine benefits and business value. With scrutiny and standards, AI washing can be recognized so authentic AI advances can flourish.

What Is AI Washing? How to See Through the Hype

Finding success in entrepreneurship is hard. I would know. I have spent many years talking about, or being involved in, entrepreneurship. I started a UK-based fashion business six years ago, and that all-encompassing dumpster fire makes up most of what I know today about marketing, e-commerce, branding, and how no one will buy your stuff if you just do things the way they’ve always been done. Four years later, I got back onto the anthropomorphic entrepreneurship horse and tried to start a German-based social business, which had the goal of providing affordable and safe housing to German sex workers. That too was a failure. Turns out it’s hard to get people to care about people who have sex for living. In addition to these half-baked experiments, I followed a handful of entrepreneurship classes at the University of Exeter (England), Universität Mannheim (Germany), and ESSEC Business School (France). Beyond what I’d learned practically, these courses gave me what I’d call a blanket theoretical knowledge of what it takes to call oneself an entrepreneur ( ). big surprise, I don’t have what it takes I only describe this to highlight the fact that the following few paragraphs are the result of careful consideration. It’s not that I’m anti-innovation or a disbeliever in disruption (or whatever another aggravating cliché the start-up community may use), or even a regulation hawk (but companies should be held accountable for the harm they may cause, sue me) : I simply think that entrepreneurship is rotten to its very core and that one way to fix it is to . change some of the things we teach about it in business schools It wasn’t always this way. Things were going pretty smoothly on the good start-up ship ten years ago, but ever since the captain/government gave the keys to the passengers/founders, there are people passed out everywhere, rowdy guests are throwing empty beer cans at the neighbor’s garden and the furniture is getting trashed. Things got so out of hand that because half the guest have enough money to make anyone’s and everyone’s life a living, dystopian nightmare (looking at you, Facebook, Amazon, Google…). the cops won’t even show up Fun metaphor aside, I believe three things should be explained to young ones to prepare them for modern entrepreneurship: Safety Nets, Shortcuts and The Five. Safety Nets Students in virtually every higher education classroom throughout the world are told that they can make it as an entrepreneur with only a great team and an astounding idea (hell, for some courses, the entire curriculum revolves around that idea). That’s, however, turning a blind eye to the most common shared trait among entrepreneurs: (family money, inheritance, or connections that allow for access to financial stability). access to financial capital It’s easier to create Snapchat if you live near Eric Schmidt, both your parents are lawyers and you can afford to have internships in South Africa. Kids are being seduced into thinking that they can just go out and pursue their dream anytime, but it’s just not true. . It could save a lot of broken dreams, dashed hopes and nuclear families. Overwhelmingly, entrepreneurs are rich, white men, with plenty of fall-back plans and safety nets, and any self-respecting entrepreneurship class should start by telling students that truth This lack of diversity is one of the key reasons for entrepreneurship’s rot: people that come from the same background, are educated in the same schools and share the same values are simply not going to create a set of businesses beneficial to society as a whole. Open a newspaper and tell me I’m wrong. I dare you . Shortcuts My second concern is that a “culture of unicorns” has created a generation of entrepreneurs looking for shortcuts to the highest possible valuation. This leads to a laziness that has become pervasive and most obvious in certain corners of entrepreneurship classes (look for the kids using the words “ICO” and “X million valuation”). Real entrepreneurship stories are about grit and risk and determination. They are about people who sacrificed for years and through hard work (and partly through luck) created something that could sustain them, their families and, perhaps more importantly, their employees. Interestingly, many of these stories come from manufacturing, not tech. I have never heard of a successful sports team that won a game by relying solely on Hail Mary plays. . The same ought to apply to start-ups. It’s not about being on stage at a TED talk or featured in an article in Quartz or closing a $20 million round or claiming LinkedIn bragging rights. It’s about continuously cold calling. It’s about continuously shipping code. It’s about putting out fires EVERYWHERE, ALL THE TIME. It’s about waking up at 4:15 am to catch a flight to a small town’s industrial zone for a $200k deal to hit your budget for the quarter, and maybe, just maybe, remain profitable. Sure, it’s not sexy. . But it’s real. And it’s stable. And it won’t leave you pants-less when the bubble crashes ( ). Victory on the field is more often than not the result of blood, sweat, tears and a cloud of dust There’s nothing inherently sexy about hard work looking at you, BitCoin I often wish I’d been beaten over the head with these facts earlier. The Five Because my generation has lived through back-to-back massive worldwide revolutions (the growth of the internet followed by the adoption of smartphones), we assume that another revolution is just around the corner, and that once again, a bunch of us can get together in a garage and write a little software to take advantage of yet another massive economic upheaval. But there is no such revolution on the way. Stop shaking your fist, put down the pitchforks and hear me out. Innovation is hard work. Future technologies include A.I, drones, AR/VR, cryptocurrencies, blockchain self-driving cars, and the Internet of Things. These technologies are, collectively, hugely important and consequential; but they are not remotely as accessible to disruption as the web and smartphones were. These new technologies are: Complicated Expensive Favour organisations that have huge amounts of data, scale, and capital already So where does all this leave entrepreneurs? , and probably hoping to be acquired by a larger company, ideally one of The Five (Google, Amazon, Facebook, Apple, Microsoft). Heck, my latest entrepreneurship project relies solely on that hope. That’s our WHOLE business model. Struggling Entrepreneurship as we teach it is dead and rotting. We live in a new world now, and it favors the rich and big, not the small, as exemplified by , , , , , , and (Goodnight sweet Prince) among many others. We should learn from their mistakes and create classes which give students the tools to fight the fair fight in a healthy way, which would be beneficial for society as a whole (institutionalised sexism, what institutionalised sexism?). Juicero YikYak Lily Drone Delicious JawBones Doppler Labs Hello Rethink Robotic I am now a strategy consultant, meaning I supposedly understand basic business fundamentals better than some of my peers (whether that’s true remains to be seen). And I’m here to say that the way we teach entrepreneurship needs to be overhauled if we don’t want to find ourselves in a world were we praise lazy white tech guys for “inventing” helicopters (Uber’s “flying taxi service”), buildings (Google’s “Landscraper”), the bus (Lyft’s “Shuttle”), room-mates (WeWork’s “WeLive”), the tent (Odd Company’s “Pause Pod”) and our winner, the vending machine (Google’s “Bodega”). We can, collectively, do better.

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