Author name: William Smith

According to news reports, the Trump administration has identified DeepSeek for potential banning while undertaking its federal initiative to combat Chinese technology companies for national security reasons. The United States may initiate a ban on DeepSeek as part of its ongoing efforts to fight against Chinese tech firms, and this move would signal an expansion of the U.S.-China tech conflict. We need to understand what DeepSeek represents and its reasons behind the current assessment process. A potential suspension of AI development by DeepSeek along with its implications for global technological competition stands as a question regarding American-Chinese diplomatic relations. Knowledge about the matter will come to you via the following information. What Is DeepSeek? DeepSeek operates as a Chinese organization that develops Large Language Models (LLMs) such as Google Gemini and OpenAI’s ChatGPT. The advanced AI capabilities of DeepSeek attract global interest because the company provides open-source models with benchmark performance that matches leading competitors. Key Features of DeepSeek: Why Is the U.S. Considering a Ban? The Trump presidency has demonstrated intense opposition toward Chinese technology enterprises because of three main factors: 1. National Security Risks 2. AI Dominance Race 3. Precedent Set by Previous Bans What Would a Ban Look Like? Potential measures could include: Possible Consequences For the U.S. ✅ Pros: ❌ Cons: For China ✅ Pros: ❌ Cons: For the Global AI Industry What’s Next? Final Thoughts The possible DeepSeek prohibition serves as another development toward U.S.-China technological conflict. Its intention to safeguard national security produces a negative side effect of severing global AI advancement. The escalating tensions are likely to lead the globe toward two separate AI environments that each have China and the United States as their leaders. The establishment of a DeepSeek ban would the United States more secure or accelerate Chinese advancements in artificial intelligence independence? Only time will tell.  

Shadow AI refers to the use of  AI tools by working professionals without the approval of the company’s leadership. It may not necessarily be done with bad intentions but because teams and individuals are just trying to get their work done faster and stay ahead of their deadlines. The issue is that teams are using these AI tools outside any compliance review by the organization. This is either because the organization hasn’t built a clear approach for integrating AI into daily work or because the risks of shadow AI just aren’t being prioritized. Risks of Shadow AI The dangers of shadow AI aren’t always immediate. They tend to show up slowly through the consequences when data slips into the wrong hands and compliance issues that come at a big cost. Here is a breakdown of two major risks of Shadow AI in businesses. Operational Risk A well-known case that highlighted the operational risks of shadow AI comes from Samsung. Some of their engineers started using ChatGPT to help with day-to-day tasks, like debugging code and summarizing internal documents. In the process, they accidentally shared sensitive source code and meeting notes without any approval or checks in place. As a result, Samsung banned external AI tools across teams and started working on their own in-house alternative with better control. Legal and Compliance-Related Risks Issues around AI copyright and ownership complicate how data and IP are handled when using external tools. If there are no policies in place at an organizational level, the legal impact of Shadow AI escalates very quickly. For instance, consider the scenario below Employees may unknowingly input private client data, financials, or Intellectual Property into public AI tools. These tools often store, process, or learn from that data, putting the company at risk of violating data laws. Here’s where it gets serious: A report by Cyberhaven found that 11% of the data employees paste into ChatGPT is confidential, including internal business details and client information. Non-compliance with data protection laws can be expensive. Under the GDPR, companies face fines of up to 4% of their global annual revenue. In the U.S., the CCPA imposes penalties up to $7,500 per intentional violation, without a cap on total fines.   Most startups haven’t set clear boundaries or systems around AI use yet. That’s understandable, but ignoring it won’t make the risk go away. We will discuss how startups can tackle shadow AI by the end of this article. Startups VS Enterprises. Which are More Vulnerable to Shadow AI At first glance, large enterprises might seem more exposed to shadow AI. But early-stage startups are often more vulnerable, not because they use more AI but because they lack the structure to manage it. Here’s why AI adoption happens earlier than policy: In fast-moving teams, tools like ChatGPT, Claude, or Midjourney often become part of the workflow before leadership even realizes it. By the time founders are thinking about policy, the behavior is already embedded. Every mistake is magnified: Unlike large enterprises with legal defenses and PR teams, startups feel the consequences immediately. One mistake, like leaking pitch decks or customer data, can derail funding or spark legal trouble that founders aren’t prepared for. We’ve covered what shadow AI is and the risks it brings to businesses. Now, let’s look at some practical ways to actually tackle it. Real-World Examples of Shadow AI Consequences Developer Integrated Unapproved AI Translation Tool A developer quietly integrated an AI translation tool into a customer portal without a security check. The tool had known vulnerabilities that attackers later exploited. The result: customer conversations leaked, service was disrupted, and the company took a financial hit that could’ve been avoided with even minimal oversight. Employees Feeding Sensitive Data into Chatbots Cyberhaven’s report found that employees at tech companies are regularly using tools like ChatGPT and Gemini through personal accounts, bypassing any IT controls. Among the data shared, customer support logs made up over 16%, source code around 13%, and R&D material close to 11%. All of it going into public AI models with zero oversight or audit trail. Customer Service Agents Using Unauthorized Generative AI Tools Zendesk found that nearly half of customer support agents are using tools like Copilot or ChatGPT without company approval. They’re trying to move faster, but without proper vetting, that speed comes at the cost of data control and compliance visibility. A Practical Solution to Tackle Shadow AI You can’t stop people from using AI tools, but you can build a system around it. The goal isn’t to block innovation. It’s to keep things safe, trackable, and aligned with company goals. Here’s a simple but workable approach to tackle shadow AI 1. Start with a survey Create a short internal form to understand the extent to which shadow AI has penetrated your workplace. Consider asking questions like the ones listed below: What AI tools have you used in the last 30 days? What is the purpose of using a particular tool? Did you input any internal data? Assure your team members that the information will be kept anonymous. As per a report by Microsoft, 52% of those who use AI for their work are reluctant to admit it. So, make it clear to your team that the goal is not to police AI use but to get a real picture of what’s happening so that better systems can be built around it. 2. Set up browser-level visibility with employee consent Use tools like Dope Security, Netskope, or Cyberhaven to get visibility into how AI tools are being used across your team. These tools can detect when someone is pasting data into public AI models like ChatGPT. Consider these simple actions: Begin with monitoring: Let the tool run silently in the background. No alerts, no restrictions. Just gather data on what AI apps are being used and what kind of internal content is being pasted into them. This gives you a clear picture without alarming your team. Move to flagging risky patterns: Once

In the heart of Silicon Valley and beyond, a new wave of American Startups is rising, driven by one of the most powerful forces in modern technology: Artificial Intelligence (AI). But this isn’t the typical AI hype we’ve seen in the past–these trailblazing companies are merging AI with sustainability to create groundbreaking solutions in green tech. From smart farming techniques to AI-powered renewable energy systems, these startups are rewriting the future of sustainability in ways we never thought possible.  AI Meets Sustainability: A Natural Evolution  For decades, the fight against climate change and the push for sustainable practices have relied on innovation. Yet, despite our best efforts, there’s always been a challenge in how to scale sustainability efforts without compromising economic growth. Enter AI–the game-changer that promises not just incremental progress, but exponential leaps in sustainable innovation.  AI with its ability to process vast amounts of data, make real-time decisions, and, optimize operations is the perfect partner for sustainability initiatives. For startups, AI is no longer just a buzzword–it’s a tool actively used to create smarter, more efficient solutions to some of the world’s most pressing environmental problems.  AI-Driven Startups Leading the Change Several American startups have already recognized the potential of AI to drive real change in sustainability, and they are racing to transform industries. Here’s a closer look at a few of the most disruptive companies in this space. It may seem unconventional, but food production is one of the most significant contributors to environmental degradation. Beyond Meat, a leader in plant-based protein is leveraging AI to revolutionize how we produce food. By using AI to fine-tune recipes and optimize production processes, Beyond Meat has successfully reduced its carbon footprint and water usage, while scaling its operations to meet growing demand.  But they aren’t stopping there. The company is constantly innovating, using AI to predict consumer preferences and identify more sustainable raw materials, all while ensuring their products are more eco-friendly and delicious. This intersection of AI and food production is a perfect example of how AI can be harnessed to reshape industries that are traditionally high in emissions.  It ensures that energy production matches demand. That’s why Grid Edge, a smart startup, steps in. Integrating AI  into the smart Grid Edge optimizes the distribution of renewable energy, ensuring that energy is used efficiently and that excess power is stored for later use.  Their AI-driven systems help utilize forecast and energy usage patterns, automate decision-making, and even balance energy lobes across grids. This reduces energy, lowers costs,  and maximizes the effectiveness of renewable sources such as solar and wind. With AI, the startup is making it possible for cities and communities to embrace clean energy while also maintaining grid stability.  Agriculture is one of the oldest industries on Earth and one of the most resource-intensive. Startups like Ripe Robotics are using AI to make farming smarter and more sustainable. By integrating AI with robotics, Ripe Robotics offers automated solutions for harvesting crops. These robots are designed to optimize the harvest process, using machine learning algorithms to identify the best time to pick fruits, reduce waste, and ensure optimal crop yield.  The result? Less water, fewer pesticides, and reduced labor costs. By making the farming process more efficient, Ripe Robotics, not only improves food security but also drastically reduces the environmental impact of agriculture.      How AI Powers Efficiency in Green Tech  What sets these startups apart is their ability to use AI to enhance the efficiency of sustainable practices. For example, AI can help reduce waste, minimize energy consumption, and enable more precise farming methods. Here’s how AI is making an impact across industries: The Road Ahead: Scaling AI for Sustainability  When AI-driven green tech is making waves, there are still challenges to overcome. Scaling these solutions to natural global demand because of investment in research and development, as well as collaboration between startups, governments, and competitions. Furthermore, the AI models used in sustainability need constant refinement and data inputs from diverse environments to ensure their secrecy across various geographic and economic growth.  Moreover, public policy will play a crucial day in ensuring that AI technologies for sustainability receive. their support, they need to thrive. The government needs to insult various adoptions of AI-powered tech solutions through tax breaks, subsidies, and funding for research. Similarly, collaboration between private-sector startups and large corporations will be essential in scaling these technologies globally.  Conclusion: The Future of Green Tech is Smart and Sustainable  The convergence of AI and sustainability isn’t just a trend– it’s the future of green tech. As American startups continue to innovate in this space, we’re likely to see even more disruptive solutions that challenge traditional methods of energy production, agriculture, and resource management.  By leveraging AI, these startups are not only improving their own bottom lines but are contributing to a more sustainable future for all of us. Their work set a powerful example for other industries, showing that innovation and sustainability are not mutually exclusive. As consumers. Employees, and investors, we must continue to support and champion these startups, ensuring that the first wave of green tech can continue to grow, scale, and ultimately reshape the world for the better. The future of our planet may depend on it.

In the rapidly evolving world of Artificial Intelligence (AI), one issue is making waves and sparking heated debate: the regulation of AI copyright. As tech giants like OpenAI and Google continue to push the boundaries of AI, the conversation surrounding how AI systems should be governed– especially in terms of intellectual property (IP)–has become a crucial topic that could shape the future of AI development.  AI’s Growing Role in Creative and Knowledge Industries  Artificial Intelligence (AI) is no longer confined to the realms of science fiction or academia. Today, it powers everything from the algorithms that recommend your next binge-watch to the AI-driven art that’s being sold at high-end auctions. As AI becomes more embedded in daily life, its ability to generate content–be it music, art, articles, or even programming code– has led to a growing question: Who owns the content created by an AI? For Instance, imagine an AI model that has been trained on vast amounts of publicly available data–everything from books and articles to music and movies– and then generates a piece of content. If this content is commercially valuable, should the model’s creators, the developers, or the users who input the data own the rights to this new creation? This is the crux of the AI copyright debate. The Concerns: Protecting Human Creativity in an AI-Dominated Future On one side of the debate, critics argue that AI should not be allowed to “steal” from human creativity without crediting the original creators. Alden Global Capital, which owns several influential newspapers in the U.S., recently published an editorial piece that criticized the push for looser AI copyright regulations. Their argument? By allowing AI systems to freely pull from public data for training, without compensating the original creators, we risk undermining the very foundation of human creativity.   This concern is particularly relevant for industries that rely on intellectual property, such as music, literature, and art. If AI can generate new works based on the billions of pieces of content it has ingested, without paying royalties to the creators whose work is learned from, the creative economy could face a seismic shift. The Tech Giants’ Perspective: Innovation Shouldn’t Be Stifled  However, tech giants like OpenAI and Google have a different take. They argue that AI should not be burdened with restrictive copyright laws because doing so could stifle innovation and slow down technological progress. OpenAI’s stance is that AI should be able to learn from publicly available data in much the same way that humans do–by reading, watching, and listening. They emphasize that AI, by its very nature, doesn’t create new content in a vacuum; instead, it synthesizes information and generic new output based on what it has learned.  The problem, they argue, lies in the outdated copyright framework that was designed in a pre-AI world. In a modern digital landscape, how do we define “fair use” when machines are involved in content generation?  A Changing Landscape: How Copyright Law May Evolve   As both sides dig in, it’s clear that we are heading towards a reckoning with intellectual property law. The question isn’t whether AI will disrupt the creative industries–this has already happened. The real question is: How can we build a legal flavor that addresses the complexities of AI-generated content while protecting human creators? One potential solution is to create a new category of rights for AI-generated content– something distinct from the traditional corporate law that applies to human creators. This could involve creating a separate class of ownership or licensing rules for AI, which would allow companies that fit AI systems to retain certain rights to the output, but all assure that human creators are not left out in the cold.  The Way Forward: A New Era of Collaboration?  As AI continues to evolve, it’s becoming increasingly apparent that we need a new framework for intellectual property that reflects the role AI plays in content creation. It’s not just about protecting the rights of creators: it’s about ensuring that AI can continue to evolve in a way that benefits society.  The debate is still ongoing, but a one-size-fits-all solution won’t work. Instead, policymakers and stakeholders must engage in thoughtful conversations to create laws that allow AI to thrive while still safeguarding the rights of human creators. As we venture further into this brave new world, we must find a balance that fosters innovation without undermining the value of human creativity.  In the end, the question isn’t whether AI will change the roots of interactive property–it’s about how we as society, will write those new rules. The AI copyright debate is far from over, but its outcome could very well define future of creativity, technology, and the intersection of the two.  

AI and no-code technology aren’t just buzzwords–they are the ultimate game-changers for startups looking to make money fast and efficiently. In 2025 these tools are breaking barriers allowing even non-technical founders to create powerful digital products, automate operations, and scale effortlessly. If you are a startup ready to dive into AI and no code monetization, here’s how you can turn ideas into revenue-generating machines.. Why AI and No-Code are a Goldmine for Startups AI is no longer a futuristic concept – it’s the fuel behind intelligent automation, data-driven decision-making, and hyper-personalized user experiences. On the other hand, no-code platforms have enabled non-programmers to build apps, automate workflows, and launch tech-driven businesses without writing a single line of code. Together, AI and no-code offer startups: Faster time-to-market with minimal technical expertise Reduced development and operational costs Scalable, automated revenue streams 1. Build and Sell AI-Powered SaaS Products The SaaS model is one of the most profitable digital businesses, and AI is supercharging it. From AI-driven analytics to automated customer support bots, businesses are willing to pay top dollar for intelligent SaaS solutions. How to Monetize: Subscription-based pricing (monthly/annual plans) Pay-as-you-go usage models Enterprise licensing for large businesses Example: DataRobot offers AI-driven predictive analytics for businesses, helping them make data-driven decisions without needing data science expertise. 2. AI-Driven Content Creation Service Content is king, and AI is the new-age writer. ChatGPT, Jasper, and Copy.ai can generate blog posts, social media content, and ad copy within seconds, helping businesses streamline content production. How to Monetize: Monthly subscription plans for AI content tools One-time fees for custom AI-generated content White-label AI content solutions for agencies Example: Copy.ai helps businesses generate marketing copy using AI, saving time and reducing the need for large content teams. 3. No-Code AI App Development Gone are the days when building an app required a team of developers. No-code platforms like Bubble, Glide, and Adalo allow startups to develop AI-driven mobile and web apps without writing a single line of code. How to Monetize: Offer app development services to businesses Sell AI-powered app templates Use a freemium model with paid upgrades Example: Voiceflow enables businesses to create AI-powered voice assistants without coding, making conversational AI accessible to everyone. 4. AI-Powered Digital Marketing Automation Startups can leverage AI to automate lead generation, targeting, email marketing, and customer segmentation. No-code platforms like Zapier and HubSpot make it easy to create powerful AI-driven marketing systems. How to Monetize: Sell subscription-based AI marketing tools Charge per lead generated using AI Offer enterprise AI marketing automation solutions Example: Drift provides AI-powered chatbots that engage website visitors, capture leads, and automate sales conversations. 5. AI Consulting and Custom AI Solutions Many businesses want AI-powered solutions but don’t know how to implement them. Startups can offer AI consultation, custom automation, and workflow optimization using no-code AI tools. How to Monetize: Charge hourly or per project for AI consulting Sell custom AI automation services Offer AI model training and customization as a service Example: Peltarion helps companies integrate AI into their businesses without needing in-house AI expertise. 6. Create AI-Powered Marketplaces AI-driven marketplaces personalize user experiences by analyzing data and making smart recommendations. Whether it’s an e-commerce platform, job board, or real estate listing site, AI can enhance engagement and retention. How to Monetize: Charge commissions on transactions Offer premium features through subscriptions Sell advertising space on the platform Example: Zillow uses AI to predict real estate prices and recommend homes based on user preferences. 7. AI-Powered Education and E-Learning Solutions The e-learning industry is booming, and AI-powered platforms are transforming the way people learn. Startups can build AI-driven education platforms offering personalized learning experiences, automated assessments, and virtual tutoring. How to Monetize: Sell courses and memberships Offer AI-driven tutoring subscriptions White-label AI learning platforms for institutions Example: Duolingo uses AI to personalize language learning, adapting lessons based on user progress. 8. AI-as-a-Service (AIaaS) API Solutions Businesses increasingly need AI capabilities, whether it’s language processing, image recognition, or automated chatbots. Startups can provide AI-as-a-Service (AIaaS) through API integrations. How to Monetize: Charge per API call or usage tier Sell enterprise AI service subscriptions Develop and license custom AI models Example: OpenAI provides API access for businesses to integrate AI-powered language models into their applications. 9. AI-Powered Personalization for E-Commerce Consumers expect hyper-personalization, and AI-driven recommendation engines can help e-commerce brands increase sales. Startups can develop AI-powered personalization tools and sell them to online retailers. How to Monetize: Sell SaaS-based AI recommendation tools White-label AI personalization solutions for retailers Offer AI-powered chatbots for customer support Example: Amazon’s AI-driven recommendation system generates 35% of its total sales through personalized product suggestions. 10. AI-Driven Business Automation Tools From HR automation to data processing, AI-powered automation tools help businesses save time and increase efficiency. Startups can create automation solutions using no-code AI tools. How to Monetize: Sell subscription-based AI automation tools Offer pay-per-use automation services Provide AI automation consulting for enterprises Example: UiPath automates repetitive business tasks, reducing operational costs for companies worldwide. 11. AI-Enabled Healthcare Solutions AI is transforming healthcare, enhancing diagnostics, patient care, and operational efficiency. Tools like IBM Watson Health and Tempus leverage AI to analyze medical data, assist in decision-making, and personalize treatment plans. How to Monetize: Subscription-based AI diagnostic tools for hospitals & clinics AI-powered telemedicine platforms with premium features Licensing AI-driven analytics solutions for healthcare providers Example: Tempus uses AI to analyze clinical and molecular data, helping doctors make data-driven decisions for personalized patient care. The Future of AI and No-Code Monetization The AI and no-code revolution is just beginning, and startups that tap into these trends can build scalable, automated, and highly profitable businesses. By combining AI intelligence with no-code accessibility, founders can create revenue-generating solutions without massive upfront investments.