DeepSeek / ChatGPT: Can China’s AI Disrupt U.S Giants?
The recent launch of DeepSeek AI R1 model has turned heads in the AI Industry. According to China, they have spent only $6 million per training run on their model, compared to the tens of millions required for U.S. competitors. This is amazing right, the social is full of the buzz Deepseek vs Chatgpt? Moreover, Its commercial pricing is also impressively low. According to DocsBots Website mentioned by Statistica, with 1 million tokens costing only 55 cents to upload. This rapid success raises important questions: can a Chinese AI model truly challenge the U.S. AI dominators without sacrificing quality and security? In this post, we’ll compare cost and performance between top U.S. and Chinese AI infrastructures, to find out best open-source LLM mainly focusing on DeepSeek vs ChatGpt and others like Qwen, Gemini and Llama. We will also explore if China’s AI disruptors can truly outperform their U.S. counterparts. Understanding AI Infrastructure and LLM Costs AI infrastructure is a combination of hardware, software, and cloud services required to train and deploy AI models. When developing cutting-edge AI models like ChatGPT, Gemini, or DeepSeek, they require massive computational power which often involves specialized chips, vast datasets, and advanced training techniques. Typically, training a large language model (LLM) involves millions of dollars in computational costs. According to analysis, running ChatGPT costs approximately $700,000 a day. That breaks down to 36 cents for each question. The US models also demand extensive datasets, advanced algorithms, and constant tuning to ensure they perform at the highest level. Technical Components LLMs Require: The Evolution of AI Training Costs (2017-2023) The evolution of AI training costs has seen an astonishing rise over the years. It reflects the growing sophistication and scale of large language models (LLMs). AI training costs have soared from modest beginnings to reach hundreds of millions today. This rise reflects the growing complexity of large language models (LLMs). Let’s examine how the increasing sophistication of AI models has led to this sharp escalation in development expenses. The above image presents a fascinating timeline of AI model training costs from 2017 to 2023. It shows a dramatic increase in investment over the years. If you see the visualization, it notes that these figures are adjusted for inflation and were calculated based on training duration, hardware requirements, and cloud computing costs, according to The AI Index 2024 Annual Report. US AI Models – The Pioneers The U.S. has long been the leader in artificial intelligence development. Here are several tech giants that are driving innovation in tech space: It was developed by OpenAI and has revolutionized as conversational AI. With iterations like GPT-3 and GPT-4, it remains one of the most advanced models on the market. Training a model like ChatGPT costs upwards of $78 million, reflecting its complexity and the computational power required. ChatGPT app development costs can range anywhere between $100,000 to $500,000. The factors that affect the cost are the dataset’s size, the chatbot’s end-use case, the services, the features required, etc. Claude AI is created by Anthropic. The ai model has emerged as a leading conversational agent as it provides an alternative to ChatGPT with a focus on safety and alignment. The development costs are significant but vary depending on deployment and specific business use cases. Meta’s Llama series is a key competitor in the open-source AI space. While the models are cheaper to access for businesses, developing applications using Llama models still incurs considerable costs mainly for larger-scale integrations. Google’s Gemini is the most expensive AI model in terms of training costs, requiring $191 million for development. It’s designed to handle more complex datasets, including multimedia formats. Despite its higher costs, Gemini is known for its reliability and performance across various tasks. China’s AI Models: A Low-Cost Revolution Recently, China has begun making waves with its innovative, cost-effective alternatives. Chinese companies are challenging the traditional AI ecosystem by introducing similar or better performance at a fraction of the price. Here are some of the newest models of AI: DeepSeek AI launch of its R1 model has sent shockwaves through the AI industry. With a development cost of just $6 million, DeepSeek has proven that cutting-edge AI can be achieved on a lean budget. Its pricing structure is also far more accessible, with 1 million tokens costing only 55 cents to upload. Despite the lower costs, DeepSeek’s model has earned strong performance reviews, often outperforming U.S. models in key benchmarks. Last night, Alibaba launched their AI offerings, including the Qwen series. It quickly gained traction as a viable alternative to expensive models like GPT-4. With a heavy focus on cloud-based AI solutions, Alibaba provides highly competitive pricing, ensuring that businesses can scale AI-powered applications affordably. Moonshot’s Kimi series is a rising star in China’s AI scene. But, it is a less-known AI architecture. However, the Kimi K1.5 has been praised for its efficiency and cost-effectiveness. As it is giving companies an affordable way to implement AI without compromising on quality. The Chinese AI model, ByteDance is known for revolutionizing social media through TikTok, ByteDance is also making strides in AI. Doubao 1.5 Pro is one of their leading LLMs, offering impressive capabilities at a significantly lower cost compared to its Western counterparts. Estimating AI Development Costs The cost of AI development varies greatly depending on the scale, complexity, and project requirements. From infrastructure to labor, software, and training, each component contributes to the overall cost. On average, businesses can expect to invest between $10,000 to $50,000 or more in AI projects. Key Cost Components: Cost Breakdown: Is DeepSeek-R1 Really a Threat? In particular, DeepSeek-R1 has been disruptive due to its low costs and strong performance. But longevity is controversial. However, that model only spends $6 million per training run, far less than models like ChatGPT or Google’s Gemini, which can cost tens of millions. Its commercial use pricing also reflects this, with 1 million tokens costing only 55 cents to upload and $2.19 to download, which is significantly cheaper than U.S.-based
Proof of Less Work:Sustainability in the Blockchain Era
Blockchain technology, celebrated for its decentralized and secure nature, has come under criticism for its environmental impact, particularly through its major use of the Proof of Work (PoW) mechanism. The PoW model, which works under major cryptocurrencies like Bitcoin, is known for its high energy consumption. To cater to these concerns, the concept of Proof of Less Work (PoLW) has emerged as a potential solution. What is Proof of Less Work (PoLW) Imagine a highly secure digital ledger where all your transactions are recorded. But there’s a problem; many blockchains, such as the one that runs Bitcoin, use a method called Proof of Work (PoW) to keep data secure. In PoW, computers solve extremely hard puzzles to add new blocks to the blockchain, which guzzles up huge amounts of electricity. Is it possible to keep blockchains eco-friendly without turning our planet into a giant oven? Yes! Instead of making computers work extra hard, the new mechanism Proof of Less Work (PoLW) uses easier tasks that require way less energy: PoLW is a different approach to adding blocks to the blockchain that uses less energy; instead of solving extremely hard puzzles, PoLW gives easier tasks that don’t require as much power from computers. These tasks still help validate and secure the blockchain but don’t require as much energy to solve; instead of those brain-melting puzzles, PoLW gives out easier tasks such as solving real-world problems that require less power i.e optimizing mathematical problems, and contributing to scientific research projects that need less intensive computing power. By using less energy, PoLW helps reduce the massive carbon footprint associated with traditional PoW. Here is an outline of how the PoLW system works: Why is Proof of Less Work (PoLW) Needed According to research conducted by Cambridge Centre for Alternative Finance, Bitcoin mining alone consumes around 121.36 terawatt-hours (TWh) per year, which is comparable to the annual energy consumption of a country like Argentina. To put it into perspective, the energy used by Bitcoin mining in a single year could power the entire city of New York for nearly four years. This massive energy requirement is driven by the need for miners to continuously run specialized hardware, known as Application-Specific Integrated Circuits (ASICs), to solve complex cryptographic puzzles. This high energy demand results in a significant carbon footprint, contributing to climate change and environmental degradation. The bulk of this energy consumption comes from specialized hardware (ASICs) running continuously to solve the puzzles. The primary critique of the traditional Proof of Work is its energy consumption; the need for massive computational power leads to substantial electricity use, contributing to a large carbon footprint. The majority of Bitcoin mining operations are powered by fossil fuels, particularly coal, which is a major source of carbon emissions. Bitcoin’s annual carbon footprint is comparable to that of countries like Qatar and Hungary, which equates to approximately 60 million metric tons of CO2 emissions per year, contributing to global warming and climate change. In Proof of Work (PoW), the competition among miners to solve puzzles first means that more powerful and energy-hungry hardware is constantly being developed and deployed. This creates a cycle of increasing energy consumption and e-waste, as older hardware becomes obsolete and is discarded. The new and improved mechanism Proof of Less Work (PoLW) enhances the economic viability of blockchain networks by lowering operational costs. Miners can use less expensive hardware and spend less on electricity, making mining more accessible and profitable. This democratization of mining can lead to a more decentralized and resilient blockchain network. To encourage miners to use PoLW, the system offers rewards or incentives for those who complete the easier tasks. Miners who use renewable energy or more efficient methods might get extra rewards, for example, a miner using solar or wind power could receive additional rewards or priority in the validation process. This helps promote environmentally friendly practices. How Do We Transition to PoLW For existing blockchain systems that use PoW, switching to PoLW can be done gradually as it would be a complicated process. The transition requires careful planning, collaboration, and a willingness to embrace new paradigms in blockchain technology which involves either of the following methods: 1- Soft Forks and Hard Forks Soft Forks: Hard Forks: 2- Hybrid Systems Gradual Transition: Example of Hybrid Implementation: How Does PoLW Add Value to Blockchain Ecosystem PoLW helps blockchain work in a way that saves energy and protects the environment by giving computers easy jobs instead of hard puzzles. This allows the network to process more transactions per unit of energy consumed. Estimates by research studies suggest that switching to PoLW could reduce energy consumption by over 90% compared to traditional PoW systems. Final Words and Future Directions One of the main technical challenges in transitioning to PoLW is ensuring that the new system can handle the same volume of transactions as PoW without compromising on performance, and developing and optimizing algorithms that are energy-efficient yet secure and effective in validating transactions is key to overcoming this challenge. Meanwhile, ensuring that PoLW maintains the same level of security as PoW is critical. This involves rigorous testing and validation of the new consensus mechanism to prevent vulnerabilities and attacks. Collaboration between academia, industry, and environmental organizations can drive this innovation and adoption of its use. In conclusion, adopting sustainable practices like PoLW will be crucial in environmental impacts and ensuring a greener future. The benefits of PoLW are bountiful; it dramatically reduces energy consumption and operational costs, making blockchain mining more accessible and profitable. This democratization of mining can lead to a more decentralized and resilient blockchain network. Furthermore, by promoting energy-efficient and renewable energy practices, PoLW contributes to a substantial reduction in the carbon footprint of blockchain technology, aligning it with global sustainability goals. To ensure successful implementation of PoLW, strong support from the blockchain community and developers is required, in addition to engaging with stakeholders through forums, workshops, and collaborative projects facilitating a much smoother transition and incentive to adopt this
Diving Into Multi Party Computations
Multi-Party Computation (MPC) is a technology where multiple computers work together to perform a computation, such as creating a digital signature, without any single computer knowing the entire input. This way, sensitive data, like a private key for a cryptocurrency wallet, is divided among several parties, enhancing the security. None of the parties have complete information, reducing the risk of theft or loss. This method ensures that no single point of failure exists, making it more secure than traditional single-key methods. Multi-Party Computation was created to enhance data security and privacy. It allows multiple parties to jointly compute a function over their inputs while keeping those inputs private; in the context of cryptocurrency wallets, MPC splits a private key among several parties, ensuring no single entity has full control. This reduces the risk of theft, fraud, and loss by eliminating single points of failure, thus providing a higher level of security for digital assets. How do Multi Party Computations Work Multiparty computation (MPC) enables multiple parties to collaboratively compute a function over their respective inputs while preserving the privacy of those inputs. The fundamental principle is that no individual party gains knowledge about others’ inputs beyond what is deducible from the final output. Here’s an overview of how MPC operates: The different protocols that are used by MPC in systems are: What Are the Technical Features of MPC Multi-Party Computation (MPC) offers many features including privacy, by distributing sensitive data among multiple parties; security, which reduces risks by eliminating single points of failure; collaborative computation, allowing joint operations while keeping inputs confidential; fault tolerance, ensuring continued functionality despite compromises; and flexibility, applicable across diverse scenarios like secure voting, private auctions, and cryptocurrency transactions. A Multi-Party Computation (MPC) wallet enhances security by splitting private keys among multiple parties, preventing any single entity from having complete control. This approach mitigates risks associated with single points of failure and provides advanced access control. While MPC wallets offer significant security benefits, they can involve higher communication costs and technical complexity. Additionally, not all MPC wallets are open-source, which can impact their interoperability with other systems. The Advantages MPC Brings to New Technology Using MPC offers benefits like enhanced security through distributed control of private keys, improved privacy by restricting data exposure, effective risk mitigation by eliminating single points of failure, and advanced access control for secure management of permissions and access. These features make MPC an attractive solution for applications requiring high levels of security and privacy. Multi-Party Computation (MPC) is mainly used in areas where data security and privacy are critical, for instance: Multi-Party Computation works by distributing a computation across multiple parties, where each party holds a piece of the input data. These parties collaboratively perform the computation without revealing their individual pieces to each other. This ensures that no single party has access to the entire input data, enhancing security and privacy. The process typically involves the following steps: The Limitations to Multi Party Computation Multi-party computation (MPC) is a powerful cryptographic technique, but it does come with certain limitations and challenges: Last Thoughts Despite these limitations, ongoing research and advancements in MPC continue to address many of these challenges, making it a promising approach for secure multiparty computations in various domains. Multi-Party Computation (MPC) stands as a robust solution for enhancing data security and privacy across various domains. By distributing sensitive computations among multiple parties without revealing complete inputs to any single entity, MPC mitigates risks associated with theft, fraud, and single points of failure. Its applications span from secure cryptocurrency wallets to healthcare data sharing and beyond, offering advanced access control and resilience against attacks. Are you interested in learning more about how Multi Party Computations can be applied in your business? Optimus Fox has all the resources you need to dive deeper into the technological world. Connect with us now at info@optimusfox.com and get your headstart into the world of Web 3 technology.
Web3 Search Engines: A New Frontier for SEO
As the digital landscape continues its relentless evolution, we are witnessing a paradigm shift in understanding and interacting with the Internet. We’ve moved from static pages in Web1 to the interactive and social Web2, and now, we stand on the precipice of a new digital frontier: Web3. With the emergence of Web3 search engines, businesses, brands, and individuals are exploring uncharted territories of digital visibility. For those offering or seeking SEO services, it’s time to brace for yet another transformative phase. What is Web3? Before diving into the intricacies of Web3 search engines, it’s essential to have a fundamental grasp of Web3 itself. Web3 represents the third era of the web, characterized by decentralized platforms and applications; unlike Web2, which primarily revolves around central entities controlling data and media, Web3 places power in the hands of its users. It operates using blockchain technology, giving birth to decentralized applications (DApps), non-fungible tokens (NFTs), and other decentralized services. Enter Web3 Search Engines Search engines have always been the backbone of the Internet, guiding users to find relevant content amongst the vast digital expanse. As the web decentralized, it was only natural that decentralized search engines would emerge, aligning with the principles of Web3. These search engines operate differently from their centralized counterparts. They focus on user privacy, transparent algorithms, and, most importantly, crawling and indexing decentralized web spaces, which traditional search engines might overlook. SEO Services in the Age of Web3 With the rise of Web3 search engines, the need for adapted professional SEO services has never been more prominent. Here’s what businesses and digital marketers should focus on: Understanding Decentralized Platforms: Web3 offers many platforms, from decentralized websites to DApps. SEO experts must understand how these platforms function and how they’re indexed. Transparent Optimization: Web3 search engines prioritize transparency. The “black box” algorithms of yesteryears are no longer as practical. SEO services now require a more precise understanding and application of optimization techniques that align with decentralized principles. User Privacy and Data Protection: One of the cornerstones of Web3 is user-centricity, with a strong focus on data privacy. Brands and websites must respect this while crafting SEO strategies, ensuring user data protection is paramount. Engaging with New Content Types: With the rise of NFTs and decentralized marketplaces, new content types are emerging. SEO strategies must adapt to optimize these novel content formats for Web3 search engines. Collaboration and Community Building: The decentralized ethos encourages collaboration. Brands should focus not just on competition but also on building communities. It switches from optimizing for keywords to emphasizing genuine user engagement and collaboration. The Role of SEO Services in This Brave New World SEO has always been a variety of solutions. It’s about understanding the ever-shifting digital landscape and adapting strategies accordingly. As Web3 platforms become more mainstream, businesses will look for SEO services to navigate this new frontier and ensure their digital visibility remains strong. Moreover, with decentralized platforms, there’s a promise of a more equitable digital space where brands, regardless of their size, have an opportunity to shine. SEO services will be crucial in leveling the playing field, ensuring that even smaller entities can compete effectively in this expansive decentralized digital universe. In Conclusion The Web3 evolution is more than just a technological shift; it’s philosophical. As the digital realm becomes more user-centric, decentralized, and transparent, it offers challenges and opportunities in equal measure. For those in the SEO services industry, it’s an exciting time, ripe with potential. By understanding the nuances of Web3 search engines and crafting effective strategies, businesses can ensure they keep relevant and ahead of the curve in this new era of digital visibility.
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