DjamgaMind: Audio Intelligence for the C-Suite (Energy, Healthcare, Finance)

Are you drowning in dense legal text? DjamgaMind is the new audio intelligence platform that turns 100-page healthcare or Energy mandates into 5-minute executive briefings. Whether you are navigating Bill C-27 (Canada) or the CMS-0057-F Interoperability Rule (USA), our AI agents decode the liability so you don’t have to. 👉 Start your specialized audio briefing today at Djamgamind.com

AI Jobs and Career

I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

• Full Stack Engineer [$150K-$220K]
• Software Engineer, Tooling & AI Workflow, Contract [$90/hour]
• DevOps Engineer, India, Contract [$90/hour]
• More AI Jobs Opportunitieshere

Job Title	Status	Pay
Full-Stack Engineer	Strong match, Full-time	$150K - $220K / year
Developer Experience and Productivity Engineer	Pre-qualified, Full-time	$160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract)	Contract	$90 / hour
DevOps Engineer (India)	Full-time	$20K - $50K / year
Senior Full-Stack Engineer	Full-time	$2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India	Contract	$20 - $30 / hour
Senior Software Engineer	Contract	$100 - $200 / hour
Senior Software Engineer	Pre-qualified, Full-time	$150K - $300K / year
Senior Full-Stack Engineer: Latin America	Full-time	$1.6K - $2.1K / week
Software Engineering Expert	Contract	$50 - $150 / hour
Generalist Video Annotators	Contract	$45 / hour
Generalist Writing Expert	Contract	$45 / hour
Editors, Fact Checkers, & Data Quality Reviewers	Contract	$50 - $60 / hour
Multilingual Expert	Contract	$54 / hour
Mathematics Expert (PhD)	Contract	$60 - $80 / hour
Software Engineer - India	Contract	$20 - $45 / hour
Physics Expert (PhD)	Contract	$60 - $80 / hour
Finance Expert	Contract	$150 / hour
Designers	Contract	$50 - $70 / hour
Chemistry Expert (PhD)	Contract	$60 - $80 / hour

What is OpenAI Q*? A deeper look at the Q* Model as a combination of A* algorithms and Deep Q-learning networks.

Embark on a journey of discovery with our podcast, ‘What is OpenAI Q*? A Deeper Look at the Q* Model’. Dive into the cutting-edge world of AI as we unravel the mysteries of OpenAI’s Q* model, a groundbreaking blend of A* algorithms and Deep Q-learning networks. 🌟🤖

In this detailed exploration, we dissect the components of the Q* model, explaining how A* algorithms’ pathfinding prowess synergizes with the adaptive decision-making capabilities of Deep Q-learning networks. This video is perfect for anyone curious about the intricacies of AI models and their real-world applications.

Understand the significance of this fusion in AI technology and how it’s pushing the boundaries of machine learning, problem-solving, and strategic planning. We also delve into the potential implications of Q* in various sectors, discussing both the exciting possibilities and the ethical considerations.

Join the conversation about the future of AI and share your thoughts on how models like Q* are shaping the landscape. Don’t forget to like, share, and subscribe for more deep dives into the fascinating world of artificial intelligence! #OpenAIQStar #AStarAlgorithms #DeepQLearning #ArtificialIntelligence #MachineLearningInnovation”

🚀 Whether you’re a tech enthusiast, a professional in the field, or simply curious about artificial intelligence, this podcast is your go-to source for all things AI. Subscribe for weekly updates and deep dives into artificial intelligence innovations.

✅ Don’t forget to Like, Comment, and Share this video to support our content.

📌 Check out our playlist for more AI insights

📖 Read along with the podcast:

Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover rumors surrounding a groundbreaking AI called Q*, OpenAI’s leaked AI breakthrough called Q* and DeepMind’s similar project, the potential of AI replacing human jobs in tasks like wire sending, and a recommended book called “AI Unraveled” that answers frequently asked questions about artificial intelligence.

Rumors have been circulating about a groundbreaking AI known as Q* (pronounced Q-Star), which is closely tied to a series of chaotic events that disrupted OpenAI following the sudden dismissal of their CEO, Sam Altman. In this discussion, we will explore the implications of Altman’s firing, speculate on potential reasons behind it, and consider Microsoft’s pursuit of a monopoly on highly efficient AI technologies.

To comprehend the significance of Q*, it is essential to delve into the theory of combining Q-learning and A* algorithms. Q* is an AI that excels in grade-school mathematics without relying on external aids like Wolfram. This achievement is revolutionary and challenges common perceptions of AI as mere information repeaters and stochastic parrots. Q* showcases iterative learning, intricate logic, and highly effective long-term strategizing, potentially paving the way for advancements in scientific research and breaking down previously insurmountable barriers.

Let’s first understand A* algorithms and Q-learning to grasp the context in which Q* operates. A* algorithms are powerful tools used to find the shortest path between two points in a graph or map while efficiently navigating obstacles. These algorithms excel at optimizing route planning when efficiency is crucial. In the case of chatbot AI, A* algorithms are used to traverse complex information landscapes and locate the most relevant responses or solutions for user queries.

On the other hand, Q-learning involves providing the AI with a constantly expanding cheat sheet to help it make the best decisions based on past experiences. However, in complex scenarios with numerous states and actions, maintaining a large cheat sheet becomes impractical. Deep Q-learning addresses this challenge by utilizing neural networks to approximate the Q-value function, making it more efficient. Instead of a colossal Q-table, the network maps input states to action-Q-value pairs, providing a compact cheat sheet to navigate complex scenarios efficiently. This approach allows AI agents to choose actions using the Epsilon-Greedy approach, sometimes exploring randomly and sometimes relying on the best-known actions predicted by the networks. DQNs (Deep Q-networks) typically use two neural networks—the main and target networks—which periodically synchronize their weights, enhancing learning and stabilizing the overall process. This synchronization is crucial for achieving self-improvement, which is a remarkable feat. Additionally, the Bellman equation plays a role in updating weights using Experience replay, a sampling and training technique based on past actions, which allows the AI to learn in small batches without requiring training after every step.

Q* represents more than a math prodigy; it signifies the potential to scale abstract goal navigation, enabling highly efficient, realistic, and logical planning for any query or goal. However, with such capabilities come challenges.

One challenge is web crawling and navigating complex websites. Just as a robot solving a maze may encounter convoluted pathways and dead ends, the web is labyrinthine and filled with myriad paths. While A* algorithms aid in seeking the shortest path, intricate websites or information silos can confuse the AI, leading it astray. Furthermore, the speed of algorithm updates may lag behind the expansion of the web, potentially hindering the AI’s ability to adapt promptly to changes in website structures or emerging information.

Another challenge arises in the application of Q-learning to high-dimensional data. The web contains various data types, from text to multimedia and interactive elements. Deep Q-learning struggles with high-dimensional data, where the number of features exceeds the number of observations. In such cases, if the AI encounters sites with complex structures or extensive multimedia content, efficiently processing such information becomes a significant challenge.

To address these issues, a delicate balance must be struck between optimizing pathfinding efficiency and adapting swiftly to the dynamic nature of the web. This balance ensures that users receive the most relevant and efficient solutions to their queries.

In conclusion, speculations surrounding Q* and the Gemini models suggest that enabling AI to plan is a highly rewarding but risky endeavor. As we continue researching and developing these technologies, it is crucial to prioritize AI safety protocols and put guardrails in place. This precautionary approach prevents the potential for AI to turn against us. Are we on the brink of an AI paradigm shift, or are these rumors mere distractions? Share your thoughts and join in this evolving AI saga—a front-row seat to the future!

Please note that the information presented here is based on speculation sourced from various news articles, research, and rumors surrounding Q*. Hence, it is advisable to approach this discussion with caution and consider it in light of further developments in the field.

How the Rumors about Q* Started

There have been recent rumors surrounding a supposed AI breakthrough called Q*, which allegedly involves a combination of Q-learning and A*. These rumors were initially sparked when OpenAI, the renowned artificial intelligence research organization, accidentally leaked information about this groundbreaking development, specifically mentioning Q*’s impressive ability to ace grade-school math. However, it is crucial to note that these rumors were subsequently refuted by OpenAI.

It is worth mentioning that DeepMind, another prominent player in the AI field, is also working on a similar project called Gemini. Gemina is based on AlphaGo-style Monte Carlo Tree Search and aims to scale up the capabilities of these algorithms. The scalability of such systems is crucial in planning for increasingly abstract goals and achieving agentic behavior. These concepts have been extensively discussed and explored within the academic community for some time.

The origin of the rumors can be traced back to a letter sent by several staff researchers at OpenAI to the organization’s board of directors. The letter served as a warning highlighting the potential threat to humanity posed by a powerful AI discovery. This letter specifically referenced the supposed breakthrough known as Q* (pronounced Q-Star) and its implications.

Mira Murati, a representative of OpenAI, confirmed that the letter regarding the AI breakthrough was directly responsible for the subsequent actions taken by the board. The new model, when provided with vast computing resources, demonstrated the ability to solve certain mathematical problems. Although it performed at the level of grade-school students in mathematics, the researchers’ optimism about Q*’s future success grew due to its proficiency in such tests.

A notable theory regarding the nature of OpenAI’s alleged breakthrough is that Q* may be related to Q-learning. One possibility is that Q* represents the optimal solution of the Bellman equation. Another hypothesis suggests that Q* could be a combination of the A* algorithm and Q-learning. Additionally, some speculate that Q* might involve AlphaGo-style Monte Carlo Tree Search of the token trajectory. This idea builds upon previous research, such as AlphaCode, which demonstrated significant improvements in competitive programming through brute-force sampling in an LLM (Language and Learning Model). These speculations lead many to believe that Q* might be focused on solving math problems effectively.

Considering DeepMind’s involvement, experts also draw parallels between their Gemini project and OpenAI’s Q*. Gemini aims to combine the strengths of AlphaGo-type systems, particularly in terms of language capabilities, with new innovations that are expected to be quite intriguing. Demis Hassabis, a prominent figure at DeepMind, stated that Gemini would utilize AlphaZero-based MCTS (Monte Carlo Tree Search) through chains of thought. This aligns with DeepMind Chief AGI scientist Shane Legg’s perspective that starting a search is crucial for creative problem-solving.

It is important to note that amidst the excitement and speculation surrounding OpenAI’s alleged breakthrough, the academic community has already extensively explored similar ideas. In the past six months alone, numerous papers have discussed the combination of tree-of-thought, graph search, state-space reinforcement learning, and LLMs (Language and Learning Models). This context reminds us that while Q* might be a significant development, it is not entirely unprecedented.

OpenAI’s spokesperson, Lindsey Held Bolton, has officially rebuked the rumors surrounding Q*. In a statement provided to The Verge, Bolton clarified that Mira Murati only informed employees about the media reports regarding the situation and did not comment on the accuracy of the information.

In conclusion, rumors regarding OpenAI’s Q* project have generated significant interest and speculation. The alleged breakthrough combines concepts from Q-learning and A*, potentially leading to advancements in solving math problems. Furthermore, DeepMind’s Gemini project shares similarities with Q*, aiming to integrate the strengths of AlphaGo-type systems with language capabilities. While the academic community has explored similar ideas extensively, the potential impact of Q* and Gemini on planning for abstract goals and achieving agentic behavior remains an exciting prospect within the field of artificial intelligence.

In simple terms, long-range planning and multi-modal models together create an economic agent. Allow me to paint a scenario for you: Picture yourself working at a bank. A notification appears, asking what you are currently doing. You reply, “sending a wire for a customer.” An AI system observes your actions, noting a path and policy for mimicking the process.

The next time you mention “sending a wire for a customer,” the AI system initiates the learned process. However, it may make a few errors, requiring your guidance to correct them. The AI system then repeats this learning process with all 500 individuals in your job role.

Within a week, it becomes capable of recognizing incoming emails, extracting relevant information, navigating to the wire sending window, completing the required information, and ultimately sending the wire.

This approach combines long-term planning, a reward system, and reinforcement learning policies, akin to Q* A* methods. If planning and reinforcing actions through a multi-modal AI prove successful, it is possible that jobs traditionally carried out by humans using keyboards could become obsolete within the span of 1 to 3 years.

If you are keen to enhance your knowledge about artificial intelligence, there is an invaluable resource that can provide the answers you seek. “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is a must-have book that can help expand your understanding of this fascinating field. You can easily find this essential book at various reputable online platforms such as Etsy, Shopify, Apple, Google, or Amazon.

AI Unraveled offers a comprehensive exploration of commonly asked questions about artificial intelligence. With its informative and insightful content, this book unravels the complexities of AI in a clear and concise manner. Whether you are a beginner or have some familiarity with the subject, this book is designed to cater to various levels of knowledge.

By delving into key concepts, AI Unraveled provides readers with a solid foundation in artificial intelligence. It covers a wide range of topics, including machine learning, deep learning, neural networks, natural language processing, and much more. The book also addresses the ethical implications and social impact of AI, ensuring a well-rounded understanding of this rapidly advancing technology.

Obtaining a copy of “AI Unraveled” will empower you with the knowledge necessary to navigate the complex world of artificial intelligence. Whether you are an individual looking to expand your expertise or a professional seeking to stay ahead in the industry, this book is an essential resource that deserves a place in your collection. Don’t miss the opportunity to demystify the frequently asked questions about AI with this invaluable book.

In today’s episode, we discussed the groundbreaking AI Q*, which combines A* Algorithms and Q-learning, and how it is being developed by OpenAI and DeepMind, as well as the potential future impact of AI on job replacement, and a recommended book called “AI Unraveled” that answers common questions about artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!

📢 Advertise with us and Sponsorship Opportunities

Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” available at Etsy, Shopify, Apple, Google, or Amazon

The Future of Generative AI: From Art to Reality Shaping

Improving Q* (SoftMax with Hierarchical Curiosity)

Combining efficiency in handling large action spaces with curiosity-driven exploration.

Source: GitHub – RichardAragon/Softmaxwithhierarchicalcuriosity

Softmaxwithhierarchicalcuriosity

Adaptive Softmax with Hierarchical Curiosity

This algorithm combines the strengths of Adaptive Softmax and Hierarchical Curiosity to achieve better performance and efficiency.

Adaptive Softmax

Adaptive Softmax is a technique that improves the efficiency of reinforcement learning by dynamically adjusting the granularity of the action space. In Q*, the action space is typically represented as a one-hot vector, which can be inefficient for large action spaces. Adaptive Softmax addresses this issue by dividing the action space into clusters and assigning higher probabilities to actions within the most promising clusters.

Hierarchical Curiosity

Hierarchical Curiosity is a technique that encourages exploration by introducing a curiosity bonus to the reward function. The curiosity bonus is based on the difference between the predicted reward and the actual reward, motivating the agent to explore areas of the environment that are likely to provide new information.

Combining Adaptive Softmax and Hierarchical Curiosity

By combining Adaptive Softmax and Hierarchical Curiosity, we can achieve a more efficient and exploration-driven reinforcement learning algorithm. Adaptive Softmax improves the efficiency of the algorithm, while Hierarchical Curiosity encourages exploration and potentially leads to better performance in the long run.

Here’s the proposed algorithm:

1. Initialize the Q-values for all actions in all states.

2. At each time step:

   a. Observe the current state s.

   b. Select an action a according to an exploration policy that balances exploration and exploitation.

   c. Execute action a and observe the resulting state s’ and reward r.

   d. Update the Q-value for action a in state s:

   Q(s, a) = (1 – α) * Q(s, a) + α * (r + γ * max_a’ Q(s’, a’))

   where α is the learning rate and γ is the discount factor.

   e. Update the curiosity bonus for state s:

   curio(s) = β * |r – Q(s, a)|

   where β is the curiosity parameter.

   f. Update the probability distribution over actions:

   p(a | s) = exp(Q(s, a) + curio(s)) / ∑_a’ exp(Q(s, a’) + curio(s))

3. Repeat steps 2a-2f until the termination criterion is met.

The combination of Adaptive Softmax and Hierarchical Curiosity addresses the limitations of Q* and promotes more efficient and effective exploration.

• 
  Why experts can't agree on whether AI has a "mind"
  by /u/timemagazine (Artificial Intelligence) on January 22, 2026 at 6:32 pm

  Research from leading AI labs suggests that AI systems are capable of lying, scheming, and surprising their creators. Whether or not AI can be conscious, it is clearly doing something markedly more sophisticated than previous generations of digital technology. These developments are forcing a reckoning with fundamental questions: What is a mind? And do AI systems have one? We explore that question in this recent article — let us know what you think. submitted by /u/timemagazine [link] [comments]

• 
  How will AI affect radiology/pathology?
  by /u/Single_Baseball2674 (Artificial Intelligence) on January 22, 2026 at 6:31 pm

  I’m a med student thinking about which specialty to pursue. I really like radiology and pathology, but I’m worried that AI could replace these jobs or push wages down. I’ve asked on radio/path subreddits, and people there said AI was still far from replacing humans, that it could barely handle simple tasks and would just be a tool for doctors. That sounds reassuring, but I can’t help wondering if it’s just wishful thinking. What do you guys think? submitted by /u/Single_Baseball2674 [link] [comments]

• 
  Feeling lost in this GenAI Ocean to study
  by /u/ScratchSpecialist505 (Artificial Intelligence) on January 22, 2026 at 6:01 pm

  I'm an experienced developer, I've trained CNNs to Qwen models. I have just started GenAI journery creating RAG agents and text2sql style agents. But I'm feeling lost on what to learn and where to learn. I would love to work in some MAANG level firm but I'm quite unsure on what they are expecting (non AI-research roles). I tried contributing the langgraph/langchain repos but those take away from GenAI rather than into it. Please help submitted by /u/ScratchSpecialist505 [link] [comments]

• 
  Transformers (LLMs) might be a dead end for reasoning, and we need to talk about "Energy" architectures.
  by /u/Aware-Asparagus-1827 (Artificial Intelligence) on January 22, 2026 at 5:37 pm

  I've been thinking a lot lately about the "plateau" we seem to be hitting with current LLMs. Don't get me wrong, GPT-4 and Claude are amazing at language, but they still fail at basic planning or maintaining a consistent internal logic over long contexts. It feels like we are trying to brute-force "intelligence" just by predicting the next token. It’s like System 1 thinking (fast, intuitive) without System 2 (slow, deliberate checking). I was reading up on Yann LeCun’s recent takes on this, and the concept of Energy-Based Models (EBMs) really stood out to me as the potential fix. For those who haven't dug into it: The core difference is that instead of just guessing the next word based on probability, an EBM defines an "energy function" that measures the compatibility between the input and the potential output. It basically asks: "Does this answer violate the rules of reality/logic?" and tries to minimize that conflict before giving an answer. It sounds much closer to how we actually reason - we don't just blurt out words; we simulate the outcome in our heads first to see if it makes sense. Do you think auto-regressive models (like the ones we use now) can ever solve the reliability/hallucination problem just by scaling data? Or are we inevitably going to pivot to objective-driven architectures like EBMs to get to AGI? Would love to hear thoughts from people working on the architecture side. submitted by /u/Aware-Asparagus-1827 [link] [comments]

• 
  Need some Physical AI (robotics) project ideas
  by /u/Nervous_Lab_2401 (Artificial Intelligence) on January 22, 2026 at 5:20 pm

  So, basically we are tasked to create a robot that have "AI features" in it, i.e. it automates some real world task. BUT the issue is we dont have a lot of hardware knowledge... Last semester they were teaching us python and then all of a sudden we were told about Arduino and ESP and about a project that requires to interact with its environment, i.e. make a robot! We failed badly lol. We burnt three esps and later we learned that our voltage was too high for sensors. anyways, The logic is we as students specializing in AI should be familiar w physical AI. So, now I found multiple projects online, like, automated delivery bot, posture detecting thing, mood detectors, ASL detectors, blind support stick, etc. But I would appreciate if I can get some guidance and project ideas 🙂 submitted by /u/Nervous_Lab_2401 [link] [comments]

• 
  [Results] #1 on MLE-Bench (among open-source systems) + #1 on ALE-Bench
  by /u/alirezamsh (Artificial Intelligence) on January 22, 2026 at 5:17 pm

  We’re sharing results on two knowledge-grounded, long-horizon benchmarks. KAPSO is a knowledge-grounded framework for autonomous program synthesis and optimization: it iteratively improves runnable artifacts under an explicit evaluator. Results: • MLE-Bench (Kaggle-style ML engineering): #1 among open-source, reproducible systems. • ALE-Bench (AtCoder heuristic optimization): #1 on ALEBench / long-horizon algorithmic discovery. Repo: https://github.com/Leeroo-AI/kapso We’ll post follow-ups with more examples and use cases. submitted by /u/alirezamsh [link] [comments]

• 
  Focusing on skills instead of constant advice
  by /u/Coffee_Talkerr (Artificial Intelligence) on January 22, 2026 at 5:02 pm

  In India, advice comes from everywhere. family, relatives, social media, even strangers. Most of it is well-intended, but it can also be overwhelming and confusing. I decided to tune out some of that noise and focus on building skills that help daily life. I’ve been learning practical AI usage through Be10X , things like planning work, organizing thoughts, and improving basic efficiency. No big expectations, just steady improvement. That alone feels grounding. submitted by /u/Coffee_Talkerr [link] [comments]

• 
  Claude's new constitution
  by /u/HimothyJohnDoe (Artificial Intelligence (AI)) on January 22, 2026 at 4:53 pm

  submitted by /u/HimothyJohnDoe [link] [comments]

• 
  What happens when large models are trained on increasing amounts of AI-generated text?
  by /u/SonicLinkerOfficial (Artificial Intelligence) on January 22, 2026 at 4:37 pm

  I've been thinking about this way too much, will someone with knowledge please clarify what's actually likely here. A growing amount of the internet is now written by AI. Blog posts, docs, help articles, summaries, comments. You read it, it makes sense, you move on. Which means future models are going to be trained on content that earlier models already wrote. I’m already noticing this when ChatGPT explains very different topics in that same careful, hedged tone. Isn't that a loop? I don’t really understand this yet, which is probably why it’s bothering me. I keep repeating questions like: Do certain writing patterns start reinforcing themselves over time? (looking at you em dash) Will the trademark neutral, hedged language pile up generation after generation? Do explanations start moving toward the safest, most generic version because that’s what survives? What happens to edge cases, weird ideas, or minority viewpoints that were already rare in the data? I’m also starting to wonder whether some prompt “best practices” reinforce this, by rewarding safe, averaged outputs over riskier ones. I know current model training already use filtering, deduplication, and weighting to reduce influence of model-generated context. I’m more curious about what happens if AI-written text becomes statistically dominant anyway. This is not a "doomsday caused by AI" post. And it’s not really about any model specifically. All large models trained at scale seem exposed to this. I can’t tell if this will end up producing cleaner, stable systems or a convergence towards that polite, safe voice where everything sounds the same. Probably one of those things that will be obvious later, but I don't know what this means for content on the internet. If anyone’s seen solid research on this, or has intuition from other feedback loop systems, I’d genuinely like to hear it. submitted by /u/SonicLinkerOfficial [link] [comments]

• 
  If AI is a Marathon and not Sprint, China Wins This One.
  by /u/ranaji55 (Artificial Intelligence) on January 22, 2026 at 4:25 pm

  China’s top models are climbing very quickly and the gap to the best US closed or top-tier models are shrinking fast. And China’s best open-source models have already overtaken the US. Open-source models spread through downloads, fine-tuning, and on-prem deployment, so leadership there can translate into faster global adoption even without controlling the top closed models. China leads on open-source models, which are released freely for developers to adapt and retrain. (More on why that matters below.) Essentially, the country has shown it can innovate around its shortfalls in high-volume, leading-edge chipmaking by developing advanced models with much less compute power than the US. Given Chinese companies’ surprising catch-up towards the AI frontier and Beijing’s centralised approach to industrial strategy, the possibility of China’s chip technology and manufacturing eventually surpassing US capabilities shouldn’t be ruled out. https://www.capitaleconomics.com/publications/china-economics-focus/chinas-ai-rollout-could-rival-us https://www.ft.com/content/d9af562c-1d37-41b7-9aa7-a838dce3f571 submitted by /u/ranaji55 [link] [comments]