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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.

https://youtu.be/0RQZJ83aaLA

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!

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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.

• 
  I tested the 85k-star "MoneyPrinterTurbo" AI video repo. Here’s why automated AI channels are a trap.
  by /u/Marvin-Celosky (Artificial Intelligence) on June 11, 2026 at 7:15 pm

  Hey everyone, If you spend any time on X or tech YouTube, you’ve probably seen the hype around "automated AI passive income channels." A repo called MoneyPrinterTurbo has been blowing up recently (85k stars, 12k forks) promising to generate complete, HD short videos with one click using an LLM, Text-to-Speech, Pexels API, and FFmpeg. Since my crypto portfolio is currently in passive management mode and I was bored, I decided to do a full technical audit and test it so you don’t have to waste your time. Here is the quick breakdown of why this architecture fundamentally breaks down for 95% of content niches. The Technical Flaw: Parallel, Blind Pipelines The tool operates by splitting tasks into silos. The LLM writes a text script. Then, the tool extracts a few global keywords from your overall topic, hits the Pexels/Pixabay API, downloads whatever stock clips match those keywords, and stitches them together using FFmpeg. There is zero semantic synchronization between the audio and the video tracks. I tested it on two specific scripts: Bittensor (Technical Crypto Niche): The script explained decentralized subnets and tokenomics. The tool generated global keywords like "blockchain." When the voiceover discussed Anthropic or validators, the video showed city traffic time-lapses and random 2017-era blockchain animations. The human brain detects this cognitive dissonance within 3 seconds and swipes away. Opossums (Niche Topic): Pexels has zero stock footage of opossums. Because the asset engine doesn’t know what to do, it fell back on adjacent terms. The video literally talked about opossum behavior while displaying high-def clips of squirrels, cheetahs, and elephants. Where It Actually "Works" The tool only succeeds in ultra-generic, broad lifestyle content—like "5 Habits of Successful People." Why? Because stock libraries are packed with generic videos of people reading books, drinking coffee, or running. The visual track doesn’t need to mean anything specific; it just needs to not actively contradict the voiceover. The Real Math on the "Money Printer" Even if you flood YouTube Shorts with generic lifestyle content, the monetization math is brutal. Spanish/broad-niche AdSense CPM sits around $1–3 per thousand views. Getting to the YouTube Partner Program requires 10 million Shorts views. You are looking at 6 to 12 months of daily, consistent automated publishing just to compete with channels that have been doing this since 2020, all for pennies. The money printer prints, but it prints the wrong content for an audience that is already drowning in it. I wrote a much more detailed breakdown, including the specific pipeline architecture and screenshots of the UI settings that screw up the retention rates. If you want to read the full post-mortem, you can check it out here: https://hodlerchronicles.substack.com/p/i-tested-moneyprinterturbo-so-you Thanks for reading! submitted by /u/Marvin-Celosky [link] [comments]

• 
  Google AI Mode & Google Lens Are Seriously Underrated
  by /u/Top-Sandwich-7829 (Artificial Intelligence) on June 11, 2026 at 6:48 pm

  Is it just me, or are Google AI Mode and Google Lens becoming everyday essentials? I use them to identify products, translate text, copy notes, summarize topics, and get quick answers in seconds. Search is starting to feel more visual and conversational than ever. What's your favorite use case? submitted by /u/Top-Sandwich-7829 [link] [comments]

• 
  True
  by /u/ExpensiveCoat8912 (Artificial Intelligence) on June 11, 2026 at 4:57 pm

  submitted by /u/ExpensiveCoat8912 [link] [comments]

• 
  AI as Radar, Not a Death Ray
  by /u/WillowEmberly (Artificial Intelligence) on June 11, 2026 at 4:51 pm

  Why the Long-Term Value of AI May Be Detection Rather Than Replacement The Popular Story Most public conversations about AI assume its primary value will come from replacing human labor. The narrative is familiar: · AI becomes "smarter" than humans · AI performs work faster and cheaper · Humans are removed from the loop · Productivity explodes This is the death ray vision of AI — a focus on direct action: replacing workers, replacing experts, replacing decision makers, replacing institutions. The assumption is simple: the greatest value of AI comes from what it can do instead of people. But history suggests a different pattern. --- A Historical Parallel In 1935, the British Air Ministry asked physicist Robert Watson‑Watt whether radio waves could be used as a "death ray" to disable enemy aircraft. The answer was no. The physics didn't work. But while disproving the weapon, Watson‑Watt and Arnold Wilkins discovered something far more important: aircraft could be detected using reflected radio waves. The death ray failed. The detection concept succeeded. That discovery became radar. Radar did not destroy aircraft. Radar made aircraft visible. --- The Dowding Problem The lesson of radar is often misunderstood. Detection alone was not decisive. Britain's advantage came from connecting detection to interpretation and action. Radar stations generated signals, but the Dowding System — filter rooms, plotting tables, communication networks, fighter squadrons — transformed those signals into operational awareness. Raw detections became orientation. Orientation became coordination. Coordination became force multiplication. A small fighter force could now be in the right place at the right time. The challenge for AI is similar. Data alone is not enough. Detection must be connected to interpretation, coordination, and response. That is the hinge of this entire argument. But there is a deeper lesson: visibility alone does not create change. Radar did not win the Battle of Britain. The Dowding System did. Detection only becomes valuable when communities, organizations, and institutions possess the capacity to respond. An instrument can reveal the storm. It cannot make people leave the beach. --- The Same Pattern Appears in AI Most discussions still treat AI as a replacement technology. But many of the most valuable uses emerging today follow the radar pattern instead. AI is often most useful when it: · notices patterns · detects drift · surfaces anomalies · reveals hidden dependencies · identifies bottlenecks · monitors changing conditions · preserves continuity across time In other words: AI frequently creates value by making systems visible. This is organizational radar, not automation. --- Why Detection Matters Most failures are not sudden. Organizations rarely collapse overnight. Teams rarely fail instantly. Projects rarely become dysfunctional in a single moment. Instead, problems accumulate: · trust erodes · knowledge disappears · coordination weakens · incentives drift · maintenance is deferred · workloads become unsustainable · assumptions stop matching reality The difficulty is not that these changes occur. The difficulty is that they are invisible while they are happening. By the time failure becomes obvious, recovery is expensive. Sometimes impossible. --- A Necessary Warning Every radar creates a surveillance risk. The same instrument that helps a community detect erosion can help an institution monitor compliance. The difference is not technical. It is governance. The question is not whether AI can see. The question is who controls the screen, who interprets the signal, and whose interests determine the response. Detection systems can be gamed, ignored, politicized, or used for control rather than stewardship. AI as radar is powerful — but only when paired with governance that prioritizes continuity over extraction. --- Human Blind Spots Humans are capable, but limited: limited attention, limited memory, limited monitoring capacity, emotional attachment, normalization of deviance, fatigue, organizational politics. People adapt to gradual degradation. What would have seemed alarming six months ago becomes normal today. This is why many disasters appear "unexpected" even though warning signs existed for months or years. The signals were present. The system simply could not see them clearly. --- AI as Persistent Observation AI introduces a new capability. Not superhuman wisdom. Not perfect judgment. Persistent attention. AI can: · continuously monitor information · compare present conditions to past baselines · identify deviations · maintain records · preserve institutional memory · surface weak signals This is less like an autonomous decision maker and more like an instrument panel. The AI does not replace the pilot. It improves the pilot's orientation. --- Concrete Examples Human TAWS – Terrain Awareness and Warning Systems do not fly aircraft. They warn pilots when terrain risk is increasing. The value comes from earlier awareness, not automated control. Organizational Diagnostics – AI may detect declining trust, rising turnover risk, communication breakdown, workload imbalance, or governance erosion. AI is not fixing the organization. It is making deterioration visible before collapse. Governance Systems – Execution-boundary governance does not decide strategy. It verifies authority, policy alignment, evidence quality, and execution legitimacy. The value comes from preventing unnoticed drift between intent and action. Knowledge Continuity – AI can preserve institutional memory, procedures, reasoning chains, and lessons learned. This reduces the risk that critical capabilities disappear when individuals leave. --- The Shift From Action to Orientation Traditional automation asks: "How can we perform actions automatically?" A radar-oriented perspective asks: "How can we improve orientation before action occurs?" Good decisions require visibility, context, timing, and understanding. AI may ultimately provide more value by improving orientation than by replacing decision makers. --- The Hidden Opportunity Weapons are easy to fund because their effects are obvious. Detection systems are harder to justify because their value is often invisible. A radar system is judged by disasters avoided. A warning system is judged by failures that never occur. Yet historically, these systems create extraordinary long-term value. Radar became weather radar. Weather radar became storm forecasting. Storm forecasting saves lives every year. The original "death ray" project ultimately produced a civilization-scale detection infrastructure. --- A Possible Future The most enduring contribution of AI may not be autonomous replacement of human beings. It may be the creation of new forms of detection: · organizational radar · governance radar · continuity radar · trust radar · resilience radar · social weather radar Systems capable of revealing hidden drift while there is still time to act. But again: detection is necessary, not sufficient. An instrument can reveal the storm. It cannot make people leave the beach. The capacity to respond — the Dowding System of each organization — must be built alongside the radar. --- The Core Idea The greatest value of AI may not be that it thinks better than humans. The greatest value may be that it helps humans see what they would otherwise miss. Just as radar made aircraft visible before they arrived overhead, AI may make emerging risks, failures, and opportunities visible before they become crises. The same way a family dinner reveals who is struggling before they say a word, AI can reveal when trust, knowledge, or coordination is silently eroding. Radar did not create more fighters. It made existing fighters more effective. In the same way, the most valuable AI systems may not replace human judgment. They may multiply it. The future of AI may belong less to autonomous decision‑makers and more to instruments that make hidden conditions visible early enough for people to respond. Because most failures do not begin with catastrophe. They begin with signals nobody noticed. --- submitted by /u/WillowEmberly [link] [comments]

• 
  We Can’t Let My Former V.C. Colleagues Buy Off Our Democracy
  by /u/Calvinball_24 (Artificial Intelligence) on June 11, 2026 at 4:25 pm

  submitted by /u/Calvinball_24 [link] [comments]

• 
  ⚡️ Anthropic and OpenAI subscriptions are more unprofitable than previously thought
  by /u/andrewaltair (Artificial Intelligence) on June 11, 2026 at 3:32 pm

  https://preview.redd.it/nyp7rp75ao6h1.png?width=1920&format=png&auto=webp&s=fd7ccf62b7d63b8d2ec50cd784a6a95939a4bc05 In January, researchers already calculated the real cost of Claude Code subscriptions when converted to API rates. Back then, a $200/month subscription would have cost ~$2,700 at API prices. SemiAnalysis repeated the experiment across all provider tiers using long coding tasks until the weekly limits were exhausted, and the current figures are noticeably higher. For Anthropic, the number has nearly tripled: claude-max-20x for $200/month is equivalent to $8,000/month via the API. OpenAI is even worse: chatgpt-pro-20x for the same $200 draws a whopping $14,000/month. SemiAnalysis believes that all new models and features will be held back exclusively for API users. And Fable (Mythos), as already known, will disappear from subscriptions starting June 22 and will only be available via extra usage. submitted by /u/andrewaltair [link] [comments]

• 
  An engineer was fired days after warning Musk’s AI about Grok’s safety risks
  by /u/Cybernews_com (Artificial Intelligence) on June 11, 2026 at 1:21 pm

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

• 
  Not all uses of AI for writing are slop
  by /u/AddlepatedSolivagant (Artificial Intelligence) on June 11, 2026 at 1:21 pm

  I'll concede that many of them are: I've certainly seen instances in which someone copy-pasted what they got from a chatbot and called it a day. But that's not what I'm talking about. I'm talking about uses of AI for writing in which the final product has zero text produced by the chatbot. My weakness (which existed long before AI) is memory. A way that AI has helped me is to get it to prompt me so that I can find out what I know. In a recent project, I had to write a paper about a data analysis that I did a few months ago, and looking over the code I wrote and the presentations I gave about it wasn't ringing any bells—it might as well have been somebody else's work. So I pointed a coding agent at those files and asked it to ask me a hundred detailed questions, from which it was to write a first draft. Note: I never had any intention of using that draft, but saying so focused the goal. I have no qualms about lying to a robot. They were all good questions. It took me the better part of a day to answer them all with a few paragraphs each. Apparently, my memory is such that if asked, "Tell me about this project," I draw a blank, but if asked, "Why did you do this here?" I can answer right away. It came back in details first, and from those details I could reconstruct in my mind the big picture. By the time I finished answering those questions, I was ready to write. But still it was helpful that the AI had written a draft, particularly because it was such a bad draft. Have you ever heard of the trick in which you can get somebody to work on something by saying, "Don't worry, I'll do it," and then doing a bad job of it? A certain type of person is triggered to fix something if they see it done badly, though they wouldn't have done it if nothing existed at all. I'm one of those people, and getting AI to make a bad draft is a way of playing that trick on myself. "Let me show you how it's done" is a strong motivator, even if the one being schooled is a robot. In all, it took two days to write the paper, which is pretty quick for this sort of thing. No words from the AI ended up in the final paper even though I had them both in the same file and replaced them little by little like a Ship of Theseus. From past experience, I can say that without AI, this would have taken much longer, but not for good reasons. Those extra days would have been spent procrastinating because I was unable to get my head into it. Maybe this technique is particular to me and my bad memory, but I'll bet there are other legitimate uses of AI for writing—uses other than "Write it for me." submitted by /u/AddlepatedSolivagant [link] [comments]

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  what do you think actually decides who comes out ahead between Anthropic and OpenAI over the next few years?
  by /u/Conscious_Ad_821 (Artificial Intelligence) on June 11, 2026 at 12:52 pm

  not asking who’s “better” right now since that flips every release. more curious what people think the deciding factor ends up being long term. is it raw model quality, or does that converge and the winner is whoever nails distribution and enterprise lock-in? OpenAI has the consumer mindshare and ChatGPT as a verb, Anthropic seems to be quietly winning on coding and enterprise/API. and does “winning” even mean one of them dominates, or do they just split into different lanes the way AWS and Azure did, where nobody really wins, they just both get huge? curious where people land, especially anyone using both heavily for actual work rather than just following the headlines. submitted by /u/Conscious_Ad_821 [link] [comments]

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  I created the better version of an ai chatbot
  by /u/DrJonah345 (Artificial Intelligence) on June 11, 2026 at 12:44 pm

  Most AI chatbots on websites work the same. a chat window opens, the user types a question, the AI writes an answer, and the user has to figure out where to click on their own. I wanted to try something different. My idea: what if the AI just shows you? It creates a full step by step guide, highlights the buttons, scrolls to the right section, walks you through each step directly on the page. The technical challenge was giving the AI enough context to actually understand what's on the screen. I ended up combining two sources 1. a DOM snapshot for structure and text content, and 2. an html2canvas screenshot for visual layout. Both get sent to Claude Haiku, which generates step-by-step guidance. A MutationObserver watches for DOM changes after each step so the AI can react when the page updates. You can install it with a single script tag so it works on any website without manual setup. It's called Phaysr if you want to check it out. Would love to hear your thoughts on this tool and if you would use something like this. submitted by /u/DrJonah345 [link] [comments]