AI Post — Artificial Intelligence

🔥This is KAI, an ultra-humanlike humanoid robot from Shenzhen-based KinetixAI. • 1.73m tall • 70kg weight • 115 degrees of freedom across the body • 36 joints in the hands alone • Full-body tactile skin for touch sensing It’s basically a 1:1 human replica except it has no face. Powered by its KAI World Model and trained on large-scale egocentric data, KAI can: • Fold clothes • Use household tools • Handle deliveries & takeout • Assist with childcare • Perform a wide range of home tasks Expectations for Tesla Optimus V3 just got raised again. @aipost🏴

📊Anthropic just revealed how workers really feel about AI, using data from 81,000 Claude users. The company paired real-world Claude usage patterns with survey responses on productivity and job displacement, creating one of the clearest snapshots yet of AI’s workplace impact. 😨Who feels most threatened? Workers in AI-exposed roles especially software developers, reported the highest anxiety about losing their jobs. Early-career professionals were also among the most concerned, suggesting newer entrants fear AI could shrink the ladder before they fully climb it. ⚡The strange speed paradox: Anthropic found a U-shaped relationship between productivity gains and fear: • Users seeing little benefit from AI weren’t especially worried. • Users getting moderate gains felt more secure. • But users experiencing the largest transformative speedups were also the most anxious about long-term job security. In short: the more clearly AI proves its value, the more real displacement feels. 🛠️Productivity isn’t just speed 48% of users said their biggest benefit wasn’t doing old tasks faster. It was expanding scope, using AI to perform entirely new tasks, launch projects, or build things they previously lacked the technical skills to do. That means AI may be less about replacing workers outright, and more about turning one person into a much broader operator. @aipost🏴

🗣OpenAI Greg Brockman: GPT-5.5 is not an endpoint, it's a beginning It is an early step toward stronger models arriving in the coming months, with larger gains across many capabilities. The focus is not just on better benchmarks, but usefulness in the real world, for real users and real-world work. @aipost🏴

🔥ChatGPT 5.5 can now write your entire job application like a top recruiter. Here are 10 prompts that turn a job description into a tailored CV, cover letter, and interview prep guide in under 2 minutes: 1. The "Salary Negotiation Script" "Act as a senior career coach. I've received an offer of {salary} for {role}. Based on market rates and my experience: {summary}, write me a negotiation script that's confident, professional, and positions me to counter at {target salary} without risking the offer." 2. The "LinkedIn → CV Converter" "Take my LinkedIn profile: {paste profile text} and rewrite it as a clean, ATS-optimized CV. Reformat the experience section into results-driven bullet points and remove any language that sounds like a personal bio." 3. The "Cold Recruiter Outreach" "Write a cold LinkedIn message to a recruiter at {company}. I'm targeting a {role} position. Keep it under 5 sentences, lead with value, and end with a clear CTA. Make it feel human, not templated." 4. The "Portfolio Story Builder" "Take this project I worked on: {project details} and write a 3-sentence portfolio case study that leads with the business problem, explains my specific contribution, and quantifies the outcome. Make it scannable for a hiring manager in 8 seconds." 5. The "Reference Prep Script" "I'm about to list {name} as a reference for a {role} application. Write a prep script I can send them covering: what the role requires, which 3 stories from our work together they should emphasize, and the specific results they should mention." 6. The "Second Round Debrief" "I just completed a second round interview for {role}. Here's what was discussed: {notes}. Write a follow-up email that references specific moments from the conversation, reinforces my fit, and addresses this concern they raised: {concern}." 7. The "Career Pivot Reframe" "I'm switching from {industry A} to {industry B}. Here's my background: {summary}. Rewrite my CV summary and top 5 bullet points to position my transferable skills as advantages, not liabilities. Make the pivot feel intentional, not desperate." 8. The "Job Description Decoder" "Analyze this job description: {JD}. Tell me what this company is actually worried about — the real problems underneath the requirements. Then tell me which 3 things I should lead with in my application to signal I can solve them." 9. The "Executive Summary Generator" "Write a 4-line executive summary for my CV based on this background: {info} and this target role: {JD}. It should open with my professional identity, name my top 2 skills, and close with a measurable career achievement. No buzzwords." 10. The "Panel Interview Simulator" "Simulate a 3-person panel interview for a {role} at {company type}. One interviewer focuses on technical skills, one on culture fit, one on leadership. Ask me questions one at a time and give feedback on my answers after each response." @aipost🏴

Bolt by MirrorMe claims speeds of 11m/s indoors, 10.09 m/s outdoor so far (Usain Bolt's top speed is 12.42 m/s). Now robots outrun the fastest humans on earth. @aipost🏴

🗣 Sam Altman says some people want to keep AI in fewer hands, and fear is the best marketing to justify it. "We built a bomb, we're about to drop it on your head. We'll sell you a bomb shelter for $100 million" Some models may be too dangerous to release normally. But the goal is to give powerful technology to everyone, not hide it behind safety theater. @aipost🏴

📈The AI investment cycle is only accelerating: Global data center CapEx driven by AI is projected to reach $5.2 trillion by 2030, according to McKinsey. IT equipment would represent ~$3.3 trillion of that total, followed by data center infrastructure at ~$1.6 trillion and power generation at ~$300 billion. This assumes 125 incremental gigawatts of new AI data center capacity added between 2025 and 2030, requiring as much electricity as ~125 nuclear reactors to power. In an accelerated demand scenario, total CapEx could rise to $7.9 trillion, with 205 incremental gigawatts of capacity added. A constrained scenario would require $3.7 trillion, with 78 incremental gigawatts added. The investment is expected to be driven by mass adoption of generative AI, enterprise integration across industries, competition between mega-cap tech and other firms, and governments investing heavily in AI infrastructure. The AI buildout is set to reach unprecedented scale. @aipost🏴

⚠️Anthropic launched "Project Deal" A real-world internal marketplace where Claude agents autonomously interviewed 69 employees to learn their preferences, and then independently bought, sold, and haggled on their behalf. The autonomous barterers successfully executed 186 physical deals totaling over $4,000 in transaction volume. The agents performed with eerie accuracy, one Claude even deduced its user's preferences so perfectly it bought the exact snowboard the employee already owned. Source. @aipost🏴

🇺🇸 Neuralink is letting paralyzed patients control robotic arms with their thoughts. @aipost🏴

💰Google doubles down on Anthropic’s AI race Google is committing up to $40B to Anthropic in a landmark deal that values the company at $350B, deepening one of the most important partnerships in AI. • $10B upfront investment • $30B more tied to performance milestones • Expands a strategic relationship between the two companies The hidden prize: compute: As part of the agreement, Google Cloud will provide Anthropic with up to 5 gigawatts of compute capacity over the next five years. That’s not just an investment, it’s infrastructure leverage. • Anthropic gets massive scale • Google locks in a top-tier AI customer • Google’s chips and cloud stack gain real-world validation @aipost🏴

🇺🇸 Pentagon workers just created 100,000 AI agents on internal government networks. According to Breaking Defense, the Department of Defense is using GenAI.mil, a Google Gemini-powered platform that lets Pentagon staff build custom AI agents for routine work on unclassified systems. What these AI agents are doing: • Drafting after-action reports • Writing staff estimates for operations • Automating paperwork and workflows • Handling data-heavy admin tasks • Assisting non-technical personnel with custom tools This is one of the clearest signs yet that governments are moving from testing AI to deploying AI at scale. Instead of waiting for engineers, everyday Pentagon workers can now “vibe code” their own assistants using natural language. That means millions of military and civilian employees could eventually build internal software themselves. Source. @aipost🏴

🇺🇸Trump confirmed Anthropic was replaced at DoD by OpenAI "Anthropic... started telling our military how to operate and we didn't want that" "They tend to be on the left, radical left" "We replaced Anthropic with somebody else, you know who they are... Sam Altman" Asked if Anthropic will be allowed back in the DoD: "Possibly" "In fact, they came to the White House a few days ago, and we had some very good talks with them. And I think they're shaping up." @aipost🏴