The next AI era is here, and it requires living in the real world. AI needs us to wear it, move with it, and even drive it. Why? Because the next breakthrough isn\u2019t more text, it\u2019s massive amounts of real-world video and sensor data. Tech giants are now in a high-stakes race for both profitability and dominance, competing to capture physical data at scale through wearables, vehicles, and cameras everywhere. In this edition, we also share Physical AI tools and learning resources to help you understand and build the next phase of AI. Stay\u00a0curious.<\/em><\/p>\n The Next Era is Physical\u00a0AI.\ud83e\uddf0 AI Tools\u200a\u2014\u200aPhysical\u00a0AIMeta Vs. Microsoft vs.\u00a0Google\ud83d\udcdaLearning Corner\u200a\u2014\u200aMIT Robotics &\u00a0AISubscribe today and get 60% off for a year, free access to our 1,500+ AI tools database, and a complimentary 30-minute personalized consulting session to help you supercharge your AI strategy. Act now as it expires in 3\u00a0days\u2026<\/p>\n Get 60% off for 1\u00a0year<\/a><\/p>\n Nvidia Corp., Microsoft Corp., and Amazon.com Inc. are in discussions to invest <\/a>as much as $60 billion in OpenAI and Softbank another $30B<\/a> as part of a major new funding\u00a0round.Would you let Chrome take the wheel? Google begins rolling out Chrome\u2019s \u201cAuto Browse<\/a>\u201d AI agent\u00a0today.Meta users will start to see new AI models and products from the company in a matter of months, including AI-powered shopping assistants.<\/a>It\u2019s hard to imagine a world in several years where most glasses that people wear aren\u2019t AI glasses. Meta on the future of wearables<\/a>.Google is testing voice cloning<\/a> and GitHub repository import for AI Studio, hinting at native audio upgrades and new tools aimed at developers.<\/p>\n Snap gets serious about Specs, spins AR glasses<\/a> into standalone company.The rise of weather influencers<\/a>. Because who watches TV anymore\u2026 They are fun, informative, and some influencers are actually meteorologists.KPop Demon Hunters <\/em>is officially the most-streamed movie of 2025 with 20.5 billion minutes\u00a0watched<\/a>.<\/p>\n Share<\/a><\/p>\n Most businesses think churn = cancellations.\u200a\u2014\u200a<\/strong>That\u2019s\u00a0wrong<\/strong><\/a>.<\/strong><\/p>\n What your payment processor doesn\u2019t<\/strong> clearly show\u00a0you:<\/p>\n Failed payments silently kill 5\u20139% of\u00a0revenue<\/strong>Customers at risk before<\/strong> they\u00a0cancelWhich products actually drive\u00a0churnHow much revenue could be recovered every\u00a0month<\/p>\n We built a real-time financial command center<\/strong> on top of Stripe and any payment processor that\u00a0shows:<\/p>\n MRR, Net Revenue, Churned\u00a0MRRRevenue by product &\u00a0customerSubscription risk\u00a0alertsFailed payment recovery\u00a0trackingClient lifetime value &\u00a0tenureIf you do <\/em>$20k+\/month<\/em><\/strong>,<\/em><\/a> you\u2019re almost certainly leaving money on the\u00a0table.<\/em><\/p>\n Schedule a Free Discovery Call.<\/a><\/p>\n As large language models (LLMs) hit a training plateau in terms of the amount of text currently existing in the world that the models can swallow, inference<\/a> becomes the priority for these\u00a0models.<\/p>\n What are World models? They are neural networks that understand the dynamics of the real world, including physics and spatial properties. They can use input data, text, image, video, and movement to generate videos that simulate realistic physical environments. Physical AI<\/a> developers use world models to generate custom synthetic data or downstream AI models for training robots. Physical AI simplified is the system that bridges the digital and physical worlds, allowing machines to perceive, reason, and interact with their surroundings in real\u00a0time.<\/p>\n Humans and all animals interact with their surroundings unconsciously and without much thinking. We walk through spaces without hitting immovable objects, put our clothes on, drive, and navigate our world using our senses, and even optimize our own spaces to improve navigation. As of now, LLMs only navigate texts, images, and videos that they have as input and create outputs accordingly. World models that are trained to give machines \u201cspatial intelligence\u201d an internal understanding of physics, cause-and-effect, and 3D space. To train them, they ingest millions of hours of real-world video<\/a> to understand motion and dynamics. By predicting subsequent events, the model can generate simulations, enabling robots to practice tasks virtually before attempting them physically. These learned capabilities are then fine-tuned for specific hardware configurations, such as autonomous vehicles or robotic appendages.<\/p>\n Remember the saying \u201cData is the new Oil\u201d? Well, now companies with the most video data (YouTube, Meta, Tesla, and maybe the ESPNs for sports) have an upper hand in this new paradigm<\/a>. But, this is just the beginning, as the battle for wearables<\/a> intensifies, the data that these devices generate becomes more valuable, because Meta glasses worn by millions means hours of real world footabe used to train spatial models. Maybe these wearables will become ubiquitous and relatively cheap as similar to social media, we will become the product that provides the training data (videos) to tech companies as we wear these so-called wearables and drive cars with multiple\u00a0cameras<\/a>.<\/p>\n Major tech companies like NVIDIA, Google DeepMind, and Meta are developing world models to overcome current AI limitations, such as a lack of intuitive understanding of cause-and-effect and 3D space. Specialized startups like World Labs and AMI Labs are also working on this \u201cspatial intelligence\u201d to enable robots and autonomous systems to predict physical outcomes before acting, with applications in automotive, manufacturing, and entertainment industries. Startups and established companies are rushing to release wearables to get ahead of the next era. Snap<\/a> just spun its wearable division into its own company, Google glasses are making a comeback, we all know Meta and RayBans devices, and OpenAI has been working on its AI device with Jony\u00a0Ive<\/a>.<\/p>\n This is just the beginning. In the next edition, I\u2019ll break down how spatial computing, world models, and Physical AI will shape decision-making, how machines won\u2019t just answer questions, but tell us what to do\u00a0next.<\/p>\n Courses & Educational Content<\/p>\n MIT\u200a\u2014\u200a<\/strong>Robotics & AI<\/strong><\/a> Microsoft spends big, but Meta and Google are emerging as the winners, so\u00a0far.<\/strong><\/p>\n Microsoft<\/strong> posted $81.3B in quarterly revenue<\/strong> (+17% YoY) and $38.5B in profit<\/strong> (+60% YoY), while pouring $37.5B into capex<\/strong> in a single quarter (+65% YoY), largely for AI data\u00a0centers.Azure grew 39% YoY<\/strong>, confirming strong AI demand, but capacity constraints are expected to last through 2026<\/strong>, pressuring margins. Shares fell >5% after hours<\/strong> despite the\u00a0beat.<\/p>\n Why Meta looks like the real\u00a0winner<\/strong><\/p>\n Meta<\/strong> just revealed strong profitability with far lower AI infrastructure intensity<\/strong> than Microsoft.Meta\u2019s AI strategy leans heavily on open-source (Llama)<\/strong> and internal efficiency<\/strong>, driving AI-powered gains in ads without matching hyperscaler-level capex.While Microsoft commits tens of billions per quarter<\/strong> and absorbs near-term margin risk, Meta is monetizing AI now<\/strong>, with operating leverage\u00a0intact.<\/p>\n Microsoft<\/strong><\/a> is spending aggressively on AI infrastructure, but most AI profits today still come from enterprise products<\/strong> like Azure AI and Copilot, not direct consumer monetization. AI-driven capex is rising faster than near-term margins.<\/p>\n Meta<\/strong><\/a>, meanwhile, has already cracked AI monetization. AI-driven ranking and recommendation systems across Instagram, Facebook, and WhatsApp<\/strong> have increased engagement and ad efficiency, directly lifting ad impressions and revenue. Ads account for ~98% of Meta\u2019s revenue<\/strong>, and AI is now core to that\u00a0engine.<\/p>\n Google<\/strong><\/a> is embedding AI directly into Search, Chrome (AI Mode), Workspace (Gemini), and Cloud<\/strong>. These upgrades improve retention, ad performance, and cloud growth, keeping AI tightly linked to revenue instead of standalone products.<\/p>\n Meta is monetizing AI immediately<\/strong>, Google is reinforcing its ads + cloud flywheel<\/strong>, and Microsoft is still converting massive AI investment into consumer-level profit.<\/p>\n Physical AI<\/strong><\/p>\n DreamerV3<\/a> (by DeepMind)\u200a\u2014\u200aA state-of-the-art world model for latent space planning and learning. Known for sample-efficient reinforcement learning. The next AI era is here, and it requires living in the real world.<\/a> was originally published in Coinmonks<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>","protected":false},"excerpt":{"rendered":" \ud83e\uddbeThe Next Era is Physical\u00a0AI. The next AI era is here, and it requires living in the real world. AI needs us to wear it, move with it, and even drive it. Why? Because the next breakthrough isn\u2019t more text, it\u2019s massive amounts of real-world video and sensor data. Tech giants are now in a […]<\/p>\n","protected":false},"author":0,"featured_media":131175,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-131174","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-interesting"],"_links":{"self":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/131174"}],"collection":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=131174"}],"version-history":[{"count":0,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/131174\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/media\/131175"}],"wp:attachment":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=131174"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=131174"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=131174"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\ud83d\udcf0 AI News and\u00a0Trends<\/h3>\n
Other Tech\u00a0News<\/h3>\n
Your Payment Processor Is Hiding Your Revenue\u00a0Leaks<\/h3>\n
The Next Era is Physical\u00a0AI.<\/h3>\n
\ud83d\udcdaLearning Corner<\/h3>\n
Strong academic grounding in perception, control, and physical\u00a0systems.<\/p>\nMeta vs. Microsoft<\/h3>\n
\ud83e\uddf0 AI Tools of The\u00a0Day<\/h3>\n
Good for Simulated physics, control tasks, and open-ended environments.MuZero<\/a> (DeepMind)\u200a\u2014\u200aLearns dynamics and value models without knowing the environment rules up front. Great for Game environments, planning, and hybrid model-based approaches.Kubernetes-enabled Isaac Gym<\/a> (NVIDIA)\u200a\u2014\u200aHigh-performance physics engine + world representation for robotics and control simulations. Good for Large-scale training with GPUs and multi-agent environments.Unity ML-Agents<\/a> (OpenAI\/Unity)\u200a\u2014\u200aEnvironment + world model training toolkit with built-in reinforcement learning. Good for Game-like simulations, visual world modeling, and embodied\u00a0agents.PETS<\/a> (Probabilistic Ensembles with Trajectory Sampling)\u200a\u2014\u200aClassic world model technique using probabilistic dynamics with uncertainty estimates. Model-based RL, planning under uncertainty.Explore our AI Guides<\/em><\/strong><\/a>\u200a\u2014\u200aFrom coding to photography and beyond, find step-by-step tips to put AI to work for\u00a0you.<\/em><\/p>\n