Brett Adcock, known for founding Figure AI, is back with a new project called Hark, and he’s going all in. Backed by $100 million of his own money, the goal is to build what he calls “the most advanced personal intelligence in the world.” Instead of launching a single product, Hark is aiming to build an entire AI system from scratch.

What makes Hark stand out is how broad the approach is. Rather than focusing on just software or models, the team is working on everything at once — foundation models, software, and custom-built hardware, all designed to work together as one system. The team itself is stacked, with talent coming from companies like Apple, Google, Tesla, and Amazon. On top of that, the timeline is aggressive, with plans to deploy large-scale GPU infrastructure soon and release their first models within months, followed by dedicated hardware shortly after.

Anthropic just dropped a bunch of new features in a really short time, and it honestly feels like they’re trying to catch up fast. With updates like Dispatch, Claude Code Channels, and Projects in Cowork, they’re basically building their own version of an AI agent system that can handle real tasks across devices. Instead of just chatting with Claude, the focus is clearly shifting toward letting it actually do work in the background.

One of the most interesting parts is how connected everything is becoming. You can message Claude from your phone while it works on your desktop, assign tasks like organizing files or writing reports, and then come back later to finished results. Developers can even interact with Claude Code through apps like Telegram or Discord, which makes it feel more like texting a coworker than using a tool. On top of that, Projects brings everything into one place, keeping files, instructions, and context organized locally, which also adds a layer of privacy.

Yann LeCun is starting fresh with a new company called AMI Labs, and the scale of it is already huge. They secured over $1 billion in funding right away, even though there’s no product yet. Instead of building another chatbot, the goal is to rethink how AI systems work at a much deeper level.

The focus is on something called “world models.” Instead of predicting the next word like most current AI systems, these models aim to understand how things change over time and how different factors influence each other. That would make AI less about generating text and more about actually understanding cause and effect. Even without a clear business model yet, major players like NVIDIA are backing the idea, which shows how much confidence there is in this direction.

OpenAI just released GPT-5.4, and it looks like they’re trying to push their models further into real professional work. Instead of focusing on one specific ability, GPT-5.4 combines reasoning, coding, and agent-style workflows into a single system designed to handle complex tasks from start to finish. The idea is basically that AI shouldn’t just answer questions anymore — it should be able to actually carry out work.

Early benchmarks suggest it’s already performing extremely well across many knowledge-work tasks. In tests covering dozens of professions, GPT-5.4 matched or even beat human experts most of the time. Another big step is that the model can now operate computers directly, navigating software and completing actions in real environments instead of just generating instructions. On top of that, improved tool handling and a massive context window mean the system can plan longer workflows and use external tools more efficiently without wasting tokens.

Perplexity just launched something called Computer, and the idea behind it is pretty ambitious. Instead of using just one AI model for everything, Computer connects more than 19 different frontier models into a single workflow system. The goal is that you describe what you want to achieve, and the system automatically breaks the task down and assigns each step to the model that’s best at it.

For example, one model might handle reasoning, another might do deep research, while others generate images, videos, or quick search results. What makes it interesting is that it doesn’t just respond to a single prompt like normal chatbots. Computer can run tasks in the background for hours or even days, managing full projects and only asking the user for input when it actually needs it. Right now it’s available to Perplexity Max subscribers, but the bigger idea is clearly to turn AI into something that manages complex workflows almost like a digital operating system.

Anthropic just released Claude Sonnet 4.6, and it’s a surprisingly big upgrade for what’s supposed to be their “mid-tier” model. Early testers are saying it performs almost like the much more expensive Opus model, especially in things like coding, reasoning, and complex tasks. What makes it interesting is that it manages to get that performance without the same cost, which could make it a much more practical option for developers and companies.

One of the biggest improvements is how well it handles real computer tasks. Users reported that Sonnet 4.6 can navigate complicated spreadsheets, fill out multi-step web forms, and work across multiple browser tabs in ways that feel almost human. On top of that, the model now supports a 1-million-token context window, meaning it can analyze huge amounts of information at once, like entire codebases or large research documents. That makes it much better for long-term reasoning and complex workflows.

ElevenLabs is starting to look less like a niche AI startup and more like a major player in the voice space. After raising $500 million and hitting an $11B valuation, they’re now pushing hard on making voice agents actually usable in real-world scenarios. The focus isn’t just on sounding realistic anymore, but on making conversations feel natural and responsive.

A big part of that is their new “Expressive Mode,” which allows voice agents to pick up on emotions like stress and adjust how they speak in real time. That means conversations can feel less robotic and more human, especially in situations like customer support. At the same time, ElevenLabs is expanding into the public sector, where their agents are already handling thousands of calls per day and solving most of them without human help. It’s a clear sign that voice AI is moving from demo to real deployment.

Elon Musk just merged SpaceX and xAI in a massive $1.25 trillion deal, creating what could be the most valuable private company ever. The move doesn’t just combine rockets and AI, it also sets up a potential blockbuster IPO that some estimate could be worth around $50 billion. It’s one of those announcements that sounds almost unreal, even by Musk standards.

The idea behind the merger is big and kind of wild. Musk wants to push AI infrastructure into space, arguing that Earth’s power grids won’t be able to handle AI’s future energy needs. SpaceX has already asked regulators for permission to massively expand Starlink into an “orbital data center system,” jumping from around 9,400 satellites today to potentially over a million. At the same time, critics point out that xAI is burning huge amounts of money and still trails competitors like OpenAI and Google, making some people see this deal as risky financial engineering rather than pure innovation.

Google DeepMind’s Project Genie lets users create and explore interactive worlds using just text prompts and images. Instead of generating a single static scene, Genie builds the environment ahead of you in real time as you move through it, which makes the experience feel much more alive. Even though it’s still a research prototype, the idea alone already feels like a big shift.

What makes Genie especially interesting is that it goes far beyond gaming. The system can simulate physics and interactions in a way that could be useful for robotics, animation, training simulations, or exploring historical and fictional environments. On top of that, Gemini’s new Agentic Vision turns image understanding into an active process, where the AI can zoom in, inspect, and manipulate visuals step by step instead of just analyzing them once.

Anthropic is clearly pushing Claude beyond being just a chatbot and more into a real productivity tool. By expanding Claude in Excel to Pro plans, a lot more users can now use it for actual spreadsheet work instead of just testing it in limited environments. The focus seems to be on making Claude fit naturally into everyday workflows, not just sit in a separate AI interface.

At the same time, Anthropic is experimenting with health data connections, allowing Claude to summarize and explain medical information when users explicitly opt in. Alongside these practical updates, they also published the full Constitution that defines how Claude should behave, outlining priorities like safety, ethics, and helpfulness. It’s a pretty transparent move that shows how seriously they’re taking the idea of AI having a defined “character.”

Anthropic released Cowork, a research preview that brings Claude Code’s agent-style abilities directly to the Claude Desktop app. Instead of just answering questions, Claude can now actually work through tasks on its own, making it feel more like a digital coworker than a chatbot.

Cowork allows users to describe a goal in plain language and then let Claude figure out the steps needed to get there. It can directly read and write local files, meaning it can create proper Excel sheets with formulas, PowerPoint presentations, and well-formatted documents without manual copying. For more complex tasks, Claude splits the work into smaller subtasks and runs them in parallel, handling things like research, data processing, and synthesis almost completely on its own.

NVIDIA used CES to clearly show where their focus is going next: physical AI. Instead of just text and images, NVIDIA is pushing AI into the real world, like robotics, self-driving cars, and systems that actually interact with physical environments. This makes AI feel a lot less abstract and way more impactful.

One big step is AlpamayO, NVIDIA’s new open-source model for autonomous driving. It doesn’t just make decisions but explains them step by step, which is huge for safety and trust. On top of that, NVIDIA introduced Cosmos, a set of simulation models that let developers train autonomous systems in virtual worlds before they ever hit real roads. Finally, the new Rubin AI platform shows that NVIDIA isn’t just doing research — they’re scaling this tech for real production, with much cheaper and more efficient hardware coming soon.

Meta announced that it bought an AI startup called Manus for over $1 billion, which is a huge move in the AI race. Manus focuses on building autonomous AI agents that can plan, use tools, and execute tasks on their own instead of just answering questions. Meta plans to integrate this system into products like WhatsApp, Messenger, and even smart glasses. What makes this deal interesting is that Manus didn’t win by having a better AI model, but by building a smarter structure around the model.

This shows a bigger trend in AI: raw intelligence isn’t everything anymore. How AI is deployed, connected to tools, and scaled across products matters just as much. By buying Manus, Meta skips years of internal development and gets a system that already works in real-world scenarios. It also positions Meta strongly for future AI assistants that actually act instead of just chatting.

Nvidia and Stanford University released NitroGen, an open-source AI that can play more than 1,000 video games. Instead of learning one game at a time, it was trained on around 40,000 hours of gameplay videos from YouTube and Twitch. By watching humans play, NitroGen learned controls, strategies, and reactions. What’s impressive is that it also performs well in games it has never seen before.

This shows real progress toward general gaming AI instead of game-specific bots. Because NitroGen is open-source, developers and researchers can improve it freely. That could speed up innovation in gaming AI a lot.

OpenAI released several updates that improve how ChatGPT handles images, coding, and daily use. With GPT Image 1.5, images are generated faster, look sharper, and handle lighting, details, and even text much better. OpenAI also upgraded Codex, making it stronger for long and complex programming tasks like refactoring. On top of that, ChatGPT got usability features like writing blocks, pinned chats, and personalization options.

These changes might not sound dramatic, but they make ChatGPT feel more polished and reliable. Instead of focusing on big promises, OpenAI is improving the small things people use every day. This makes the tool more practical for school, work, and creative projects.

Disney announced that it’s investing $1 billion into OpenAI and signing a three-year licensing deal that lets people create AI-generated videos and images using over 200 characters from Disney, Pixar, Marvel, and Star Wars. These creations will be made using tools like Sora and ChatGPT Images, which shows that Disney is no longer just watching AI from the sidelines. What makes this even crazier is that just a day earlier, Disney had sent Google a cease-and-desist letter over large-scale copyright issues.

This move shows a big strategy change: instead of fighting AI everywhere, Disney is choosing to license its content where it makes sense. One major issue with AI is that it can basically memorize famous characters, which creates legal risks—often called the “Snoopy problem.” By licensing its characters, Disney turns a legal headache into something officially allowed. On top of that, Disney is becoming a premium data partner at a time when AI companies are running out of high-quality training material. Its huge character library is now a powerful asset, not just something to protect.

ByteDance released a new AI video model called Vidi2, and it’s beating some of the strongest AI systems on video understanding benchmarks. The model is especially good at understanding what’s happening across time in videos, finding specific moments, and answering questions about video content. What makes this impressive is that it combines several skills—like tracking objects, understanding scenes, and answering questions—into one system instead of separate tools.

Vidi2 outperformed competing models on multiple benchmarks, especially when it comes to understanding motion across frames and quickly finding very short video moments. It can handle videos ranging from just a few seconds up to half an hour, which makes it useful for real-world applications. Because of this, the model isn’t just for research but also fits professional workflows like video editing, automatic camera switching, and tracking characters across scenes. Overall, it shows how fast video-focused AI is improving.

U.S. President Donald Trump signed an executive order launching the “Genesis Mission,” a massive national project designed to speed up scientific discovery using AI. The idea is similar to the Manhattan Project, but instead of weapons, it focuses on science and technology. The U.S. government wants to combine powerful supercomputers, huge federal datasets, and AI agents to automate research and test scientific ideas faster than humans alone could. This would all run on existing government research infrastructure.

The Department of Energy will be in charge of building the platform, connecting national labs, universities, and approved private companies. The plan moves very fast: within a few months, officials must identify major scientific challenges and quickly show real results. These challenges include areas like biotech, nuclear fusion, quantum computing, semiconductors, and advanced manufacturing. Strict cybersecurity rules are meant to protect sensitive research while still allowing collaboration. Overall, the project shows how seriously the U.S. is taking AI as a strategic tool for science and national security.

ElevenLabs, best known for realistic AI voices, is now expanding into images and video with a new Image & Video platform (currently in beta). Instead of building everything from scratch, ElevenLabs connects top image and video models like Sora, Veo, and Kling into one unified workspace. The idea is to let creators generate visuals, add AI voiceovers, include music, and layer sound effects all in one place. This turns ElevenLabs from a voice tool into a full content creation platform.

What makes this move strong is the workflow focus. Users don’t need to jump between different apps for images, video, and audio anymore. Everything happens inside one timeline, from the first idea to the final export. By targeting creators, marketers, and content teams, ElevenLabs is positioning itself as a serious alternative to using multiple separate tools. It’s less about having the best single model and more about making creation faster and smoother.

OpenAI released GPT-5.1, an update that makes ChatGPT feel smarter, warmer, and more adaptable depending on the situation. The new version can adjust how much it thinks before answering, so it responds quickly to simple questions but takes more time on harder ones. This makes answers clearer and more accurate, especially for math and coding, while still feeling fast in normal conversations. On top of that, OpenAI added detailed controls that let users change ChatGPT’s tone and style.

One big change is that ChatGPT now has different personality presets like Professional, Friendly, or Quirky, which affect how it talks across all chats. This directly responds to feedback that earlier versions felt too cold or robotic. By combining smarter reasoning with personality controls, ChatGPT feels more human and easier to use. It’s less about raw intelligence now and more about how the AI communicates with people.

Google revealed Project Suncatcher, a long-term project that explores building AI infrastructure directly in space using solar-powered satellites. The idea is to use satellite constellations equipped with Google’s TPUs and fast optical links to process data in orbit instead of on Earth. This sounds extreme, but it makes sense when you realize how much energy the Sun produces and how much more efficient solar panels can be in space, where they get constant sunlight. Google is basically testing whether space could become the next place for massive data centers.

Google has already tested key parts of this idea, including TPUs that survived intense radiation and satellite-to-satellite communication speeds fast enough for serious data transfer. Two prototype satellites are planned to launch by early 2027 in partnership with Planet to test how well everything works in orbit. If launch costs continue to fall, space-based AI infrastructure could eventually cost about the same as Earth-based data centers. That would completely change how and where computing happens in the future.

Grammarly made a pretty unusual branding move by renaming its parent company to “Superhuman” after acquiring the email app with the same name. Instead of fully absorbing the Superhuman brand, Grammarly flipped the structure and made Grammarly itself a product under the Superhuman umbrella. It’s a confusing change at first, but also a bold one that shows the company wants to be seen as more than just a grammar checker.

Along with the rebrand, Grammarly launched an AI assistant called Superhuman Go, which connects to tools like Gmail, Google Drive, Calendar, and Jira. The idea is that the AI understands what you’re working on and helps you write better while also automating small tasks, like logging tickets. With earlier acquisitions like Coda and Superhuman, Grammarly is now building a full productivity suite instead of just a writing tool. This puts it in direct competition with platforms like Notion and Google Workspace.