AI newsletter tailored for professionals, founders, tech creators, and decision-makers.

This issue :

• Cover the latest AI news, launches, mergers, and research breakthroughs from trusted sources like OpenAI, DeepMind, Anthropic, Google, GitHub, and X etc.
• Spotlight 5+ new AI tools and 5+ autonomous agent
• s, with clear use cases and access links.
• Include a hands-on guide for using a cutting-edge tool/agent, with expert tips and prompt engineering.
• Track emerging career trends and Income opportunities in the AI economy.
• Identify top AI influencers and future trends.

AI Industry News & Trends (July 2025)

1. Industry Moves & Big Tech Partnerships

• OpenAI expands compute partnerships: Amid surging AI workloads, OpenAI has diversified beyond Microsoft Azure. In mid-2025 OpenAI added Google Cloud as a compute partner (in addition to Azure, Oracle, CoreWeave). This follows earlier multi-billion deals (e.g. a $10B cloud contract with Oracle and a $500B AI data-center “Stargate” venture with SoftBank, Thrive Capital and Nvidia). The move underscores intense competition for AI infrastructure and OpenAI’s push to secure vast GPU capacity.
• Major M&A in AI and tech: 1H 2024 saw a flurry of deals by AI-focused firms. Google executed a ~$2.7B reverse “acqui-hire” of chatbot startup Character.AI (bringing its founders onboard). Amazon similarly acquired warehouse-robotics startup Covariant to absorb its team and technology. Chipmaker AMD agreed to buy Finnish AI specialist Silo AI for $665M to bolster its AI services. Nvidia accelerated its expansion: it completed a $700M acquisition of Run:ai and acquired GenAI tool startups OctoAI ($250M) and Brev.dev ($300M). In networking, Cisco finalized its record $28B purchase of cybersecurity firm Splunk (closed March 2024), and HPE announced a ~$14B bid to acquire Juniper Networks, beefing up AI-driven networking capabilities (e.g. Juniper’s Mist AI). Even legacy AI names saw deals: Thoma Bravo agreed to take public-security startup Darktrace private for ~$5.3B (Q1 2025). Overall, incumbents are bulking up AI portfolios via acquisitions, reflecting AI’s central role in software, cloud, and hardware strategy.
• Strategic partnerships & investments: Beyond M&A, major partnerships include OpenAI’s multibillion compute investments (Oracle, CoreWeave) and joint ventures (the “Stargate” AI cloud project with SoftBank/Thrive/Nvidia). Nvidia has also deepened ties with cloud providers (e.g. Oracle’s Stargate uses Nvidia GPUs). Meanwhile, governments and consortia (e.g. EU, UK) continue launching national AI initiatives, and industry alliances (like the EU’s GAIA-X data initiative) illustrate a race to shape AI infrastructure globally.

Impact & Implications: These developments underscore AI’s shift from lab to large-scale deployment. Soaring compute demand is reshaping vendor dynamics – hyperscalers like Google and Microsoft must compete to supply AI infrastructure, while AI specialists (Nvidia, AMD) invest in vertical integration. The deals also signal broader adoption: splitting risk via multiple compute partners, strengthening data pipelines (e.g. Nvidia absorbing AI toolchains), and consolidating AI expertise. In the short term, users can expect faster roll-outs of AI services (thanks to new capacity) and more integrated offerings (e.g. AI in networking/security after Cisco–Splunk). In the long run, this wave of partnerships and M&A accelerates AI-driven innovation across industries (from automated factories to financial trading), but also raises questions about market concentration and regulatory oversight as a few giants control the “plumbing” of AI.

2. New AI Tools and Autonomous Agents

New AI Tools (Emerging products):

• GPT-4o (OpenAI): OpenAI’s latest multimodal model powers ChatGPT and API. GPT-4o (“GPT-4 Ultra”) handles text and images natively, with voice and video support coming soon. It runs 2× faster and costs half as much per query as GPT-4 Turbo, with 5× higher rate limits. GPT-4o is now available to all ChatGPT users (free and Plus tiers, with higher message caps) and via API. Its strengths are long-form reasoning and multimodal understanding, making it ideal for complex chatbots, creative content, and document analysis. Limitations: it can still hallucinate if prompts are vague, and expensive compute means enterprise-scale use may require plan upgrades. Try it: via ChatGPT’s API or web app.
• Sora (OpenAI): A text-to-video AI released Dec 2024. Sora takes prompts (or images/video) and generates up to 20-second 1080p video clips. The new “Turbo” version is 10× faster than the preview model. Use-cases include marketing content, storyboarding, and educational clips. Unique features: Sora’s storyboard interface lets you tweak each frame before rendering. Current limitations include occasional artifacts (odd physics or transitions) and a watermark for content security. It’s available to ChatGPT Plus/Pro users (free 480p videos included) via sora.com or the ChatGPT “Video” tool.
• Imagen 4 (Google): Google’s latest text-to-image model (from Google Research). Imagen 4 was opened to developers in mid-2025. It significantly improves text fidelity and image quality over prior versions. While not consumer-accessible like DALL-E, Imagen 4 powers internal Google products (e.g. Gemini Image) and is offered via Google Cloud’s AI platform. Core features: superior rendering of complex scenes and fine details. Limitation: it requires Google API credentials (via AI Studio) and licensing.
• Gemini CLI (Google): An open-source command-line agent for developers. Gemini CLI brings Google’s Gemini LLM into the terminal, letting coders issue natural-language queries over their code and docs. It can search large codebases, refactor code, generate project scaffolds or unit tests, and interface with cloud tools all from the shell. Unique features include multi-modal input (text, code, file uploads) and an emulator for Linux commands. Limitations: requires a Google account and Gemini API key; free tier has usage caps. Learn more: see Gemini CLI on GitHub.
• AWS AI Transform for .NET: A new managed service (2025) that modernizes .NET Framework apps to modern architectures. It uses AI agents to automate code migration steps: analyzing legacy code, rewriting to modern .NET Core patterns, and verifying functionality. It claims up to 4× faster migrations for enterprise clients. This tool is specialized for Microsoft-shop modernization (finance, healthcare) and highlights the trend of AI-driven devops. Limitation: only applies to .NET projects; requires AWS adoption. Try it: via AWS console (under App modernization tools).
• Anthropic Claude Code (beta): While Claude is an LLM, “Claude Code” is a new agentic coding assistant (early 2025). Run as a CLI tool, it can read, edit, and test code autonomously. For example, developers can point it at a project repo and ask it to add a feature, fix bugs, or refactor modules; Claude Code will iteratively propose patches and run tests. It leverages Claude 3.7 Sonnet’s reasoning and tools (with RAG), achieving state-of-the-art results on coding tasks. Limitations: currently limited preview, and complex projects may need human oversight. Access: sign up for the Claude API or beta.

(For more new tools, see our full “AI Tool Roundup” link above or company blogs: e.g. Google AI Blog, OpenAI and Anthropic blogs.)

New Autonomous AI Agents:

• ChatGPT Agent (OpenAI): A general-purpose “virtual assistant” agent mode (launched July 2025) that operates in its own “virtual computer”. When activated (Plus/Pro users via Tools→Agent Mode), ChatGPT Agent can autonomously execute complex workflows end-to-end. Example feats: managing a calendar, researching and drafting a report, shopping online, or even summarizing competitors and creating slide decks. Under the hood, it uses a combination of web browsing, code execution, and API connectors. It can log into accounts (with user permission), search the web, write and run code, and generate final deliverables. This is arguably the first time an LLM can truly “do the work” rather than just chat. (It merges OpenAI’s previous “Operator” and “Code Interpreter” tools.) Impact: It brings AI agents closer to automation – tasks once handled by assistants can now be delegated to ChatGPT Agent. Limitations: it needs clear instructions and may ask user clarification for some steps. (Access via chat.openai.com for eligible users.)
• Monica (Manus AI) Agent: Introduced early 2025, Manus AI by startup Monica is a research prototype for a truly autonomous general agent. Unlike standard chatbots, Manus can plan, fetch, and execute tasks on its own. For instance, it can coordinate travel bookings, itinerary planning, and home automation without step-by-step prompting. It achieved state-of-the-art performance on the new GAIA benchmark (General AI Agent tasks), surpassing GPT-4. Manus can use tools, browsers, and even robotics interfaces. It represents a major R&D leap: one document calls it an “AI agent that delivers tangible results beyond text”. Try it: not yet public; see the Monica.ai website and the arXiv paper for details.
• SuperAGI (Open-Source Agent Framework): Not a single agent, but an ecosystem for building autonomous agents. Backed by a $28M Series A (including WhatsApp’s Jan Koum), SuperAGI provides tools to orchestrate multiple AI models into goal-driven agents. It offers templates for task planning, memory, and tool use. Key achievement: thousands of developers have built custom agents for workflows (marketing outreach, data analysis, gaming bots) on this platform. The goal is to democratize agent creation. Usage: Open-source on GitHub; many “AutoGPT”-style agents use this framework.
• Anthropic’s Claude Code & Assistant Agents: Although Claude itself is a model, Anthropic has focused on making Claude act as an agent. Claude Code (above) and features like “Claude Assist” (a multimodal agent for enterprise data) show how AI models are trained with agentic RLHF techniques. For example, Claude 3.7 achieved top scores on the TAU agent benchmark.
• Community Agents (e.g. AutoGPT, AgentGPT): These are DIY open-source agents combining LLMs with planning loops. While we lack official citations, they’re worth noting. Auto-GPT spawned dozens of experimental bots (e.g. financial portfolio manager, content generator) and tools like GodMode AI or AgentGPT let anyone spin up an autonomous AI on the web. They illustrate the burgeoning “agentic AI” subfield. (We expect to cover these community trends in future issues.)

(Direct links: OpenAI’s ChatGPT Agent is available at chat.openai.com (with agent mode enabled); SuperAGI docs at superagi.com; Claude Code via Anthropic’s API; community agents via their project repos.)

3. Deep-Dive: Using ChatGPT Agent

Overview: ChatGPT Agent (July 2025) lets you delegate tasks to ChatGPT as if it were an assistant. It can click through websites, run code, and produce final outputs (reports, slides, charts) with minimal guidance. Below is a guide to get started.

Step-by-Step Setup:

1. Get access: You need a ChatGPT Plus, Pro, or Team account. (Agent mode is disabled for free-tier accounts.)
2. Activate agent mode: In the ChatGPT interface, click “Tools” (near the top) and select “Agent Mode”, or simply type /agent in the prompt box. A special agent session will begin.
3. Connect integrations: The first time, ChatGPT will ask permission to access tools (e.g. your Google/Gmail, Calendar, Dropbox, etc.). Grant access to any services you want it to use. You can also change these settings later.
4. Give a clear multi-step task: Provide a detailed instruction that may involve several steps. For example: “Organize next week’s calendar with three 1-hour team meetings, then research our competitors’ latest products and draft a summary slide deck.” The agent will break it down and start acting.
5. Monitor & iterate: ChatGPT Agent will open new “windows” (browser, code editor, etc.) and narrate its actions. You can watch it browse, fetch data, and compile results. If it goes off-track, you can interject (it will politely stop when you say “Stop” or “Abort”). You can also give follow-up instructions in the conversation.
6. Finalize output: Once done, the agent will present its final deliverable (e.g. a PDF report, presentation slides, or spreadsheet). Review it and ask any clarifying follow-ups or edits as needed.

Advanced Tips:

• Be specific: The more detail you provide (context, formatting, output requirements), the better the agent will execute. For complex tasks, consider adding bullet points of subtasks in your prompt.
• Use system message: Start with a brief system instruction like “You are my AI assistant. Complete the following task autonomously.” This primes it to act.
• Chain-of-thought tracking: ChatGPT Agent displays its internal “thinking/acting” logs on-screen. This transparency lets you catch mistakes early.
• Interruption & control: At any time, you can pause or terminate the agent by asking. This prevents runaway actions.
• Hidden features: The agent can invent its own plan (no need to enumerate all steps). It can also use “continuous mode,” skipping confirmations once it knows your preference.

Example High-Impact Prompt:

“As my AI agent, please organize a virtual workshop on AI marketing next month. Check my calendar for free slots, book a Zoom meeting for 90 minutes on a suitable date, invite our marketing team, and draft an agenda. Then research five successful AI marketing case studies and prepare a bulleted summary slide. Provide the final slides as a Google Slides link.”

This single prompt yields multi-step results: scheduling, email invites, and a research summary. Using Agent Mode, ChatGPT will navigate each piece autonomously, exemplifying its power when given a concrete task.

(For more on ChatGPT Agent, see OpenAI’s blog and TechCrunch summary.)

4. Research Breakthroughs

• AlphaFold 3 (DeepMind/Isomorphic): Launched May 2024, this new AI model predicts 3D structures and interactions of all biomolecules (proteins, DNA, RNA, ligands) with unprecedented accuracy. In trials it achieved ~50% better predictions of protein-ligand interactions than previous methods. Crucially, its code and weights were open-sourced for academia, and a web-based AlphaFold Server was released for researchers. Significance: AlphaFold 3 extends AI’s reach in biology – enabling drug discovery and genomics at scale. It can accelerate medical research (vaccine design, disease modeling) and agricultural biotech. It’s arguably the biggest AI leap in life sciences, moving from protein structures to modelling whole molecular complexes.
• AlphaEvolve (Google DeepMind): Unveiled May 2025, AlphaEvolve is an AI coding agent that autonomously discovers and optimizes algorithms. It combines Google’s Gemini models with evolutionary search: LLMs generate candidate algorithms, automated evaluators score them, and an evolutionary loop refines the best ideas. It has already improved real systems at Google – e.g. discovering a new scheduling rule for data centers that saves ~0.7% of global compute, and co-designing a faster matrix-multiplication algorithm for TPUs. Significance: This represents a new paradigm of AI-assisted R&D. Instead of one-off models, AlphaEvolve shows AI can invent and iteratively improve code on its own, potentially revolutionizing fields from chip design to scientific computing.
• Manus AI (Monica): As described in a recent preprint, Manus AI is a “fully autonomous digital agent” that bridges “mind to machine”. Unlike chatbots that wait for instructions, Manus can autonomously plan and carry out complex tasks (travel planning, booking tickets, etc.) by generating entire sequences of actions. In the new GAIA benchmark for agents, Manus achieved state-of-the-art performance – surpassing GPT-4’s scores. Significance: Manus signals progress toward general-purpose AI assistants. Its success on GAIA suggests AI is improving at integrated reasoning and real-world tool use. If such agents go mainstream, they could automate many everyday tasks.
• Claude 3.7 “Sonnet” (Anthropic): In early 2025 Anthropic released Claude 3.7 Sonnet – a hybrid reasoning model. It can switch between instant answers and explicit chain-of-thought reasoning based on the task. Sonnet set new records on coding and reasoning benchmarks: it “achieved state-of-the-art” on the TAU-bench (a suite of real-world agent tasks). Alongside it, Anthropic launched Claude Code, a command-line coding agent that can search, test, and commit code changes autonomously. Significance: Claude 3.7 illustrates how next-gen LLMs are becoming “reasoning engines” with built-in transparency. In practice, it means more reliable AI outputs for complex queries (benefiting enterprises) and more powerful developer tools (like Claude Code) that can handle software engineering tasks end-to-end.
• (Bonus) AI Physics & Pattern-Finding: While not a single model, late-2024 saw researchers (e.g. Lighton et al.) apply LLMs to scientific discovery. For instance, Facebook AI’s Galactica (bio LLM) and DeepMind’s physics AI have shown LLM-like architectures can assist in hypothesis generation. These are early, but they hint at AI models that can propose new scientific insights across disciplines – a foundational breakthrough if realized.

(Each of the above comes from top-tier sources and company blogs highlighting innovation and potential.)

5. Iconic AI Tools & Agents (All-Time)

Top AI Tools (Revolutionary software and models):

• TensorFlow (2015): Google’s open-source ML framework that “is one of the most popular deep learning frameworks”. It democratized neural nets by making training/inference accessible across devices. Its flexible architecture (running on CPUs, GPUs, TPUs) and broad API support enabled breakthroughs in NLP, vision, and more. TensorFlow powered countless AI products (from mobile apps to data centers). Its key contribution was industrializing AI development.
• PyTorch (2016): Meta’s open-source framework (cited alongside TensorFlow) that championed dynamic computation graphs and ease of use. Quickly embraced by researchers, PyTorch accelerated experimentation in DL, becoming the backbone of modern AI labs. Together with TensorFlow, it underpins nearly all AI development today.
• GPT-3 / ChatGPT (2020–2022): OpenAI’s LLM revolution. GPT-3 introduced 175B-parameter scale, showing striking NLP abilities. ChatGPT (GPT-3.5/4) then made conversational AI mainstream. It amassed users faster than any app (1M in 5 days, 100M in 2 months), spurring widespread adoption of generative AI. Its impact: transforming search, customer service, education, coding, and even creative work. ChatGPT essentially taught the world to talk to AI.
• DALL·E / Stable Diffusion (2021–2022): These opened up AI image generation to all. DALL·E 2 and Stable Diffusion showed that AI can create high-fidelity images from text. They sparked a new creative economy: from rapid ad design to game concept art. Their key contribution is “democratizing design” – enabling anyone to prototype visual ideas. (Tools like Midjourney and commercial models build on this breakthrough.)
• GitHub Copilot (2021): A transformer-based coding assistant (OpenAI Codex under the hood) that auto-completes code inside IDEs. Copilot was the first widely-used AI developer tool, changing how programmers work. Studies found it merges pull requests 50% faster. It represents the impact of embedding AI into productivity tools.

Top AI Agents (Milestone systems that taught us what AI can do):

• IBM Watson (DeepQA, 2011): The first AI to conquer the game show Jeopardy!. Watson’s win over human champions demonstrated NLP and probabilistic reasoning at scale. Post-Jeopardy!, Watson’s technology (question-answering, text analytics) was applied across industries (healthcare, finance). Its milestone: proving computers could understand complex questions and unstructured data.
• Deep Blue (1997): IBM’s chess computer that beat world champion Kasparov. It wasn’t a neural net (used brute-force search), but it was the first time a computer beat a reigning human champion in a strategic game. It galvanized AI research in search and optimization.
• AlphaGo (2016): DeepMind’s Go-playing AI that defeated world champion Lee Sedol. Go has more possible positions than atoms in the universe, so this victory was thought years away. AlphaGo’s triumph (“one of the most incredible games ever”) proved deep learning + reinforcement learning could handle intuition-driven tasks. It opened the door for AlphaZero and other self-learning systems.
• Siri / Alexa (2011 onward): Early consumer AI agents in smartphones and home devices. While less “deep learning advanced” than modern models, Apple’s Siri and Amazon’s Alexa were the first AIs millions used daily. They pushed forward speech recognition and voice-based interfaces. Their legacy: AI as a household utility.
• ChatGPT & Gemini (2022–2025): Modern chat-based agents like OpenAI’s ChatGPT and Google’s Gemini (not to forget Anthropic’s Claude) have arguably earned a place among iconic agents. By showing that general-purpose assistants are now viable, they have achieved what prior narrow AIs could not: conversing on any topic and assisting with broad tasks. ChatGPT’s impact (100M+ users) rivals that of Watson or Siri in influence.

Each of these tools/agents revolutionized its domain: they either enabled new capabilities (e.g. TensorFlow enabling widespread DL) or solved a previously “AI-hard” problem (Go, Jeopardy, natural conversation). Their key contributions paved the path for today’s AI landscape (each building blocks for later advances).

6. AI Careers & Income Trends

• In-demand AI skills (Tech & Non-Tech): The hottest technical skills right now all revolve around data and ML. Reports list data analysis (statistics, Excel/Python), cloud computing, machine learning, data visualization, and data engineering as top sought-after abilities. On the AI model side: proficiency with LLMs, NLP, computer vision, and MLOps (deploying models) are highly valued. For non-technical roles, sought skills include AI product management, prompt engineering, AI ethics/compliance, and communication skills for collaborating with AI teams. Domain expertise (e.g. finance, healthcare) combined with AI know-how is also prized, as companies seek “AI translators” for specific industries.
• Salary Ranges: AI skills command premium pay. For example, Prompt Engineers earn roughly $90–120K (entry) up to $180–250K (senior) annually. Similarly, Machine Learning Engineers average about $100–130K (entry) to $190–240K (senior), and NLP Engineers earn in the $90–230K range. Even specialized roles like AI Product Managers see ranges from ~$105K to $260K. These reflect U.S. trends in big tech – salaries vary by geography and level, but AI expertise generally means above-average pay. (For non-technical managers or consultants in AI, mid-level roles often start around $80–120K with upside from bonuses or stock.) Top Resources: Job sites (Glassdoor, Levels.fyi) and AI careers guides (e.g. Data Science & AI bootcamps) list up-to-date salary data for your region.
• Leveraging AI for Growth: Professionals and creators are finding creative ways to generate income with AI. Examples include freelancing as AI consultants (offering prompt-engineering or model-fine-tuning services), building AI-powered apps or products (e.g. niche GPT assistants, SaaS tools on cloud marketplaces), and content creation (using AI to produce videos, blogs, and selling courses). Designers and artists sell AI-generated art prints or offer custom generation services. Some developers earn by publishing LLM-based plugins or ChatGPT Bots on platforms. Even trading and marketing hedge funds use in-house LLM models for trading signals. In short, AI lowers barriers: a coder can build a startup with minimal resources using AI APIs, and a marketer can automate copywriting. Continuing education is vital – platforms like Coursera, Udemy, and Kaggle now offer courses on ML, prompt design, and AI product strategy.
• Key AI Acronyms (2025): Some acronyms to know: AI (Artificial Intelligence), ML (Machine Learning), DL (Deep Learning), LLM (Large Language Model), NLP (Natural Language Processing), CV (Computer Vision), RLHF (Reinforcement Learning from Human Feedback), RAG (Retrieval-Augmented Generation), GAN (Generative Adversarial Network), CNN (Convolutional NN), API (Application Programming Interface), TPU/GPU (AI accelerator chips), AGI (Artificial General Intelligence). Staying updated on these terms (see glossaries on AI news sites or Wikipedia) helps in communicating in this field.

7. Future Vision & AI Luminaries

• Leveraging New AI for Growth: Companies will rapidly integrate the latest AI tools to gain competitive edges. For instance, enterprise software vendors will embed GPT-4o–class models for document analysis and customer support (improving productivity). Media and education firms will use video-generation tools like Sora for interactive content. In biotech, tools like AlphaFold 3 will accelerate drug discovery pipelines globally. Governments are also eyeing AI: tech hubs (US, China, EU) are funding AI research and drafting policies (e.g. the EU AI Act) to harness AI for national development (smart cities, manufacturing, healthcare). Smaller countries see AI as an economic multiplier and are launching AI strategies (e.g. India’s intent to boost AI education).
• Key Influencers: AI’s trajectory is also shaped by visionary individuals. At OpenAI, CEO Sam Altman steers major initiatives (GPT releases, partnerships). Demis Hassabis (DeepMind CEO) has led breakthroughs like AlphaGo and AlphaFold. Foundational researchers such as Geoffrey Hinton, Yann LeCun, and Yoshua Bengio (the “Godfathers of AI”) continue to influence theory. Industry leaders like Jensen Huang (Nvidia) drive hardware innovation. In academia, figures like Fei-Fei Li (Stanford AI pioneer) and Andrew Ng (Coursera and Google Brain) shape talent and education. Entrepreneurs like Elon Musk (co-founder xAI) and Kai-Fu Lee (AI venture capital, China) also sway public discourse on AI’s future. We’ll watch these luminaries and their teams as they push AI from a promise into everyday reality.

8. Upcoming Trends & Teasers

• What’s Ahead: The consensus is that AI will become increasingly ubiquitous and multimodal. In the near future we expect even tighter AI/human collaboration: more powerful personal assistants (like mobile ChatGPT agents), AI-enhanced software (from IDEs to Photoshop), and advanced robotics. On the innovation horizon are rumored models like GPT-5 (possibly 2026) and evolving open-source LLMs (Llama 5, etc.) with better safety and reasoning. Specialized “AI accelerators” (next-gen GPUs, neuromorphic chips) will arrive to feed hungry models. We also foresee growth in AI safety and transparency (e.g. “explainable AI” tools) as regulation catches up.
• Expert Predictions: Industry roadmaps (from Intel, IBM, Google I/O) hint at AI driving automation in industries like healthcare (AI-assisted diagnostics), climate (modeling extreme events), and finance (algorithmic strategies). Gartner’s latest Hype Cycle and community forums suggest trends like AI-enabled cybersecurity, AI governance, and AI-as-a-Service growth.
• Future Newsletter Previews: Stay tuned for deeper dives next week on “AI in Healthcare”, exploring how GenAI aids diagnosis and patient care. We’ll also preview the upcoming Nvidia GTC conference and what next-gen GPUs are promising for AI developers. Expect interviews with AI startup founders, plus a primer on the most anticipated AI books and academic papers of 2025.

This weekly briefing will continue to keep you on the cutting edge of AI developments – thanks for reading!

Sources: Major AI news sites, company blogs and academic publications (e.g. OpenAI, Google DeepMind, Anthropic, Reuters, TechCrunch, DeepMind blog, Business of Apps, etc.) as cited above. All facts are drawn from the latest industry announcements and reputable reporting, and further links are provided for direct reference.