• 📰 AI News and Trends

• Why are Tech Executives Fearing for Their Lives?

• What is this New Open-Weights Model by Thinking Machines?

• 🧰 AI Tools - AI & Tech in Crisis Response

• When the Government Stalls, Citizens Code - AI in Disaster Relief

• 📚 Learning Corner

📰 AI News and Trends

• ASML has room to raise prices, and they will.

• Anthropic moves closer to mega-IPO as bankers line up investor meetings

• How OpenAI’s Sol Finally Learned Design Taste

• Apple has received key approval in China that paves the way for the release of its Apple Intelligence on-device AI service in the country

• DeepSeek’s Annualized Revenue Nears $500 Million, Boosting Fundraising and IPO Plans

• Elon Musk quietly buys a $1 billion gas turbine company to power Grok

• Hack Reveals Suno AI Music Generator Scraped YouTube, Deezer, and Genius

• Moroccan intelligence insider reveals widespread use of Pegasus hacking software

• Nvidia Releases New Robotics AI Model

A New Open-Weights Model by Thinking Machines

Thinking Machines has just released Inkling, a powerful new 975B-parameter Mixture-of-Experts foundation model that natively reasons across text, images, and audio.

Designed as a broad, balanced generalist rather than a narrow benchmark-chaser, Inkling excels at agentic coding, tool use, and controllable thinking effort, allowing developers to balance performance with cost and latency. Because its full weights are open, it serves as an excellent base for customization. You can start experimenting with it right away by chatting with the model in the new Inkling Playground on the Tinker console, or you can begin fine-tuning it for your specific use cases directly on the Tinker platform today.

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When the Government Stalls, Citizens Code - AI in Disaster Relief

After twin earthquakes devastated northern Venezuela last month, the state’s response was agonizingly slow, but citizen developers stepped into the void. Armed with AI tools like Claude and Replit, volunteers from the Venezuelan diaspora rapidly spun up emergency response platforms in mere hours. These grassroots tools included “Desaparecidos Terremoto Venezuela”, which used AI and donated facial recognition software to process over 30,000 missing-person reports in two days, and “Ayuda en Camino”, a logistics hub built in four hours that used a WhatsApp assistant to coordinate donations in low-connectivity areas.

While experts warn that these AI-driven, citizen-led initiatives require robust data privacy measures and cannot permanently replace state accountability, they have undeniably democratized crisis response, proving that when institutions fail, communities can use AI to save lives.

🧰 AI Tools of The Day

AI & Tech in Crisis Response

If the Venezuela story caught your eye, here are 4 established apps and platforms that use AI and crowdsourced tech to coordinate disaster relief and find missing persons globally:

1. AIDR (Artificial Intelligence for Disaster Response) - A free and open platform developed by the Qatar Computing Research Institute. AIDR uses machine learning to filter and classify massive volumes of social media messages during emergencies, helping humanitarian organizations pinpoint exactly where aid is needed in real-time.

2. Ushahidi - Originally developed to map reports of violence in Kenya, Ushahidi is now a gold-standard open-source platform used in over 30 countries. It crowdsources crisis information, allowing citizens to report incidents via SMS, email, or web, and maps the data to empower rapid community response during natural disasters.

3. Google Person Finder - An essential web application activated during major crises. It serves as an open-source registry and message board for survivors, families, and emergency responders to post and search for the status of missing loved ones.

4. Zello - While not strictly AI, Zello is a vital disaster-response tool. It turns smartphones into walkie-talkies (push-to-talk) and operates effectively on low-bandwidth networks. It is widely used by volunteer rescue fleets (like the “Cajun Navy” during hurricanes) to coordinate real-time logistics when traditional communications infrastructure fails.

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Why are Tech Executives Fearing for Their Lives?

A wave of violent threats, and at least one serious real-world attack, is targeting AI company executives and their properties. The most alarming incident was an attempted firebombing of OpenAI CEO Sam Altman’s home in April 2026, carried out by a Texas man who was later charged with attempted murder and attempted arson. Police found a manifesto on him explicitly calling for the killing of AI CEOs and investors. Around the same time, a man tailgated his way into Anthropic’s lobby, claiming an executive “was going to be killed.” A third man threatened to show up at Anthropic’s office with a pistol over a billing dispute. According to the threat intelligence firm Liferaft, the volume of digital threats targeting AI executives and data centers grew sevenfold between late February and May 2026.

Why Are People Threatening Them

The anger is rooted in a deep and growing fear that AI is dismantling livelihoods and concentrating power in the hands of a tiny elite, which is true. Three overlapping grievances are driving the backlash:

1. Job loss. Companies are openly attributing layoffs to AI-driven efficiencies. When Meta announced ~1,400 layoffs in Washington state during an AI pivot, online commenters called for Zuckerberg’s yacht to be sunk or blown up. A laid-off Pinterest designer captured the sentiment bluntly: “You can’t go back to serfdom. It really feels like the people in power want to be kings.”

2. Economic anxiety. Ordinary people blame AI companies for rising rents, high energy costs, and a broader sense that the technology is being imposed on them without consent or recourse.

3. Powerlessness and alienation. Palantir CEO Alex Karp acknowledged at a conference that when people are told “your job is going to disappear,” they “go for the pitchfork.” A Quinnipiac survey from March 2026 found that Americans concerned about AI outnumber those who aren’t by more than 4 to 1, and 55% believe AI is doing more harm than good.

How Executives Are Protecting Themselves

Across the S&P 500 tech sector, the share of companies disclosing executive protection spending jumped from 26.8% in 2021 to 38.1% in 2025. Specific measures include: traveling with armed guards (though tech execs prefer low-profile, “slender” security over conspicuous bodyguards), running 24/7 security operations (Anthropic has done so since 2024), tracking “persons of interest” to catch escalation patterns early, and discouraging employees from wearing company logos in public to avoid being targeted. Some executives have also gone quieter on AI’s risks to jobs, pivoting their public messaging toward its potential benefits.

The main issue is how the industry that is simultaneously accelerating its most disruptive technology and grappling with the social fallout it is generating, is dealing with the situation. Karp put it starkly: “None of us are going to make any money when the country blows up.” The tension between the enormous financial incentives pushing AI forward and the mounting public fury at its human costs is, for now, unresolved — and the executives building these systems are increasingly living with that contradiction behind armed guards.

📚 Learning Corner

Visit our AI Guides

AI is rapidly changing the workplace, and these productivity agents are helping employees accomplish more in less time. While they promise significant gains in efficiency, they also raise an important question that if AI can do more of the work, what happens to the workers? Is this a tool to train your replacement?

But wait… Help may be on the way, or at least policymakers are paying attention. The Federal Reserve has announced a new task force to study the economic and monetary impact of AI, including its effects on productivity, jobs, and inflation. The group includes AI supporters who are against regulation and who, thanks to their investments, benefit handsomely from AI prowess. But shall see how this develops.

Let’s dive in and explore what all of this means. Stay curious, and have a great weekend!

• 📰 AI News and Trends

• The Feds are stepping in to curb AI Monetary Damages

• Claude Cowork has a competitor… Well, Maybe

• Cursor is also developing a Cowork Competitor

• 🧰 AI Tools - Claude and ChatGPT CoWork Alternatives

• 📚 Resource Corner - ChatGPT Work is here

📰 AI News and Trends

• Meta has released an app for making generative AI games

• Apple sues OpenAI over trade secrets. Apple has lost significant talent to OpenAI as the frontier lab prepares to unveil its first hardware device this year.

• OpenAI introduced a new model that listens and speaks at the same time. No more having to repeat what we already said, and hopefully stops talking when I want to say something.

• SpaceXAI launched Grok 4.5 as its strongest model for coding, agentic tasks, and knowledge work, noting that it was trained alongside Cursor.

• Meta AI introduces Muse Image, an image-generation model now live in Meta AI apps and Instagram Stories, offering AI image creation and editing.

• Video Remix in Google Photos allows us to change backgrounds and edit existing videos and photos. Will be handy if you recently got divorced, but it may lead to some serious bullying and hacking.

• Small firms are replacing tools like Salesforce and HubSpot with custom AI-built applications, cutting software costs and challenging the traditional SaaS stack

• China plans to let its top AI firms buy Nvidia H200 chips

• Tencent leads deal to unwind Meta’s $2bn Manus acquisition

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Claude Cowork has a competitor… Well, Maybe

OpenAI has unveiled ChatGPT Work, a major expansion of ChatGPT that moves it closer to becoming an all-in-one AI productivity platform for knowledge workers. Powered by the new GPT-5.6 model, the service combines conversational AI with advanced coding capabilities, allowing non-technical users to create documents, presentations, websites, and other business assets through simple prompts. OpenAI says the new models deliver similar performance to larger AI systems at up to one-fifth the cost, making advanced AI more accessible to businesses.

The launch intensifies OpenAI’s competition with Anthropic’s Claude Cowork and Microsoft Copilot, as the AI race shifts from chatbots to autonomous workplace agents that can complete complex, multi-step tasks with minimal supervision. ChatGPT Work will initially be available to Pro, Enterprise, and Education customers before expanding to Plus and Business users, alongside new features including a desktop app and hosted website publishing. AI companies are racing to build the “super app” for work, where a single AI assistant can replace multiple productivity tools while dramatically reducing the time and cost of everyday knowledge work.

Additionally, OpenAI launched GPT-5.6, featuring models Sol, Terra, and Luna, with Sol leading in intelligence and efficiency for coding, cybersecurity, and science. The models excel with fewer tokens at lower costs, with Sol notably outperforming competitors like Claude Fable 5. Enhanced capabilities include multi-agent parallel processing and improved design judgment, advancing cybersecurity and scientific research while maintaining strong safety measures.

But that’s not it…

Cursor is also developing a Cowork Competitor

Cursor is undertaking a major strategic pivot from an AI-powered code editor to a general-purpose productivity agent, moving into direct competition with established tools like Anthropic’s Claude Cowork and OpenAI’s ChatGPT Work. Internally referred to as “Sand,” Cursor’s upcoming agent is designed to manage everyday office tasks, such as handling emails, organizing spreadsheets, and processing text messages, signaling a significant shift to capture the broader knowledge worker market beyond software developers. This move reflects a broader industry trend where agentic AI is evolving from passive chatbots into autonomous systems that can execute multi-step workflows across a user’s desktop and file system . The expansion comes at a pivotal moment for Cursor, following reports that SpaceX has agreed to acquire its parent company, Anysphere, for $60 billion in an all-stock transaction.

The competitive landscape for these autonomous workplace agents is rapidly intensifying as companies race to solve the “last mile” of knowledge work. Anthropic’s Claude Cowork, which launched earlier in 2026, already operates as a desktop agent capable of reading local files, coordinating parallel sub-agents, and delivering finished work across applications . While Anthropic built Claude as a general-purpose model first and then layered productivity features on top, Cursor is attempting the reverse—leveraging its deep expertise in a specific vertical (coding) to expand outward into general office automation . With the AI agent market projected to exceed $10.8 billion in 2026 , Cursor’s evolution from “write code for me” to “handle work for me” positions it to capture a massive segment of everyday business operations, provided it can successfully translate its developer-focused precision into reliable, general-purpose enterprise execution.

🧰 AI Tools of The Day

CoWork Alternatives

Claude Cowork and now ChatGPT CoWork are getting all the attention, but there are alternatives, especially when you run out of credits on those.

• Microsoft 365 Copilot / GitHub Copilot: Great for enterprise settings, integrating multi-model agent technologies directly into your office apps and coding suites.

• Gumloop: A node-based, cloud-first platform that is LLM-agnostic, meaning you can plug in Claude, GPT, or Gemini depending on the task at hand. [1, 2]

• Eigent: A powerful open-source workspace alternative featuring hundreds of tools (MCP) and multi-agent capabilities. [1]

• eesel AI: Specialized for businesses, this acts like a new human hire that you train on your team’s chat history and help desk tools (like Zendesk) to automate customer support. [1]

Check Our AI Guides Repository

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The Feds are stepping in to Curb AI Monetary Damages

The Federal Reserve has launched five high-level task forces to review and modernize its monetary policy, signaling that the central bank is preparing for an economy increasingly shaped by AI, shifting labor markets, and new financial realities. Chairman Kevin Warsh said the Fed remains committed to its dual mandate of price stability and maximum employment. Still, he acknowledged that today’s economy has changed dramatically and that the institution’s tools, data, and policy framework must evolve with it.

The task forces will focus on communications, balance sheet policy, economic data, productivity and jobs, and inflation frameworks, bringing together leading economists, former central bankers, business executives, and technology leaders. Notably, the Productivity and Jobs group is composed of very AI-forward individuals who champion no AI and tech regulation, and a group that has and will handsomely benefit financially from AI adoption. The group includes Marc Andreessen, Charles Jones, and Microsoft’s Asha Sharma, highlighting the Fed’s growing interest in understanding how artificial intelligence and other general-purpose technologies could reshape productivity, employment, and future interest rate decisions. The initiative suggests that AI is no longer viewed solely as a technology trend; it is becoming a key factor in how policymakers assess the future of the U.S. economy.

📚 Learning Corner

ChatGPT Work is here

OpenAI will not let Claude take the reins in the AI space, and they have just released ChatGPT Work. Check the tutorial above to get acquainted with it, and please let us know how it compares with Claude Work.

Visit our AI Guides

For decades, American leaders have championed free markets, private enterprise, and capitalism while criticizing Socialism, state ownership and centralized economic planning. Yet today, the U.S. government is taking equity stakes in more private companies and using public investment to shape strategic industries such as semiconductors, defense, energy, and critical technologies. We discuss this today.

We also explore the bigger question: Can the United States keep pace with China in the race for AI, semiconductors, advanced manufacturing, and other critical technologies? As global competition accelerates, understanding these policies has never been more important. Let’s dive in, and stay curious.

• 📰 AI News and Trends

• U.S. Government Equity Stakes in Private Enterprise and its Socialists Efforts.

• 🧰 AI Tools - API-first platforms for Video AI Agents

• China’s AI Is No Longer Playing Catch-Up

• 📚 Learning Corner - Peptide Design, Principles & Methods.

📰 AI News and Trends

• Anthropic has begun early-stage work on its own AI chip and held talks with Samsung Electronics as a potential manufacturing partner.

• SpaceX Showed Investors Prototype of a New AI Device

• For the First Time, a Cell Built From Scratch Grows and Divides

• Meta Is Planning a Cloud Business to Sell AI Computing Power

• Claude Fable 5 and Mythos 5 have been redeployed. Fable 5 will be included for up to 50% of weekly usage limits until July 7, after which it will be available via usage credits. Mythos 5 has been restored for some organizations in the US.

• Singapore seizes $42m mansion over Nvidia chip smuggling

• The AI data center buildout is forcing companies and legislators to rethink their climate pledges.

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U.S. Government Equity Stakes in Private Enterprise and its Socialists Efforts.

A significant shift in American industrial policy is underway as the U.S. government, under the Trump administration, increasingly acquires equity stakes in private companies. What began as a strategic maneuver to secure critical supply chains has expanded into a multi-billion-dollar portfolio spanning semiconductors, quantum computing, critical minerals, nuclear energy, and artificial intelligence.

The Expanding Federal Portfolio

Since January 2025, the U.S. government has invested billions of dollars across numerous deals involving direct ownership or quasi-equity stakes. This marks a departure from traditional tools like grants, loans, and tax incentives, pivoting toward a venture-capital-style approach to industrial policy.

The OpenAI Proposal and AI Sovereign Wealth Fund

The most recent and high-profile development involves OpenAI who has reportedly proposed handing the U.S. government a 5% stake in the company, worth approximately $42.6 billion based on its recent $852 billion valuation. OpenAI CEO Sam Altman argued this is the best way to “share the upside of AI with the public”.

This proposal aligns with broader discussions about creating a “public wealth fund” or an AI sovereign wealth fund. The current administration has been exploring options to give the public a stake in leading AI companies. Surprisingly, this concept has found bipartisan support, with Senator Bernie Sanders introducing legislation to create a $7 trillion AI sovereign wealth fund by taking 50% stakes in major AI companies.

Key Corporate Investments

The government’s equity acquisition spree extends well beyond AI, focusing heavily on sectors deemed critical to national security and technological dominance.

Motivations and Beneficiaries

The Administration’s Rationale

The Trump administration justifies these unprecedented investments through the lens of national security and “taxpayer upside.” Commerce Secretary Howard Lutnick and other officials argue that if the government is going to subsidize industries to compete with China and secure domestic supply chains, taxpayers should share in the profits rather than just footing the bill. The strategy is framed as necessary to combat Chinese state-backed monopolies in critical minerals and advanced technologies.

Who Truly Benefits?

While the stated goal is public enrichment, the reality of who benefits is more complex and raises significant concerns about conflicts of interest and crony capitalism.

1. The Companies - Firms receiving government investment benefit from an immediate influx of capital and a massive boost in market confidence. When the government announces an equity stake, it signals that the company is “too strategic to fail,” often leading to a surge in stock prices. For example, quantum computing firms saw their stocks jump 25-33% following the announcement of government equity deals.

2. The Trump Family and Allies - There are mounting concerns about the Trump family profiting from these policy shifts. The Trump Organization has expanded heavily into cryptocurrency, launching World Liberty Financial and selling tokens that brought in hundreds of millions, only to benefit the family. Furthermore, President Trump’s personal financial disclosures showed he traded stock (inside trading) in companies like Intel and Nvidia around the time his administration was making major policy decisions affecting those firms.

3. The “Pay-to-Play” Environment - The shift to “government-by-deal” creates an environment where companies may feel compelled to offer equity or favorable terms to secure government support or avoid regulatory hurdles. Critics argue this replaces a neutral policy framework with personalized bargains, where success depends on winning favor from the administration.

The Ideological Contradiction: Republican Socialism?

Perhaps the most striking aspect of this policy shift is the profound ideological contradiction it represents. The Republican Party has long positioned itself as the champion of free-market capitalism and the fiercest critic of socialism.

Socialism, in its classical definition, involves government ownership of the means of production. By actively purchasing equity stakes in private corporations, the Trump administration is engaging in practices that closely align with this definition. As Senator Rand Paul noted regarding the Intel deal, “If socialism is government owning the means of production, wouldn’t the government owning part of Intel be a step toward socialism?”

Despite this, the GOP has largely remained silent or actively praised Trump’s dealmaking. This complacency highlights a significant shift within the party. The abandonment of conservative free-market principles in favor of economic nationalism and loyalty to Trump’s transactional approach. The fact that Trump’s policies on AI wealth funds and corporate equity stakes overlap significantly with proposals from Democratic Socialist Senator Bernie Sanders underscores the bizarre political realignment occurring around industrial policy.

The is definitely a new era for the U.S. government as it aggressively moves into corporate ownership known as the “portfolio state.” While driven by legitimate concerns over national security and supply chain resilience, the strategy carries immense risks. It distorts capital markets, invites severe conflicts of interest, and blurs the line between public policy and private profit. Furthermore, it exposes a deep hypocrisy within the Republican Party, which has embraced state ownership of private enterprise while continuing to wield “socialism” as a political attack against its opponents.

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🧰 AI Tools of The Day

API-first platforms for Video AI Agents

• Runway AI - Video generation & editing

• Creatomate - Template-based video generation

• Shotstack - Server-side video rendering

• Synthesia - Talking avatars

• HeyGenAI - Presenters & Translation

Check Our AI Guides Repository

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China’s AI Is No Longer Playing Catch-Up

Beijing-based startup Z.ai just released GLM-5.2, a model that is “right up there with GPT-5.5 and just a tick below Anthropic’s Opus 4.8” at one-sixth the cost. It already ranks above some Anthropic’s models, and it arrived at the worst possible moment for the U.S. when Anthropic’s top models were temporarily export-banned by Washington, and OpenAI’s GPT-5.6 rollout was delayed, creating a vacuum that a cheaper, capable Chinese model rushed to fill.

The main barrier keeping it out of large U.S. enterprises is data security concerns, not performance, and that wall is eroding fast. Startups are already switching, and a RAND study found Chinese LLMs’ global market share jumped from 3% to 13% in just two months after DeepSeek launched in early 2025. I have been fascinated by Breakneck, the book by Dan Wang that highlights how China is becoming the world leader on many different fronts and industries due to its engineering prowess and America lawyerlee society, and their focus on process knowledge. Reading this book led to think that it may take a generation or more to be able to compete with China as they have turned their entire population into an engineering culture, taht thrives on work, knowledge, and takes on tasks that I dont see Americans or other cultures taking. An example is the extraction or rare metals, which may not be that rate, vbut tha rarity is in the findiong the people willing and able to extract it and this is what Chine is undertaking as well and the undertalking of many other dirty or so called challenging industries where margin are thin, but with lots of potential.

For years, Chinese AI models were derivative and clearly behind , the performance gap was as wide as “31 percentage points” as recently as May 2023. Then came “January 2025’s DeepSeek shock”: a Chinese model that briefly matched the top U.S. model at a fraction of the training cost, wiped hundreds of billions from Nvidia’s market cap in a day, and proved that algorithmic efficiency could substitute for the advanced chips U.S. export controls had denied China.

The floodgates opened after that Alibaba’s Qwen, MiniMax, StepFun, and now GLM-5.2 followed in rapid succession. By April 2026, Stanford’s AI Index confirmed the gap had “collapsed to just 2.7%”, while the U.S. was spending “23 times more” to hold it. Looking ahead, the outlook is unsettling, AI talent migration to the U.S. has dropped 89% since 2017, China dominates global AI patents and research output, and Chinese open-source models are becoming the default stack across the developing world. The next 18 months will determine whether the U.S. can pull ahead, or whether the gap disappears entirely.

📚 Learning Corner

Peptide Design: Principles & Methods

Peptides are everywhere right now. From professional athletes and weekend warriors chasing faster recovery to the rise of GLP-1 medications and the “lookmaxxing” trend, interest in peptides has exploded. But what are they, how do they work, what are the potential benefits and risks, and are they actually worth using? Here’s a practical guide to help you understand the science and decide whether they’re right for you.

Visit our AI Guides

Data is like a seed that continually creates new life. It gives greater meaning to events, helps uncover hidden patterns, and even creates new events of its own. And we are only scratching the surface of how data can be collected, where it can come from, and how it can be used or abused. Government agencies such as ICE and other immigration authorities increasingly rely on large contracts with private technology companies to track individuals through facial recognition, behavioral analysis, the vast network of cameras surrounding us, satellites overhead, and the enormous amount of information we willingly share on social media and digital platforms.

Now, researchers are developing electronics that can be printed onto living tissue, skin, hair, and other biological surfaces, creating the possibility of tracking systems that are even more pervasive than today’s wearables. The world of sports is also being transformed by the unprecedented volume of granular data that can now be collected, structured, and analyzed. In the long run, the sheer scale of this information may become impossible for humans to fully comprehend; only computers will be able to process it. And those systems, potentially controlled by a small number of powerful organizations, could increasingly shape decisions that people follow without question. But what happens if those systems are compromised? And how will all of this be powered? That’s exactly what we explore today. Stay curious.

• 📰 AI News and Trends

• Will all Safety Measure Fail?

• Data Gathering by Any Means

• 🧰 AI Tools - Database of Claude skills that you can download and use right now

• The struggle to Power Data Centers

• 📚 Learning Corner - Privacy and Civil Liberties

📰 AI News and Trends

• Perplexity Brings AI-Powered Research, Contract Review, and Legal Workflows Into a Single Platform for Lawyers.

• OpenAI Enters the Chip Wars with a the New Broadcom-Powered Chip ‘Jalapeño’ Accelerator which Promises Faster, Cheaper, and More energy Efficient AI Inference.

• Amazon vs. Perplexity is the Legal Fight That Could Redefine How AI Agents Use the Internet.

• Electronics can now be printed onto living tissues.

• Apple Hikes Prices on Almost Everything Except iPhones

• Oracle Reports AI Adoption Contributes to 21,000 Job Cuts

• Anthropic accuses Chinese rival Alibaba of illicitly extracting AI capabilities

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Will all Safety Measure Fail?

A growing number of leading AI researchers in both the U.S. and China are warning that the race to build more powerful AI systems is moving too fast. At a recent AI conference in Beijing, experts from both countries highlighted concerns that advanced AI agents could accelerate cyberattacks, create new security vulnerabilities, and potentially trigger a “Chernobyl moment” for artificial intelligence if safety measures fail. While Washington continues restricting China’s access to advanced AI technology, researchers argue that AI risks are global and require international cooperation, similar to past nuclear safety efforts. The debate is becoming even more urgent as open-source AI models from companies like Alibaba, Moonshot, and Nvidia grow increasingly capable, raising concerns that future models could be exploited for hacking, cyber warfare, or other malicious uses. Some Chinese AI firms are reportedly reconsidering open-source releases altogether as security risks begin to outweigh the benefits of openness.

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🧰 AI Tools of The Day

A large database of Claude skills that you can download and use right now:

Claude Skills

Discover Claude Skills, Claude Code skills, Codex skills, and ChatGPT agent skills built around the open SKILL.md format.

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Data Gathering by Any Means

A new report reveals that spending on AI-powered immigration surveillance technology has surged to record levels under the Trump administration, with ICE and Customs and Border Protection awarding more than $513 million in contracts in 2026, up from $310 million in 2025 and less than $50 million a decade ago.

The funding is fueling a rapidly expanding arsenal of tools from companies like Palantir and Anduril, including facial recognition systems, social media monitoring, data brokers, phone-hacking tools, AI-powered drones, biometric tracking, and autonomous border surveillance towers. Researchers warn that immigration agencies are not only purchasing these technologies but actively funding their development, raising concerns about privacy, civil liberties, government oversight, and the growing influence of major tech firms in shaping the future of domestic surveillance.

📚 Learning Corner

Privacy and Civil Liberties

The counterbalance to surveillance.

Key concepts: Fourth Amendment protections, Digital rights, Consent, Data ownership, Encryption, Anonymity.

Organizations worth following:

• Electronic Frontier Foundation (EFF)

• American Civil Liberties Union (ACLU)

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The struggle to Power Data Centers

Tesla, Sunrun, and Renew Home have unveiled plans to create a 16.8-gigawatt virtual power plant by linking hundreds of thousands of home batteries, smart thermostats, and connected devices across the U.S.

The initiative is designed to help meet the rapidly growing electricity demands of AI data centers without building new power plants or transmission infrastructure. The companies already have 300 megawatts ready for deployment in Virginia’s “Data Center Alley” and expect capacity to expand significantly over the coming years. As AI-driven data center power demand is projected to rise from 41 GW in 2026 to 66 GW in 2027, the partnership aims to turn idle residential energy assets into a flexible grid resource, potentially reducing electricity costs by up to $170 billion over the next decade while accelerating the deployment of new AI infrastructure.

Computer Vision is getting more powerful every day. Midjourney, the AI image company, is building a 60-second MRI tool that soon may be accessible to anyone. Other companies are also building or already have tools that allow users to scan images to find if they have cavities, and tools that allow us to read X-rays and other medical images that usually only trained doctors can decipher. Democratizing all this technology can be great to learn more about our health, but it should not replace a professional, especially now that our judgment is becoming more impaired every minute we use the same technology that is allegedly making our lives better. Today, we dive into these computer vision tools, how our judgment is eroding, and marketers are prepping for a bot-to-bot world. Stay Curious.

• 📰 AI News and Trends

• Midjourney Is Building a 60-Second Alternative to MRI

• 🧰 AI Tools - Clinical Dental AI (Finding Cavities)

• Marketers prepare for Bot-to-Bot World

• 📚 Learning Corner - Learn how to properly use Claude Skills

• Our Judgement is eroding, but we think otherwise

📰 AI News and Trends

• Coinbase really registered an AI agent as an investment advisor with the SEC

• Pentagon boasts of using AI to write reports mandated by Congress; it will be interesting to see how this will come back to bite them

• Norway bans generative AI tools in elementary schools to protect kids’ basic learning skills

• Two of Google’s Top AI Minds Exit in One Week, one for OpenAI and the other for Anthropic.

• The New global order now includes the top AI CEOs as heads of nation-states

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Midjourney Is Building a 60-Second Alternative to MRI

AI image company Midjourney is entering healthcare with a new full-body scanner that uses ultrasound instead of magnets or radiation. The system features around 500,000 grain-sized ultrasound sensors that create a 3D map of the body with MRI-like quality, capturing details as small as 0.5 millimeters.

While a traditional full-body MRI takes 60–90 minutes, Midjourney’s prototype already completes scans in about 20 minutes, with an ambitious goal of reducing that to just 60 seconds. The scanner generates roughly 17GB of data every second, requiring 21 servers and around 2 petaflops of computing power to reconstruct the images.

The company, which became profitable shortly after launching in 2022 and exceeded $500 million in annual recurring revenue (ARR) in 2025, plans to deploy 50,000 scanners capable of performing 1 billion scans per month by 2031. The first location is scheduled to open in San Francisco next year, initially focusing on body composition mapping before expanding into diagnostic capabilities through incremental FDA approvals.

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🧰 AI Tools of The Day

Clinical Dental AI (Finding Cavities)

• Pearl Second Opinion: A leading clinical imaging tool with comprehensive FDA clearances. The system uses machine learning to visually tag cavities, tartar, and structural anomalies on 2D and 3D dental radiographs.

• VideaHealth (VideaAI /): Deeply integrated into standard clinic software like Henry Schein Dentrix. It works automatically as X-rays are captured, boosting early caries detection by highlighting suspicious spots with color-coded boundaries.

• Overjet: Another dominant clinical AI suite widely utilized by dental practices and insurance companies to automatically measure bone levels and assist in identifying cavities.

Check Our AI Guides Repository

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Marketers prepare for Bot-to-Bot World

L’Oréal is betting that the future of marketing lives inside AI conversations and not websites. The beauty giant has partnered with OpenAI to power its in-house AI marketing platform and bring Maybelline’s virtual makeup try-on experience directly into ChatGPT. The company has already created more than 50,000 AI-generated marketing assets, cut production costs by 40%, trained 70,000 employees in AI, and invested nearly $2 billion in technology.

This partnership signals a major shift for every brand. Consumers are increasingly asking AI assistants what products to buy instead of searching on Google or visiting websites. As a result, brands will need to optimize not just for search engines, but for AI conversations. This is a big shift as marketers start to think about how to attract online users with AI conversations and adopt different strategies to learn what AI will respond. We are optimizing for the way bots and large language models converse and provide feedback instead of optimizing for human verbal and written communication.

This signals a world where winners may be the companies whose products are easiest for AI systems to understand, recommend, and personalize, turning chat interfaces into the new storefronts of the internet and a bot-to-bot world.

📚 Learning Corner

Learn how to properly use Claude Skills

Skills extend Claude's capabilities by giving it access to specialized knowledge and workflows. This guide shows you how to enable, discover, and use skills in Claude.

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Our Judgement is eroding, but we think otherwise

A new study from MIT suggests that while AI assistants like ChatGPT and Claude can make us more effective in the short term, they may also weaken our ability to think independently over time.

Researchers tracked 67 participants over four weeks as they identified fake headlines and manipulated images. Participants using AI were 21% more accurate at spotting misinformation, but their performance became 15.3% worse when asked to make similar judgments without AI assistance. Even more concerning, about 25% of participants believed their skills were improving despite objectively declining performance. The findings highlight a growing phenomenon known as cognitive offloading, our tendency to outsource mental tasks to technology. We’ve seen versions of this before with GPS reducing our sense of direction, calculators weakening mental math skills, and smartphones replacing memory for facts and phone numbers.

However, generative AI is different because it increasingly handles higher-order functions such as writing, research, decision-making, and judgment itself. The concern isn’t that society is becoming less intelligent, but that independent reasoning may become increasingly rare. In a world where people ask AI what happened in the news, which stocks to buy, what medical advice to follow, or even what business strategy to pursue, there is a risk that fewer people will develop the ability to question assumptions, evaluate evidence, and navigate uncertainty on their own. As AI becomes more capable, the ability to exercise sound human judgment when the machine’s answer is becoming the top skill.

• 📰 AI News and Trends

• Gemini Introduces Skills, but do they compete with Claude Skills?

• Anthropic’s Fable 5 Sparks AI Security Showdown

• Federal Data Center Rules To Expire soon and the Feds will lets them expire.

• 🧰 AI Tools - Investing workflows

• 📚 Learning Corner - Visit our AI Guides

📰 AI News and Trends

• Meta reportedly moves to unwind $2B Manus deal after Beijing’s demand

• OpenAI faces investigation from state attorneys general on user data, child safety and advertising, this as their LLM model, ChatGPT, crosses 1 Billion users.

• Meta to give Free AI Glasses for Every Blind Veteran in America

• CRISPR Study Destroys “Undruggable” Cancer Cells by Shredding Tumor DNA in Breakthrough Approach

• Britain’s plans to ban social media for under-16s. Tech companies says teens will be driven to more dangerous sites.

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Anthropic’s Fable 5 Sparks AI Security Showdown

Anthropic’s highly anticipated Fable 5 and Mythos AI models were abruptly taken offline just days after launch when the Trump administration warned they posed a potential national security risk.

The move came after Amazon researchers reportedly demonstrated that portions of the models could be “jailbroken,” prompting the White House to threaten sweeping export controls. The decision has ignited a fierce debate across the tech industry, with more than 40 cybersecurity leaders from companies including Adobe, Zoom, Sophos, and Nvidia arguing that restricting access hurts defenders more than attackers. The episode highlights a growing reality in AI where governments and tech companies are writing the rules for advanced AI systems in real time, with national security, innovation, and global competitiveness increasingly colliding.

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🧰 AI Tools of The Day

Investing workflows

• AlphaSense (institutional research OS): Heavy use in hedge funds and PE firms. Searches across earnings calls, filings, broker research, and internal notes. Great for Enterprise-grade signal extraction.

• Koyfin (AI-assisted financial terminal alternative) - Lightweight alternative to Bloomberg Terminal. Strong for visualizing macro, equities, ETFs, and fundamentals. Increasingly adding AI layers for screening and narrative explanations

• FiscalNote (policy + regulatory intelligence layer) - Used to track regulation, legislation, and geopolitical risk. Monitors bill movements, policy shifts, sanctions, and government actions in real time. Important for sectors like AI, defense, energy, healthcare, and semiconductors

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Federal Data Center Rules To Expire in 2026

The U.S. government is reportedly preparing to let the Federal Data Center Enhancement Act (FDCEA) lapse, potentially ending key standards that govern federal data center security, resiliency, and energy efficiency. The law currently requires protections against cyberattacks, power outages, and natural disasters while mandating transparency through public reporting and congressional oversight. If allowed to expire, agencies could gain more flexibility to rapidly build AI infrastructure, but experts warn it may lead to fragmented security standards, reduced public visibility into federal IT spending, and higher energy consumption across government data centers.

Gemini Introduces Skills, but do they compete with Claude Skills?

Google is testing the integration of Skills Marketplace inside Gemini Enterprise. But how do they differ from Claude SKills?

The big difference is that Claude’s Skills are essentially persistent prompts with tools attached, whereas Gemini seems to be building an app ecosystem on top of skills. Google is reportedly developing three pieces simultaneously: a Skills Builder, Skills Management UI, and a Skills Marketplace where organizations can publish and discover reusable capabilities.

For businesses, this could evolve into something resembling an AI app store:

• Sales Dashboard Skill → Pulls data from Google Sheets, BigQuery, and Gmail

• Customer Support Skill → Reads support tickets and drafts responses

• Investor Briefing Skill → Monitors news, earnings, and creates reports

• Content Approval Skill → Generates articles and routes them for review

Google’s strategy appears to be turning Gemini into a unified enterprise workspace that combines chat, agents, app-building, and business integrations under one interface.

For someone like you who builds AI workflows and teaches automations, Gemini Skills could eventually become the equivalent of selling AI templates or mini-apps. Instead of giving someone a complex Make.com blueprint, you could potentially publish a “Newsletter Research Agent” or “Lead Generation Agent” that a company installs from the marketplace and uses immediately. That’s closer to an AI plugin ecosystem than what Claude Skills currently offers.

📚 Learning Corner

Visit our AI Guides

• 📰 AI News and Trends

• AI Agents Will Soon Shop and Pay for You

• AI Revolution in Pro Soccer

• 🧰 AI Tools - Keep Track of your AI Tokens

• 📚 Learning Corner - AI-Powered Lead Generator in Make.com

📰 AI News and Trends

• ByteDance is developing AI chips similar to those from Nvidia partner Groq, a design that could help the company work around U.S. export restrictions.

• Google in talks with Samsung to make part of next-gen chip

• Warner Music Group acquired Sureel AI, a company that develops tools for tracking how intellectual property is used by generative AI models.

• SpaceX’s I.P.O. Could Turn 4,400 Employees Into Millionaires

• Waymo built a virtual driver to study how humans react to surprises on the road, and it is open source for any company to use

• Solar generates more energy (12.8% of US electricity in May) in the US than coal for the first time, even as Trump boosts coal over clean energy

• Fully autonomous drones have killed human soldiers for the first time

• Anthropic pursues data center leases, seeks financial backing from Google

• China just approved the world’s first commercial brain implant.

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AI Agents Will Soon Shop and Pay for You

OpenAI and Visa are partnering to bring AI-powered commerce closer to reality by enabling AI agents to make purchases and payments on users' behalf within predefined limits.

The integration combines OpenAI’s agent technology with Visa’s global payments infrastructure, enabling users to set spending caps, merchant restrictions, and approval requirements while Visa handles fraud protection, chargebacks, and refunds. Beyond online shopping, the system could eventually allow AI agents to autonomously pay business invoices, purchase software services, APIs, cloud computing resources, and other digital products. Visa says AI is already influencing more than 20% of consumer transactions, highlighting the growing role of large language models in purchasing decisions.

While the partnership lays the groundwork for agentic commerce, both companies acknowledge that the final user experience is still evolving, and ChatGPT is unlikely to become a full-scale shopping platform anytime soon.

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🧰 AI Tools of The Day

Keep Track of your AI Tokens

• Braintrust - Automatically collects detailed token metrics for every LLM call, prompt tokens, cached tokens, completion tokens, and reasoning tokens.

• Laminar - open-source observability platform designed to automatically track token usage across AI workflows. Captures input and output token counts as soon as it’s set up at your application’s entry point.

• Prompts.ai - AI orchestration platform, uniting over 35 LLMs within a single, streamlined interface. Tailored for organizations looking to manage AI expenses at scale, it combines real-time token tracking with robust financial controls to help prevent overspending.

Check Our AI Guides Repository

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AI Revolution in Pro Soccer

The World Cup opened today, and researchers and AI labs are ready to capture as much data as possible to monetize their models and to, allegedly, make the game better. Researchers at Belgium’s KU Leuven are helping drive a data revolution in soccer by using AI and machine learning to uncover winning strategies that were previously invisible to coaches and analysts.

Led by Jesse Davis, the team has analyzed more than 1.4 million passes and 60,000 throw-ins, revealing surprising insights such as how intentionally kicking the ball out of bounds in the opponent’s half can increase scoring opportunities. Their open-source tools, including expected goals (xG) and player valuation models, are now used by professional clubs across Europe and national soccer organizations. The lab’s research has also shown that teams like Chelsea could have gained an estimated 1.6 additional goals per season by increasing long-range shots by 20%.

As AI-powered analytics become increasingly embedded in the sport, these tools are transforming everything from player scouting and tactical planning to automated game analysis, making soccer one of the fastest-evolving data-driven sports in the world.

📚 Learning Corner

AI-Powered Lead Generator in Make.com

This week, I’m sharing an AI lead generation workflow I built in Make.com that automatically finds prospects, enriches their contact information, generates company intelligence reports, and stores everything in a Google Sheet.

How it works:

1. Webhook Trigger – Submit a company website or business name through a simple form.

2. Contact Discovery – An HTTP module connects to Hunter.io to find verified email addresses and key decision-makers within the organization.

3. AI Intelligence Report – A second HTTP module sends company information to Google AI Studio, which generates a detailed report including:

   • Company overview

   • Products and services

   • Target customers

   • Competitive advantages

   • Potential sales opportunities

4. Google Sheets Database – All contacts and AI-generated insights are automatically saved to a spreadsheet for outreach and prospect management.

What you get:

• Verified business contacts

• AI-generated company research

• Centralized lead database

• Automated workflow with minimal manual effort

👉 View the Make.com scenario

👉 Need help connecting APIs or customizing the workflow? Contact Me

• 📰 AI News and Trends

• Sports, AI, and the Algorithm of Fandom.

  • The Data Is Now the Product

  • Micro-Betting + Personalization = Behavioral Shift

  • Where does the Money Flow?

  • Fanaticism, Quantified

• 🧰 AI Tools - Check Our AI Guides Repository

• 📚 Learning Corner - Meta Workforce Academy

📰 AI News and Trends

• OpenAI to go public on the heels of SpaceX and Anthropic. Where are you putting your money on?

• The new “Siri AI” is rolling out this fall. Alongside a new Google-powered update to Apple’s on-device Foundation Models, as well as tighter integration of all these AI capabilities across Apple’s many operating systems.

• Xiaomi, the Chinese tech company mostly known for its phones, cheap electric scooters, and air purifiers, has released an AI model that is allegedly faster than ChatGPT and Claude

• Perplexity plans IPO in 2028

• World’s first wind-powered underwater data center starts operating in China

• Meta Deletes Face-Recognition System From Its Smart Glasses App

• JPMorgan, Citi, and Big Banks Plan New Tokenized Deposit System to Answer Crypto

• China’s exports surged past analyst expectations last month as the global AI boom fed demand for Chinese chips, EVs, and other high-tech components.

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Sports, AI, and the Algorithm of Fandom.

I was watching Game 3 of the NBA Finals in the fourth quarter when it hit me. The team owners, investors, the networks, the French TV networks (Wemby), the refs, and the cities involved, and the fans, literally nobody in this ecosystem wants these series to end.

The Knickerbockers are pulling in ~$90 million per home Finals game. New York City? Half a billion per game. NBA refs average a $200k salary plus $20k per Finals game, and suddenly, a quick sweep becomes a massive financial loss. Meanwhile, the World Cup is quickly approaching. I was talking to a friend of mine who works for LA Metro, and he shared that the parking at SoFi Stadium for World Cup matches is climbing to $400 a vehicle, that’s insaneeee. This isn’t a conspiracy (or is it). I want to believe it’s incentive alignment. The system wants more games, more days, more eyeballs, more transactions. It’s all about the Benjamins…

Now layer on what’s actually changed in 2026: AI-powered prediction markets, real-time personalization, and micro-betting that turns fanaticism into quantifiable engagement. Suddenly, the economics of sports aren’t just about who wins the trophy, but about who can extract the most value from the attention economy while the games unfold.

The Data Is Now the Product

The NBA tracks 29 data points per player across all 29 arenas in real-time. AWS processes millions of data points per game. That data used to stay with teams and broadcasters. Now it flows directly into fan-facing apps, personalized highlight reels, and, critically, betting algorithms.

Platforms tracking 50+ input variables show 28% accuracy improvements in upset predictions vs. human analysts. This matters because it means AI models now achieve 75-85% accuracy in predicting game outcomes, up from 50-60% for traditional methods.

The betting market knows this. Sharp money moves on AI predictions faster than expert takes move online. Your favorite analyst’s gut feel isn’t competing fairly anymore when neural networks trained on decades of play-by-play data can come up with the perfect prediction in seconds all day, every day.

Micro-Betting + Personalization = Behavioral Shift

Here’s where the system gets clever. Streaming-synced micro-markets now offer point-by-point wagering in tennis, pitch-by-pitch in baseball, play-by-play in football, turning every moment into a decision.

We are actively trading on sub-game events in real-time. A momentum shift in the third quarter? Bet it. A key defensive stop? Wager on the next possession. The emotional peak that used to last three seconds now gets monetized. AI-generated market layers can theoretically multiply wagering opportunities around the kind of sudden, high-emotion game events that drive viewer engagement.

And it’s personalized. No two fans see the same broadcast, highlights, or betting suggestions anymore. Generative AI is accelerating hyper-tailored fan feeds, predictive insights, and personalized highlight experiences as critical infrastructure in sports media in 2026.

Engagement gets quantified. Emotional reaction maps to a wager. Wager maps to data about what drives you as a fan. Every data point feeds back into the next personalized experience. The system learns your fanaticism and commercializes it.

Where the Money Flows

• Infrastructure: Companies building agentic sports systems. Salesforce just became an Official World Cup 2026 supporter, bringing Agentforce 360 to fan engagement and tournament operations across a 5+ billion viewer audience.

• Prediction & Personalization: SpeedLabs raised $6.5M to build AI-powered momentum markets that create new wagers from in-game moments in summer 2026. This is just the beginning. Every sportsbook needs a personalization engine now. Every platform needs real-time odds automation.

• Data Ownership: The leagues and broadcasters are realizing their real product isn’t the game, it’s the data the game generates. Whoever owns the personalization layer owns the fan relationship.

Fanaticism, Quantified

The 2026 sports season is the moment fanaticism became a measurable, tradeable, personalized commodity. We are not fans anymore. We are a data source, a prediction node, and a micro-transaction waiting to happen.

The games will be longer. The betting will be more granular. The personalization will be eerily accurate. The cities, teams, refs, and platforms will all optimize for engagement length, not sporting merit.

All we can do is watch or not. But the algorithm knows exactly how to make it irresistible.

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🧰 AI Tools of The Day

Check Our AI Guides Repository

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📚 Learning Corner

Meta Workforce Academy

Meta is redirecting resources toward its AI ambitions. After laying off thousands of employees, the company has expanded efforts to analyze workplace tasks to improve AI systems while investing billions in new data centers. Its newest workforce academy is focused on training technicians and operators to build and manage the infrastructure powering future AI models, signaling that data centers have become a strategic priority for the company.

• 📰 AI News and Trends

• Apple Is Preparing to Disrupt the Smart Glasses Industry

• OpenAI vs. Anthropic Is Becoming a $100 Million Political Fight

• 🧰 AI Tools - Three Underrated AI Apps you need to try

• 📚 Learning Corner - How World Models Work

• The Rise of World Models Explained

📰 AI News and Trends

• Three upcoming NotebookLM features, including Personal Intelligence, Connectors, and Canvas, will soon expand workspace capabilities.

• As Minnesota Cracks Down on Prediction Markets, Kalshi and Polymarket Double Down With Crypto Perpetuals and New Betting Products

• The U.S. Space Force awarded a $4.16 billion contract to SpaceX as part of a program to deploy space-based sensors to track and target airborne threats. Talk about timing and connections

• Meta is developing an AI-powered pendant that it plans to start testing in the next year

• SoftBank to commit to developing and operating five gigawatts of AI data center capacity in France, and invest up to 75 billion euros ($87.5 billion). This wine country is attractive for its nuclear energy infrastructure to power these centers.

• Nvidia Has a Plan to Put Its Chips in Personal Computers

• Governments Consider Letting AI Pull the Trigger

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OpenAI vs. Anthropic Is Becoming a $100 Million Political Fight

The battle between OpenAI and Anthropic has spilled into U.S. politics. Two rival super PACs aligned with the competing AI giants have already committed nearly $24 million to the 2026 midterm elections, which will take place tomorrow, Tuesday, June 2nd, with more than $100 million expected to follow.

At the heart of the conflict is a fight over how AI should be regulated, of course. Anthropic-backed groups generally favor stronger oversight and AI safety measures, while OpenAI-aligned organizations are pushing for a lighter regulatory touch to accelerate innovation. The rivalry has become so intense that candidates are reportedly avoiding discussions about AI altogether to escape becoming targets in the crossfire. As AI increasingly shapes economies, jobs, and national security, Silicon Valley is now investing heavily in shaping the lawmakers and policies that will govern the technology’s future.

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🧰 AI Tools of The Day

Three Underrated AI Apps you need to try (If you haven’t)

1. bolt. new — build AI apps without code

2. NotebookLM — turns docs into podcasts, slides, mental maps, and more.

3. Napkin AI — turns text into diagrams

Check Our AI Guides Repository

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Apple Is Preparing to Disrupt the Smart Glasses Industry

Apple is preparing its next major AI-powered hardware push with smart glasses that could do to the $200 billion eyewear industry what the Apple Watch did to traditional watches. After disrupting watch brands like Fossil and Swatch and building a wearables business that generates an estimated $17 billion annually, Apple is now targeting the billions of people who wear prescription glasses or sunglasses. The company is reportedly developing AI-enabled glasses deeply integrated with the iPhone and a revamped Siri assistant, positioning them as both a consumer electronics device and an everyday fashion accessory. The move puts Apple on a collision course with Meta, whose Ray-Ban smart glasses have gained early traction. Still, Apple is betting that its ecosystem of more than 2 billion active devices, retail presence, and AI capabilities can reshape the market. At the same time, Apple is accelerating its AI roadmap with a dedicated Siri chatbot app that syncs conversations across devices via iCloud. In contrast, next-generation Apple TV and HomePod mini devices are being upgraded to support Apple Intelligence features. Together, these efforts signal that Apple sees AI-powered wearables, not smartphones, as the next major computing platform.

📚 Learning Corner

How World Models Work

• World Models (David Ha & Jürgen Schmidhuber) - This is the original paper that popularized the term “world model.”

• NVIDIA Cosmos Platform - This is NVIDIA’s vision for Physical AI.

• Fei-Fei Li’s World Labs - Fei-Fei Li believes current AI systems understand language but not reality.

The Rise of World Models Explained

The next major frontier in AI isn’t chatbots, it’s teaching machines to understand and interact with the physical world. This is why companies like NVIDIA, Google DeepMind, World Labs, Figure AI, and Sanctuary AI are investing billions into “world models” AI systems that learn physics, spatial reasoning, cause and effect, and how objects, people, and environments behave.

While large language models learn from text, world models learn from videos, sensors, robot movements, simulations, and real-world interactions, allowing robots to predict what will happen before taking action. The ultimate goal is to create machines capable of performing the countless tasks humans do with ease, from folding laundry and stocking shelves to assisting in hospitals and factories. The biggest challenge isn’t intelligence, but dexterity. Many experts now view the human hand as the ultimate test for physical AI because tasks like picking up fragile objects, threading a needle, or handling tools require an extraordinary understanding of force, touch, and movement. To solve this, companies are collecting massive datasets through teleoperation, motion-capture gloves, cameras, and synthetic simulations that let robots practice millions of tasks in virtual environments before entering the real world. The prize is enormous; analysts estimate humanoid robotics alone could become a $5 trillion market by 2050, making world models the foundational technology that could power the next industrial revolution and transform industries ranging from manufacturing and logistics to healthcare and transportation.

• 📰 AI News and Trends

• AI’s Progressive Challenge: Democrats won’t let AI be loose.

• How to Master Dynamic Workflows, a new feature by Claude.

• 🧰 AI Tools - AI Guides Repository

• It’s Not Just Nvidia anymore, it’s war…

• 📚 Learning Corner - Claude’s Dynamic Workflows

📰 AI News and Trends

• Analysts forecast that lithium demand will increase three- to sixfold in the next decade, but a mining boss in China argued that even these bullish forecasts were underestimates.

• Nvidia CEO Jensen Huang is set to join the advisory board of Tsinghua University School of Economics and Management, which counts Tim Cook, Elon Musk, and Mark Zuckerberg among its 65 members.

• Anthropic on Thursday announced a new funding round valuing it at $965 billion, surpassing rival OpenAI as the world’s most valuable AI startup.

• Chinese AI company launches “95% accurate” pet translator

• Radical AI’s ‘self-driving’ lab designs and tests new alloys, and creates new materials, speeding up a material R&D process that often takes human scientists 20 years or more.

• A Google employee allegedly used inside information to win $1.2 million on Polymarket

• Apple to Overhaul iOS 27 Siri and lots of AI Features

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AI’s Progressive Challenge

A growing group of progressive Democrats is taking a tougher stance on artificial intelligence, creating a widening divide inside the Democratic Party over the future of AI regulation.

Leaders like Bernie Sanders, Alexandria Ocasio-Cortez, and Ro Khanna are pushing back against the rapid expansion of AI infrastructure, particularly large data centers tied to companies like Meta. Their concerns range from environmental damage and water usage to worker displacement and the growing influence of AI money in politics. Sanders has proposed a moratorium on new data centers and renewed calls to eliminate super PAC influence, while AOC has highlighted pollution concerns linked to AI facilities in Georgia.

Khanna, meanwhile, is advocating for a “Work for America” initiative that would retrain up to 1 million Americans for tech and infrastructure jobs as AI automation accelerates. The debate signals that AI policy is quickly becoming one of the most politically divisive issues in Washington, with progressives demanding stricter oversight as the AI industry races to expand.

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How to Master Dynamic Workflows

In Claude Code, dynamic workflows allow the agent to move beyond linear chatting by writing and executing its own orchestration scripts. It acts as a “manager,” spawning multiple parallel sub-agents to tackle complex, multi-part tasks simultaneously.

Dynamic workflows transform Claude Code from a single agent into an orchestrator. Instead of solving a massive problem step-by-step in one thread, Claude breaks the problem down, creates a fleet of sub-agents to handle the sub-tasks in parallel, verifies their output, and merges the results into a final solution. This is best for large-scale refactors, documentation generation, or complex bug hunting across large codebases. Be aware, your tokens may run out quickly if limited, or send you a large bill.

How to Use Dynamic Workflows

Since this is currently in Research Preview, you access it through specific commands in the Claude Code CLI or the supported IDE extensions.

1. Enable/Invoke via CLI

In the Claude Code terminal, you don’t usually need a special “mode” switch; you simply prompt Claude with a high-level, complex task that implies parallelization.

• Example Prompt: “Refactor all export statements in the /src directory to use named exports instead of default exports, and update all corresponding imports.”

• Observation: You will see Claude mention that it is “starting a dynamic workflow” or “spawning sub-agents.”

2. Scoping the Task (Best Practice)

Because this feature consumes significantly more tokens (as it is running many instances of Claude at once), start small:

• Target a specific folder: Instead of “fix the whole repo,” try “fix all type errors in src/components/.”

• Be specific about the goal: “Generate README files for every sub-package in this monorepo.”

3. Monitor Progress

While the workflow is running, the CLI will typically show a progress indicator or a list of active sub-tasks.

• Claude will automatically handle “retries” if a sub-agent fails.

• It will perform a “validation” phase at the end to ensure the parallel work didn’t break the codebase.

4. Requirements

• Plan: You must be on a Pro, Team, or Enterprise plan.

• Tools: Ensure you are using the latest version of the Claude Code CLI (npm install -g @anthropic-ai/claude-code) or the VS Code extension.

Warning: As mentioned above, in case you dint’t read all the way down here, watch your token usage/billing. Since dynamic workflows run hundreds of parallel calls, a single command can consume a large portion of your rate limit or budget much faster than a standard chat session.

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🧰 AI Tools of The Day

• Check Our AI Guides Repository

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It’s Not Just Nvidia anymore, it’s war…

The global AI boom has sparked an infrastructure arms race. While Nvidia still commands 80%–90% of the market (briefly hitting a $4 trillion valuation), the world’s tech giants are tired of waiting in line. Faced with 10-week to 6-month lead times, the industry is shifting toward “Vertical AI Infrastructure,” designing custom silicon to bypass the middleman.

The Challenger Bench

Nearly every major “hyperscaler” now has a custom chip program to lower costs and boost performance:

• The Big Three: Google (TPUs), Amazon (Trainium/Inferentia), and Microsoft (Maia) are building in-house to power their massive cloud ecosystems.

• Social & Media: Meta is using its MTIA chips for ad targeting, while ByteDance (TikTok) is reportedly developing custom CPUs to escape supply chain bottlenecks.

• Specialized Tech: Tesla (Dojo) is focused on autonomous robotics, while Apple continues to dominate on-device AI via its Neural Engine.

• The Direct Rivals: AMD (MI300 series) remains Nvidia’s strongest commercial GPU competitor, while Intel fights for relevance with its Gaudi accelerators.

Why Go Custom?

For these companies, the move isn’t just about saving money; it’s about survival. Custom silicon offers:

• Optimization: Chips tuned specifically for their own AI models (like Gemini or Llama).

• Efficiency: Lower power consumption and faster “inference” (running the AI).

• Sovereignty: Protection against global shortages and export restrictions.

From Earth to Mars

The race has even reached deep space. NASA recently tested its High Performance Spaceflight Computing (HPSC) chip. This radiation-hardened processor delivers 100x to 500x the performance of current space-grade tech, allowing future Mars rovers and lunar habitats to navigate autonomously without waiting for instructions from Earth.

Companies understand that creating the models and code is not enough; they must own the sand it runs on.

📚 Learning Corner

Claude’s Dynamic Workflows

• Introducing Dynamic Workflows

• Orchestrate subagents at scale with dynamic workflows

• 📰 AI News and Trends

• Data Centers in Your Backyard… Literally.

  • The Dirt AI leaves behind

  • The Ugly Opportunity

  • What about the Law?

• 🧰 AI Tools - Research

• 📚 Learning Corner - To prevent or restrict data center construction in your community

📰 AI News and Trends

• Data collection experiments by on-demand service platforms have drawn attention to the emerging physical AI ecosystem, with safety experts raising concerns about how such data is collected, processed, and shared

• China’s EV exports surged 40% year-on-year in April, further cementing its global dominance of the industry

• Huawei Says It Has Workaround to Match Leading Chips by stacking multiple layers of circuits within a single chip and reducing the time it takes to move data among them, they can be ready within 5 years.

• Japan’s New Hypersonic Engine Could Make 2-Hour Flights To The US A Reality

• Pope Leo XIV warned that artificial intelligence “threatens to normalize an anti-human vision” and turns humans into cogs, and said that the concentration of immense digital power in the hands of a few private actors must be countered.

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Data Centers in Your Backyard… Literally.

As I drive through the streets of Southern California, I can't help but notice the number of empty commercial and residential buildings. For-rent, for-lease, and for-sale signs are everywhere. We definitely don't have a homeless issue in SoCal, we have a drug epidemic, and it has made a large part of our population dysfunctional enough to not be able to sustain proper housing, but that’s a different story, a story that I would like to dive deeper into sometime.

The empty buildings make me think of the Data center hurdles that tech behemoths face today. Citizens and certain governments are all against data centers and they are opposing the building of these structures anywhere near humans. Data centers significantly strain and pollute local waters through excessive consumption, chemical discharge, and the thermal pollution of nearby water bodies.

The Dirt AI leaves behind

AI data centers are becoming some of the largest consumers of electricity and water in the U.S., with a single modern facility using as much power as 100,000 homes and up to 5 million gallons of water per day. As these facilities expand, homeowners are beginning to feel the impact through rising electricity bills and greater risks of grid instability and summer brownouts. Their constant energy needs are also extending the life of fossil-fuel power plants, slowing clean-energy transitions in many states.

The Ugly Opportunity

All these available real estate locations in SoCal, and any other states, are slowly becoming small Data Centers. That piece of commercial real estate that has been vacant for a few months can easily become the place your device connects next time it searches online. Google just declared that every search will be an AI search, so they are desperate for edge computing and distributed data centers worldwide. This isn’t science fiction. Major homebuilders, startups like NVIDIA, SPAN, which creates a wall battery-like device to be attached outside your home (that can attract thieves), and established data center operators are actively testing and deploying small fractional compute nodes on residential and commercial properties. The infrastructure that powers our AI queries, video streams, and autonomous vehicle decisions is being distributed and moved from the centralized hubs of Northern Virginia and Texas to the edges of neighborhoods, towns, and small cities.

For founders, operators, investors, and real estate professionals, this shift signals a massive opportunity window. But it also surfaces critical operational, regulatory, and valuation challenges that most players haven’t yet solved.

Hyperscalers are land-banking hundreds of acres and locking in multi-gigawatt power agreements across states. But power capacity is the constraint. Placing compute infrastructure closer to end-users reduces latency, for real-time applications like autonomous vehicles, augmented reality, medical diagnostics, and financial trading, milliseconds matter. Edge computing isn’t a new concept, but the scale, cost reduction, and AI advancement have made it suddenly viable at the residential and small commercial level.

What about the Law?

Communities in the nation have become very vocal against Data Center sin the their backyard. Citizens are quickly learning the perils and dangers of them near residences. NDC is a measure to be passed in Monterrey Park, CA, to prohibit the data centers in that community, and many other cities are adopting their own measures and getting organized. The battle will not be easy are no one has deeper pockets than the hyperscalers, and lobbying is something they have mastered, and as we know, everyone has a price. Even the current administration, which is actively unraveling federal limits on PFAS ("forever chemicals") in tap water, has launched historic deregulatory actions that significantly relax environmental protections. Residential zones typically don't permit compute infrastructure. Commercial zones do, but restrictions vary widely as of today. Some jurisdictions require data center facilities to be set back 500+ feet from residential property lines. Rezoning will be the game to play; some will attempt to use it to make data center development more difficult, while some politicians will do the opposite. As the hunger for compute and electricity grows, these data centers in your backyard, literally in your backyard trend will become more controversial.

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Robots may not take over the world after all… Cause they already id

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🧰 AI Tools of The Day

• Elicit: An AI research assistant that extracts key claims, methodologies, and sample sizes directly from your search results to help build systematic reviews.

• Consensus: An AI search engine that reads millions of research papers and provides direct, evidence-backed answers to specific questions.

• Connected Papers: A visual tool that builds graphs of academic papers, allowing you to easily discover related works and the “family tree” of a specific study.

• ResearchRabbit: Often called the “Spotify for papers,” this tool visualizes citation networks and sends updates when new papers are published on your saved topics

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📚 Learning Corner

To prevent or restrict data center construction in your community

• NAACP Stop Dirty Data Centers: Provides resources to track data center proposals, demand transparency, and protect frontline communities from water depletion and air pollution. Use the Report Dirty Data Centers Near You form to log planned sites.

• Food & Water Watch: The “Stop Data Centers Now!” campaign offers comprehensive strategies for organizing local petitions, applying pressure to local leaders, and demanding rigorous public votes.

• Halt the Harm Network: Offers a co-created toolkit that provides background on the issue and strategic insight into the legal and regulatory interventions available to block bad data center projects.

• Community Environmental Defense Services (CEDS): Connects concerned citizens with a nationwide network of attorneys, environmental experts, and engineers to oppose zoning changes and special exceptions.

• AlgorithmWatch Guide to Data Centers: A practical European and global guide that details how to check local land use zoning maps, access environmental impact assessments, and fight back against mega-projects

• 📰 AI News and Trends

• Meta has cut the Employees that Trained AI to replace them

• Anyone can be Nudifyed

• NVIDIA’s Reign Continues, but for how long?

• 🧰 AI Tools - Track the Market (Monitoring the Shift)

• 📚 Learning Corner - Learn to Orchestrate (AI Agent Frameworks)

📰 AI News and Trends

• The SpaceX IPO may hit a $2 trillion valuation. BUT, although SpaceX recorded $18 billion in revenue in 2025, a 33% jump from 2024, it is running at a net loss of $4.9 billion. Their cash cow is Starlink, a key profit-generating business line providing satellite high-speed internet, which surpassed 10 million subscribers earlier this year and accounted for nearly 70% of SpaceX's revenue in 2025. The US government, one of SpaceX's major customers and a significant purchaser of Starlink, represented more than 20% of SpaceX's total revenue in 2025. The excitement may wear off.

• What helps SpaceX is that Anthropic Is Paying $15 Billion a Year to Access Its Data Centers, SpaceX IPO Filing Reveals…

• And… while we are at IPOs, OpenAI has moved closer to its IPO. Sam Altman and Elon Musk will continue their love affair even after Musk took an “L” in court.

• And why wait to go public, knowing that their main rival’s revenue is set to more than double in the second quarter to $10.9 billion. Anthropic's quarterly revenue is now growing faster than Google's and Facebook's in the run-up to their IPO.

• The EU Is Going Through a Trump-Fueled Breakup With Big Tech. France is already moving on from Zoom and Microsoft Teams in favor of homegrown alternatives, and other countries are quickly following suit.

• Anyone can be nudifyed on line. All images shared on social media are at risk. Entire communities are nudifying women by request

• GitHub confirmed a major breach after a cybercriminal group known as TeamPCP used a malicious Visual Studio Code extension to compromise an employee's device, granting them access to ~3,800 repositories

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NVIDIA’s Reign Continues, but for how long?

NVIDIA reported a massive 85% revenue surge to $81.6 billion, with growth accelerating for the fourth consecutive quarter as the AI boom intensifies. The company’s Blackwell-series chips are hitting their stride, and standalone CPU sales are on pace for $20 billion this year to power autonomous agents like Anthropic’s Claude Code. With an astonishing $48.6 billion in free cash flow, Nvidia rewarded investors by increasing dividends and authorizing $80 billion in additional stock buybacks.

Despite its dominance, Nvidia faces rising competition as rivals like AMD grow and Chinese giants like Alibaba accelerate their own AI chip development to bypass U.S. trade restrictions. While major customers are attempting to develop in-house alternatives, Nvidia is maintaining its lead by tripling sales of networking equipment and preparing the next-generation Vera Rubin-series chips. Jensen Huang warns that total trade bans will only backfire, forcing China to build an independent ecosystem completely outside of U.S. control.

Strategic Pulse

• Pros: Massive cash reserves and triple-digit sales growth in networking equipment to lock in customers.

• Cons: High customer concentration and the risk of major clients switching to proprietary silicon.

• Competition: AMD remains a primary U.S. rival, while Alibaba pushes specialized AI silicon to counter geopolitical trade barriers.

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📚Learning Corner

Learn to Orchestrate (AI Agent Frameworks)

Managing the “AI Agents that do the work” requires understanding how to link multiple models together to handle complex workflows.

• CrewAI & AutoGen: These are the leading open-source frameworks for orchestrating multi-agent systems. Their documentation and GitHub repositories are essential “how-to” guides for delegation.

• LangChain / LangGraph: These tools allow you to build “stateful” agents that can remember past interactions and follow multi-step business logic.

• DeepLearning.AI: Look for their “AI Agentic Workflows” short courses, which teach the strategy of breaking down large tasks into smaller pieces for agents to solve.

Meta has cut the Employees that Trained AI to replace them

Meta has cut 8,000 jobs (10% of its workforce) to pivot toward massive AI infrastructure spending, projected to reach up to $145 billion. CEO Mark Zuckerberg signaled that AI now allows smaller teams to complete projects that previously required large groups, effectively trading human payroll for automated efficiency. While 7,000 employees are being reassigned to a new Applied AI Engineering division, those roles are specifically tasked with building the automation that replaces traditional functions in engineering, recruiting, and sales.

This “AI-driven reshuffling” has already led to over 100,000 layoffs across the tech sector, with giants like Google and Microsoft following Meta’s blueprint of cutting staff to fund the AI arms race. For employees, the risk is real: workers are essentially “training” the systems that will eventually automate their tasks. To survive this trend, the focus must shift to AI orchestration and oversight, as the industry moves away from human-led operations toward prioritizing automation at scale.

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🧰 AI Tools of The Day

Track the Market (Monitoring the Shift)

To stay ahead of the shrinking sectors and find the hiring hotspots:

• Layoffs.fyi: Use this to track real-time headcount reductions across the tech sector.

• LinkedIn Economic Graph: This provides insights into which “AI-related” skills are seeing the highest growth in job postings globally.

• TrueUp: A specialized job board that aggregates “future-proof” tech roles and tracks company growth versus layoffs.

Entire communities are Nudifying women by request

Research from the Institute for Strategic Dialogue (ISD) reveals a systematic and collaborative ecosystem on 4chan where men, referred to as “wizards,” create nonconsensual explicit deepfakes at the request of other users. The investigation underscores a chilling reality: anyone whose likeness is public may be subjected to this invasive exploitation. Whether the photos are from a vacation or a professional profile, any image posted online is at peril, and users often do not even know they have become targets.

Details from the ISD report and current trends include:

• Social Hierarchy: The community is built on a power structure where “wizards” (men skilled in digital manipulation) are worshiped with titles like “sir” or “master” by followers who supply photographic material and degrading command prompts.

• Targeting Personal Connections: The solicitation of these images is frequently tied to personal relationships, with users requesting “nudification” of family members, friends, colleagues, bosses, or people they simply find on Instagram and Facebook.

• Scale of Abuse: Analysis identified over 100,000 posts related to “nudification” within a three-month period spanning late 2025 to early 2026 across 4chan and other platforms.

• Motivations and Harassment: While some claim these images are for personal gratification, others openly aim to use them to coerce, ruin lives, or cause public fallout within private group chats.

• Technological Proliferation: The ecosystem is fueled by the dissemination of nudification apps and home-installed AI generators, making this form of digital violence increasingly systematic and normalized.

• 📰 AI News and Trends

• AI has not replaced human-written articles, yet…

• 🧰 AI Tools - Top 3 AI Chinese video models

• Why Anthropic’s $930B Valuation May Signal the End of OpenAI’s Dominance?

• 📚 Learning Corner - 144 Specialized Agents across 12 divisions

📰 AI News and Trends

• Everyone seems to be getting A’s in High school and college, but few are learning. Universities and colleges were already concerned about how many students were earning A's and B's. Still, they now must worry that graduates are leaving AI-proficient rather than knowledgeable about their subjects of study.

• Google I/O 2026 starts tomorrow. We expect Gemini AI's new features and improvements, Android XR, Android 17 updates, and a potential smart glasses collab with Samsung

• The first phase of the Musk v. Altman trial has concluded, and the nine-person jury will begin deliberating today

• After several authors and class members raised objections to Anthropic’s $1.5 billion settlement over its widespread book piracy to train AI, a federal judge has delayed final approvals of the settlement.

• Chinese AI groups pull ahead of US rivals in video generation race

• SpaceX accelerates IPO timeline, targets June 12 listing on Nasdaq

• Would you connect your bank account to ChatGPT? OpenAI launches ChatGPT for personal finance, which lets you do that.

• In an effort to compete with Starlink and Amazon Leo, AT&T, T-Mobile, and Verizon Plan to launch a new joint venture that Helps End Dead Zones using satellites

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AI has not replaced human-written articles, yet…

Despite the surge of AI-generated content following the launch of ChatGPT, new data indicate that machine-written articles have not overtaken the web, instead leveling off to represent approximately half of all new online articles.

While AI-generated content rapidly climbed to 48% of new articles within two years of ChatGPT’s release, it has hovered near the 50% mark since early 2025, suggesting a current limit to its dominance over human writers. However, the distinction between human and machine is increasingly blurred as many creators now use AI for outlining, drafting, or editing, leading to content that is often indistinguishable in quality from human-only work.

This plateau offers a brief reprieve from fears of a complete AI takeover, but the long-term balance remains uncertain as researchers warn that an over-reliance on AI could create a “feedback loop” of low-quality content if independent human knowledge creation stalls.

The Future of AI vs. Human Writers

Pros of AI Integration:

• Efficiency and Speed: AI can rapidly generate outlines, drafts, and rewrites, significantly accelerating the content creation process.

• Improving Quality: Modern AI models produce content that is frequently as good as, or even superior to (depends on the writer), human-written work, making it highly effective for standard articles and listicles.

• Collaborative Potential: Instead of a total replacement, AI acts as a powerful assistant for humans in the drafting and editing phases.

But, the Risks of AI Integration are:

• Knowledge Feedback Loops: If humans stop creating independent knowledge, AI models, which rely on human data for training, may begin training on machine-generated content, leading to a decline in overall information quality.

• Difficulty in Detection: As AI content becomes harder to distinguish from human writing, maintaining transparency and verifying the authenticity of online information becomes increasingly complex.

• Market Saturation: The “flood” of AI writing can lead to a massive volume of low-quality, machine-generated content that clutters the internet.

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📚Learning Corner

The Agency: AI Specialists Ready to Transform Your Workflow

• 🎭 144 Specialized Agents across 12 divisions

• 📝 10,000+ lines of personality, process, and code examples

• ⏱️ Months of iteration from real-world usage

• 🌟 Battle-tested in production environments

• 💬 50+ requests in first 12 hours on Reddit

Why Anthropic’s $930B Valuation May Signal the End of OpenAI’s Dominance?

The rivalry between AI giants OpenAI and Anthropic has reached a significant turning point, with Anthropic now outpacing its rival in valuation and corporate adoption.

• Valuation Crossover: Anthropic has signed a term sheet for a deal that places its expected post-money valuation at $930 billion, surpassing OpenAI’s $852 billion valuation for the first time.

• Equity Efficiency: Investors are paying approximately 1.8 times more per equity dollar for Anthropic compared to OpenAI. Anthropic shows a 9.0x equity return ratio ($930 billion on $103 billion in equity), while OpenAI holds a 4.9x ratio ($852 billion on $173 billion).

• Enterprise Adoption: For the first time on record, Anthropic has overtaken OpenAI in U.S. business adoption, with 34.4% of businesses using Anthropic compared to 32.3% for OpenAI. A year ago, Anthropic’s adoption was under 10%.

• Revenue and Margins: Anthropic’s gross margins have surged to above 70%, up from 38% just one year ago. In contrast, OpenAI faces challenges with a massive consumer base where 94% of its 900 million weekly active users remain on the free tier.

• Investment Concentration: High-profile firms, including Dragoneer, Greenoaks, Sequoia, and Altimeter Capital, are leading Anthropic’s latest round, with some firms like Greenoaks and Altimeter committing over 10% of their total assets under management to the company.

• Path to IPO: Analysts suggest Anthropic currently has a “cleaner path” to an IPO due to a lack of corporate restructuring issues and less reliance on a single customer compared to OpenAI’s 35% dependency on Microsoft.

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🧰 AI Tools of The Day

Top 3 AI Chinese video models you can use today

• Kling AI - Currently considered one of the strongest performers globally, Kling is capable of generating 4K high-definition videos with strong motion realism and synchronized audio.

• Seedance 2.0 (by ByteDance): Developed by the parent company of TikTok, Seedance 2.0 is noted for its high quality and usability, particularly in synchronizing audio and stabilizing voices. It is frequently cited as a “game-changer” for independent filmmakers due to its creative freedom and high scores on usage leaderboards.

• Hailuo AI (by MiniMax): This model is highlighted for its efficiency and ability to produce realistic or animated videos quickly from simple text prompts. It features a unique Camera Path Editor that allows users to set dynamic movements like pans and tilts at specific timestamps for more cinematic results.

• 📰 AI News and Trends

• The AI Energy Crisis - The Problem and Possible Solutions

• 🧰 AI Tools - Avoid Brain Rot

• AI Is Rotting Everyone’s Brain, and We’re Just Getting Started

• 📚 Learning Corner - Google’s 9 Hour AI Prompt Engineering Course In 20 Minutes

📰 AI News and Trends

• Cerebras Prices IPO at $185 – Cerebras Systems Inc. priced its shares at $185 ahead of its initial public offering, raising $5.5 billion, making it one of the largest technology IPOs in years and indicating strong investor demand for AI chips competitive with Nvidia.

• AI Cybersecurity Investments Outpace Acquisitions – Q1 2026 saw investment volumes in AI security startups ($3.8 billion) exceed the value of AI security acquisitions ($2.6 billion), an unusual trend where bigger investment bets are being placed on hot startups rather than smaller acquisitions.

• LinkedIn Plans Workforce Reduction – Microsoft-owned professional networking platform LinkedIn is reportedly planning to lay off 5% of its staff.

• Andreessen Horowitz Is Spending over $110 Million in Super PACs aligned with AI and Crypto efforts, as well as donating to MAGA and the Republican National Committee

• Sam Altman holds more than $2 billion in companies that have done business with OpenAI, creating a conflict of interest, a court document showed.

• TikTok is launching a new hub called TikTok GO that connects travel content with booking services that let users recreate the experiences they discover in the app.

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The AI Energy Crisis - The Problem and Possible Solutions

Data centers consume 6% of UK/US electricity, 3x the global average, and AI is the culprit. In just two years, AI has driven global data center energy consumption up 15%. The International Data Centre Authority warns that community backlash becomes “significant” once a country’s data centre footprint hits 5% of national grid consumption. The UK and US have already crossed that threshold. But the hidden scandal is that 13% of US datacenters’ power is “zombie services”, unused apps still running in the background, wasting 3+ GW annually. That’s electricity equivalent to powering millions of homes, consumed by code nobody remembered to shut down. This means higher energy bills for regular people, strain on water supplies, and a new justification for keeping fossil fuel plants online.

Solutions exist and are scaling fast. Liquid cooling technology can allegedly reduce data centre energy use by 40% and water consumption by up to 96% compared to traditional air cooling. The technology works by circulating dielectric coolant directly over the hottest components, removing nearly all the heat instead of letting it dissipate wastefully. On the software side, companies are helping organizations eliminate “zombie” cloud resources, idle instances, orphaned storage, and forgotten load balancers that continue consuming costs without delivering any value. Studies suggest that up to 30% of enterprise cloud spending is wasted this way, and cleanup efforts generate significant returns. The scale of the problem means the infrastructure gets built anyway, but now the choice is either old air-cooled systems that waste energy or new liquid-cooled systems that don’t. Either way, the AI boom is coming. The question is whether we build it sustainably.

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📚Learning Corner

Google’s 9 Hour AI Prompt Engineering Course In 20 Minutes

AI Is Rotting Everyone’s Brain, and We’re Just Getting Started

Developers report that using AI to write code is making them dumber. They produce working output but can’t understand how it works or fix what breaks. Research shows developers who fully delegate to AI perform 17% worse on conceptual tasks afterward.

Scale that across billions of knowledge workers, add teenagers using TikTok and AI simultaneously, and you’re looking at a structural collapse in human cognition nobody planned for. Human attention span has collapsed from 12 seconds in 2000 to 7.6 seconds in 2026, a 36% erosion in 26 years. Children with 4+ hours of daily screen time show 34% steeper attention declines. The deeper problem is that when children encounter coding, writing, or problem-solving for the first time while delegating to AI, they never develop the neural pathways to understand it. There’s no muscle to atrophy because the muscle was never built. Meanwhile, AI models themselves degrade from exposure to low-quality viral social media, creating a feedback loop where both humans and AI are fed the same junk.

Tech executives brag about AI writing 75% of Google’s code and 95% of Microsoft’s by 2030, but these gains translate into mass layoffs, not better products or shorter work weeks. Some guardrails are emerging, human-in-the-loop approaches, screen time limits, and refusing core thinking tasks to AI. But they’re not solutions; they’re just guardrails. By most metrics, AI will rot everyone’s brains. Are we going to notice in time to build systems or institutions that protect human cognitive development, or will we optimize for productivity until we wake up having outsourced the thinking itself?

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🧰 AI Tools of The Day

4 Tools to avoid Brain Rot

• Freedom (Focus Blocker) — Behavioral Guardrails - Blocks distracting websites and apps across all devices, with customizable rules that create barriers to mindless AI use.

• Lumosity or Peak — Targeted Cognitive Training - Neuroscientist-designed games targeting memory, attention, flexibility, speed, and problem-solving. Not entertainment, measurable cognitive exercises.

• iA Writer — Minimalist Writing to Block Distraction - A distraction-free writing environment that strips away every formatting option, sidebar menu, and AI suggestion.

• Endel or Noisli — Adaptive Soundscapes for Deep Focus - AI-generated soundscapes that adapt to time of day, heart rate, and activity—creating a sonic environment optimized for concentration.

• 📰 AI News and Trends

• 🧰 AI Tools - 20 Essential Tools for Small Business

• 💰 A New Class of Millionaires expanding the AI divide

• 📚 Learning Corner - AI Agents Full Course 2026 (2 Hours)

• 🌐 To Close the “AI Divide,” Microsoft Pledges $50B.

📰 AI News and Trends

• The cloud services company Akamai is the latest to sign Anthropic as a customer (a deal worth $1.8 Billion), extending gains to trade up over 25% following the news.

• Nvidia agreed to invest up to $3.2 billion in glass maker Corning and signed a deal for the right to invest up to $2.1 billion in data center operator IREN. The company now has over $40 billion in its investment bets.

• Coursera and Udemy have completed their merger, creating a massive online learning platform built for workers and employers, just as AI changes the skills needed for nearly every job.

• NVIDIA and PulteGroup are partnering with startup Span to install mini data centers on the walls of new homes. Each unit packs 16 Nvidia Blackwell GPUs, 4 AMD EPYC CPUs, and 3TB of RAM and taps unused home electrical capacity to run AI inference workloads. The question is, what unused electricity? Don’t we pay for what we use? And aren’t these expensive data centers loud and attractive to thieves?

• ByteDance boosts 2026 capex by 25% due to AI and memory costs

• In Cannes, Mathieu Kassovitz, the French director behind the 1995 film La Haine, predicted that “in two years from now nobody will care” whether characters on screen are human or generated by AI.

• Lime, the Uber-backed electric scooter and bike-sharing company, filed for an IPO after years of pivoting and restructuring.

• The “public” data we leave behind has become a searchable goldmine for AI surveillance. LLMs can now instantly analyze bulk data to identify patterns, sentiment, and personal identities with startling accuracy. Our entire public digital footprint is now an open book that can be summarized and profiled by employers or governments, far outpacing current privacy laws.

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💰A New Class of Millionaires expanding the AI divide

A massive financial “divide” is opening up in tech. OpenAI recently allowed over 600 employees to sell a collective $6.6 billion in shares, all before the company had even gone public.

The Highlights:

• Instant Wealth: Roughly 75 employees walked away with $30 million each after the company tripled its “selling cap” for staff.

• 100x Returns: Early employees saw their stock value grow 100 times over in just seven years, crushing the performance of the regular stock market.

• The Talent War: To keep up, rivals like Meta are offering top researchers $300 million pay packages.

• The “San Francisco Effect”: This sudden surge of “AI money” is spiking local rents and creating a massive wealth gap in the city.

As AI compensation hits unprecedented heights, the gap between tech “elites” and everyone else is widening faster than ever. Developers and researchers looking for work in the industry want to land that lottery ticket where they can cash out on stocks as well as get a 6-figure salary. The concentration of these companies in certain metros is making those cities more unequal, leading to serious socioeconomic complications.

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📚Learning Corner

• AI Agents Full Course 2026 (2 Hours) - This 2026 YouTube course covers how to build and deploy agentic AI systems, with all course files freely available.

To Close the “AI Divide,” Microsoft Pledges $50B.

At the 2026 India AI Impact Summit, Microsoft leaders Brad Smith and Natasha Crampton warned us that AI usage in the “Global North” is currently twice as high as in the “Global South.” To prevent a new era of economic inequality, Microsoft is committing $50 billion by 2030 to bridge this gap.

The Plan

• Building the Grid: As we have been hearing, it’s about power. Microsoft is building massive datacenter hubs in Mexico, Africa, and India, while aiming to bring internet access to 250 million people in underserved areas.

• The “Elevate” Skilling Surge: They are launching Elevate for Educators, a program to train 2 million teachers in India alone. Globally, the goal is to help 20 million people earn AI credentials by 2028.

• Beyond English: Most AI is “English-first,” which leaves billions behind. Microsoft is investing in projects like LINGUA Africa to make AI work fluently in local languages and cultural contexts.

• Real-World Tech: AI isn’t just for chatbots. New initiatives use satellite data to help farmers in Kenya and India predict food security and manage water usage more efficiently.

Microsoft is betting that AI will be the biggest “catch-up” opportunity of the 21st century. For students, this means a massive expansion of the global tech workforce and a shift toward localized, multilingual AI that goes far beyond Silicon Valley.

The goal is to move AI from a “luxury tool” to a “universal utility” like electricity.

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🧰 AI Tools of The Day

AI Top 20 Tools Playbook 2026 - 20 Essential Tools for Small Business

Biggest Student Ransomware Ever

The recent ransomware hack of Canvas is being called the “biggest student data privacy disaster in history,” affecting over 275 million users across thousands of schools.

Beyond typical info like IDs and emails, the breach exposed billions of private messages that often contain highly sensitive details, including medical disclosures, sexual assault allegations, and accessibility accommodations. This disaster highlights the “all eggs in one basket” danger of centralized EdTech, as a single vulnerability at parent company Infrastructure allowed hackers to lock students out of their own education while potentially weaponizing their most personal conversations for future phishing attacks.

• 📰 Today’s Top 5 Tech Stories

• This Week’s Top 5

• 🧰 AI Tools -

• The AI Infrastructure is in a Crisis

  • The Energy Squeeze

  • The Global Backlash

  • Hydrological Stress

• The Potential Solutions being deployed

  • Radical Cooling Innovations

  • “Circular” Energy: Waste Heat Recovery

  • Decoupling from the Grid: On-Site Power

  • Algorithmic and Hardware efficiency

• 📚Learning Corner - 10 GitHub repos that distill the world’s smartest people into AI you can run on your laptop.

📰 Today’s Top 5 Tech Stories

1. Apple and Intel Strike a Landmark Chip-Making Deal
   Apple has reached a preliminary agreement for Intel to manufacture chips for its devices, a historic reunion after Apple ditched Intel processors for its own silicon back in 2020. The Trump administration played a direct role in brokering the deal, with Commerce Secretary Howard Lutnick personally lobbying Apple CEO Tim Cook; Intel’s stock surged 15% on the news.

2. US Nears $400 Million Settlement with TikTok Over Child Privacy
   The Trump administration is closing in on a $400 million settlement with TikTok to resolve an ongoing federal lawsuit over alleged violations of children’s privacy laws. The settlement funds are reportedly earmarked for Washington, D.C. “beautification” projects, an unusual twist that underscores the political nature of the deal.

3. America’s Biggest Power Grid Sounds the Alarm Over AI
   PJM Interconnection, the grid operator serving 65 million people and the data center-dense Northern Virginia corridor, released a stark white paper warning it has “years, not decades” to overhaul how it operates. Surging AI data center demand has overwhelmed its capacity pipeline, with one major utility, American Electric Power, threatening to pull out of PJM altogether.

4. The Musk vs. OpenAI Trial Gets Explosive
   Two weeks into the $150 billion federal jury trial in Oakland, co-founder Greg Brockman’s private diary entries were read aloud in court, revealing he predicted Musk would wage a “very nasty fight” over OpenAI’s nonprofit-to-for-profit conversion.

5. Google I/O 2026 Preview: Gemini 4, Aluminium OS, and Smart Glasses
   With Google I/O kicking off on May 19, expectations are sky-high for the unveiling of Gemini 4.0, the debut of “Aluminium OS” (Google’s Android-based laptop operating system), and a new wave of Android XR smart glasses hardware. Google is also releasing Android 17 QPR1 Beta 2 this week, signalling the stable Android 17 release is on track for June.

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This Week’s Top 5

Week of May 5–8, 2026

1. Big Tech Posts Blockbuster Q1 Earnings and Commits $725B to AI
   Amazon, Alphabet, Meta, Apple, and Microsoft all beat earnings expectations, with Meta leading YoY revenue growth at 33% and Amazon’s AWS clocking its fastest growth in 15 quarters at 28%. Collectively, the “Magnificent Five” have now committed a record ~$725 billion in AI capital expenditure for 2026, sending free cash flow to a decade low.

2. Anthropic Shocks the Industry with a SpaceX Compute Deal
   In one of the week’s most jaw-dropping stories, Anthropic signed an agreement with SpaceX to access the Colossus 1 supercomputer, 220,000 NVIDIA GPUs, despite Musk having publicly called Anthropic “evil.” The deal signals that xAI is rapidly pivoting into a commercial cloud provider, and that Anthropic’s explosive growth is straining every available compute resource. This is another move to make sure OpenAI doesn’t win the AI race.

3. Samsung Hits $1 Trillion Valuation on AI Memory Boom
   Samsung became only the second Asian company after TSMC to crack the $1 trillion market cap milestone, with shares surging more than 15% in a single session, its largest single-day gain ever. The milestone cements Samsung’s place in the trillion-dollar club alongside Apple, Microsoft, Nvidia, and TSMC, driven entirely by insatiable global demand for AI memory chips.

4. Tech Layoffs Surpass 128,000 in 2026 as AI Reshapes the Workforce
   More than 128,000 tech jobs have been cut across the industry in the first five months of 2026 alone, already eclipsing the full-year 2025 total of 124,201. Coinbase (14%), Freshworks (11%), Meta (~10%), Snap (16%), and Oracle (30,000 globally) are among the biggest cutters, with AI-driven automation cited as the primary driver in nearly every case.

5. China Weighs $50 Billion State Investment in DeepSeek
   Beijing is reportedly preparing a state-backed investment in AI lab DeepSeek at a $50 billion valuation, a move that would transform the startup into a strategic national asset. The funding push comes as China doubles down on domestic AI champions in the face of U.S. chip restrictions, turning the AI race into an explicit industrial-policy battle between superpowers.

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📚Learning Corner

• 10 GitHub repos that distill the world's smartest people into AI you can run on your laptop.

The AI Infrastructure is in a Crisis

The rapid scaling of artificial intelligence is way beyond the digital realm, triggering a physical crisis at the intersection of energy, water, and public policy. As tech giants race to build “hyperscale” data centers, the digital economy is colliding with the hard limits of natural resources and public tolerance.

The Energy Squeeze

The U.S. power grid is facing an existential capacity crunch. A single AI data center can consume as much electricity as 100,000 homes, leading to a nearly 1,000% surge in capacity prices for the PJM Interconnection grid. This volatility is destabilizing the utility market, with giants like American Electric Power threatening to exit the grid due to the disproportionate burden placed on the system by AI demands.

Hydrological Stress

AI cooling is an ecological “thirst” problem. Hyperscale facilities use evaporative cooling, removing up to 5 million gallons of water daily from local watersheds, equivalent to the needs of a small city. Alarmingly, two-thirds of new data centers are located in drought-prone regions, forcing tech companies into direct competition with agricultural and residential water needs.

The Global Backlash

A sophisticated “anti-data center” movement has emerged, shifting from local complaints to legislative bans.

• In the U.S., Monterey Park, CA, and the Seminole Nation of Oklahoma have passed historic construction bans, while federal lawmakers have proposed a national moratorium on new facilities.

• Internationally: Ireland has frozen new data center connections through 2028, and activists in Spain and Latin America are successfully derailing projects over resource depletion.

The AI boom is limited by the fundamental elements of power, water, and public consent. Without radical breakthroughs in resource efficiency and community engagement, the infrastructure required for the “cloud” risks being grounded by the very physical world it serves.

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🧰 AI Tools of The Day

AI Top 20 Tools Playbook 2026 - 20 Essential Tools for Small Business

Potential Solutions being deployed

1. Radical Cooling Innovations

Traditional air-conditioning and evaporative towers are being phased out in favor of systems that use little to no water:

• Direct Liquid Cooling (DLC): Instead of cooling the entire room, chilled liquid is piped directly to “cold plates” on top of the AI chips. This is up to 30% more efficient than air cooling.

• Immersion Cooling: Servers are completely submerged in a non-conductive (dielectric) fluid that absorbs heat much more effectively than air. This eliminates the need for massive water evaporation and allows for much higher “rack density.”

• Subsea Data Centers: Following experiments like Microsoft’s Project Natick, companies are deploying modular data centers on the ocean floor. The cold surrounding seawater acts as a natural, infinite heat sink, requiring zero freshwater and reducing cooling energy costs by up to 90%.

2. “Circular” Energy: Waste Heat Recovery

Rather than venting heat into the atmosphere, new facilities are being designed as “thermal power plants” for their neighbors:

• District Heating: Data centers are being integrated into municipal grids to provide hot water and heating for local homes, schools, and hospitals.

• Greenhouse Co-location: High-grade waste heat is being redirected to power year-round industrial greenhouses, turning a digital byproduct into a solution for local food security.

3. Decoupling from the Grid: On-Site Power

To bypass the aging and overstressed national power grids (like PJM), tech giants are becoming their own utilities:

• Small Modular Reactors (SMRs): Companies like Amazon and Google are investing in “plug-and-play” nuclear reactors. These are smaller than traditional plants, can be built in factories, and provide 24/7 carbon-free power directly to the data center campus without taxing the public grid.

• Microgrids and Storage: Large-scale battery arrays and on-site hydrogen fuel cells allow data centers to “shave” their peak demand, drawing power from the grid only when it is abundant and running on stored energy during high-stress periods.

4. Algorithmic and Hardware Efficiency

The crisis is also being fought at the code level to reduce the “compute-per-query” footprint:

• Specialized AI Chips: Newer generations of hardware (like NVIDIA’s Blackwell and beyond) are designed to deliver more “intelligence per watt,” significantly reducing the total energy required to train a model.

• Edge Computing: By moving “inference” (the part of AI that answers your questions) to smaller local devices or “edge” centers, the industry can reduce the need for massive, centralized hyperscale facilities that create localized resource “hotspots.”

📰 AI News and Trends

• Google is testing a new Omni model for video generation, potentially unifying its video and image-generation tools.

• DeepSeek-V4-Pro, the new largest open weights model. It’s larger than Kimi K2.6 (1.1T) and GLM-5.1 (754B) and more than twice the size of DeepSeek V3.2 (685B) for a fraction of the price.

• Elon Musk Seemingly Admits xAI Has Used OpenAI’s Models to Train Its Own

• Meta Platforms has acquired Assured Robotics Intelligence, a startup developing AI models for humanoid robots that understand and adapt to human behavior across a range of environments

Other Tech News

• The company that built TikTok’s algorithm is now designing drugs for diseases in pharma called undruggable

• As the US depletes its war arsenal fighting dozens of wars, Ukraine is becoming the main exporter of weapons by building up its drone capabilities and bolstering defense partnerships.

• Ask.com shut down after nearly 30 years.

• Apple was surprised by AI-driven demand for Macs. Mac mini and Mac Studio devices sold out in recent weeks.

• GameStop Makes Bid to Buy eBay for $56 Billion

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The Race to Mine the Metal Powering AI

KoBold Metals, a Silicon Valley-based mineral exploration company backed by high-profile billionaires, has officially broken ground on the Mingomba copper mine in Zambia. Valued at over $2.3 billion, this deep underground project aims to produce more than 300,000 tonnes of copper annually by the early 2030s. The project is notable not only for its scale but also for its pioneering use of artificial intelligence in mineral discovery. What is the technology behind the discovery, the investors driving the project, the critical role of copper in AI and data centers, environmental and labor considerations, and how Mingomba compares to other global copper assets?

AI-Driven Mineral Exploration

Traditional mineral exploration relies heavily on human geologists analyzing limited datasets, often missing subtle patterns. KoBold Metals disrupted this paradigm by deploying proprietary AI platforms, TerraShed and Machine Prospector, to analyze over a century of geological data, satellite imagery, geochemical surveys, and field reports.

TerraShed acts as a massive data integration engine, standardizing and aligning diverse datasets spatially and temporally. Machine Prospector then applies machine learning algorithms trained on known global deposits to identify high-probability exploration targets. In the case of Mingomba, the AI identified a massive, highly concentrated copper deposit deep underground that traditional geologists had overlooked for decades. Furthermore, the AI models adapt in real-time as new field data emerges, significantly reducing the time and capital wasted on false targets.

Investors and Valuation

In January 2025, the company closed a $537 million Series C funding round, achieving a valuation of $2.96 billion.

Key investors include (nothing new here):

• Breakthrough Energy Ventures: Backed by Bill Gates.

• Apollo Projects: Backed by Sam Altman (CEO of OpenAI).

• Other Backers: Jeff Bezos, Michael Bloomberg, Andreessen Horowitz Growth, T. Rowe Price, Durable Capital Partners, Earthshot Ventures, Equinor, and Mitsubishi.

This convergence of tech billionaires and climate funds underscores the strategic importance of securing critical minerals for the energy transition and the AI revolution.

The Mingomba project is operated by Mingomba Mining Limited (MML). The ownership structure is a joint venture by KoBold Metals: 80% ownership, and ZCCM Investments Holdings Plc: 20% ownership, a Zambian state-backed investment company.

The AI and Data Center Connection

Copper is the “metal of electrification” and is critical to the infrastructure supporting AI. While a conventional data center requires between 5,000 and 15,000 tons of copper, a hyperscale AI data center can consume up to 50,000 tons per facility. Copper is essential for powering servers, cooling systems, and grid connections.

The International Energy Agency (IEA) projects that data center electricity demand will more than double by 2030, driven largely by AI. Consequently, data centers could consume over half a million metric tons of copper annually by the end of the decade. With the IEA warning of a potential 30% global copper supply deficit by 2035, securing new, high-grade sources like Mingomba is critical for the tech industry’s supply chain.

Comparable Mines and Investment Opportunities

Mingomba’s projected output of 300,000+ tonnes per year places it firmly in the “major producer” category globally.

Investment Opportunities: Zambia is experiencing a copper boom. Beyond KoBold, major players are expanding operations. Barrick Gold is executing a $2 billion “Super Pit” expansion at its Lumwana mine to double output to 240,000 tonnes. First Quantum Minerals recently commissioned a $1.25 billion expansion in Zambia. These developments present significant opportunities for infrastructure, logistics, and local supply chain investments.

Environmental Impact and Labor Conditions

While copper is vital for green technology, its extraction poses severe environmental risks. Zambia has a troubled history with mining pollution. The city of Kabwe, site of a former lead and zinc mine, is considered a “sacrifice zone” with extreme soil contamination and widespread childhood lead poisoning.

More recently, in early 2025, a tailings dam collapsed at a Chinese-operated copper mine in northern Zambia, releasing millions of gallons of toxic waste into the Kafue River, killing fish and contaminating farmland.

Deep underground mining is inherently hazardous, involving risks of rockfalls, extreme heat, and poor ventilation. KoBold Metals has emphasized its commitment to local employment, stating that over 90% of its Zambia team and project leadership are Zambian nationals. The company is also partnering with local universities to train the next generation of exploration geologists.

KoBold Metals’ Mingomba project represents a fascinating intersection of Silicon Valley AI innovation and traditional heavy industry. If successful, it will provide a crucial supply of copper to feed the insatiable infrastructure demands of the AI revolution. However, the transition from AI-driven discovery to executing a $2.3 billion, 1,700-meter-deep mine in a wet geological zone is fraught with engineering, environmental, and labor risks. The project will serve as a bellwether for whether modern, tech-backed mining can deliver critical minerals responsibly and efficiently.

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📚Learning Corner

• How to Run LLMs locally. A step-by-step guide.

Pentagon Expands AI Arsenal After Anthropic Clash

The U.S. Department of Defense just reshaped the AI landscape, fast. On May 1, 2026, it signed deals with 8 companies, including OpenAI, Google, Microsoft, Amazon Web Services, NVIDIA, Oracle, SpaceX (via xAI), and startup Reflection AI, all to deploy AI on top-secret military systems.

This came after a public fallout with Anthropic, which walked away from a $200M contract rather than allow its models to be used for mass surveillance or fully autonomous weapons. In response, the government labeled it a “supply chain risk” and quickly replaced it with vendors willing to agree to “lawful operational use,” giving the military broad flexibility over how AI is deployed.

The DoD’s internal AI platform already reached 1.3 million users in just five months, and these new systems will operate at the highest classification levels. AI governance is moving away from company-imposed ethical limits toward government-defined rules, signaling a new phase where speed, scale, and national security priorities outweigh internal guardrails.

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🧰 AI Tools of The Day

Pentagon Tools.

• Project Maven (Palantir) - The Pentagon’s core AI battlefield platform (now a “program of record”). Used across 10+ combatant commands, ~20,000+ users. Ingests data from 150+ sources (satellites, drones, radar, signals). Uses computer vision to detect targets in real time and generates ~1,000 targeting recommendations per hour.

• GenAI.mil - The Pentagon’s internal “ChatGPT-like” AI platform. 1.3 million users in ~5 months. This is becoming the operating layer for AI inside the DoD.

• Joint All-Domain Command and Control - A next-gen military network connecting all branches (Army, Navy, Air Force, Space Force). Links sensors, weapons, and command systems into one network. Uses AI to fuse data and speed up decisions.

• Distributed Common Ground System - A long-running military intelligence platform that collects and analyzes surveillance + reconnaissance data, integrates satellite imagery, signals intelligence, and reports

• Aegis Combat System - Navy’s advanced missile defense + combat system. Tracks 100+ targets simultaneously, automates threat detection and interception decisions for missile defense on ships, real-time combat response.

• Big Tech’s $700B Capex Spree and Anthropic’s $900B Valuation Question

  • Alphabet’s Q1 2026: The AI Bet Pays Off

  • The $700 Billion Capex Arms Race

  • Breakdown of Big Tech 2026 Capex Guidance

  • Anthropic’s $900 Billion Valuation: Justifiable or Froth?

• 🧰 AI Tools - Best Open Source models for Local LLMs

• What Are AI Evals and Why Do They Matter?

• 📚Learning Corner - How to Run LLMs locally. A step-by-step guide.

📰 AI News and Trends

• OpenAI has abandoned much of its Stargate plan, and it will no longer develop its own data centers

• Citi is rolling out a new internal AI platform that lets employees create agents, tapping into top models within one secure system that can scale those agents across the firm.

• Nvidia’s Banned B300 Servers Hit $1 Million in China’s Black Market. US export controls and a crackdown on chip smuggling have driven the price to twice as much. Chinese tech firms are scrambling for the hardware despite the risk of US sanctions exposure, with some turning to rentals as high as 190,000 yuan per month as grey-market supply dries up.

• Samsung's Chip Profits Explode 49-Fold as AI Memory Crunch Deepens. Samsung Electronics posted a record Q1 2026 operating profit of 57.2 trillion won (~$38.5B), with its chip division alone surging from 1.1 trillion to 53.7 trillion won in a single year, a nearly 49-fold leap.

• Big Tech's AI Spending Spree Tops $700 Billion and Counting. Alphabet, Amazon, Microsoft, and Meta collectively plan to spend as much as $725 billion on capital expenditures in 2026, almost entirely to build out AI data center infrastructure.

• Google's AI Bet Pays Off in a Big Way Cloud Revenue Surges 63% Alphabet blew past Wall Street expectations in Q1 2026, reporting $109.9B in total revenue (up 22% YoY) with Google Cloud alone hitting $20B, a staggering 63% jump driven by AI demand.

Other Tech News

• Musk was fine with a for-profit OpenAI when he thought he could control it, but turned against it when he realized he wasn’t able to control it, and now acts as the AI saviour. But his products post racist messages, create nonconsensual images of adults, and generate explicit images of children.

• Meta told it’s violating EU law by not doing enough to keep children off Facebook and Instagram.

• Anthropic, OpenAI Splurge on London Offices in Leasing Wave, while Startup Offices in NYC Are Flashy but Mostly Empty

• Emergency First Responders Say Waymos Are Getting Worse

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Big Tech’s $700B Capex Spree and Anthropic’s $900B Valuation Question

I am becoming a bit skeptical of the Anthropic’s valuations. Can they justify raising yet another $100B to reach almost the $1 Trillion valuation? How can they keep up with costs? Are their clients as loyal as they may think? I do understand that the artificial intelligence landscape in early 2026 is defined by staggering numbers. As Big Tech companies report their first-quarter earnings, the narrative is clear: the AI boom is generating massive revenue, but it requires an unprecedented scale of capital expenditure to sustain. At the center of this frenzy are the hyperscalers, Alphabet, Amazon, Microsoft, and Meta, and the frontier model builders like OpenAI and Anthropic.

Alphabet’s blowout Q1 earnings were driven by Google Cloud, the collective $700 billion capital expenditure (capex) plans of Big Tech, and Anthropic’s reported pursuit of a $900 billion valuation.

Alphabet’s Q1 2026: The AI Bet Pays Off

Alphabet’s first-quarter 2026 earnings report served as a powerful vindication of its massive investments in AI. The company reported total revenue of $109.9 billion, a 22% year-over-year increase that comfortably beat Wall Street’s $107.2 billion estimate.

The undisputed star of the quarter was Google Cloud. The division reported $20.03 billion in revenue, representing a staggering 63% year-over-year growth. This performance significantly outpaced analysts’ expectations of $18.05 billion and marked a sharp acceleration from the 48% growth seen in the previous quarter.

Alphabet CEO Sundar Pichai highlighted that enterprise AI solutions have become the primary growth driver for Google Cloud for the first time. The company’s full-stack approach to AI is yielding results across the board, with Search queries hitting an all-time high and the Gemini Enterprise platform seeing a 40% quarter-over-quarter increase in paid monthly active users.

Despite the massive revenue surge, Google remains compute-constrained. “Our cloud revenue would have been higher if we were able to meet the demand,” Pichai noted during the earnings call. This constraint underscores the necessity of the massive infrastructure build-out currently underway across the industry.

The $700 Billion Capex Arms Race

The demand for AI compute has triggered what analysts are calling the largest concentrated infrastructure cycle in tech history. The four major hyperscalers, Alphabet, Amazon, Meta, and Microsoft, are collectively projected to spend between $650 billion and $725 billion on capital expenditures in 2026.

This spending is almost entirely directed toward building out AI data center infrastructure, including land, power, buildings, servers, and highly sought-after AI accelerators (GPUs and TPUs).

Breakdown of Big Tech 2026 Capex Guidance

• Amazon: ~$200 Billion

• Microsoft: ~$190 Billion (Up 61% from 2025)

• Alphabet: $180 Billion – $190 Billion

• Meta: $125 Billion – $145 Billion

The sheer scale of this investment is causing some unease among investors, with concerns about free cash flow and the potential for a “capital misallocation” if AI revenue doesn’t keep pace with infrastructure costs. For instance, Amazon reported that its free cash flow decreased to $1.2 billion for the trailing 12 months, a 95% drop year-over-year, driven primarily by a $59.3 billion increase in property and equipment purchases.

However, tech executives remain resolute. Amazon CEO Andy Jassy compared the current environment to the early days of AWS, noting that “the faster AWS grows, the more short-term capex we’ll spend”. Microsoft CFO Amy Hood attributed part of their $190 billion capex forecast to a $25 billion impact from higher component prices, specifically citing soaring memory costs driven by the global AI hardware crunch.

Anthropic’s $900 Billion Valuation: Justifiable or Froth?

Against the backdrop of Big Tech’s infrastructure spending, frontier AI lab Anthropic is reportedly in talks to raise fresh capital at a valuation of more than $900 billion. If successful, this would push Anthropic past its chief rival, OpenAI, which was valued at $852 billion in March 2026.

Anthropic’s valuation has skyrocketed over the past year. In February 2026, the company raised $30 billion at a $380 billion valuation. A jump to $900 billion in just a few months raises the critical question: Can the company justify this price tag?

The Bull Case for $900 Billion

The justification for Anthropic’s soaring valuation rests on its explosive revenue growth, its deepening entrenchment in the enterprise sector, and the breakout success of its latest models.

1. Hyper-Growth Revenue: Anthropic recently announced that its annualized revenue run rate has surpassed $30 billion, a dramatic increase from approximately $9 billion at the end of 2025. Some sources suggest the current run rate is closer to $40 billion.

2. Enterprise Dominance: Unlike OpenAI, which has a massive consumer footprint with ChatGPT, Anthropic has focused heavily on B2B applications. Over 55% of Anthropic’s revenue comes from enterprise and API usage. The company reported that the number of business customers spending over $1 million annually doubled from 500 to over 1,000 in less than two months.

3. The “Mythos” Factor: In early April, Anthropic unveiled a new model called Mythos, purportedly able to detect and exploit vulnerabilities in critical software. Deemed too dangerous for wide release, it is currently being tested by a limited group of companies. The model has sparked global alarm, high-profile meetings with the Trump administration, and even testing by the NSA. The massive compute required to run Mythos is a key driver behind Anthropic’s current fundraising push.

4. Strategic Backing: Anthropic has secured massive financial and infrastructure commitments from Big Tech. Google recently committed to invest $10 billion at a $350 billion valuation, with plans to invest up to another $30 billion if performance targets are met. Amazon is also investing $5 billion at the same $350 billion valuation, with plans to inject $20 billion more over time. Furthermore, Anthropic has secured multiple gigawatts of next-generation compute capacity through partnerships with Google, Broadcom, and Amazon.

The Valuation Math and IPO Horizon

If Anthropic is generating a $30 B to $40 B annualized revenue run rate, a $900 billion valuation implies a revenue multiple of roughly 22x to 30x. While high by traditional software standards, this multiple is actually a compression compared to earlier stages of the AI boom. The rapid growth in actual revenue is helping the company “grow into” its massive valuation.

This fundraising push comes at a critical time. Anthropic is reportedly considering an initial public offering (IPO) as soon as October 2026. As Anthropic gains momentum, it is putting immense pressure on OpenAI, which is also expected to go public soon but has reportedly missed some revenue and user growth targets amid the fierce competition.

The intersection of Alphabet’s 63% cloud growth, Big Tech’s $700 billion capex commitments, and Anthropic’s push for a $900 billion valuation paints a picture of an industry in the midst of a historic platform shift.

The hyperscalers are spending unprecedented sums because the enterprise demand for AI, evidenced by Google Cloud’s numbers and Anthropic’s $30B+ run rate, is materializing faster than expected. Anthropic’s potential $900 billion valuation, while staggering, is anchored by real, hyper-scaling enterprise revenue, the breakout success of models like Mythos, and massive compute resources secured through its Big Tech partnerships. Securing the capital and infrastructure to deploy AI at a global scale is the real AI war today.

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📚Learning Corner

• How to Run LLMs locally. A step-by-step guide.

What Are AI Evals and Why Do They Matter?

Artificial Intelligence evaluation (evals) is the critical process of testing and measuring the capabilities, reliability, and safety of AI models and agents. Similar to crash tests for vehicles or clinical trials for pharmaceuticals, evals provide standardized benchmarks to assess AI performance before and during deployment.

According to the article “AI evals are becoming the new compute bottleneck” by Avijit Ghosh et al., AI evaluation has transformed from a relatively inexpensive process into a significant computational and financial challenge impacting the entire AI ecosystem.

Types of AI Evals

AI evals have evolved to match the increasing complexity of AI systems:

• Static LLM Benchmarks: Initially, evals for Large Language Models (LLMs) involved static datasets, where models were graded on predefined questions. These were efficient, with methods like Flash-HELM allowing for 100× to 200× compression while maintaining accurate model ranking.

• Agentic Benchmarks: With the rise of autonomous AI agents capable of multi-step tasks (e.g., web navigation, coding), evals became more complex. Agentic benchmarks like GAIA and SWE-bench are costly and intricate, as a single task involves numerous steps, and failure at any point can invalidate the entire run.

• Training-in-the-Loop Benchmarks: The most advanced evals now require AI to perform actual machine learning research or train new models. Benchmarks such as The Well (for scientific ML) and PaperBench (for replicating research) demand significant GPU resources and time, essentially making the evaluation a full training process.

Why Evals Are Crucial for Everyone

Despite their technical nature, AI evals have broad implications for society, governance, and the future of AI:

1. Defining “Good” AI: Evals establish the standards for AI progress. However, leaderboards that prioritize raw accuracy over cost and reliability can be misleading, fostering inefficient development. “Cost-blind leaderboards reward waste,” encouraging excessive compute usage without necessarily improving efficiency or reliability.

2. Revealing Unreliability: Modern evals highlight that AI agents often lack reliability. A model might perform well once but fail inconsistently on repeated attempts. For instance, performance on the τ-bench dropped from 60% to 25% when requiring 8-run consistency. Rigorous, repeated testing is vital to prevent deploying seemingly capable but unpredictably failing AI systems.

3. Creating an Accountability Barrier: The cost of comprehensive evaluations has escalated dramatically, with single runs costing thousands and full evaluations potentially hundreds of thousands of dollars. This financial burden creates a barrier for academic institutions, AI Safety Institutes, and independent journalists, limiting their ability to independently verify claims made by frontier AI labs. As the article states, “Whoever can pay for the evaluation gets to write the leaderboard”.

4. Shaping AI Safety and Governance: Evals are fundamental for AI regulation, measuring safety and risk. Governance bodies need to understand the disparity between a model’s single-run accuracy and its consistent reliability. The high cost of measuring this gap hinders regulators’ capacity to audit powerful AI systems effectively.

The Way Forward

The escalating cost of evals necessitates a collaborative solution. Standardized documentation and data sharing are the most effective ways to reduce costs and enhance transparency. By publishing full evaluation traces, including prompts, scaffolds, and tool-call logs, researchers can build upon existing data, avoiding redundant testing. Initiatives like the EvalEval Coalition’s “Every Eval Ever” project aim to create a shared format for this data, promoting reuse and analysis within the community.

In essence, AI evals are no longer a minor technical detail; they are the foundational infrastructure for measuring, trusting, and governing artificial intelligence. Ensuring their rigor, transparency, and accessibility is paramount for the safe and equitable advancement of AI.

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🧰 AI Tools of The Day

Best Open Source models for Local LLMs - As discussed yesterday with Dr. Sam Illinworth, getting an open source local LLM model for your personal research and queries is the best option. Here are 3 to consider:

1. Qwen 3.5 (The Best All-Rounder) - The gold standard for local users. It consistently beats proprietary models like GPT-5-mini in benchmarks and offers incredible performance in coding and multilingual tasks.

   Best For: General purpose, high-end coding, and 200+ languages.

2. Llama 4 Scout (The Context King - 109B total / 17B active parameters) - The first choice for users who need to process massive amounts of data locally.

   Best For: Document analysis, long-form writing, and RAG (Retrieval-Augmented Generation).

3. DeepSeek-R1 (The Reasoning Powerhouse) - If your goal is complex problem-solving, math, or deep logic, DeepSeek-R1 remains the top choice. It uses a “Chain-of-Thought” (CoT) reasoning process similar to OpenAI’s o1 series.