• 📰 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

Check Our AI Guides Repository

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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.

• China Blocks Meta’s $2 Billion Acquisition of Manus AI

  • The Meta-Manus Deal: A Geopolitical Flashpoint

  • The New Era of Investment Restrictions

  • Chinese startups.

  • State-Driven vs. Private-Sector Models

  • The “Dual-Exit” Trap

• 🧰 AI Tools - Top Chinese AI Models

• Tech Deals and Contracts at Risk in the Wake of the Meta-Manus Block

• 📚Learning Corner - The Elements of AI Free Courses and A 4-hour Claude Code Course. Definitely worth it.

📰 AI News and Trends

• Google to invest up to $40 billion in AI rival Anthropic, days after Amazon pledged $25B. Both companies are now Anthropic’s largest investors, sole infrastructure providers, and direct competitors. Talk about circular deals.

• DeepSeek previews new AI model that ‘closes the gap’ with frontier models

• Meta has signed a deal to use millions of AWS Graviton chips to power its growing AI needs

• OpenAI releases GPT-5.5, bringing the company one step closer to an AI ‘super app.’

Other Tech News

• Samsung execs worried the company could lose money on smartphones for the first time due to high memory costs.

• Tesla’s Cybercab is now in production at the company’s Gigafactory in Austin, Texas, but they are unusually cautious about the rollout.

• Instagram’s new app is yet another riff on Snapchat, allowing users to post messages that disappear after a set period of time.

• China Blocks Meta’s $2 Billion Acquisition of AI Startup Manus and Plans to Restrict Tech Firms From Receiving U.S. Investments.

• X Money is expected to launch to the public before the end of this month. The banking and payments platform will provide free peer-to-peer transfers, a personalized metal Visa debit card, and an AI concierge that tracks spending and sorts through past transactions, getting X closer to a Super App

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China Blocks Meta’s $2 Billion Acquisition of Manus AI

I was honestly very surprised when I heard the news that Meta was buying Manus for $2B in December of 2024. I had and have been using Manus for quite a while now, and find the model very powerful and better than some of the US rivals. SO this news seemed to me great for Meta, which I am not a fan of, and Manus investors looking to cash out, but detrimental for Manus the Model. The decision by China’s National Development and Reform Commission (NDRC) to block Meta Platforms Inc.’s $2 billion acquisition of the agentic AI startup Manus marks a watershed moment in the escalating technological rivalry between Washington and Beijing. This move, announced on April 27, 2026, represents a significant escalation in China’s efforts to prevent the “leakage” of critical artificial intelligence technology to the United States. While the deal was initially seen as a successful exit for a startup with global aspirations, it has now become a cautionary tale for the “dual-exit” risks facing Chinese-founded tech firms.

The Meta-Manus Deal: A Geopolitical Flashpoint

Manus AI, operated by Singapore-based Butterfly Effect Pte Ltd, gained global prominence for its advanced “agentic AI” autonomous systems capable of performing complex tasks independently. Founded by Chinese entrepreneurs Xiao Hong (known as Red Xiao) and Ji Yichao (Peak Ji), the startup was seen as a bridge between Chinese engineering talent and global markets. Meta’s acquisition, valued at approximately $2 billion, was intended to bolster its own AI agent capabilities.

However, the deal triggered immediate scrutiny from Beijing. Shortly after the announcement in December 2025, the NDRC launched a probe into what it termed “illegal foreign investment and tech exports.” By March 2026, the founders were reportedly barred from leaving China, and the deal was ultimately ordered to be cancelled. The NDRC’s intervention signals that Beijing now views AI talent and intellectual property as national assets that cannot be traded freely, even if the company is headquartered offshore in Singapore.

The New Era of Investment Restrictions

The blocking of the Manus deal is not an isolated incident but part of a broader strategy to curb US investment in Chinese tech companies. As reported by Bloomberg, Beijing is tightening its grip on the flow of capital and technology, driven by several factors:

1. Stemming Brain Drain: Beijing is increasingly concerned that its top AI researchers are being “hired away” through acquisitions by US tech giants like Meta, Google, and Microsoft.

2. Technology Sovereignty: The NDRC is acting as a “Chinese CFIUS” (Committee on Foreign Investment in the United States), reviewing deals not just for financial compliance but for their impact on national security and technological independence.

3. Reciprocal Restrictions: In response to US efforts to limit investment in Chinese AI and semiconductors, China is implementing its own “negative list” for US companies, making it nearly impossible for American firms to acquire strategic Chinese startups.

State-Driven vs. Private-Sector Models

The Manus case highlights the fundamental difference between the Chinese and American models of technological advancement. As detailed in the USCC report on Made in China 2025, China’s progress is characterized by a “comprehensive mobilization of state resources.”

The Chinese Model: State-Led Innovation

China’s approach, exemplified by the Made in China 2025 (MIC2025) initiative, relies on a multi-pronged strategy of government support. This includes massive subsidies, tax breaks, and “Government Guidance Funds” that direct capital into strategic sectors. The state also uses market entry barriers and procurement policies to favor domestic firms. This model has seen significant success in sectors like Electric Vehicles (EVs), shipbuilding, and space equipment, where China has met or exceeded its ambitious targets.

The US Model: Private-Sector Dynamism

In contrast, the US model has traditionally been driven by private enterprise, venture capital, and a highly liquid public market. Innovation is decentralized, with companies like Meta and OpenAI competing for talent and market share. However, the US is increasingly adopting an “industrial policy” of its own, such as the CHIPS Act, to counter China’s state-driven gains.

The “Dual-Exit” Trap

The blocking of the Meta-Manus deal creates a permanent “chill” in the global AI ecosystem. For founders with Chinese roots, the path to a global exit is now fraught with peril. If they sell to a US company, they risk being blocked by Beijing; if they accept US venture capital, they may face restrictions from both sides.

Moving forward, we can expect a further decoupling of the AI industry. China will likely double down on its state-driven model, providing even more capital to domestic startups to ensure they don’t feel the need to seek US buyers. Meanwhile, US companies will find it increasingly difficult to tap into the vast pool of Chinese AI talent through traditional M&A. The “Agentic AI” race, once a competition between companies, has officially become a frontline in the geopolitical struggle for technological supremacy.

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📚Learning Corner

• The Elements of AI - A series of free online courses created by MinnaLearn and the University of Helsinki. Learn what AI is, what can (and can’t) be done with AI, and how to start creating AI methods.

• A 4-hour Claude Code Course. Definitely worth it.

Tech Deals and Contracts at Risk in the Wake of the Meta-Manus Block

The National Development and Reform Commission’s (NDRC) decision to block Meta’s acquisition of Manus AI has sent shockwaves through the global technology sector. This intervention is not merely a single-case rejection but a signal of a new, more aggressive phase in China’s “technology sovereignty” strategy. The following analysis outlines the specific deals, partnerships, and contracts that are now at high risk.

1. High-Profile AI Startups: The Funding Freeze

Beijing has moved to insulate its “national champions” in the AI sector from American influence. Regulators have reportedly issued direct guidance to several top-tier AI startups to reject US capital in future funding rounds.

For these companies, the “dual-exit” trap is now a reality. They are effectively barred from being acquired by US tech giants (the Meta-Manus precedent) and are increasingly restricted from accessing the world’s deepest pool of venture capital.

2. Strategic Partnerships and Global Alliances

The “chilling effect” extends beyond direct acquisitions to complex global partnerships that involve US technology and Chinese-linked entities.

•Microsoft and G42: Microsoft’s $1.5 billion investment in the UAE-based AI firm G42 is under intense scrutiny. Because G42 has historical ties to Chinese hardware and talent, US regulators fear it could serve as a “backdoor” for tech leakage. Conversely, China may now view G42’s pivot toward Microsoft as a betrayal of technological cooperation, potentially leading to the cancellation of G42’s existing contracts within China.

•OpenAI’s Global Expansion: As OpenAI seeks to expand its footprint in Asia and the Middle East, any partnership involving entities with significant Chinese ties will be a non-starter. The Meta-Manus block confirms that even “neutral” hubs like Singapore or the UAE are no longer safe harbors for US-China tech collaboration.

3. Supply Chain and Operational Contracts

The most significant financial impact may be felt in the multi-billion-dollar supply chain contracts that underpin the global hardware industry.

The Nvidia “H20” Crisis

NVIDIA developed the H20 chip specifically to comply with US export controls while still serving the Chinese market. However, Beijing has reportedly retaliated by directing Chinese firms to stop purchasing Nvidia hardware altogether in favor of domestic alternatives like the Huawei Ascend series. Reports of Nvidia halting H20 production suggest that this multi-billion dollar revenue stream is effectively at an end.

Apple’s “China-Light” Strategy

Apple is accelerating its shift of iPhone production to India and Vietnam, aiming for India to produce the majority of US-bound iPhones by the end of 2026. This transition puts long-term contracts with Chinese manufacturing giants like Luxshare and Foxconn’s mainland facilities at risk. As Apple moves its “center of gravity” away from China, Beijing may respond by making it harder for Apple to operate its retail and services business within the country.

Tesla’s Data Security Tightrope

Tesla’s rollout of Full Self-Driving (FSD) in China is contingent on complex data security contracts and government approvals. In the current climate, Beijing may demand full “data sovereignty,” requiring Tesla to store and process all AI training data locally and potentially barring the export of any “learned” weights back to the US. This would effectively split Tesla’s AI development into two incompatible silos.

4. The Future of Venture Capital

The era of the “global venture fund” is effectively over. The split of Sequoia Capital into separate US and China (HongShan) entities was the first major tremor; the Meta-Manus block is the earthquake. US pension funds and endowments, which have historically been the primary backers of Chinese tech growth, are now facing immense pressure to divest. This capital flight will force Chinese startups to rely entirely on state-backed “Guidance Funds,” further cementing the state-driven development model.

Conclusion: The Decoupling of the AI Ecosystem

The Meta-Manus block has proven that technology is no longer a private commodity but a national security asset. Any contract or deal that involves the transfer of “agentic” or “frontier” AI capabilities across the US-China divide is now considered high-risk. We are witnessing the birth of two distinct, non-interoperable technological ecosystems, where the price of entry is total alignment with one side or the other.

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🧰 AI Tools of The Day

Top Chinese AI Models

• 🥇 1. DeepSeek V4-Pro - Trailing only Google’s Gemini 3.1-Pro on world knowledge, and falling only marginally short of GPT-5.4 overall. Like its predecessors, V4-Pro is fully open-source, which has been China’s key competitive strategy, rapidly scaling adoption while working around US chip export restrictions.

• 🥈 2. Kimi K2.6 - Scores 80.2% on SWE-Bench Verified, supports agentic workflows, and became the first open-weight model to beat GPT-5.4 on the SWE-Bench Pro coding benchmark. For coding and agent workloads, it’s the top Chinese pick, and costs roughly 75% less than GPT-5.4 at comparable quality.

• 🥉 3. Qwen 3.6 Plus — Go-to for long-context tasks, offering 1 million token context windows, and is currently free during its preview period. Alibaba’s Qwen family now has the most user-generated variants on Hugging Face, more than Google and Meta combined.

• 4️⃣ GLM-5.1 (Z.AI / Zhipu AI) - #3 on benchmark leaderboards with a score of 83, with strong math (89) and instruction following (93). GLM-5 Reasoning leads Chinese models in agentic capability, with scores of 88 in reasoning and 86 in agentic tasks. GLM-5.1 beat Claude Opus 4.6 on a key coding benchmark earlier this quarter.

• 5 - Step 3.5 Flash - ships at $0.10/$0.30 per million tokens, 25× cheaper than GPT-4o with comparable math reasoning. For agent workloads with stable system prompts, effective input cost can drop to as low as $0.03–0.07 per million tokens.

📊 Quick Comparison

Bottom line: Chinese AI labs are shipping frontier coding models faster than most developers can keep up — and at least three Chinese models now score above 75% on SWE-Bench Verified, putting them in direct competition with GPT-5.4 and Claude 4.5 Sonnet. The price gap alone makes these worth integrating into your client service stack.

• The New Panopticon - Corporate Surveillance, Hacker Tradecraft, and the AI Data Gold Rush

  • The Orwellian Parallel. Surveillance Capitalism in the Workplace

  • The Hacker Strategy

  • The Broader Trend

  • The Value of Behavioral Data for AI

• 🧰 AI Tools - Fraud and Surveillance. Tools to be aware of or used responsibly

• The Robot Just Served an Ace, And It’s a Bigger Deal Than You Think

  • Why Table Tennis? Why Does This Matter?

  • Where will we be in 5 years

  • The Real Takeaway for You

• 📚Learning Corner - Data Privacy, Ethics, and Responsible AI Specialization

📰 AI News and Trends

• TSMC Snubs ASML's $410M Chipmaking Machine as "Too Expensive" TSMC announced it will delay adopting ASML's cutting-edge high-NA EUV lithography machines until at least 2029, with co-COO Kevin Zhang calling them "very, very expensive" at roughly $410 million per unit. The rebuff sent ASML's stock lower and signals that even the world's most advanced chipmaker has limits on how fast it will absorb next-generation tooling costs.

• Google Unveils Two New AI Chips to Challenge Nvidia, its eighth-generation Tensor Processing Units, the TPU 8t (for training) and TPU 8i (for inference), at Google Cloud Next 2026, explicitly designed for the “agentic era” of AI.

• Microsoft Drops $18 Billion on Australian AI Infrastructure, covering Azure AI supercomputing, cloud expansion, and national cybersecurity.

• Tencent and Alibaba are in talks to invest in DeepSeek, valuing the company at over $20 billion.

• Google builds elite team to close the coding gap with Anthropic

Other Tech News

• Elon Musk announced the Optimus V3 robot unveil has been pushed to late July or early August to prevent rivals from copying the design before mass production begins.

• Tesla reported a strong Q1 2026 earnings beat, with revenue up 16%, global energy crisis has been part of this increase. Now the company is tripling its spending to $25B as it pivots hard into AI and robotics, funding everything from custom chips to robotaxis and humanoid robots.

• Kalshi prediction site suspends three political candidates for betting on their own races

• Crypto scam lures ships into the Strait of Hormuz, falsely promising safe passage

The New Panopticon

Corporate Surveillance, Hacker Tradecraft, and the AI Data Gold Rush

The recent revelation that Meta is deploying software to track the mouse movements, clicks, and keystrokes of its employees to train artificial intelligence models has sparked significant backlash and raised profound questions about workplace privacy. This initiative, dubbed the Model Capability Initiative (MCI), represents a significant escalation in corporate surveillance, blurring the lines between employer oversight, dystopian fiction, and malicious hacker tradecraft.

The Orwellian Parallel

Surveillance Capitalism in the Workplace

The comparison to George Orwell’s 1984 is not merely hyperbolic; it is structurally accurate. In Orwell’s dystopian society, the “telescreen” served as a two-way device that relentlessly broadcast propaganda while simultaneously monitoring individuals’ every move, ensuring total compliance and eliminating the possibility of dissent.

Meta’s MCI functions as a modern, digital telescreen. By recording every keystroke, mouse movement, and taking periodic screenshots of employee workstations, the company achieves a level of granular surveillance that mirrors the totalizing oversight of the Party in 1984. The chilling effect is immediate: employees have expressed that the tracking makes them “super uncomfortable,” recognizing that such pervasive monitoring inherently stifles free expression and creates an environment of constant scrutiny.

This phenomenon is a stark manifestation of what Harvard professor Shoshana Zuboff terms “surveillance capitalism.” Zuboff defines this as the unilateral claiming of private human experience as free raw material for translation into behavioral data. While initially applied to consumer data harvested by tech giants to create “prediction products” for targeted advertising, this logic has now turned inward. Employees’ digital labor and physical interactions with their machines are no longer just the means of production; they are the product itself, the “behavioral surplus” required to train the next generation of AI.

The Hacker Strategy

The methods Meta is employing to gather this data are functionally identical to established hacker tradecraft. In the cybersecurity domain, the practice of recording keystrokes and mouse movements is known as Input Capture, specifically categorized under the MITRE ATT&CK framework as technique T1056.

Keylogging (T1056.001)

The most prevalent form of input capture is keylogging. A keylogger is a type of surveillance technology, often deployed as malware, used to monitor and record each keystroke on a specific device. Adversaries use keyloggers to intercept sensitive information, such as passwords, financial details, and confidential communications.

Advanced Persistent Threats (APTs), sophisticated, well-resourced cyberattack groups often sponsored by nation-states, frequently utilize input capture to maintain long-term access and gather intelligence from compromised networks. The fact that a major technology corporation is deploying the same mechanism on its own workforce highlights a disturbing convergence between corporate management tools and malicious cyber espionage techniques.

The Broader Trend

Meta’s initiative is not an isolated incident but part of a broader, aggressive trend in corporate data collection driven by the insatiable data requirements of AI models. As large language models (LLMs) exhaust publicly available internet data, AI companies are desperately seeking new sources of high-quality, human-generated content.

Liquidating Defunct Startups for Data

One of the most striking examples of this trend is the emerging market for the digital remains of failed companies. Defunct startups are increasingly selling their internal communications, including Slack archives, Jira tickets, and email threads, to AI labs as training data. Companies like SimpleClosure, which assist in shutting down startups, report processing numerous deals where internal workplace data is sold for hundreds of thousands of dollars.

This practice raises severe privacy concerns. Even if attempts are made to anonymize the data, internal communications are inherently rich in personally identifiable information, candid discussions, and proprietary business logic. The transformation of private employee interactions into commodified training data without explicit, informed consent represents a massive breach of trust and a redefinition of workplace privacy.

The Rise of “Bossware” and AI Monitoring

The use of employee monitoring software, often termed “bossware,” has skyrocketed, particularly following the shift to remote work. Recent statistics indicate that 74% of US employers now use online tracking tools, and 61% utilize AI-powered analytics to measure employee productivity or behavior.

This surveillance takes a significant toll on workers. Employees in high-surveillance environments report stress levels of 45%, compared to 28% in low-surveillance settings, with 59% reporting stress or anxiety directly caused by workplace monitoring.

The Value of Behavioral Data for AI

Current AI models are highly capable of generating text and answering questions, but they struggle with executing complex, multi-step tasks within software environments. They lack the contextual understanding of how humans actually navigate interfaces, use keyboard shortcuts, switch between applications, and correct errors in real-time.

Behavioral data, the exact sequence of keystrokes, mouse movements, and clicks, provides the “ground truth” necessary to train AI agents to perform these actions autonomously. As Meta CTO Andrew Bosworth noted, the goal is to build agents that “primarily do the work,” requiring models to learn from “real examples of how people actually use [computers]”. "We are training our replacements, with or without our consent."

In the contact center industry, for example, while AI can transcribe calls and suggest responses, it cannot yet replicate the complex system navigation an experienced human agent performs to resolve a customer issue. By harvesting this behavioral layer, companies aim to bridge the gap between conversational AI and autonomous AI agents capable of replacing human labor in knowledge work.

Future Trajectories: Beyond the Keyboard

The current focus on keystrokes and mouse movements is likely only the beginning. As the demand for behavioral and emotional data grows, corporate surveillance is poised to become even more invasive, incorporating advanced biometric and physiological monitoring.

Emerging technologies and future trends include:

•Emotion AI and Facial Analysis: Systems that use webcams to analyze facial expressions and micro-expressions to determine an employee’s emotional state, engagement level, or stress .

•Advanced Behavioral Biometrics: Moving beyond simple tracking to create unique digital fingerprints based on typing cadence, mouse movement patterns, and interaction speed, used for continuous authentication and behavioral profiling .

•Brain-Computer Interfaces (BCIs) and EEG: While currently niche, the use of electroencephalogram (EEG) headsets to monitor brainwaves for alertness and cognitive load is already being tested in high-risk industries and could eventually migrate to standard office environments.

Conclusion

Meta’s decision to track employee keystrokes and mouse movements for AI training is a watershed moment in corporate surveillance. It perfectly illustrates the mechanics of surveillance capitalism applied to the workforce, utilizing techniques indistinguishable from malicious hacker tradecraft to extract behavioral surplus. As the AI industry’s hunger for data drives the commodification of every digital interaction—from the Slack messages of dead startups to the micro-movements of current employees—the fundamental right to privacy in the workplace is being systematically dismantled. Without robust regulatory intervention and a reassertion of digital labor rights, the future of work risks resembling the totalizing surveillance of 1984, optimized not just for control, but for the automated replacement of the workers themselves.

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🧰 AI Tools of The Day

Fraud and Surveillance - Tools to be aware of or used responsibly

1. BioCatch - Behavioral Biometrics & Fraud Detection - Collects over 3,000 behavioral signals, mouse movements, typing rhythm, swipe patterns, and interaction speed, to build a unique behavioral fingerprint for every user.

2. Teramind — AI-Powered Employee Monitoring - Captures keystrokes, records screens in real-time, logs application usage, tracks email content, and generates AI-driven productivity scores.

3. Clearview AI — Facial Recognition at Scale - It has scraped billions of public facial images to build a database used by law enforcement and government agencies to identify individuals from a single photograph.

4. Scale AI — AI Training Data Pipeline - The company that processes and labels the raw behavioral, image, and text data that AI companies need to train their models. Owned by Meta.

5. FullStory — Session Replay & Behavioral Analytics -Records every mouse movement, scroll, click, and keystroke on websites and apps, creating a full video replay of user sessions.

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The Robot Just Served an Ace, And It’s a Bigger Deal Than You Think

The moment AI moved from the screen into the physical world.

In 1997, IBM’s Deep Blue defeated Garry Kasparov, the world’s greatest chess champion, and the world changed overnight. Not because chess mattered, but because of what it proved: that a machine could master a domain once considered exclusively human.

Last week, we got that moment for physical robotics.

Sony AI published research in Nature introducing Ace, an autonomous robot that just beat elite human table tennis players. Not in simulation. Not with special rules or handicaps. On a regulation Olympic-sized court, under official ITTF rules, with licensed umpires judging every point. Ace won 3 out of 5 matches against elite players.

Why Table Tennis? Why Does This Matter?

Chess is information. Table tennis is physics at the edge of human capability.

The ball travels at over 20 meters per second. Spin can exceed 1,000 radians per second. The time between shots? Often less than half a second. It’s real-time, adversarial, and brutal. To compete, Ace needed to solve three hard problems simultaneously:

• See faster than humans - It uses event-based cameras that track spin at 400–700Hz (your eye blinks in ~150ms)

• Decide faster than humans - Its RL-trained policy updates every 32 milliseconds

• Move faster than humans - A custom 8-joint robot arm hitting balls at up to 16.4 m/s

What makes this the “Deep Blue moment” for robotics is the transfer problem. Deep Blue played in a virtual, perfectly defined world. Ace operates in a messy, noisy, unpredictable reality — with spin, air drag, table bounce variation, and a human opponent actively trying to beat it. That’s an entirely different class of problem.

This Didn’t Happen in Isolation

The same week Ace made headlines, 21 humanoid and bipedal robots completed a half-marathon in Beijing, covering 21 kilometers on the same course as human runners. Some stumbled. Some needed resets. But they finished.

Both events point to the same underlying shift: the sim-to-real gap is closing fast.

For years, robots could do incredible things in controlled lab environments but fell apart in the real world. That gap is now being bridged through better physics simulation, reinforcement learning trained on synthetic data, and hardware built to handle edge cases at scale.

Where Are We in 5 Years?

Compound this trajectory, and the picture gets serious:

• 2026–2027: Physical AI agents become competitive in narrow, high-speed domains (sports, warehouse logistics, precision manufacturing). Early commercial humanoid deployments at scale (Figure 1X, Tesla Optimus).

• 2027–2028: Multi-task physical AI. Robots that don’t just do one thing well, but adapt across tasks in the same environment, the shift from “specialized robot” to “general-purpose robot.”

• 2028–2030: The labor market starts feeling it. Not replacement, augmentation, and redefinition. Roles in manufacturing, fulfillment, elder care, and field service begin to structurally shift. Early investors in robotics infrastructure and AI-physical stack plays will look very smart.

The compounding dynamic here mirrors what we saw in LLMs from 2020–2024: each breakthrough enables the next one faster. Better sensors → better training data → better policies → better hardware → better sensors again.

The Real Takeaway for You

Kinjiro Nakamura, a 1992 Olympian who watched Ace play, said it best:

“No one else would have been able to do that... but the fact that it was possible means that a human could do it too.”

That’s the pattern every time physical limits get broken, by machines or by humans. The ceiling moves. The definition of possible expands.

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📚Learning Corner

Data Privacy, Ethics, and Responsible AI Specialization - Addresses the legal, ethical, and technical dimensions of everything discussed today, from AI training data collection and behavioral surveillance to privacy law and responsible AI governance.

• Big Tech’s Carbon Retreat Could Become Climate’s Next AI Casualty

• 🧰 AI Tools - Autonomous Agents

• Huawei, DeepSeek, and the Big Threat.

• 📚Learning Corner - The Complete Perplexity Mastery Guide

📰 AI News and Trends

• Anthropic shifts its enterprise pricing to charge businesses based on the amount of AI they actually use. This will add the client’s cost, especially those using Claude code.

• Claude now allows you to create polished visual work like designs, prototypes, slides, one-pagers, and more, a feature to compete with Figma.

• Google has assembled a strike team of researchers and engineers to improve its AI coding models, automate more of its own coding, and ultimately improve its AI research.

• Cursor is in talks to raise over $2 billion at a $50 billion valuation, led by investors like Andreessen Horowitz and Thrive Capital.

Other Tech News

• QVC Group, owner of QVC and HSN, will file for Chapter 11 bankruptcy protections. The internet has killed another business. Audiences that once watched sellers on TV have moved to 24/7 livestream platforms like Whatnot, TikTok Shop, or Temu.

• Meta targets May 20 for the first wave of layoffs, which will be ~8000 layoffs; additional cuts later in 2026

• An upcoming AI-generated movie about Bitcoin, of course, a development technology, is still developing a protocol, which knocked production costs down from $300 million to around $70 million

• Sam Altman may get fired, again… OpenAI Investors Aren’t Sure Sam Altman Is the Guy to Take Them Public

• Uber will now return your packages; no need to leave your home or office.

• Gucci-branded Google smart glasses are coming next year

Big Tech’s Carbon Retreat Could Become Climate’s Next AI Casualty

Microsoft’s reported pullback from future carbon removal purchases is the clearest sign yet that the AI boom is colliding with Big Tech’s climate promises. According to Heatmap, Microsoft has told some suppliers and partners it would pause future purchases, although the company says its carbon removal program has not ended and that it may simply adjust pace and volume as it refines its strategy. Even that softer wording matters. Microsoft has been the backbone of the young carbon removal market, accounting for roughly 87% of purchases in 2025 and 56% in the first quarter of 2026, while Heatmap reported it made about 90% of global purchases last year.

The pressure is coming from electricity. As companies race to build AI data centers, their power demand is surging faster than clean energy supply can keep up. The Associated Press reported that Google’s emissions have risen nearly 50%, Amazon’s 33%, Microsoft’s more than 23%, and Meta’s more than 60% as the sector struggles to reconcile years-old climate pledges with the immediate need for more computing power. In that sense, Microsoft is not alone. It is the most visible company stepping back from carbon removal buying, but Google, Amazon, and Meta are also showing how AI expansion is weakening climate commitments in practice, even if they have not formally abandoned them.

This is a big issue for two reasons. First, carbon removal was never supposed to replace emissions cuts, but climate scientists say it is still necessary to reach global temperature goals. If the largest corporate buyer slows down, a fragile industry loses the demand signal it needs to scale. Second, if tech companies decide that power growth matters more than climate follow-through, they risk locking in more gas-fired electricity, higher emissions, and a market message that sustainability promises can be postponed when AI profits are at stake. That would hurt not only carbon removal startups, but also the credibility of corporate climate pledges across the economy.

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🧰 AI Tools of The Day

Autonomous Agents

• AgentGPT - A browser-based autonomous agent tool that lets users launch agent-like workflows that plan and execute tasks from a prompt.

• BabyAGI - A lightweight autonomous task management agent that allows “task creation, prioritization, and execution” style that made agent tools popular.

• OpenDevin - An open-source autonomous software agent project that aims to let an AI agent perform real actions in a working environment, especially for technical tasks.

• SuperAGI -An open-source AI agent framework and platform that focuses on building, running, and managing autonomous agents with tools and memory.

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Huawei, DeepSeek, and the Big Threat.

In a recent interview, Jensen Huang pointed out that Huawei is becoming a threat to Nvidia and to the US tech landscape entirely. Huawei has not made a better chip than Nvidia. And it may not be near that. The more important point here is that Huawei may be becoming good enough, especially when paired with a leading Chinese AI company such as DeepSeek. That is what makes Jensen Huang’s warning significant. If DeepSeek can run a major model on Huawei’s Ascend platform, then China no longer needs to match Nvidia chip-for-chip to weaken Nvidia’s position or reduce America’s leverage over the AI stack.

What makes this a potential blow to Nvidia is that Nvidia’s moat has never been just about having the fastest silicon. Its real strength has been the combination of chips, software, developer tools, and industry lock-in. Researchers build for CUDA because CUDA is the default. Startups buy Nvidia because its software was written for Nvidia. Cloud providers stock Nvidia because customers expect Nvidia compatibility. Once a serious Chinese lab proves that frontier-class models can be adapted to Huawei’s stack, that lock-in begins to weaken.

That matters beyond DeepSeek itself. DeepSeek is important because it can act as a proof of concept for the rest of China’s AI sector. If one of the country’s most visible AI firms shows that Huawei hardware is commercially usable, then other Chinese companies may decide that the cost of switching away from Nvidia is worth paying. Even if Huawei chips remain less powerful, they can still become strategically decisive if they are sufficiently available, sufficiently optimized, and supported by a growing domestic software ecosystem. In technology markets, good enough plus local control can be more disruptive than a technically superior but politically constrained foreign supplier.

The foldable-phone example matters for the same reason. Huawei’s launch of a widely available premium foldable ahead of Apple and Samsung is not directly an Nvidia story, but it strengthens the broader Huawei narrative: this is a company that sanctions failed to kill. Instead of being frozen out of the market, Huawei has re-emerged as a company capable of setting the pace in selected categories. That matters because perception influences procurement, partnerships, and state backing. A company that looks like a survivor in smartphones is more likely to be trusted as a long-term platform in AI infrastructure as well.

For U.S. companies more broadly, the danger is not only lost sales. The danger is the emergence of a parallel technology system. If Huawei supplies chips, operating systems, cloud infrastructure, and flagship devices, then Chinese firms may no longer need American suppliers in the most strategically important layers of the stack. NVIDIA loses accelerator demand. U.S. cloud companies lose influence. American software ecosystems lose default status. And Washington loses part of the coercive power that came from controlling the tools on which advanced AI depended.

This is why Huang’s comment is so striking. He is effectively saying that the real risk is not that Huawei becomes better than Nvidia next quarter. The risk is that Huawei and DeepSeek together prove that China can build an AI path that is independent enough to be viable. Once that happens, export controls stop being a choke point and start becoming an incentive for China to accelerate replacement. In that scenario, Huawei’s recent wins are not isolated product victories. They are signs that an alternative AI and consumer-tech ecosystem is becoming more real.

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📚Learning Corner

• The Complete Perplexity Mastery Guide

Had an interesting conversation with a colleague about lawsuits affecting webmasters and how AI is accelerating the trend. I did some digging to find ways to protect ourselves and support other entrepreneurs. Additionally, TSMC, the backbone of ML and the latest technologies, continues to grow, signaling the bubble may not be ready to pop just yet. Let’s dive into this and, as usual, stay curious.

• How AI Is Fueling a New Wave of ADA Lawsuits Online

  • Requirements for Full Compliance

  • Tools of the Trade

  • The Webmaster’s “To-Do List.”

• 🧰 AI Tools - Audit & Guide Compliance

• 📚Learning Corner - Start Building with Anthropic

• TSMC keeps hitting it out of the park. Why does it matter?

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📰 AI News and Trends

• Why didn’t I buy Allbirds shares? Former shoe brand pivots to AI computing after $39M sale

• Adobe is bringing its Firefly creative agent into Claude, allowing users to move from idea generation to image editing directly inside the assistant.

• The Gemini app is now available as a native macOS experience, bringing you a faster, more integrated way to get help from AI right on your desktop.

• OpenAI upgrades its Agents SDK with sandboxing and tougher testing tools with native sandboxing and an in-distribution harness for testing long-running agent tasks, aiming squarely at enterprise reliability. AI agents are moving from flashy demos toward safer, more controlled real-world deployment.

Other Tech News

• Age and user verification are here. EU says its “age verification app” is ready to go, and Anthropic will ask Claude users to verify their identities for a few use cases’

• S&P 500, Nasdaq 100 hit records as ceasefire hopes drive rally

• Jane Street signs $6 billion AI cloud deal with CoreWeave, boosts stake

• Google’s old SpaceX bet is suddenly looking enormous as they own ~5% stake in SpaceX, worth a staggering sum if SpaceX ever reaches the kind of valuation bulls expect.

How AI Is Fueling a New Wave of ADA Lawsuits Online

Just had a great conversation with a social media consultant who shared that a webmaster client of hers was hit with a $15K lawsuit due to a website he built that was not fully compliant with disability standards like the ADA or EAA. The webmaster had designed the site to be highly navigable, including ALT text describing links, purposes, and images, but a small compliance gap was still enough to trigger legal action. The case nearly pushed him out of business and significantly reduced his willingness to take on future clients.

Behind the claim was a broader operation led by law firms leveraging teams equipped with modern AI tools to systematically scan and identify small, vulnerable businesses with even minor compliance gaps. These cases often lead to quick settlements, but even then, a ~$10K–$20K hit can be enough to derail a small business or solo operator.

A few years ago, while working at a radio station, I encountered attorneys promoting a similar model offline, hiring individuals in wheelchairs to visit physical locations and identify ADA violations like a lack of proper access. That same playbook has now scaled digitally.

This online and offline trend creates both risk and opportunity. Businesses need to proactively audit and fix accessibility gaps, while consultants and agencies can step in to offer compliance audits, remediation services, and ongoing monitoring.

To be fully compliant (WCAG 2.1/2.2 Level AA), you must address four layers: the Theme Code, your Content, your Legal Documentation, and Continuous Monitoring.

1. Requirements for Full Compliance

• Technical (The Code): The site must be navigable by keyboard and compatible with screen readers. This includes “Skip to Content” links, ARIA labels for buttons, and proper heading structures (H1, H2, H3).

• Visual/Media: High color contrast (4.5:1 ratio), text that can be zoomed to 200% without breaking, and Alt Text for every image.

• No “Traps”: Users must be able to open and close all pop-ups and menus using only the Esc or Tab keys.

• Accessibility Statement: A dedicated page in your footer stating your commitment to accessibility, what you’ve fixed, and how users can contact you if they have trouble.

2. 🧰 AI Tools of The Day

AI Tools to Audit & Guide Compliance

These tools use AI to scan your site, identify failures, and provide a “to-do list” for your webmaster.

• AccessiBe (accessScan): An AI-powered auditor. You enter your URL, and it gives you a compliance score and a detailed PDF report of exactly what failed (e.g., “Button lacks a name,” “Image missing alt text”).

• Lumar (formerly DeepCrawl): An enterprise-level AI crawler that identifies accessibility issues across thousands of pages at once. It’s great for large stores to find “hidden” errors in deep pages.

• UserWay: Their AI doesn’t just find errors; it can automatically apply “remediations” to your code in real-time. It also provides a dashboard for your developer showing “High Priority” vs. “Low Priority” fixes.

• Evinced: This tool uses AI to “see” a website like a human does. It detects components that look like buttons but aren’t coded as buttons—a common issue that standard scanners miss.

3. The Webmaster’s “To-Do List.”

If you hire a developer or do it yourself, these are the manual tasks AI tools will likely flag:

1. Fix Navigation Order: Ensure the “Tab” key moves through the menu, then the body, then the footer (in that order).

2. Add ARIA Labels: Any icon-only button (like a magnifying glass for “Search” or a “Shopping Bag” icon) must have a hidden text label for screen readers.

3. Correct Heading Hierarchy: Ensure you only have one H1 per page and that headings don’t skip levels (e.g., don’t go from H2 to H4).

4. Audit Third-Party Apps: Review every pop-up, chat widget, and review app. If they aren’t keyboard-friendly, your webmaster must find an alternative or code a fix.

5. Remove Autoplay: Ensure videos or sliders do not play automatically, or can be paused immediately by the user.

I wonder how Substack and social media companies deal with this, as we are all free to post text and images that may not be properly labeled. But again, these are not small firms that lawyers want to bully and get into a long, drawn-out ring fight with. Additionally, their legal agreements usually state that the “publisher” (you) is responsible for the legality of the content, including its accessibility.

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📚Learning Corner

• Start Building with Anthropic

TSMC keeps hitting it out of the park. Why does it matter?

TSMC’s results matter because the company sits at the center of the global AI supply chain. When its profits and forecasts jump, it usually means demand is not just healthy at one customer, but broad-based across the entire market for advanced computing.

A 58% rise in first-quarter net income, along with revenue beating expectations, suggests that major clients such as Nvidia and other high-performance chip designers are still ordering aggressively, while TSMC’s decision to raise its outlook indicates management sees this momentum continuing rather than fading after a short burst of enthusiasm. That is especially important because investors have been watching for any sign that the AI spending cycle might be overheating or slowing under macro pressure, supply constraints, or geopolitical risk. Instead, TSMC’s commentary points in the opposite direction, meaning that advanced chips remain in heavy demand, capacity is still valuable, and the race to build AI data centers, models, and supporting infrastructure is continuing to translate into real manufacturing revenue at the most important foundry in the world.

Tech valuations are insane and not dropping, although some companies may not be able to justify their lofty valuations. What is true is that consumers are getting to enjoy, and sometimes to be overwhelmed by, the rapid advancements. Anyone can literally code any app, and on the hardware side, wearables are just getting prettier, sleeker, and more affordable. All these advancements bring security issues, privacy problems, data bottlenecks, and other social implications. Are we going to slow down? Let’s dive into two particular tech industries where deceleration may not be seen soon. Stay curious.

This is what we have today:

• AI Smart Glasses Shipments Explode 322% as Wearable AI Goes Mainstream

  • The 2025 Wearables Boom

  • Meta is running the show

  • Apple gets ready

  • The Android Squad and global rivals

• The Trillion-Dollar Vibe Coding Race: Hype, Layoffs, and the Future of Software

  • The Contenders: A Stack, Not a Monopoly

  • The Hype vs. Reality

  • Layoffs, Burnout, and “AI Psychosis.”

  • Who wins the race

• 🧰 AI Tools - AI-powered mobile app builders.

• 📚Learning Corner - Lovable

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📰 AI News and Trends

• Stalking victim sues OpenAI, claims ChatGPT fueled her abuser’s delusions and ignored her warnings

• Meta shifts top engineers into AI unit as it and AI-led productivity push

• Nvidia’s Blackwell Boom Is Making AI Compute Even Pricier

  A new price index shows that hourly rental rates for Nvidia Blackwell GPUs have jumped sharply in just two months, driven by surging demand for agentic AI workloads. In plain English, the hottest chips in AI are getting more expensive fast, and that could squeeze smaller players while the giants keep scaling.

• Anthropic Pushes Further Into the Office With Claude for Word

• Meta must face a youth addiction lawsuit by Massachusetts

• Now, UK Regulators Sound the Alarm on Anthropic’s Claude Mythos, and as their US counterparts did last week, they are now preparing to warn banks, insurers, and exchanges that Anthropic’s Claude Mythos Preview may expose serious cybersecurity risks.

• Microsoft Plots New Copilot Features Inspired by OpenClaw

Other Tech News

• SpaceX reported a $5B loss in 2025 despite $18.5B in revenue, scaling AI through xAI, and space infrastructure is expensive. On top of that, the last xAI cofounder (out of 11) has left the company. The $1.75T IPO still stands.

• Apple plans to sell its first smart glasses in 2027, with a possible unveiling at the end of this year. The big takeaway is that Apple’s AI strategy is starting to look a lot more like hardware you can actually wear, not just software demos and promises.

• TikTok Doubles Down on Europe with Second €1 Billion Data Center in Lahti, Finland as part of its “Project Clover” initiative to store European user data locally. The new facility, with a capacity scalable up to 128 MW, will operate by 2027, following its first Finnish center in Kouvola, set to launch later this year.

• FBI says Americans lost roughly $21 billion to cybercrime last year in 2025, a 26% jump year over year, driven by investment scams, business email compromise, tech support fraud, and breaches. That figure may grow this year due to AI making cybercriminals more powerful

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AI Smart Glasses Shipments Explode 322% as Wearable AI Goes Mainstream

The wearable tech scene is getting a massive glow-up, and it looks exactly like your favorite pair of shades. AI-powered smart glasses shipments just skyrocketed by an insane 322% in 2025, hitting 8.7 million units. Thanks to major upgrades in AI and way lighter designs, smart glasses are officially crossing over from “tech bro gadget” to a mainstream must-have.

The big tech players are racing to build what they think will be the next smartphone.

The 2025 Wearables Boom

In 2025, global shipments for things like smartwatches and earbuds grew 9.1%, reaching a staggering 611.5 million units. But the real plot twist happened in the extended reality (XR) space, where device shipments jumped 44.4%, but traditional VR headsets actually saw a drop in sales. The entire surge was driven by sleek, display-less smart glasses. People do not want to be isolated in a headset anymore; they want hands-free cameras, audio, and AI assistants built into glasses they can actually wear in public without looking weird.

Meta is Running the Show

Right now, Meta is absolutely dominating. They captured a massive 85.2% of the global AI glasses market in 2025, shipping around 7.4 million units.

The second-gen Ray-Ban Meta glasses, a partnership that has been the game differentiator, look like classic Wayfarers but pack serious tech. You can snap photos, record TikToks, listen to Spotify, and talk to Meta AI without ever touching your phone. The company thinks these glasses could eventually replace our phones entirely. They even just dropped new prescription-ready models for $499.

Apple gets ready

You knew Apple wasn’t going to sit this one out. Leaks suggest Apple is currently testing four different frame styles for its own smart glasses, including a sleek design similar to the ones CEO Tim Cook wears.

Code-named “N50,” Apple’s glasses are expected to skip the built-in display and focus on premium materials like acetate. They will act as the ultimate iPhone accessory, using cameras and sensors to feed real-world info to an upgraded Siri. Expect an official announcement sometime in late 2026 or early 2027.

The Android Squad and Global Rivals

• The China Surge: Mainland China is the fastest-growing market for AI glasses right now. Brands like Rokid and Xiaomi are dropping affordable, feature-packed glasses and quickly climbing the global ranks.

• Google & Samsung: The Android XR ecosystem is waking up. Google and Samsung are reportedly teaming up for a 2026 smart glasses launch. Google even partnered with Warby Parker, copying Meta’s eyewear-brand strategy.

• Snapchat is Back: Snap is partnering with Qualcomm to drop the next generation of their Spectacles AR glasses.

The era of wearable AI is officially here. Omdia predicts global shipments will blow past 15 million units in 2026. The race to replace the smartphone is happening right before our eyes.

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🧰 AI Tools of The Day

AI-powered mobile app builders.

• Rork - Create native mobile apps by chatting with AI. Idea to phone in minutes, to App Store in hours

• A0.dev - Describe what you want in natural language, and the platform generates a complete React Native project using Expo. That includes the component tree, navigation, styling, and configuration files.

• Bolt.new - Runs entirely in your browser. It focuses heavily on web applications, but it gives you direct access to the underlying code and terminal right in the browser.

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

The 20 essential AI tools for small businesses in 2026, curated and written by our team. Just honest, opinionated takes on the tools that actually move the needle.

The Trillion-Dollar Vibe Coding Race: Hype, Layoffs, and the Future of Software

“Vibe coding, ”the act of building software by simply describing what you want to an AI, has gone from a quirky internet phrase to a multi-billion-dollar arms race. But as valuations soar and new features drop weekly, a darker narrative is emerging: mass tech layoffs, developer burnout, and existential dread over the future of the coding profession.

The Contenders: A Stack, Not a Monopoly

Industry watchers expected a “winner-take-all” scenario. Instead, developers are assembling a composable AI coding stack, layering different tools for orchestration, execution, and review.

• Cursor (The Orchestrator): Valued at a staggering $29.3 billion, Cursor is the control center. Its new “Agents Window” lets developers manage fleets of parallel AI agents across local machines and cloud sandboxes.

• Claude Code (The Executor): Anthropic’s terminal-based tool is the developer's favorite for actually writing the code, boasting a 46% “most loved” rating. It excels at nuanced reasoning across long context windows.

• OpenAI Codex (The Reviewer): OpenAI is fighting back with a unified Codex “superapp” and a new “Scratchpad” feature for parallel tasks. They even released an official plugin that installs directly inside Claude Code, allowing developers to use Claude to write code and Codex to review it.

• Lovable & Replit (The Builders): These platforms are democratizing software creation for non-technical users. Lovable hit $400 million in annual recurring revenue (ARR) at a $6.6 billion valuation. Replit recently raised $400 million, tripling its valuation to $9 billion, and launched “Agent 4” for autonomous building.

The Hype vs. The Reality

Vibe coding compresses timelines and lets solo founders ship products that previously required entire teams. But the honeymoon phase is ending.

Developers are discovering that while AI optimizes for immediate correctness, it struggles with software architecture. As one developer noted, “I am trying to fix a Vibe-coded application... the overall application is a dog’s breakfast of spaghetti” . 74% of developers feel more productive, but AI-generated code reportedly contains 1.7x more critical bugs.

Layoffs, Burnout, and “AI Psychosis”

The rise of these tools coincides with a brutal labor market. Nearly 245,000 tech jobs were cut globally in 2025, and Q1 2026 saw over 50,000 more. Tech CEOs are increasingly pointing to AI efficiencies as a justification for leaner teams.

This has created palpable anxiety. Junior developers face a hollowing out of entry-level roles. Even senior developers are grappling with the psychological toll. While some report that AI has “re-ignited a passion” for coding, others feel they are losing their fundamental skills. “If you don’t use it, you’re going to lose it,” warned one software engineer.

Furthermore, the sheer speed of AI-assisted development is leading to burnout. Developers report that managing swarms of AI agents is exhausting their “internal buffers” and disrupting sleep cycles, a phenomenon some are dubbing “AI Psychosis”.

Who Wins the Race?

The AI coding market is projected to reach $12.8 billion in 2026. But the winner isn’t a single company; it’s the composable stack.

The role of the software engineer is fundamentally shifting. The job is moving from writing lines of code to architectural design, prompt engineering, and managing fleets of AI agents. As one veteran coder put it: “AI coding still requires our expertise to guide it. I’m not sure if that will be the case in a year, but it is today”.

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📚Learning Corner

Lovable Tutorial: How to Build an App Step by Step (NXCode) - A complete beginner’s guide to building a full-stack app, connecting a database, and deploying it.