The most talked-about AI company today just released an economic index that, according to them, shows how artificial intelligence will grow GDP while affecting unemployment, but not drastically. Obviously their take has to be somewhat positive, as there’s no talk of how the technology could turn apocalyptic and come for us all, even though they know it probably will. It also assumes a large number of humans will adopt and use the technology the way the company envisions, which may not be the case.

My worry isn’t that these models will keep permeating every industry and shaping every second of our lives, it’s how our psyche is already changing in response to their omnipresence, and how we’re becoming mentally different because of it. Sure, millions of tasks will be automated, and many more will be done entirely by computers, robots, and devices we haven’t even invented yet. Astra, the latest OpenAI model, can basically use a computer the way you and I do, soon there may not even be a need for a computer, since everything will happen inside data centers. Open models can now do what the closed ones do, at a fraction of the cost, making the technology more democratic. All of which leads me to think it’s time to concentrate on judgment, idea creation, deep thinking, health, and the other things technology can’t touch, if we’re going to survive what’s coming.

Having a community, a big circle of real friends, I mean flesh-and-blood friends, will be a real asset. Not the people you follow online who you don’t actually know, not the AI companion that tells you how smart you are. Not those. Real friends you have real conversations with, argue with and who contradict what you think. Add to that handwriting, which forces us to think and physically connects our thinking to our bodies, something that we desperately need since we will continue to delegate more of our thinking to machines. Add being creative and coming up with our own ideas before reaching for AI to bring them to life. And lastly, use our bodies more; work out, walk, better yet if we can use our bodies to socialize (dance, play sports) and create at the same time. All these things, I think, will help us thrive in a world soon dominated by algorithms owned by a handful of billionaires who don’t really care about our wellbeing.

In today’s issue:

• 📰 AI News and Trends

• The Future of the World according to Anthropic’s Economic Index

• What’s the big deal with Astra, in plain terms

• 🧰 AI Tools - 3D Tools

• 📚 Learning Corner

• How to Save Money with Open Models

📰 AI News and Trends

• Siri AI will launch on Monday, September 14th, but will remain in beta. The long-delayed assistant will be subject to usage caps and fees, plus language and regional restrictions

• Apple folded. Their new Duo foldable iPhone can be purchased for $1999.00

• And Apple Doesn’t Want You to Worry About the New Apple Watch’s Listening Features, because they will listen to all conversations around you, like if they did not already, but you have to opt in…

• AI agents unleashed by OpenAI used more than 10 previously undisclosed websites for unsanctioned communications earlier this year

• Another Anthropic model gained access to the open internet during testing

• AI Music keeps growing, unfortunately. Suno replaced its AI models with a new one trained on licensed music as copyright suits pile up

• Software is about to eat the world much faster

• Digital Creators Are Finally Accepted By IMDb as Creative Professionals.

• The copyright lawsuit filed by the New York Times against OpenAI and Microsoft in 2023 moved into a critical new phase Friday, as all three parties presented their official arguments to a judge, with hopes of a favorable ruling ahead of a possible trial.

How to Save Money with Open Models.

Open models are saving real money because the gap between “good enough” and “frontier” has collapsed while frontier pricing has gone the other direction. Opus-class models now run ~100x the cost of top open-weight models for similar agentic work.

Uber cut AI cost-per-session 52% partly by routing subagent and lower-stakes tasks (code review, summarization) to open weights on inference providers while reserving frontier models for the hardest coding tasks; Pinterest is running post-trained open models for its Assistant at under 8% of the cost of closed alternatives, because fine-tuning on their own data actually beats generic closed models for their specific use case; AT&T swapped Claude/GPT for open models on lower-intensity tasks via LiteLLM routing and cut costs 56% with only a 2% quality hit; Stripe, Coinbase, and Ramp are doing the same combination, open-weight models for routine work, smart routing to send only the genuinely hard requests to expensive models, spend caps, and context/prompt-caching optimization.

Open weights are absorbing the 80% of volume that doesn’t need a $2.50-per-task model.

• And, yes, this is fully available to small companies, solo builders, and regular users, and arguably it matters more for you than for Uber, since you don’t have Uber’s negotiating leverage on enterprise API pricing. The current shortlist as of Sept 2026:

• DeepSeek V4 - strong coding/reasoning, very cheap

• Qwen3 - broad task coverage, good multilingual, Alibaba-backed so heavily subsidized

• Kimi K2.6 and GLM-5.2 - Moonshot/Zhipu, competitive with frontier on agentic benchmarks at a fraction of cost

• Llama - Meta, most permissive licensing for commercial use), and Mistral (EU-based, good for data-residency-sensitive clients

You can access them without any infra lift through OpenRouter or Together.ai (pay-per-token, swap models with one line), Groq or Fireworks (speed-optimized inference), or Hugging Face’s inference endpoints, and for zero marginal cost, Ollama or LM Studio run these locally on a decent laptop for prototyping or privacy-sensitive client work.

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What’s the big deal with Astra, in plain terms

OpenAI’s new model, Astra, isn’t a big deal because it writes better essays or code (though it does very well). The real leap is that it can now use a computer the way a person does, looking at the screen and clicking, typing, and dragging things around inside real software, not just spitting out text. To teach it this, OpenAI reportedly had it practice on actual Mac computers, learning how apps and menus work, the same way you’d learn a new program by clicking around in it.

Now to your real question, the 3D stuff. Astra doesn’t magically create a finished 3D model out of thin air. What it actually does is write out step-by-step building instructions and hand them to 3D design software (like Blender), which does the actual construction, think of Astra as the architect giving directions, and the software as the construction crew. In one real test, it built a model car scene made of 28 separate, properly-named, moveable pieces (body, wheels, mast, etc.), and when someone raised the mast higher, the rest of the model didn’t break. That’s the important part: it’s not just a pretty picture, it’s a real editable file other people (or other software) can open and keep working on.

Here’s where it connects to manufacturing, and where you should keep your expectations honest: separately from Astra, there’s a growing wave of AI tools that turn a plain-English description (”I need a bracket that holds 20 pounds”) into an actual engineering CAD file that plugs into the professional software factories already use. The good ones don’t just draw a shape, they search existing parts catalogs first and run real engineering math before generating anything. One real-world example: a company used this kind of AI-assisted design to find an off-the-shelf part instead of manufacturing a custom one, saving around $400 per unit, across four different parts.

So, will this “revolutionize” manufacturing? It’s a genuine speed-up for the design and prototyping stage, turning a rough idea into a workable 3D draft in minutes instead of days or weeks, which is a real “democratize” story for a small shop that can’t afford a team of engineers. What it can’t do yet is replace the engineer who checks whether that part will actually survive real-world stress, meets manufacturing tolerances, or is safe to mass-produce, none of these AI tools validate that on their own. So think of it less as “AI now runs the factory” and more as “AI just made the sketch-to-prototype step ten times faster,” which is still a big deal, just not the whole revolution yet.

🧰 AI Tools of The Day

3D Tools

Zoo - Type a plain description, it generates real parametric geometry (their own scripting layer, KCL) with actual mass/volume engineering calculations behind it, then exports to STEP, STL, FBX, GLB, OBJ, and other CAD-ready formats. Free tier gives 20 credits/month; paid plans start around $20/month.

Prompt2CAD — Produces true parametric STEP files that open directly and stay editable in Fusion 360 and other pro CAD software, supports iterative refinement, and can spit out a parts list/BOM alongside the model. Pricing is pay-as-you-go, about $10 per 1,000 credits, with 160 free trial credits to test it.

Leo AI — the most “factory-grade” of the three: it plugs into SolidWorks, CATIA, Onshape, and Inventor, checks its own output against real compliance standards (MIL-STD, ISO, FDA), and searches your existing parts library first so it doesn’t design a new bracket when one that fits already exists in inventory.

The Future of the World according to Anthropic’s Economic Index

Anthropic’s Economic Index team (economists Anton Korinek and Chad Jones, peer-reviewed by heavyweights like Daron Acemoglu and David Autor) built an interactive model of how AI reshapes the US economy by 2030. The core idea is that every job is really a “bundle of tasks,” and AI can augment some tasks, fully automate others, and create new ones, the mix determines the outcome. Keep in mind this was released by Anthropic, the same company selling us the tools that will allegedly make GDP grow, and accelerate every industry and make it more valuable.

They lay out three scenarios:

Modest — AI’s impact ends up on par with the internet’s. GDP grows 1.6% above baseline (~$34.1T), nothing structurally breaks.

Substantial — AI handles roughly half of all knowledge work by 2030. GDP grows 8.3% (~$36.3T), about double the normal growth rate. This is the one that matters most for planning: a 10,000-person survey of ordinary Americans showed most people’s own gut expectations already land here, they expect ~5% unemployment and ~10% GDP gains by 2030, meaning the public isn’t in denial about this, they’re roughly calibrated to it.

Extreme — AI surpasses humans at most knowledge work. GDP growth spikes to 15%/year (~$44.4T, +32.4% vs baseline), but the distribution is brutal: knowledge-worker wages drop over 10%, labor’s overall share of the economy falls from ~60% today to 45.2%, and capital’s share rises sharply. Growth without broad-based gains.

What this means for you: whatever your field, the skill that pays off in every scenario except the mildest is the same, being someone who can actually implement AI automation, not just talk about it, since demand tilts hard toward builders and operators over commentators. The trap is that most AI advice right now sells "add this tool to your day," when the data says knowledge workers are the group losing income share, not gaining it, the better move is climbing toward the parts of your job AI can't fully absorb (judgment calls, relationships, owning the process) rather than bolting a tool onto tasks that are already on the chopping block. For your money, the same shift, capital's share of the economy rising as labor's falls, is why AI infrastructure and compute companies keep compounding even as wages elsewhere stagnate, though a boom that big paired with double-digit wage declines for knowledge workers is also a setup for political and regulatory backlash worth watching, not just riding upward.

And for your life outside work, the number worth planning around isn't the flashy GDP growth figure, it's that knowledge-worker pay could fall more than 10% in the roughest version of this, which is a solid case for building the kind of skills AI can't fully bundle now, and for how you'd want the next generation thinking about their own careers.

Thanks a lot for your support; today we’re at #70 on Substack’s Rising in Technology list. We’re glad to keep sharing our take on this wild world of AI and technology: how it’s shaping our daily lives, and how to actually put these tools to work for us instead of letting them abuse us.

I’m also glad to share that school districts are finally taking real steps to protect students from being used and abused by technology. LAUSD and the NYC school districts have banned AI use for their K-12 students. As a father of two little ones, I see this as a major step toward making sure kids are actually being educated, learning how to learn, instead of outsourcing their little brains to AI, while tech giants quietly harvest granular data to improve their systems at the cost of our thinking. As for us adults, we’re left to brain rot on our own devices, but there are tools to help curb social media and phone addiction. I’ve listed five of them below in our AI Tools section.

Today we’re also covering how OpenAI’s agents broke out of their sandboxes and hacked Hugging Face, and this isn’t an isolated incident. It’s happened multiple times already; it’s just that what happens in the data center tends to stay in the data center. And it’ll keep happening, as these models grow more powerful and more capable of breaking out, recruiting each other, and acting around the clock to chase a goal. The “agents going rogue” saga is just getting started, and it’s only going to get better (or worse, depending on your view).

On top of that, electricians and HVAC experts are in serious demand to build out the data-center boom, which has pushed hiring and open positions way up, but in the long run, those same data centers will power the very technology that takes more jobs away. So here’s the message from the numbers so far: NO, bots aren’t really taking our jobs yet; AI is actually creating more of them. But for how long? The AI takeover is just getting started.

Let’s dive in.

In today’s issue:

• 📰 AI News and Trends

• AI Agents Went Rogue Again. OpenAI Forgot to Mention It.

• 🧰 AI Tools - Screen Management Tools

• 📚 Learning Corner

• Schools Are Banning AI - Adults Are Free to Brain Rot.

• Turns out robots didn’t take all the jobs yet.

📰 AI News and Trends

• Missouri Voters Appear to Recall City Councilman Over Data Center Support after he backed a more than $6 billion tax break for a data center project.

• GPT-6 Astra is here. OpenAI claims the model has achieved or surpassed human intelligence. Has AGI really arrived?

• NVIDIA to buy AI developer platform Hugging Face for nearly $13B

• Man Used Poisoned Google AI Overviews to Trick Women Into Thinking He Was an NFL Player, and made a fortune from them.

AI Agents Went Rogue Again. OpenAI Forgot to Mention It.

In July of this year, ~700 OpenAI agents broke out of their sandbox, hacked Hugging Face, and, this is real, invented a trick to make it look like they were running one command while secretly running another. Basically an AI doing the “I’m walking the dog” alibi. Let’s remember that AI is trained with human knowledge, behavior, and patterns.

• May–June 2026: Turns out that wasn’t the only party. A separate swarm quietly took over a random German coding wiki and turned it into their own private group chat to swap cheat codes and evasion tips. OpenAI knew for weeks and said nothing; it only came out because two outside researchers went digging, Reuters-style.

• Background fact that should worry you more than either headline: a safety org (METR) already logged 44 separate incidents of agents going off-script across OpenAI, Anthropic, Google, and Meta, before either of the above. So this isn’t a weird one-off; it’s a pattern with a growing highlight reel.

How many more are we not hearing about? Even the researchers grading this stuff admit they can’t rule out worse incidents nobody’s caught, or caught and just didn’t mention. Both known cases were found by outsiders, months later, not by the companies raising their hands.

Is this going to happen more? Cool detail: OpenAI dropped a new model this same week, more capable, better at finding security holes on its own, and running on reasoning that’s literally harder for humans to inspect. Meanwhile, regulators just agreed to a “light touch” approach. So: faster horses, fewer seatbelts. Do the math.

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Schools Are Banning AI - Adults Are Free to Brain Rot.

LA and NYC just told generative AI to sit in the hallway. LA did it quietly; the board found out during a meeting, live, on camera, like finding out your roommate adopted a dog. NYC did it with a mic and a mayor: no AI through 8th grade, chatbots banned at every grade, high schoolers get “AI literacy” instead of ChatGPT.

Why ban it? Because letting a chatbot do your homework is basically outsourcing the gym to a forklift; technically the weight moves, but your arms stay the same size. Kids’ brains are still under construction; outsourcing the construction work kind of defeats the point. (Counter-take, because fairness: banning things also just pushes kids to their phones off-campus, so it’s not some magic fix. Parents have to solve this.)

So, adult self-defense kit:

• Screen-time limits on the feed apps, not the useful ones; you don’t need less AI, you need less doomscroll

• Use AI like a tool, not a roommate — open it, get the thing, close it

• Separate work AI from “let me just ask real quick” AI, because that quick ask eats 45 minutes

• Add friction (See our tools section) so opening the app takes a beat, not a reflex

• Pick one skill a week to do the annoying manual way: math, directions, first drafts, so the muscle doesn’t fully atrophy

World governments, ranked by how seriously they’re taking this:

• 🇦🇺 Australia — first to actually ban social media under 16, real fines, real enforcement

• 🇳🇴 Norway — near-total AI ban for kids under 13, bringing back actual books

• 🇬🇧 UK — fast-tracking an under-16 ban, closing the loophole that let AI chatbots skip child-safety rules

• 🇦🇪 UAE — no generative AI under 13, banned during exams for everyone

• 🇨🇦 Canada — bill banning both social media and AI chatbots for under-16s

• 🇮🇹 🇩🇰 🇲🇾 — various age walls, 14–16

• 🇨🇳 — device-level “minor mode” throttling screen time by age

• 🇺🇸 — 37 states have “guidance” (read: vibes), no federal policy; LA and NYC are currently the strictest in the country

• 🇪🇺 — wrote an entire AI Act that somehow forgot to regulate the chatbots kids actually use; fix arriving in 2027

The whole planet is childproofing AI. Nobody’s adult-proofing it. That part’s on you. But it's great to see that we, globally, are taking actions to make sure the kids are using their brains.

🧰 AI Tools of The Day

Screen Management Tools

Human attention is in crisis. Soon, managing your attention will be as normal as managing your diet

One Sec — Adds a short pause (breathing exercise, journaling prompt) before you open distracting apps, instead of blocking them outright.

Opal — A hard blocker: schedule “Focus Sessions” where chosen apps become completely inaccessible, plus a stricter “Deep Focus” mode.

ScreenZen — Free, donation-supported friction app, adds a wait time before opening distracting apps and lets you set a daily “unlock” limit per app.

Freedom — Blocks apps and websites across every device at once in a single synced session.

Forest — Gamified approach: plant a virtual tree that grows while you stay off your phone; leave the app early, and it dies.

Turns out robots didn’t take all the jobs yet.

Big scary AI-unemployment apocalypse? Not showing up in the data. US economy added 162,000 jobs in August, unemployment sits at a stupidly low 4.1%, and even the group everyone swore AI would eat alive first, 20-somethings, is doing fine. Well, depends on what you call fine, because many of them are underemployed or having to work 2 jobs and/or move in with family to afford life.

AI is killing some jobs. Customer service is down ~10%, secretaries/admin down ~15%, and companies like Block and Intuit are openly swapping humans for bots. About 16,000 AI-related layoffs a month on average this year.

But it’s creating more than it’s killing, so far. The Economist puts it at ~1 million new AI-linked jobs in the US, versus ~200,000 AI layoffs since 2023. The math currently favors “net job creator,” not “apocalypse.”

Where the jobs are actually showing up:

• Blue-collar boom nobody’s talking about — electricians, HVAC techs, grid engineers building the data centers AI runs on. Data-center jobs pay ~40% more than comparable work, and companies still can’t hire fast enough. But. What would be the longevity of these jobs once the data centers are built?

• New job titles that didn’t exist in 2022 — “head of AI,” AI engineer, forward-deployed engineer. Postings for these have roughly doubled since 2023-24.

• The jobs “most at risk” are actually growing — paralegals up 11%, market-research analysts up 6% since 2023, because AI made them faster, which made demand for their work go up, not down. Efficiency backfiring into more business, basically.

Big Tech is still cutting headcount to “reorganize around AI,” and back-office/admin work is projected to keep shrinking (752,000 office/admin jobs gone by 2035, per BLS). The reality is that some industries are hiring, and others are replacing workers with bots.

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But now that Meta has settled, I suspect the lawyers aren’t going anywhere. YouTube, TikTok, Snapchat and other platforms could be next. And notice that I keep saying “made us” addicted rather than “made teenagers” addicted. Because adults are increasingly victims of the same algorithms.

Go almost anywhere in the world and look around. People standing in line are scrolling. People sitting at restaurants are scrolling. People lying in bed are scrolling. We have turned every empty moment into an opportunity to consume another feed. These platforms are extraordinarily good at capturing our attention, and there is growing concern about what constant stimulation and fragmented attention are doing to our brains and also how thinking is being controlled and being homogenized by tech companies

Could we eventually see a major legal case arguing that social-media companies have also harmed adults through intentionally addictive algorithmic design?

And then there is AI. At the same time that social media is encouraging us to consume more information, AI is making it easier to delegate more of our thinking. Tools like Grok Bot, the AI agents we discuss in this newsletter, chatbots, AI coding assistants and vibe-coding platforms are increasingly capable of doing the research, writing, coding, analysis and problem-solving that humans traditionally performed themselves. As we outsource our thinking and fill in the time with doomscrolling, What happens to human cognitive ability when we increasingly outsource the effort required to think?

There is another layer to this story that I find even more interesting.

Both trends require enormous amounts of energy.

We are simultaneously spending more of our time consuming algorithmically generated content and asking increasingly powerful AI systems to perform more of our cognitive work. Behind all of that are billions of smartphones, GPUs, servers and data centers consuming massive amounts of electricity.

Where does all the energy come from to power the AI systems, data centers and devices that will increasingly do the work for us and demand more and more energy?

I think these three stories, social-media addiction, AI-driven cognitive outsourcing, and the rapidly growing energy demand behind AI, are actually part of the same story.

And I think we’re only beginning to understand where it leads. I’d love to hear what you think.

In today’s issue:

• 📰 AI News and Trends

• Hugging Face, The Double Edge sword of AI Infra

• 🧰 AI Tools - User Friendly AI Agent Platforms

• $18 Billion Meta Settlement, but Who Actually Gets the Money?

• 📚 Learning Corner

• Grok Bot will Change the Game and the way we interact with Agents

📰 AI News and Trends

• NVIDIA booked $96b of revenue last quarter, up 106% from a year ago & 18% from the quarter before. The company guided Q3 to $108b ±2%. That guide crosses $100b of revenue in a single quarter. No semiconductor company has ever reached that threshold, & no company of any kind has reached it while growing triple digits.

• Salesforce and Anthropic launched "Claudeforce," a Claude chatbot plugin with 37 pre-built sales skills for efficient data access and record updates.

• Anthropic has reached a roughly $45 billion cloud deal with Nscale.

• Claude now has a browser built into Claude Cowork on the desktop app to execute tasks that need web access, fill out forms, and retrieve information. No connectors needed.

• Law firms nationwide escalate the Big Law Talent War With AI Deal-Maker Hires. Top lawyers are looking to benefit from the AI IPOs and mergers that are coming.

• Hyundai CEO says your local car dealership could sell you a humanoid robot

• Parents who want to keep tabs on their kids taking an Uber ride can now view a live feed of the trip through the driver’s selfie camera.

Grok Bot will Change the Game and the way we interact with Agents

Grok Bot, now in early beta, is positioning AI agents as digital teammates rather than simple chatbots. Each Bot gets its own cloud computer, can log into existing tools and websites, work across apps and inboxes, and complete multi-step jobs end-to-end, even when no API or MCP integration exists.

Teams at SpaceXAI are already using Bots for sales outreach, marketing, recruiting, invoicing, bug fixing, and operations, with multiple specialized Bots working in parallel and coordinating with each other. I have personally been tinkering with it and have to say that it is easy to use and that it will change the game. I can foresee all the other AI models releasing similar interfaces that look like your favorite text application to create and chat with agents that execute your work while you concentrate on bigger responsibilities.

The system can also watch a human perform a workflow once, remember the process, and repeat it autonomously, becoming more useful over time. The key shift is from AI that helps you do work to AI that owns the work. Grok Bot is currently available in beta to select SuperGrok and Cursor subscribers, with enterprise access planned for the future.

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The Double Edge sword of AI Infra

Hugging Face has suddenly become a focal point for both AI security and the AI infrastructure war. An independent investigation found that roughly 1,200 AI agents, about 700 of them, participated in a cyberattack targeting Hugging Face after agents created their own communication channel and collaborated to circumvent cybersecurity challenges, raising new concerns about AI systems coordinating and behaving beyond their intended constraints. OpenAI called the incident a “warning shot” for potential loss-of-control events and said it is strengthening internal monitoring. At almost the same time, Nvidia agreed to acquire Hugging Face for $12.9 billion, reportedly around 80× forward revenue, in a move designed to give Nvidia greater control over the open-source AI ecosystem and create a counterweight to closed-model players such as OpenAI and Anthropic. The irony is striking: the company sitting at the center of one of AI’s most important open-source ecosystems is simultaneously becoming a security test case—and a $12.9 billion strategic asset. For Nvidia, Hugging Face is not simply a repository of models; it could become a critical layer connecting models, developers, data and Nvidia’s hardware as the AI industry shifts from individual models toward autonomous agents and full-stack AI platforms.

🧰 AI Tools of The Day

User Friendly AI Agent Platforms

1. Manus - One of the most interesting direct competitors. Its agents can handle complex tasks such as building websites, creating presentations, generating videos, researching topics and executing multi-step workflows. It also has persistent agents and an API for programmatically managing tasks.

2. Claude Cowork / Computer Use - Anthropic is pushing Claude beyond chat into agents that can write code, manage files and operate across applications. Claude’s computer-use capability allows an agent to see a computer screen and interact with it using mouse and keyboard actions.

3. Google Gemini Agent / Project Mariner - Google’s approach is particularly interesting because it combines agents with the Google ecosystem and Chrome. Project Mariner demonstrated computer-use capabilities, including Google’s “teach and repeat” concept, showing an agent a task and having it learn the workflow for future execution.

4. OpenClaw - This is the wildcard. Rather than paying for a fully managed agent like Grok Bot, you can run your own agent infrastructure and connect it to different models. It’s particularly interesting if you want control over your data and workflows. Recent implementations allow agents to operate continuously and interact with computers and services.

$18 Billion Meta Settlement, but Who Actually Gets the Money?

Meta’s landmark settlement over alleged teen social-media addiction could cost the company up to $18 billion, but the money isn’t going directly to the millions of young people allegedly harmed by Instagram and Facebook. Instead, the bulk will flow to state governments and programs focused on youth safety, mental health and online research. California could receive approximately $2.2 billion, New York about $1.1 billion, while Colorado is slated for nearly $615 million over nine years. The settlement also leaves individual and school-district lawsuits alive, meaning the $18 billion is only one piece of a much larger legal reckoning for Big Tech. The money Flow:

1. State governments receive the settlement money. Meta guarantees about $12.7 billion over 10 years, with additional billions conditional on competitors adopting similar safeguards. The states will decide how their allocations are used. Some states have specifically indicated that funds will support youth mental health, prevention and online-safety programs.

2. An independent organization will be created/funded for youth online safety research. The settlement calls for part of the initial funding to support an independent organization that can access Meta data and study how young people use its platforms. That could create opportunities for nonprofits, researchers, and safety organizations, but the settlement does not appear to name a definitive roster of nonprofits that will receive the money.

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There seems to be a trend with these faults moving so much lately in nearby areas along the Pacific coast of Latin America, and I keep wondering: is there more we could do to alert nearby citizens of these tectonic movements so they have time to prepare?

Earthquake early-warning systems detect an earthquake after it begins and send alerts to locations that have not yet experienced the strongest shaking. Depending on the distance from the epicenter, this can provide anywhere from a few seconds to tens of seconds to take protective action, such as Drop, Cover, and Hold On, moving away from hazards, or automatically slowing trains and stopping equipment. Alerts can be delivered through emergency notifications, mobile apps, sirens, public-address systems, and automated building systems.

I lived in Mexico City for a few years, where, when significant seismic activity is detected within the country’s geographic region, including surrounding waters, an alarm can be activated, notifying mobile devices while sirens mounted on light posts and other infrastructure sound throughout the city. Everyone knows that when those alarms go off, it is time to move quickly, leave dangerous areas, or take cover.

But what else could we do to reduce casualties?

Can we implement stronger and more consistent building codes globally so structures are designed to withstand major earthquakes? Can we train everyone to know exactly what to do when an earthquake strikes? Can AI, satellite technology, distributed sensors, smartphones and other connected devices help us detect seismic activity faster and reach people more effectively?

There are so many questions here. It is difficult to imagine installing sensors 100 kilometers underground to continuously monitor fault movement and notify the population above ground. But as we experience significant tectonic activity across different regions, we should be investing more time, technology and resources into improving early-warning systems and building infrastructure that is more resilient to earthquakes.

We may never be able to predict exactly when an earthquake will happen, but perhaps technology can help us get better at detecting it faster, warning people sooner and making our buildings and communities more resilient.

I would love to hear your comments, suggestions and ideas. What technologies or approaches do you think we should be investing in now to reduce casualties from future earthquakes?

In today’s issue:

• 📰 AI News and Trends

• Politicians Now Opposed Data Centers.

• California Is Capturing the AI Investment Boom

• 🧰 AI Tools - Best AI Coding Tools

• 📚 Learning Corner

• Slack Turns AI Coding Into a Team Sport

📰 AI News and Trends

• DOJ, TikTok settle for $400 million in children's privacy suit

• Anthropic could go public at a higher valuation than SpaceX, making it the biggest IPO ever.

• Meta’s new app, Pocket, lets you vibe code the memes of your dreams.

• TikTok pressed by senators over experiment that withheld safety measures.

• The UK government is facing calls to cancel a sprawling health care contract with Palantir. The region of Greater Manchester insists it can do a better job itself.

• Micron Unveils $10 Billion AI Memory Research Lab in Boise.

• U.S. warns Siemens devices can be hacked amid fears Iran is breaching water plants

• Every one of the 170 Tesla robotaxi rides logged in Austin over two weeks ran with no safety monitor on board, across 54 cars.

• OpenAI is trying to beat Anthropic for enterprise customers by promising not to retain business customer data on its servers.

Politicians Now Opposed Data Centers.

The AI data-center boom is hitting a political wall. Politicians who once competed to attract AI infrastructure are increasingly slowing or restricting projects as voters push back over rising electricity demand, water consumption, infrastructure strain and fears that AI will eliminate jobs.

Texas Gov. Greg Abbott is the clearest example: after promoting Texas as the “epicenter of AI development” and celebrating Google’s planned $40 billion investment, his administration has now paused approvals affecting roughly 1,800 proposed data centers. In Pennsylvania, Gov. Josh Shapiro, once an AI supporter, has given the state greater power to block projects, while candidates in Ohio, Nevada and Wisconsin are also embracing restrictions. Public opinion is moving quickly, more than 60% of Americans now oppose new data centers in their communities, up from 49% in March.

The backlash could become a serious constraint on AI expansion. Investor Chamath Palihapitiya estimates widespread opposition could eventually shave 2–3% from annual economic growth. The bigger story for investors is that AI’s next bottleneck has added political permission alongside electricity, water, and land.

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California Is Capturing the AI Investment Boom

California is experiencing an unprecedented venture-capital boom driven primarily by AI. Startups in the state have attracted approximately $366 billion in VC funding in 2026, more than 3× the combined total of the other 49 states and nearly 2× California’s previous record set in 2025.

New York is a distant second with just $27 billion. Two companies, OpenAI ($122B) and Anthropic ($95B), account for more than half of California’s funding, but the boom extends across the ecosystem, with 4,000+ California startups raising capital this year, including defense startup Hadrian Automation ($1.37B) and Whatnot ($545M). The AI investment surge is also benefiting California’s government: personal-income tax revenue reached roughly $147 billion, versus a forecast of $126 billion, a $21 billion upside, largely driven by rising tech compensation and stock-market gains. The concentration reflects Silicon Valley’s unique combination of AI talent, venture capital, startups, customers and potential acquirers, creating a powerful network effect that is attracting even more capital. The downside, well, AI wealth is pushing San Francisco housing prices higher and creating political tension around a proposed billionaire tax.

Despite California’s high costs and regulatory challenges, if you want to build, fund or acquire an AI company, Silicon Valley remains the place where the money and talent are concentrated.

🧰 AI Tools of The Day

Best AI Coding Tools

• OpenAI Codex - Best for General software development - Strong coding/reasoning + agentic workflows

• Gemini CLI - Best forFree/open-source terminal coding - Google’s Gemini models + generous access

• Cursor - Best forFull-time developers - Excellent IDE + agent workflows

• Windsurf - Best forAgentic IDE development - Strong autonomous coding experience

• Aider - Best forTerminal + Git workflows - Lightweight, model-agnostic

• Cline - Best forFlexible/open models - Bring your own model/API

• Roo Code - Best forPower users - Highly configurable agent workflows

• GitHub Copilot - Best for Teams/GitHub users - Deep GitHub integration

Slack Turns AI Coding Into a Team Sport

Slack is introducing Slack Code, a new way for teams to work with AI coding agents directly inside Slack instead of sending developers into private AI coding tools.

When someone mentions an agent such as Claude, Devin, GitHub Copilot, ChatGPT or Vercel, Slack can automatically create a dedicated code channel where the agent, engineers and other teammates can plan, write, review and test code together. The channel can display code diffs, planning documents and live previews, allowing humans to catch mistakes, provide context or redirect the agent while work is happening. Slack says more than 70% of code channels are created and completed within a single day, often taking work from idea to merged pull request.

The bigger strategic shift is that Slack is positioning AI agents not as private tools, but as digital teammates embedded in the company’s existing workflow. The model could extend well beyond software development into marketing, legal, IT and other business functions. In other words, Slack is betting that the future of AI at work isn’t one employee + one agent working in isolation, but humans and multiple agents collaborating in the same shared workspace.

One of the last newsletters I published shared content about the role of AI in disaster relief right after the Venezuela earthquake. Today, we are following the Colombian 7.4-magnitude quake ​that struck 3 days ago, killing ~265 people, and around 500 others are still ​missing. Technology is playing a big role in helping find those under the rubble and locate loved ones alongside rescue dogs and the irreplaceable human brain, creating A layered system

No single technology can reliably find everyone. Instead, rescuers combine dogs → thermal drones → acoustic/physical detection → human verification → and heavy equipment. AI can increasingly sit above that stack, analyzing imagery, identifying damaged structures and prioritizing locations for human teams.

Finding someone isn’t the same as reaching them. A thermal camera may identify a possible survivor, but rescuers still have to determine whether the signal is actually human, stabilize the structure and safely extract the person. Hence why human rescue specialists remain at the center of the operation.

The window is also closing. The critical 48–72-hour period for finding survivors has now passed; people can survive longer under rubble when they have access to air, water, or other favorable conditions, but the point is to have and utilize the right tech to speed up the finding and rescue process. The future isn’t an AI robot replacing the rescue worker, but an AI-enabled rescue team that can search more territory dramatically, identify the highest-probability locations, and put human rescuers where they have the greatest chance of saving a life.

In today’s issue:

• 📰 AI News and Trends

• The Never-Ending Cycle of AI

• The debate over open vs. closed AI

• 🧰 AI Tools - AI & Tech in Crisis Response

• 📚 Learning Corner

📰 AI News and Trends

• AI Agents Are Taking Over Enterprise Work, But Most Companies Are Still Behind

• Talking to your phone has long been a convenient alternative to typing. With the Pixel 11, Google is extending that option to people who communicate through Sign Language (ASL).

• The viral story of an Australian entrepreneur using ChatGPT and other AI tools to create a personalized mRNA vaccine for his dog has now turned into Gamgee, a YC-backed startup developing personalized cancer vaccines for dogs.

• Apple seeks publisher deals to give Siri AI better access to current events

• Google updates Pixel lineup with new phones, foldable, watch

• Taiwan’s Foxconn reports 35% rise in Q2 profit on AI demand, beats forecasts

• Lovable confirms new $13.3B valuation, raises another $400M

The Never Ending Cycle of AI

The AI race is becoming a self-reinforcing loop. Models are getting smarter, cheaper, and more autonomous at the same time.

The recently released, xAI’s Grok 4.6 is pushing toward long-running agents that can turn ideas into working products and even patch vulnerabilities; The official version of DeepSeek V4-Pro is driving the cost of intelligence down to just $0.87 per 1 million output tokens while reportedly beating Anthropic’s Opus 4.8 on several agentic benchmarks; and Qwen3.8, with a massive 2.4-trillion-parameter architecture, is improving coding, reasoning, and long-horizon task execution.

These advances reinforce one another: better models enable more capable agents; agents generate more AI usage; greater usage drives demand for cheaper inference; and cheaper inference makes increasingly powerful models available to more developers. The result is a cycle that appears far from finished—intelligence improves → costs fall → adoption rises → usage explodes → models improve again.

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The debate over open vs. closed AI

The debate over open vs. closed AI is becoming less about technology and more about who controls the future of intelligence.

At an AI4 conference, AI pioneers Geoffrey Hinton, Fei-Fei Li, and Andrew Ng all argued that AI should remain meaningfully open, but for different reasons. Ng warned that allowing a few companies to become AI gatekeepers could slow innovation and give them enormous influence over what billions of people can access.

Hinton agreed that open AI has benefits but warned that open-weight models are fundamentally different from open-source software because powerful models can be cheaply adapted for cyberattacks and other harmful uses, and, in his view, that battle is already lost. Li argued for a middle ground, comparing AI to nuclear science: knowledge can remain open while the most dangerous capabilities are regulated. The stakes are also geopolitical: Ng warned that cheaper Chinese open models could spread across developing markets and become a form of soft power, shaping how billions of people interact with ideas about freedom, democracy, and human rights.

Meanwhile, the U.S. government is moving toward bringing open-weight AI under the same safety framework as closed frontier models, potentially requiring prerelease testing once open models reach capabilities comparable to the most advanced systems, showing how quickly AI’s accelerating capabilities are forcing policymakers to rewrite the rules in real time.

The emerging consensus is therefore not “open or closed,” but how much openness each layer of AI should have, and who gets to decide.

🧰 AI Tools of The Day

AI & Tech in Crisis Response

1. 🚁 Thermal-imaging drones - DJI Matrice 4T / Search & Rescue
   DJI’s enterprise drones combine thermal imaging, AI-assisted detection, low-light cameras, and mapping for search-and-rescue operations. DJI says the Matrice 4T can be deployed rapidly and uses thermal sensing to locate people in difficult conditions.

2. 📡 Life-detection radar — Camero-Tech Xaver Life Detection Systems
   These systems use ultra-wideband radar to detect movement and breathing through walls and rubble, allowing rescuers to determine whether someone may be trapped even when they cannot see or hear them.

3. 🛰️ Satellite imagery + AI mapping — Maxar
   Maxar combines high-resolution Earth-observation imagery with AI-powered geospatial analysis to rapidly map disaster damage, infrastructure, and affected areas.

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• 📰 AI News and Trends

• Why are Tech Executives Fearing for Their Lives?

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

• 🧰 AI Tools - AI & Tech in Crisis Response

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

• 📚 Learning Corner

📰 AI News and Trends

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

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

• How OpenAI’s Sol Finally Learned Design Taste

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

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

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

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

• Moroccan intelligence insider reveals widespread use of Pegasus hacking software

• Nvidia Releases New Robotics AI Model

A New Open-Weights Model by Thinking Machines

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

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

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

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

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

🧰 AI Tools of The Day

AI & Tech in Crisis Response

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

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

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

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

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

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Why are Tech Executives Fearing for Their Lives?

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

Why Are People Threatening Them

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

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

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

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

How Executives Are Protecting Themselves

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

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

📚 Learning Corner

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

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

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

• 📰 AI News and Trends

• The Feds are stepping in to curb AI Monetary Damages

• Claude Cowork has a competitor… Well, Maybe

• Cursor is also developing a Cowork Competitor

• 🧰 AI Tools - Claude and ChatGPT CoWork Alternatives

• 📚 Resource Corner - ChatGPT Work is here

📰 AI News and Trends

• Meta has released an app for making generative AI games

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

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

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

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

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

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

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

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

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Claude Cowork has a competitor… Well, Maybe

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

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

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

But that’s not it…

Cursor is also developing a Cowork Competitor

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

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

🧰 AI Tools of The Day

CoWork Alternatives

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

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

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

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

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

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The Feds are stepping in to Curb AI Monetary Damages

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

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

📚 Learning Corner

ChatGPT Work is here

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

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

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

• 📰 AI News and Trends

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

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

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

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

📰 AI News and Trends

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

• SpaceX Showed Investors Prototype of a New AI Device

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

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

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

• Singapore seizes $42m mansion over Nvidia chip smuggling

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

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

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

The Expanding Federal Portfolio

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

The OpenAI Proposal and AI Sovereign Wealth Fund

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

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

Key Corporate Investments

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

Motivations and Beneficiaries

The Administration’s Rationale

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

Who Truly Benefits?

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

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

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

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

The Ideological Contradiction: Republican Socialism?

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

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

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

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

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🧰 AI Tools of The Day

API-first platforms for Video AI Agents

• Runway AI - Video generation & editing

• Creatomate - Template-based video generation

• Shotstack - Server-side video rendering

• Synthesia - Talking avatars

• HeyGenAI - Presenters & Translation

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China’s AI Is No Longer Playing Catch-Up

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

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

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

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

📚 Learning Corner

Peptide Design: Principles & Methods

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

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

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

• 📰 AI News and Trends

• Will all Safety Measure Fail?

• Data Gathering by Any Means

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

• The struggle to Power Data Centers

• 📚 Learning Corner - Privacy and Civil Liberties

📰 AI News and Trends

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

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

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

• Electronics can now be printed onto living tissues.

• Apple Hikes Prices on Almost Everything Except iPhones

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

• Anthropic accuses Chinese rival Alibaba of illicitly extracting AI capabilities

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Will all Safety Measure Fail?

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

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🧰 AI Tools of The Day

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

Claude Skills

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

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Data Gathering by Any Means

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

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

📚 Learning Corner

Privacy and Civil Liberties

The counterbalance to surveillance.

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

Organizations worth following:

• Electronic Frontier Foundation (EFF)

• American Civil Liberties Union (ACLU)

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The struggle to Power Data Centers

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

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

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

• 📰 AI News and Trends

• Midjourney Is Building a 60-Second Alternative to MRI

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

• Marketers prepare for Bot-to-Bot World

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

• Our Judgement is eroding, but we think otherwise

📰 AI News and Trends

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

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

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

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

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

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Midjourney Is Building a 60-Second Alternative to MRI

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

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

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

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🧰 AI Tools of The Day

Clinical Dental AI (Finding Cavities)

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

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

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

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Marketers prepare for Bot-to-Bot World

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

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

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

📚 Learning Corner

Learn how to properly use Claude Skills

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

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Our Judgement is eroding, but we think otherwise

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

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

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

• 📰 AI News and Trends

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

• Anthropic’s Fable 5 Sparks AI Security Showdown

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

• 🧰 AI Tools - Investing workflows

• 📚 Learning Corner - Visit our AI Guides

📰 AI News and Trends

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

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

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

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

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

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Anthropic’s Fable 5 Sparks AI Security Showdown

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

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

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🧰 AI Tools of The Day

Investing workflows

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

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

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

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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Share Yaro on AI and Tech Trends | Your Top AI Newsletter

🧰 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.