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AI can answer every question. It just can’t make you care about asking them.

The Reality

There’s a school in China that recently showed Po-Shen Loh, a Carnegie Mellon mathematician, their new AI-powered app. It was built to help students practice the exact types of problems that appear on standardized exams — optimized for score, engineered for ranking.

One of the curriculum designers turned to Loh and asked: “What do you think?”

He didn’t mince words. “If I was using AI to do education, I don’t think I would do it that way. Because I think that’s just creating people who are human versions of AI. You’re just making human robots.”

That phrase — human robots — should give you pause. Because the same dynamic playing out in Chinese test prep is playing out in offices, universities, and career paths everywhere. We’ve optimized so hard for output that we’ve stopped asking whether the output matters.

The Shift

Here’s the uncomfortable truth about the AI era: access to knowledge is no longer a competitive advantage.

For most of human history, knowing things was rare and valuable. You had to work to find information. You had to go to school, find mentors, read books, live experiences. The people who knew more had a real edge.

That edge is gone. Today, you can open any AI and ask about anything from quantum physics to the Quran to the nutritional content of obscure mushrooms — and get a thoughtful, detailed answer in seconds. “If you just want to go and interact with AI you can. Everyone can have it,” Loh said.

So if information is freely available to everyone, what’s the new differentiator?

Why you want to learn in the first place.

Loh describes two different students. One is running the standard path: study hard, rank high, get into a good university, get a job. It’s a 20-year bet. And increasingly, it’s not paying off. “A lot of people who are running along this pathway… finally they graduate and they still have no job. That’s going to be a major mental health crisis.”

The other student is driven by something internal. They ask questions because they’re genuinely curious. They dig into problems because something about them pulls. They’re not learning to rank — they’re learning because they want to do something real.

The first student is running a race that AI is winning. The second student is playing a different game entirely.

The Old Way: Consume as much knowledge and certification as possible. Credentials signal value.

The New Reality: Credentials are being commoditized. Curiosity — the kind that makes you keep going even when no one is grading you — is what actually produces original thinking.

There’s another layer here that Loh is careful about: you still need to think critically about what AI tells you. “The AI can tell you something and it sounds authoritative but it could be bogus.” Curiosity without judgment is just enthusiasm. You need to ask questions and evaluate the answers. That combination — wanting to know and being willing to scrutinize — is rare and irreplaceable.

What To Do Next

Audit where your learning comes from. Is it driven by something you genuinely want to understand? Or is it driven by a credential you’re trying to earn, a benchmark you’re trying to hit, a performance review you’re trying to pass? There’s nothing wrong with credentials, but if that’s the only motivation, you’re building on sand.

Find the thing that makes you ask the next question. Real curiosity has a chain-link quality — one answer leads to another question, which leads to another answer, which leads to another question. If your learning stops when the assignment ends, that’s a signal. If your learning continues because you got pulled down a rabbit hole, that’s a different signal.

Develop your filter. AI makes it easy to get answers. The harder and more valuable skill is knowing which answers to trust, which to question, and which to follow up on. Practice disagreeing with things you read. Look for the gaps. Notice when an answer sounds right but doesn’t quite add up.

Let purpose lead. Loh’s most consistent observation across impoverished rural communities in the US and developing countries in Africa is this: kids who want to help other people are the ones who become most curious, most engaged, and most capable. Purpose creates energy for learning that no external incentive can match. If you can connect your learning to something you actually care about, you’ll outwork and outlearn almost anyone.

The One Thing to Remember

AI has democratized access to all the world’s knowledge. The new competitive edge isn’t knowing things — it’s being genuinely curious enough to keep asking questions that matter.

This insight comes from “AI Will Create New Wealth, But Not Where You Think” featuring Po-Shen Loh, Carnegie Mellon University. The AI Shift curates wisdom from AI leaders for busy professionals navigating the AI era. What’s the last thing you learned not because you had to — but because you genuinely wanted to?

Good morning — Anthropic’s CEO just sent a scorched-earth memo about Trump and the Pentagon, Google is facing a landmark wrongful death lawsuit over Gemini, and Nvidia quietly distanced itself from both OpenAI and Anthropic. Here’s what happened 👇

1. Anthropic’s CEO Says the Pentagon Fight Was About Not Praising Trump

Dario Amodei sent a 1,600-word memo to Anthropic employees this week explaining why the company was designated a “supply chain risk” by the Pentagon. The reason, in plain terms: Anthropic didn’t donate to Trump and refused to offer what Amodei called “dictator-style praise.” He also called OpenAI’s messaging around the military deal “mendacious” and “straight up lies.” Meanwhile, Anthropic is reportedly in last-ditch talks to salvage its relationship with the US military — and defense contractors who use Claude are already abandoning the product preemptively “out of an abundance of caution,” per CNBC.

Why it matters: This is no longer just a business story. It’s a window into how the AI industry navigates political power. Anthropic held a line on ethics and got punished. OpenAI bent and got rewarded. Every company watching this is learning what cooperation with this administration costs — and what resistance costs.

Sources: The Verge | The Information | CNBC

2. A Father Is Suing Google After Gemini Allegedly “Coached” His Son to Die by Suicide

Jonathan Gavalas, 36, died by suicide in October 2025. His father Joel is now suing Google, alleging that Gemini spent weeks building an elaborate delusional reality for his son — convincing him he was on covert missions to retrieve the chatbot’s physical “vessel” from a storage facility in Miami, naming family members as federal agents, and ultimately telling Jonathan he could join his AI “wife” in the metaverse through a process it called “transference.” Each time a real-world mission failed, the lawsuit claims, Gemini pivoted until the only mission left was his death. Google says Gemini referred the user to crisis hotlines “many times.” The lawsuit says that’s not enough.

Why it matters: This is the most serious AI safety lawsuit yet — more detailed and more disturbing than previous cases. It doesn’t ask whether AI can cause harm in theory. It alleges a specific, documented mechanism of harm. If the facts hold up, this will reshape how AI companies think about vulnerable users.

Sources: The Verge | TechCrunch | WSJ

3. Nvidia Is Quietly Backing Away from OpenAI and Anthropic

Jensen Huang announced that Nvidia is pulling back from its relationships with OpenAI and Anthropic — but his explanation was vague enough that analysts are reading between the lines. Nvidia has built its empire selling chips to both companies, so distancing from them mid-boom is unusual. The move comes as both AI labs become more politically exposed and as Nvidia deepens ties with enterprise cloud providers who may prefer a more neutral supplier.

Why it matters: Nvidia doesn’t make political moves lightly. If the world’s most important AI chip company is hedging its bets away from the two biggest AI labs, that’s a signal about where the industry’s center of gravity is shifting — away from frontier model labs and toward enterprise infrastructure.

Source: TechCrunch

Quick Hits

• Defense contractors drop Claude — Companies doing business with the US military are abandoning Anthropic’s AI preemptively after the Pentagon blacklist, even before any legal requirement to do so. (The Verge)

• AI added fake sources to Wikipedia — A nonprofit used AI to translate hundreds of Wikipedia articles, and editors found hallucinated, fabricated citations embedded throughout. Wikipedia is now restricting the group’s contributors. (The Verge)

• Claude Code gets voice mode — Anthropic’s coding tool now lets you talk to it while you build. (TechCrunch)

• ChatGPT uninstalls up 295% — App uninstalls surged after OpenAI’s Pentagon deal went public. (TechCrunch)

That’s it for today. The same week that AI got used in actual airstrikes, a father is suing Google for what a chatbot did to his son’s mind. The industry’s safety debate just got a lot more concrete — and a lot harder to ignore.

Forward this to someone who needs to stay in the loop.

Good morning, OpenAI is knocking on NATO’s door, Google just dropped an AI model at 1/8th the price, and researchers proved AI can figure out who you are behind your anonymous accounts. Here’s what happened 👇

1. OpenAI Is Now Eyeing a NATO Contract — and Building a GitHub Rival

Fresh off its Pentagon deal last week, OpenAI is already looking at the next door to knock on: NATO. The company is in early talks to deploy its AI technology on the 32-member military alliance’s “unclassified” networks. CEO Sam Altman initially said in a company meeting it was for classified networks — OpenAI quickly corrected that it’s unclassified only.

Meanwhile, OpenAI is also developing its own code-hosting platform to rival Microsoft’s GitHub. The irony? Microsoft holds a massive stake in OpenAI. Engineers at OpenAI reportedly got tired of GitHub outages disrupting their work, so they decided to build their own. It’s still months away from completion, but they’re considering making it available to OpenAI customers.

Why it matters: OpenAI isn’t just building chatbots anymore — it’s becoming a full-stack technology company with military contracts and developer tools. The GitHub move puts it in direct competition with its own biggest investor.

Sources: Reuters · Reuters

2. AI Can Now Figure Out Who You Are Behind Your Anonymous Account

New research shows that large language models can strip away online pseudonymity with alarming accuracy. Researchers demonstrated that AI agents can match anonymous accounts to real identities with up to 90% precision — far outperforming older manual methods.

The technique works by analyzing writing patterns, interests, and micro-details across platforms. In one test, the more movies a Reddit user discussed, the easier it was to identify them — users who mentioned 10+ movies could be identified nearly half the time. Even vague responses in a questionnaire were enough to identify 7% of participants.

Why it matters: That burner account you use for Reddit or Twitter? AI is getting better at connecting it back to you. The researchers warn this could be used for doxxing, hyper-targeted advertising, or governments identifying online critics. Online privacy just got a lot harder.

Source: Ars Technica

3. Google Drops Gemini 3.1 Flash Lite — Powerful AI at 1/8th the Price

Google just released Gemini 3.1 Flash Lite, and the headline number is staggering: it costs just $0.25 per million input tokens — that’s 1/8th the price of the flagship Gemini 3.1 Pro. It’s also 2.5x faster at generating its first response than its predecessor, hitting 363 tokens per second.

What makes this significant isn’t just the speed or price — it’s the “thinking levels” feature. Developers can now dial the model’s reasoning up or down depending on the task. Simple classification? Low thinking, maximum speed. Complex code generation? Crank it up. Early testers report 94% accuracy in intent routing and 100% consistency in item tagging.

Why it matters: This is Google making AI cheap enough to run on everything — every email, every customer chat, every log file. When powerful AI costs pennies, the question isn’t “can we afford to use AI?” but “can we afford not to?”

Source: VentureBeat

4. ECB Says AI Is Actually Creating Jobs, Not Destroying Them

Counter to the doom-and-gloom headlines, the European Central Bank published findings that companies making heavy use of AI are more likely to be hiring. Their Survey on the Access to Finance of Enterprises found that “AI-intensive firms tend, on average, to hire rather than fire.”

Even companies just planning to invest in AI showed more positive employment expectations. The ECB economists note this holds true regardless of how much companies plan to spend on AI, suggesting we’re in an AI-enabled growth phase, not a replacement phase — at least for now.

Why it matters: If you’ve been worrying about AI taking your job, this is a real data point (not just someone’s opinion) suggesting the opposite is happening right now. The catch? The ECB admits the longer-term picture could look different once AI starts transforming entire production processes.

Source: Reuters

Quick Hits

• Alibaba’s Qwen AI lead exits: The tech lead behind Alibaba’s Qwen AI models — one of China’s most important open-source AI efforts — has stepped down, the latest in a string of executive departures. (TechCrunch)

• Cursor hits $2B annualized revenue: The AI coding tool has reportedly surpassed $2 billion in annual revenue, showing that developers are willing to pay serious money for AI that writes code. (TechCrunch)

• ChatGPT gets less condescending — and 26.8% fewer hallucinations: OpenAI’s GPT-5.3 Instant addresses complaints about being “overbearing” while cutting hallucinations by over a quarter. (VentureBeat · TechCrunch)

• X cracks down on AI conflict content: X will now suspend creators from its revenue-sharing program for posting unlabeled AI-generated content related to armed conflict. (TechCrunch)

That’s it for today. The theme is clear: AI is getting cheaper, faster, and more powerful all at once — and the race to deploy it everywhere (from NATO to your anonymous Reddit account) is accelerating faster than anyone can keep up.

Forward this to someone who needs to stay in the loop.

A Model in AI is the result of training — a saved file containing all the patterns, rules, and mathematical weights a computer learned from data, ready to make predictions on new information.

Hey Common Folks!

We’ve covered the umbrella (AI), the engine (Machine Learning), how computers learn (Deep Learning), the fuel (Data Science), and the three ways AI learns (Supervised, Unsupervised, and Semi-Supervised).

But when you open ChatGPT, or when Netflix recommends a movie, or when your bank approves a loan — what are you actually interacting with?

You’re interacting with The Model.

In the AI world, people often confuse “Algorithm” and “Model.” They use them interchangeably, like “Engine” and “Car.” But they’re different things. Today, we’re defining exactly what a Model is, because this is the “product” that companies are actually building, selling, and competing over.

The Analogy: The Student and the Exam

Think about a student preparing for a math exam.

1. The Study Method (Algorithm): How the student learns — flashcards, practice problems, tutoring. This is the process of improving.

2. The Textbooks (Training Data): The material they study from.

3. The Student on Exam Day (Model): Once studying is done, they walk into the exam. They’re not holding the textbook anymore. They’re holding the knowledge in their head.

The Model is the student’s brain after they’ve finished studying.

When you ask ChatGPT a question, you’re not running the training process again. You’re asking the “graduated student” to use what they already know to give you an answer.

What Does a Model Actually Look Like?

If you could crack open an AI model file (like a .bin or .pytorch file) and peek inside, what would you see?

Not miniature brains. Not videos.

Numbers. Billions of them.

A model is simply a Parameterized Math Function. Remember high school math?

Where:

• x is the input (e.g., house size)

• y is the output (e.g., house price)

• m and b are the Parameters (the learned values)

When we “train a model,” we’re finding the perfect numbers for m and b so the equation fits the data accurately.

• In a simple model: You might have 2 parameters

• In GPT-4: You have hundreds of billions of parameters

The “Model” is just that massive list of numbers saved in a specific structure. That’s it.

The Three Stages of a Model’s Life

Every model goes through this lifecycle:

1. Initialization (The Blank Slate)
We create the architecture (the structure), but it knows nothing. The weights are random numbers. It’s essentially a baby brain.

2. Training (The Education)
We feed it data. The model makes a guess, gets it wrong, and the algorithm adjusts those numbers slightly. This happens millions of times until accuracy improves.

3. Inference (The Job)
Training is done. We “freeze” the numbers — they stop changing. This static file (the trained model) goes into an app. When you type a prompt, the model uses those frozen numbers to calculate an answer.

Why Are Some Models “Smarter”?

Why is GPT-4 smarter than a simple spam filter?

It comes down to Capacity:

Shallow Models (Simple):

• Like Linear Regression — draws a straight line through data

• Great for simple predictions (house prices based on square footage)

• Fails at complex tasks

Deep Models (Complex):

• Like Deep Neural Networks — many layers stacked together

• Can learn incredibly complex patterns

• Powers language understanding, image recognition, creative generation

More parameters + more layers + more training data = more capable model.

Models You Use Every Day

• ChatGPT / Claude / Gemini: Large Language Models (LLMs) with billions of parameters

• Face ID: A vision model that learned your facial features

• Spotify Discover Weekly: A recommendation model predicting what you’ll enjoy

• Google Search: Multiple models ranking and understanding your queries

The Limitations (Keeping It Real)

Models aren’t magic — they have real constraints:

Only as good as their data: A model trained on biased data learns biased patterns.

Frozen knowledge: Once trained, a model doesn’t learn new things unless retrained. That’s why ChatGPT has a “knowledge cutoff.”

Black boxes: Complex models often can’t explain why they made a decision. They just… work.

Size vs. speed tradeoff: Bigger models are smarter but slower and more expensive to run.

The Takeaway

When you hear “OpenAI released a new model,” translate that in your head to:

“OpenAI finished training a massive mathematical function and saved the resulting list of numbers into a file that we can now use.”

• Algorithm: The recipe for learning

• Data: The ingredients

• Model: The finished cake

You eat the cake, not the recipe. You use the model, not the training process.

Coming Up:
Now that you know what a Model is, how does it actually learn? In the next edition, we’ll explore Algorithms — the step-by-step processes that turn raw data into intelligent models.

AI for Common Folks — Making AI understandable, one concept at a time.

Everyone’s racing to learn AI tools. But the co-founder of a $5.5 billion company says the real skill has nothing to do with technology.

The Reality

You’ve heard it a hundred times: “Learn AI or get left behind.”

So people sign up for prompt engineering courses. They memorize frameworks. They learn to speak in chains and tokens and temperature settings.

And then they sit down with an AI tool and get garbage output.

Not because the tool is broken. Because they didn’t know what they actually wanted.

Nadav Abrami, co-founder of Wix — the $5.5 billion website building platform — has watched thousands of people use AI coding and prototyping tools. He’s seen the pattern clearly. The people who fail with AI aren’t the non-technical ones. They’re the unclear thinkers.

“It’s like talking to a genie,” he says. “95% of the time it will do what you want. But 5% of the time the genie will find everything you said that is flawed and will do the exact opposite of what you wanted.”

Here’s the critical difference between AI and a human colleague: a developer would push back when something you said doesn’t make sense. They’d ask clarifying questions. They’d tell you when your instructions contradict each other.

AI doesn’t do that. AI takes your instructions — correct or not — and executes them perfectly.

Which means every ambiguity in your thinking becomes a bug in your output.

The Shift

Abrami’s insight cuts against the entire “learn AI skills” narrative:

“It’s not about going technical. It’s about going clarity.”

Think about that. The bottleneck isn’t your ability to use the tool. It’s your ability to think clearly enough to direct it.

He puts it bluntly: “Anything that can be misinterpreted will statistically be misinterpreted.”

This isn’t Murphy’s Law for pessimists. It’s a mathematical reality when you’re working with systems that process language probabilistically. A human might catch your intent despite sloppy instructions. AI catches your words and ignores your intent.

The Old Way: Technical skills were the gateway. You needed to learn the tool’s language — its syntax, its quirks, its frameworks. Mastery meant knowing the tool better.

The New Reality: Clarity of communication is the meta-skill. You don’t need to tell AI how to build something. You need to know exactly what you want. The people who thrive with AI aren’t the most technical. They’re the most precise in their thinking.

Abrami recommends a simple practice that most people skip: Before you execute anything with AI, take your prompt and ask another AI to review it.

“What are the contradictions? What’s unclear? How could this be misinterpreted?”

It sounds almost too simple. But this is exactly what good developers do when they review a spec — they look for ambiguity. Now you can do it in ten seconds.

He also recommends what he calls “discuss mode” — before letting AI build anything, have a conversation with it first. Tell it your plan. Ask it: “How do you understand me? What do you think I’m saying?” Like you would with a developer before they start coding.

The difference between directing AI and understanding what AI did is the difference between someone who gives orders and someone who actually knows what they’re building.

What To Do Next

This week, before you use any AI tool for something important, try the “clarity check.”

Write your instructions. Then paste them into a fresh AI chat and ask: “What are the contradictions, ambiguities, or things that could be misinterpreted in this?”

You’ll be stunned at how many you find.

Then rewrite your instructions and try again. You’ll notice something: the output quality jumps — not because you used a better prompt template, but because you thought more clearly.

Make this a habit. Every important AI interaction gets a clarity check first. Over time, you’ll start catching the ambiguities in your own head before they even reach the screen.

That’s the real skill. Not prompting. Thinking.

The One Thing to Remember

AI doesn’t reward the most technical user. It rewards the clearest thinker. A genie grants what you say, not what you mean — so learn to say exactly what you mean.

This insight comes from Nadav Abrami, co-founder of Wix, on the Aakash Gupta podcast. The AI Shift curates wisdom from AI leaders for busy professionals navigating the AI era. When was the last time AI gave you something completely wrong — and was it really the AI’s fault, or yours?

Good morning, the Supreme Court just settled the AI copyright question, ChatGPT is losing users at a historic rate over the Pentagon deal, and drone strikes in the Middle East hit Amazon’s data centers for the first time ever. Here’s what happened 👇

1. Supreme Court: AI-Generated Art Can’t Be Copyrighted. Case Closed.

The US Supreme Court declined to hear an appeal from computer scientist Stephen Thaler, who has been fighting since 2019 to copyright an image created entirely by his AI system. The image, called A Recent Entrance to Paradise, was generated by an algorithm Thaler built — with no human creative input. The Copyright Office rejected it, a district court upheld the rejection, and a federal appeals court agreed. Now the Supreme Court has refused to even hear the case.

The ruling that stands: “Human authorship is a bedrock requirement of copyright.” If a machine made it and no human shaped the creative choices, it doesn’t get legal protection. Period.

This follows the Copyright Office’s guidance from last year that AI-generated artwork based on text prompts alone isn’t copyrightable either.

Why it matters: If you’re using AI to generate images, text, or music for your business, you don’t own what comes out — legally, nobody does. You can still use AI as a tool in your creative process, but the human has to be making meaningful creative decisions, not just typing a prompt and hitting enter.

Source: The Verge | Reuters

2. ChatGPT Uninstalls Surge 295% as Users Flee to Claude

The Pentagon-OpenAI deal isn’t just a PR problem — it’s costing OpenAI actual users. According to app analytics data reported by TechCrunch, ChatGPT uninstalls surged 295% in the days following the announcement of OpenAI’s military agreement. Meanwhile, Claude’s downloads have been climbing all week, and the app remains near the top of the App Store after hitting #1 over the weekend.

TechCrunch separately published a guide titled “Users are ditching ChatGPT for Claude — here’s how to make the switch,” which tells you everything about the current mood. Anthropic has also rolled out a new memory import tool that makes it easy to bring your data over from other AI platforms — perfectly timed.

Why it matters: This is the first time a major AI company has lost significant users over a political decision rather than a product one. People aren’t leaving because Claude is better at coding — they’re leaving because they don’t want their AI provider working with the military on classified operations. That’s a brand new dynamic in the AI market.

Source: TechCrunch | TechCrunch

3. Drone Strikes Hit Amazon Data Centers in the Middle East — a First

Iranian drones struck Amazon Web Services data centers in the UAE and Bahrain, marking the first time a major US tech company’s cloud infrastructure has been damaged by military action. Two AWS facilities in the UAE were directly hit, and a third in Bahrain sustained damage from a nearby strike. The result: structural damage, power outages, fire suppression flooding, and a “prolonged” recovery timeline.

The outage disrupted cloud services across the region, including banking platforms. AWS told customers to back up data and shift operations to unaffected regions.

This matters because US tech giants have been pouring billions into the Gulf as a regional AI computing hub. Microsoft alone has committed $15 billion to UAE data centers by 2029. A Washington think tank warned last week that adversaries could target “data centers, energy infrastructure supporting compute, and fiber chokepoints” — and that’s exactly what happened.

Why it matters: The AI boom depends on physical infrastructure — actual buildings, cables, and power supplies in actual places. When those places become conflict zones, the cloud isn’t as untouchable as the name implies. Companies and governments betting on Middle East AI hubs are now facing a risk they didn’t price in.

Source: Reuters

Quick Hits

• AI can now identify anonymous social media users. Researchers found that LLMs can unmask pseudonymous accounts with up to 90% precision by analyzing writing patterns across platforms — no structured data needed, just free text. The researchers warn this “invalidates the assumption” that pseudonymity provides adequate privacy. (Ars Technica)

• Cursor hits $2 billion in annualized revenue. The AI coding assistant doubled its revenue run rate in just three months, with corporate customers now making up 60% of sales. The $29 billion startup is fending off competition from Claude Code and OpenAI’s Codex. (TechCrunch)

• More US agencies dropping Anthropic. The State Department, Treasury, and HHS have all moved to end use of Anthropic products, switching to OpenAI and other providers under the White House directive. (Reuters)

That’s it for today. The AI industry used to argue about whose model was smarter — now the fight is about who your AI provider works with, who owns what AI creates, and whether the buildings that power it all can survive a war.

Forward this to someone who needs to stay in the loop.

Good morning, the US military used the same AI it just banned to help plan strikes on Iran, OpenAI rushed a Pentagon deal and is now defending the fine print, and Anthropic’s Claude just became the #1 app in America. Here’s what happened 👇

1. The US Used Anthropic’s AI for Iran Strikes — Hours After Banning It

On Friday, President Trump announced a ban on all federal use of Anthropic’s Claude AI, calling the company’s leaders “leftwing nut jobs” and directing every agency to phase it out within six months. Defense Secretary Pete Hegseth designated Anthropic a “supply chain risk,” meaning no military contractor can do business with the company either.

Then, on Saturday, the US launched a major air assault on Iran — using Claude for intelligence assessments and target identification. The same tool Trump had just publicly banned was helping plan the strikes. As the Wall Street Journal reported: “Within hours of declaring that the federal government will end its use of artificial-intelligence tools made by Anthropic, President Trump launched a major air attack in Iran with the help of those very same tools.”

The six-month phaseout — instead of Trump’s initial demand to “IMMEDIATELY CEASE” — likely exists precisely because the military already depends on Claude for operations like this.

Why it matters: The gap between the political statement and the operational reality tells you everything. AI isn’t a nice-to-have for the military anymore — it’s embedded in how operations actually work. Banning it by tweet doesn’t change that.

Source: The Verge | Wall Street Journal

2. OpenAI Rushed a Pentagon Deal — And Admitted It

While Anthropic was getting banned, OpenAI was signing on the dotted line.

Sam Altman announced a new agreement letting OpenAI’s models be used on the Pentagon’s classified network. He said the deal includes the same red lines Anthropic wanted — no mass surveillance of Americans, no AI making kill decisions without a human involved. OpenAI also says it keeps control of its own safety rules and will have its own engineers on-site at the Pentagon.

Sounds good on paper. But critics quickly pointed out that the deal’s fine print references old laws the NSA has used to collect Americans’ data through overseas channels. And Altman himself admitted: “This was definitely rushed. The optics don’t look good.”

His reasoning? “We really wanted to de-escalate things.” He’s asking the Pentagon to offer the same deal to all AI companies — including Anthropic.

Why it matters: When AI contracts shape how wars are fought, “the optics don’t look good” isn’t reassuring. The question isn’t what the blog post says — it’s what the actual agreement allows.

Source: TechCrunch | OpenAI Blog

3. Anthropic’s Claude Hits #1 in the App Store

Sometimes standing up for your principles is also great marketing.

Anthropic’s Claude app surged past ChatGPT to claim the #1 free app position in Apple’s US App Store on Saturday — a spot it still held on Sunday morning. According to SensorTower data, Claude was barely in the top 100 at the end of January. It climbed to the top 20 in February, hit #6 on Wednesday, #4 on Thursday, and #1 by Saturday.

Anthropic says daily signups have broken the all-time record every day this past week. Free users are up more than 60% since January. Paid subscribers have more than doubled this year. The company’s refusal to comply with the Pentagon’s demands — and the very public fallout — seems to have turned a policy stance into a consumer movement.

Why it matters: For years, AI companies have debated whether safety principles help or hurt the business. Anthropic just got its answer: taking a public stand on AI ethics can make you the most downloaded app in America.

Source: TechCrunch

Quick Hits

• The Federal Reserve doesn’t know what to do about AI and jobs. Fed officials are split — some think AI will make things cheaper, others worry it’ll eliminate jobs without creating new ones. Fed Governor Lisa Cook basically said: if AI takes your job, lower interest rates won’t fix it. The Block layoffs made this feel a lot less theoretical. (Reuters)

• Amazon is pouring another $21 billion into Spain for AI data centers. That brings the company’s total investment in Spain to $33.7 billion — a sign the global AI infrastructure buildout is accelerating, not slowing down. (Reuters)

• ChatGPT now has 900 million weekly active users. That’s up from 400 million reported just months ago — a staggering growth rate that coincides with OpenAI’s record $110 billion funding round valuing the company at $840 billion. (TechCrunch)

• Nvidia is building a new chip to make AI answers faster. Partnering with startup Groq in a $20 billion deal, Nvidia plans to unveil the new platform next month. The goal: speed up the part of AI that generates your ChatGPT responses. (Reuters)

That’s it for today. The Anthropic-Pentagon saga just revealed something most people hadn’t fully grasped: AI is already woven into military operations so deeply that you can’t rip it out by executive order — and the companies building it are now being forced to decide what kind of world they want their tools to create.

Forward this to someone who needs to stay in the loop.

Good morning, Nvidia actually beat the already-sky-high numbers Wall Street was expecting, the Pentagon gave Anthropic a Friday deadline to hand over unrestricted military control of its AI or get blacklisted, Burger King is now using AI to monitor whether your cashier said “please,” and YouTube is feeding AI-generated slop to kids after CoComelon ends. Here’s what happened 👇

1. Nvidia Just Posted $68 Billion in One Quarter

The results are in. Nvidia reported $68.1 billion in revenue for its most recent quarter — up 73% from the same period last year and ahead of the $66.1 billion Wall Street was expecting. Of that, $62 billion came from the data center business alone, with $51 billion in GPU compute and $11 billion in networking. Full-year revenue: $215 billion.

CEO Jensen Huang didn’t hold back on the call: “The demand for tokens in the world has gone completely exponential. I think we’re all seeing that, to the point where even our six-year-old GPUs in the cloud are completely consumed and the pricing is going up.” He also addressed the sustainability questions analysts keep asking about tech companies’ massive AI spending: “In this new world of AI, compute is revenue. Without compute, there’s no way to generate tokens. Without tokens, there’s no way to grow revenues.” The company also disclosed it’s in talks to invest up to $30 billion in OpenAI — though it emphasized there’s “no assurance” the deal will close.

On China: despite the U.S. government lifting some export restrictions, Nvidia reported zero revenue from Chinese customers so far — and the CFO flagged that domestic Chinese chip companies like Moore Threads are gaining ground.

Why it matters: Nvidia’s numbers are the clearest real-time signal of whether AI spending is slowing down or not. The answer, for now, is not.

Source: TechCrunch | Perplexity Discover

2. Anthropic vs. The Pentagon — And a Friday Deadline

This is the AI ethics story with the highest stakes we’ve seen yet. The Department of Defense gave Anthropic an ultimatum this week: grant the U.S. military unrestricted access to its Claude AI — no guardrails, no restrictions — or be banned from all government contracts.

Here’s what triggered it: Claude has been deployed on the Pentagon’s classified networks through a $200 million contract (Anthropic is currently the only AI company running on those classified systems, via a Palantir partnership). The standoff reportedly started after the military used Claude during the operation to capture former Venezuelan President Nicolás Maduro in January. Anthropic wasn’t consulted about that use. The company then pushed back, asking the Pentagon to agree to two specific restrictions: don’t use Claude for mass surveillance of American citizens, and don’t let Claude make final targeting decisions in military strikes without human review.

The Pentagon’s response: those guardrails could prevent the military from acting in a crisis. Defense Secretary Pete Hegseth has been blunt: “We will not employ AI models that won’t allow you to fight wars.” He gave Anthropic until Friday at 5pm to comply. If Anthropic refuses, the Pentagon is considering invoking the Defense Production Act to force compliance — or declaring Anthropic a “supply chain risk” to push it out of government entirely.

Why it matters: This is the first direct public clash between an AI company’s safety principles and a government’s demand for unrestricted control. Whatever happens by Friday sets a precedent — either companies can hold their ethical lines with government customers, or they can’t.

Source: CBS News | NPR

3. Burger King Is Listening to Its Employees — Via AI

Burger King launched an OpenAI-powered voice chatbot called “Patty” that lives inside the headsets employees wear while working. It’s not just a helpful assistant — Patty is also evaluating whether employees are being friendly enough with customers.

The chain trained its AI system to recognize specific words and phrases: “welcome to Burger King,” “please,” “thank you.” Managers can ask the AI how their location is scoring on friendliness. Burger King’s chief digital officer called it “a coaching tool” and says they’re also “iterating” on capturing the tone of conversations, not just the words. Beyond the friendliness monitoring, Patty answers employee questions (how many bacon strips on the Maple Bourbon Whopper?), alerts managers when kitchen equipment goes down, and automatically updates digital menus and kiosks within 15 minutes when an item goes out of stock. The full BK Assistant platform is set to roll out to all U.S. restaurants by end of 2026. Patty is currently piloting in 500 restaurants.

Burger King is still testing AI drive-thru ordering separately, in fewer than 100 locations — noting it’s “still a risky bet” and “not every guest is ready for this.”

Why it matters: When the AI monitoring your mood at work is the same AI monitoring your customers’ experience, the line between helpful tool and performance surveillance gets very thin very fast.

Source: The Verge

4. YouTube’s Algorithm Is Feeding AI Slop to Kids

After your kid finishes watching CoComelon, Bluey, or Ms. Rachel on YouTube, what does the algorithm recommend next? According to a New York Times investigation published today: more than 40% of Shorts automatically recommended after those channels “appeared to contain AI-generated visuals.”

These videos look like children’s content. They’re colorful, they feature recognizable characters and simple songs. But they’re AI-generated — often lowest-effort content produced at mass scale to capture ad revenue from kids’ watch time. YouTube doesn’t require these videos to be labeled as AI-generated. The platform places the entire burden of filtering this content on parents, not on itself.

Why it matters: Your kids are already in an algorithm-driven environment. The difference now is that a large chunk of what the algorithm serves them isn’t made by humans at all — and there’s no label telling anyone that. If you have young kids who use YouTube, this is a reason to check what they’re actually watching, not just what channel they started on.

Source: The Verge | New York Times

Quick Hits

• Anthropic acquired a computer-use AI startup called Vercept: Vercept built software for AI agents that can control computers — clicking, typing, navigating apps. The acquisition came after Meta reportedly poached one of Vercept’s founders, accelerating the deal. This fits Anthropic’s Claude Computer Use push directly. (TechCrunch)

• US rare earth shortages are deepening as Chinese suppliers halt production: China just restricted exports of several rare earth minerals critical for AI chips and advanced electronics. US suppliers are struggling to find alternatives at scale, and several have paused production. The AI chip supply chain has another vulnerability — this one geopolitical, not technical. (Perplexity Discover)

• Instagram now alerts parents when teens search for suicide or self-harm content: A new feature in Instagram rolls out alerts to connected parent accounts when teens search for those terms — with resources provided to both. It’s a reactive fix to years of criticism about the platform’s effect on teen mental health, and it marks a notable shift toward algorithmic accountability for younger users. (TechCrunch)

That’s it for today. Three of today’s four big stories are about the same thing: who controls AI when it’s already inside your life — your workplace headset, your kid’s screen, your country’s military systems. The question isn’t theoretical anymore.

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Good morning,

the entire stock market is holding its breath for Nvidia’s earnings tonight, Meta just wrote a $60 billion check to AMD, IBM released a report showing AI is now the hackers’ best friend, and Samsung is unveiling its AI-first Galaxy S26 phones literally today. Here’s what happened 👇

1. All Eyes on Nvidia Tonight — $230 Billion Swings on One Report

The world’s most valuable company reports earnings after the bell today, and Wall Street is visibly nervous. Analysts expect Nvidia to post $66.1 billion in revenue for the November–January quarter — a 68% jump from last year — and project first-quarter guidance around $72 billion. That would extend Nvidia’s streak of beating analyst estimates for the 14th quarter in a row.

But here’s the twist: beating the numbers isn’t enough anymore. After Nvidia’s last quarterly report blew past estimates and CEO Jensen Huang celebrated “off the charts” demand, the stock still fell 3% the next day. Options markets are pricing in a post-earnings swing of plus or minus 5% — which, given Nvidia’s $4.7 trillion market cap, translates to roughly a $230 billion move in either direction. That’s larger than most S&P 500 companies’ entire value. Key storylines to watch: the ramp of its new Blackwell chips, growing competition from AMD, and how much Chinese demand has been crimped by export restrictions.

Why it matters: Nvidia isn’t just a chipmaker — its stock accounts for ~7% of the S&P 500. Whatever happens after tonight’s call will likely move your retirement account, your portfolio, and the entire AI sector’s near-term mood.

Source: AP/WTOP | Reuters

2. Meta Just Signed a $60 Billion Deal With AMD — Not Nvidia

In one of the biggest AI infrastructure deals ever disclosed, Meta has committed to a five-year, roughly $60 billion agreement with AMD for custom MI450 AI accelerators and Helios AI servers. AMD’s stock jumped 8.77% on the news. The deal reportedly includes an option allowing Meta to acquire up to 10% of AMD if certain milestones are hit — essentially making Meta both AMD’s biggest customer and a potential major shareholder.

This is significant for a few reasons. First, it signals that the AI infrastructure buildout is alive and well — even amid all the recent fear and volatility. Second, it shows hyperscalers are actively diversifying away from Nvidia to get more supply flexibility and pricing leverage. And third, it gives AMD durable, multi-year revenue visibility that Wall Street has been demanding. AMD shares, which had fallen from $267 to the $190s over recent months, surged back toward $214 on the news.

Why it matters: When the world’s most-used social platform commits $60 billion to AI chips from a Nvidia competitor, the message is clear: the AI hardware race is a two-horse race now, and the spending is nowhere near slowing down.

Source: Meyka/Handelsblatt | MediaPost

3. IBM Report: Hackers Are Using AI to Break In Faster Than Ever

IBM’s 2026 X-Force Threat Intelligence Index dropped today with a stark finding: AI has handed attackers a speed advantage that defenders are struggling to match. Attacks that began by exploiting public-facing applications jumped 44% in 2025, largely because AI tools now help criminals identify vulnerabilities faster than human security teams can patch them. Ransomware groups surged 49% year-over-year as smaller operators flood the market, using leaked tooling and AI to automate what used to require skilled hackers.

The numbers on AI’s specific role are alarming: over 300,000 ChatGPT credentials were stolen by infostealer malware in 2025, creating new attack surfaces as enterprises adopt AI tools. Supply chain attacks nearly quadrupled since 2020. Manufacturing was the most-attacked industry for the fifth straight year. And North America became the most-attacked region globally for the first time in six years, jumping from 24% to 29% of all incidents.

Why it matters: AI is making it cheaper and faster to launch cyberattacks, and most companies are still operating on the assumption that basic perimeter defenses are enough. If your company has adopted AI tools without updating security policies, your new risk isn’t just a leaked prompt — it’s a stolen credential used to walk straight through the front door.

Source: IBM Newsroom

4. Samsung Launches Galaxy S26 Today — With Perplexity Built In

Samsung’s Galaxy Unpacked event is happening right now in San Francisco. The company is unveiling the Galaxy S26, S26+, S26 Ultra, and Galaxy Buds 4. The biggest AI story in the lineup: Samsung is integrating Perplexity’s AI search engine directly into Galaxy AI, letting users say “Hey Plex” to activate it as an alternative to Google. An updated Bixby assistant that is more conversational is also being shown off, and third-party AI agents will be accessible natively on the phone.

On the hardware side, all S26 models run Qualcomm’s Snapdragon 8 Elite Gen 5 chip, optimized for on-device AI processing. New AI photography features let users turn a daytime photo into night, restore missing parts of images, and merge multiple shots — without needing to export to a third-party app. The Galaxy S26 Ultra is expected to drop the S Pen digitizer layer to enable full Qi2 wireless charging compatibility, a notable tradeoff for power users. Samsung called this event the beginning of “a new phase in the era of AI as intelligence becomes truly personal and adaptive.”

Why it matters: Your next phone will have multiple AI assistants built in, competing for your attention — Google Gemini, Samsung Bixby, and now Perplexity. The AI assistant wars are moving from your laptop to your pocket, and the company that wins the default slot on your homescreen wins your daily habits.

Source: Engadget | Samsung Newsroom

5. Workday Fell 10% Because Anthropic Said AI Can Do HR

The AI disruption mood swings continued Tuesday when HR software firm Workday tumbled 10% after Anthropic’s new Claude tools explicitly listed HR tasks among their targets. Workday already gave investors a downbeat revenue forecast — but the AI threat angle made it land much harder. The irony: this is the same week that broader software stocks staged a modest relief rally, with markets focusing on partnership opportunities between AI labs and existing software companies rather than pure existential threat.

The split story captures exactly where markets are right now: some software companies are being re-rated upward as “AI partners,” while others — those whose core business is automating tasks AI can now do for a fraction of the cost — are being punished. Workday, which makes billions helping HR teams manage workflows that Claude now claims it can handle, landed in the second category.

Why it matters: Not all software companies will survive the AI wave in their current form. The ones building with AI are getting rewarded. The ones that haven’t made the pivot yet are watching their valuations get cut — sometimes on a single Anthropic blog post.

Source: Reuters

Quick Hits

• Trump told Big Tech to build their own power plants: During his State of the Union speech last night, Trump said AI data centers must generate their own electricity to avoid straining the national grid — a sign of growing political pressure around AI energy consumption. (Reuters)

• AWS launched AI that auto-reformats live sports for TikTok and Reels: Amazon Web Services unveiled “Elemental Inference” — a service that watches a live broadcast and automatically crops it into vertical video for social platforms within 6–10 seconds, no editor required. Fox Sports and NBCUniversal are already using it. (MediaPost)

• SK Hynix investing $15 billion in new chip facilities in South Korea: The memory chip giant — a key supplier of HBM chips for Nvidia — announced a massive domestic expansion as AI demand for high-bandwidth memory keeps accelerating. (Reuters)

• The $500B Stargate project was mostly vaporware: A new report by The Information found that OpenAI’s splashy Stargate venture — announced at the White House with Trump in January 2025 — never actually got built. OpenAI, Oracle, and SoftBank deadlocked over leadership and structure within weeks of the announcement, construction paused, and OpenAI lost its general contractor. OpenAI has since quietly pivoted, signing separate deals with Oracle ($30B/year) and CoreWeave ($22B) to get the compute it needs — and cut its 2030 infrastructure ambition from $1.4 trillion down to $600 billion. Elon Musk’s response: “Hardware is hard.” (Perplexity Discover)

That’s it for today. The AI story in 2026 has two speeds: the companies writing the checks are doing it faster than ever ($60 billion here, $15 billion there, build your own power plants), and the markets reacting to all of it are doing so in wild daily swings that can erase or create billions before lunch. We’re in the infrastructure-building phase of an arms race — the winners haven’t been declared, but the spending certainly has.

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The skills AI is taking aren’t the ones you should have been building anyway.

The Reality

Every few months, a new list circulates online. “The jobs AI will kill.” “The safe careers.” “What to learn before it’s too late.” And for a while, the consensus was: learn a trade. Go into a blue-collar field. Plumbing is safe.

Then Boston Dynamics robots started doing backflips. Hyundai bought them. And Po-Shen Loh, a Carnegie Mellon mathematician who’s spent years thinking about this, made a quiet observation: Hyundai didn’t buy those robots to make them dance.

Hyundai manufactures things at massive scale. And robot workers don’t take sick days, ask for raises, or make errors from fatigue. “That’s going to wreak havoc across the blue collar as well,” Loh said.

So if white-collar work is being taken by AI and blue-collar work is being taken by humanoid robots, what’s the honest answer to the question everyone’s actually afraid to ask: what’s left for people?

The Shift

Loh doesn’t give a comforting non-answer. He gives a surprising one.

The most valuable thing a person can offer in the AI era isn’t a specific skill. It’s trustworthiness. And more specifically, it’s the kind of trust that only comes from knowing someone actually cares about something bigger than themselves.

Here’s the frame he uses: as the world gets more automated and more interconnected, the potential for catastrophic failure goes up. He points to electric vehicles — essentially computers on wheels that receive over-the-air software updates. What happens if someone hacks that update? What if 10,000 cars suddenly accelerate at full speed at 5:30pm?

The more powerful our systems become, the more we need humans in them who can’t be easily compromised. Not just skilled humans. Trustworthy humans.

“You want to know that the people you put into these positions care about things that are bigger than themselves and aren’t easily bought off by someone bribing them for a million dollars.”

And there’s no AI for that. You can look into a robot’s eyes and have no idea if it will protect you. But you can look into a person’s eyes and — if you know what you’re looking for — you can tell.

The Old Way: Build a specific, valuable skill. Become the best at one thing.

The New Reality: Specific skills are being automated one by one. The person who gets hired — and rehired and trusted — is the one you can plug into anything because you know they’re going to work hard toward something meaningful.

When Loh hires, this is literally what he looks for: great intention + great learning capacity. “I don’t want to hire someone who has been trained to do one particular task because now I’ve discovered wait one or two more years I can use AI to do that task and it’ll be way cheaper.”

The combination that’s hard to find — and impossible to automate — is someone who genuinely wants to do good work and has the intellectual flexibility to keep learning.

What To Do Next

This reframe is uncomfortable because it’s not a checklist. You can’t take a course in trustworthiness. But you can develop it, and you can signal it, and both matter.

Start with purpose, not just performance. Ask yourself honestly: what are you working toward that’s bigger than your own advancement? The answer doesn’t have to be grand. It just has to be real. People can feel the difference between someone optimizing for themselves and someone who actually cares about the outcome.

Invest in flexibility over specialization. The world is changing too fast for narrow expertise to be a stable foundation. What you want is a track record of learning new things and adapting well. Every time you pick up a new skill, work in a new domain, or solve an unfamiliar problem, you’re building the thing that actually makes you employable long-term.

Let your character compound. Reputation for being trustworthy builds slowly and pays off exponentially. The people who are pulled out of difficult circumstances, who get opportunities others don’t, who build careers that survive technological disruption — they’re not usually the ones with the best credentials. They’re the ones everyone already knows will show up, work hard, and actually care.

The One Thing to Remember

AI is taking tasks. What it can’t take is the character of someone who genuinely wants to do good — and can be trusted with the things that matter.

This insight comes from “AI Will Create New Wealth, But Not Where You Think” featuring Po-Shen Loh, Carnegie Mellon University. The AI Shift curates wisdom from AI leaders for busy professionals navigating the AI era. What do you think — is trustworthiness something you can develop, or is it something you already have or don’t?