• Consumer AI is still early, but spending is accelerating

• 🧰 AI Tools - AI Top 20 AI Tools

• Are AI data centers driving a fossil fuel comeback?

• 📚Learning Corner - AI Energy Infrastructure

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📰 AI News and Trends

• DeepSeek’s New AI Model Will Be a Victory for Huawei

• OpenAI is acquiring TBPN, a business talk show that’s popular among Silicon Valley elites, as it continues to battle its negative public image.

• ElevenLabs releases a new AI-powered music-generation app

• LLMs Will Protect Each Other if Threatened

• The Trump administration is appealing a federal judge's order temporarily blocking the Pentagon's ban on Anthropic, per a filing yesterday.

• Alibaba Releases Third Closed-Source AI Model in Three Days, Signals Profit-First Strategy

Other Tech News

• Students Are Now Renting Smart Glasses to Cheat on Exams

• The Artemis II mission has started its 10-day journey around the moon

• Scientists Engineered a Plant to Produce 5 Different Psychedelics at Once

• Amazon is buying Globalstar to compete with SpaceX. This move will bolster Amazon’s Leo satellite internet service.

• Meanwhile, SpaceX has held talks with a Saudi fund for a possible $5 billion investment in an IPO

• Indie Pass, a new subscription service just for indie games, launches on April 13th with more than 70 games for $6.99 per month.

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Consumer AI is still early, but spending is accelerating

Consumer AI adoption is high, but monetization is just starting. Only 3% of households pay for AI services, despite ~40% YoY growth in paying users since Feb 2024.

Spending depth is improving:

• 40%+ of paying users now spend >$20/month.

• Higher-spend tiers are gaining share, signaling a stronger willingness to pay.

Demographics are driving the shift:

• Gen Z and younger Millennials increased AI spend by ~54% YoY.

• Even then, only ~5% of Gen Z households pay for AI.

Consumer AI is still under-monetized with massive headroom. Usage is widespread, but conversion is low, creating a long runway for subscription expansion and premium tiers.

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🧰 AI Tools of The Day

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

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

Are AI data centers driving a fossil fuel comeback?

A Google-backed data center in Texas highlights a growing shift taking place where AI infrastructure is increasingly powered by natural gas.

The data:

• Planned gas plant emissions: 4.5M tons CO₂/year (≈ 970K cars)

• >10x higher than a typical gas plant; exceeds average coal plant emissions

• Site includes 900 MW gas + 265 MW wind

Why it’s happening:

• AI demand is outpacing grid capacity

• Data centers are turning to “behind-the-meter” power (on-site energy) for speed

• Nearly 100 GW of gas capacity is now in development in the U.S. just for data centers

Industry trend:

• Projects like OpenAI–Oracle could emit 14M tons/year

• Microsoft signed deals for 2.5 GW of gas-powered capacity

• Big Tech emissions have risen ~50% in 5 years, despite climate commitments

AI growth is now directly tied to energy infrastructure. Natural gas is emerging as a short-term bottleneck solution, creating opportunities in energy, but also long-term regulatory and ESG risk.

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📚Learning Corner

AI Energy Infrastructure - Research from Global Energy Monitor (tracks the 100 GW gas buildout). The next AI winners need to build models and control distribution (consumer apps) or compute + energy (infrastructure layer).

• California Defies New AI Safety Regulations

• 🧰 AI Tools - 20 Essential AI Tools for Small Business

• The Tipping Point is that 46.6% of Businesses Now Pay for AI

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📰 AI News and Trends

• Yahoo has launched Scout, an AI-powered answer engine now available to its 250 million U.S. users, aiming to simplify online search and deliver more personal results tied to each person’s interests.

• ChatGPT app store falters six months after launch.

• 15% of Americans say they’d be willing to work for an AI boss, according to a new poll. Would you trade your manager for a chatbot?

• Mistral, the French LLM company, raises $830M to build Nvidia-powered AI centers in Europe.

Other Tech News

• Speaking to students at Harvard University yesterday, Fed Chair Jerome Powell said that although energy prices are rising due to the war in Iran

• Nvidia’s PE sinks to a seven-year low as war and AI angst weigh

• Bill Ackman says it’s one of the best times in a long time to buy quality stocks

• Quantum technology firms race to market as the industry sees ‘inflection point.’

• Pete Hegseth’s broker attempted to make defense investments before the Iran war

• New rule could fast-track SpaceX IPO for Nasdaq index inclusion

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California Defies New AI Safety Regulations

California Governor Gavin Newsom has signed a major executive order establishing strict new standards for artificial intelligence companies, setting up a direct legal and political confrontation with the Trump administration’s deregulation agenda.

The Key Details:

• State-Level Guardrails: Companies seeking contracts with the State of California must now prove their models prevent the distribution of child sexual abuse material (CSAM) and violent pornography.

• Anti-Bias Protections: AI firms will be required to demonstrate how they avoid “harmful bias” and prevent the use of their tech for unlawful discrimination, detention, or surveillance.

• Content Transparency: The order directs the state to develop best practices for “watermarking” AI-generated or manipulated images and videos to combat misinformation.

• Federal Friction: This move directly defies President Trump’s December executive order, which discouraged “cumbersome” state rules in favor of a national policy focused on rapid innovation. The White House has already tasked the DOJ with challenging state-level AI regulations.

Why It Matters: As the “birthplace of innovation,” California is using its massive purchasing power to force ethical standards on the industry, even as the federal government threatens litigation to keep the sector deregulated.

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🧰 AI Tools of The Day

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

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

The Tipping Point is that 46.6% of Businesses Now Pay for AI

In December 2025, “46.6% of U.S. businesses paid for AI services,” a 1.6 percentage point jump from November that marked the largest month-over-month spike since April 2025. Unlike survey data, this figure from Ramp's AI Index (tracking actual transactions across 50,000+ companies) captures real money moving to vendors, not just experimental pilots, but committed infrastructure spending.

April 2025 previously held the record for monthly growth, driven by early "AI agent" hype and viral image generation tools. That December 2025 matched this velocity after months of Gartner's "trough of disillusionment" suggests businesses moved past hype into pragmatic, necessity-driven adoption.

The Shifting Vendor Landscape

OpenAI drove December's growth, reversing a two-month decline to hit 36.8% adoption. But the strategic story is Anthropic: growing from 16.7% to 19.5% by February 2026, winning 70% of new head-to-head deals against OpenAI, a complete reversal from 2025. Technical teams increasingly prefer Claude's coding capabilities, while OpenAI benefits from consumer familiarity with ChatGPT. Google lags at 4.7%, though this excludes Gemini bundled in Workspace.

The reality is that companies aren't expanding budgets, but cutting low-ROI tools and contractors to fund AI. The 46.6% represents hard choices. Ramp's data excludes free usage, personal accounts, and bundled tools like Microsoft Copilot. Including these, actual AI usage likely exceeds 70-80%. However, paid contracts represent institutional commitment; free usage can be banned or abandoned when costs arise.

Crossing the Threshold

By February 2026, paid adoption reached 7.6%. In tech and finance (where adoption exceeds 60%), AI is now baseline infrastructure. The remaining half of businesses aren't just missing a productivity tool; they're accumulating a competitive disadvantage. The question is how quickly laggards can close the gap with the 46.6% already paying for it.

• Quantum Computing Threats, Hacking Crypto, and the post-quantum transition.

  • What is Quantum Computing?

  • Real-World Benefits and Applications

  • The Cryptographic Threat: Q-Day

  • Can quantum computing hack a Bitcoin Wallet?

  • How You Can Start Using Quantum Computing Today

• 🧰 AI Tools - 20 Essential AI Tools for Small Business

• Suno, the AI Music Generator, has Competition.

• 📚Learning Corner - Google Quantum AI — Cirq Tutorials (Free)

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📰 AI News and Trends

• OpenAI Pulls the Plug on Sora, Its AI Video Generator, Less than six months after its splashy public launch.

• Arm Launches Its First-Ever In-House Chip, the AGI CPU, after 35 years of exclusively licensing chip designs to others. Meta and OpenAI are 1st clients.

• OpenAI has quietly put its plans for an adult AI chatbot on hold after pushback from both staff and investors.

• Google's TurboQuant is a compression algorithm that reduces the memory footprint of large language models while also boosting speed and maintaining accuracy.

• Major AI conference flooded with peer reviews written fully by AI

Other Tech News

• Meta Ordered to Pay $375 Million in Landmark Child Safety Verdict…

• Additionally, Meta and YouTube were Found Liable in the landmark social media addiction trial, ruling their negligence and failure to warn users about the dangers of their addictive platform designs, awarding $6M in damages, $4.2M from Meta, and $1.8M from YouTube.

• Uber Partners with Pony AI and Verne to Launch Europe's First Commercial Robotaxi Service

• Meta Lays Off ~700 Employees, Including Reality Labs Staff

• SpaceX Aims to File for IPO as Soon as This Week

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Quantum Computing Threats, Hacking Crypto, and the post-quantum transition.

Quantum computing represents a paradigm shift in technology, promising to solve complex problems that are currently intractable for classical supercomputers. However, this immense computational power also poses an existential threat to the cryptographic foundations of the modern internet. Recently, Google accelerated its timeline to defend against this threat, setting a firm 2029 deadline for its post-quantum cryptography (PQC) migration.

What is Quantum Computing?

At its core, quantum computing harnesses the principles of quantum mechanics to process information in fundamentally new ways. While classical computers rely on bits that represent either a 0 or a 1, quantum computers use quantum bits, or qubits. Qubits possess unique properties that enable exponential computational power

• Superposition: A qubit can exist in a state of 0, 1, or a weighted combination of both simultaneously. This allows quantum computers to process vast amounts of possibilities concurrently, rather than sequentially like classical machines.

• Entanglement: Qubits can become intrinsically linked, meaning the state of one qubit instantly influences the state of another, regardless of distance. This allows quantum systems to handle complex correlations and multidimensional computational spaces.

• Interference: Quantum algorithms use interference to amplify the probability of correct answers and cancel out incorrect ones, guiding the system toward the optimal solution.

These properties allow quantum computers to excel at modeling complex physical systems and identifying patterns in massive datasets, tasks where classical brute-force methods fail.

Real-World Benefits and Applications

The potential applications of quantum computing span multiple critical industries, offering breakthroughs that could reshape society:

• Pharmaceuticals and Healthcare: Quantum computers can simulate molecular behavior and biochemical reactions with unprecedented accuracy. This capability is expected to drastically accelerate drug discovery, allowing researchers to design complex molecules and personalized treatments without relying solely on expensive, time-consuming physical synthesis.

• Financial Modeling: In the financial sector, quantum algorithms are being explored for portfolio optimization, risk analysis, and market modeling. By evaluating countless variables simultaneously, quantum systems can provide more robust financial strategies and stabilize complex economic models.

• Climate Science and Energy: Quantum computing holds the key to optimizing energy grids and developing new materials for carbon capture. It can also accelerate fluid dynamics calculations at the core of climate models, providing deeper insights into climate change mitigation.

The Cryptographic Threat: Q-Day

Quantum computing is rapidly turning into a real cybersecurity threat, not just a theoretical one. The moment known as “Q-Day” could break today’s encryption systems (like RSA and TLS), exposing everything from financial transactions to government secrets. More concerning is that this risk is already active through “harvest now, decrypt later” strategies, in which attackers collect encrypted data today with the intent to unlock it later, putting any data with long-term value (10–30+ years) at immediate risk. Google is targeting a full transition to post-quantum cryptography by 2029, while standards bodies like NIST and organizations including Apple, Signal, and Cloudflare are already deploying quantum-resistant systems. With mandates like the NSA’s 2033 deadline, the transition window is shrinking fast, and organizations that delay could face large-scale, retroactive data exposure.

Can quantum computing hack a Bitcoin Wallet?

Don't get too happy yet. Shor's Algorithm can theoretically break the elliptic curve cryptography protecting Bitcoin wallets, but "theoretically" is doing a lot of heavy lifting there.

To crack a real 256-bit private key, you need roughly 1 million stable, error-corrected logical qubits, and each of those requires about 1,000 noisy physical qubits to error-correct just one reliable one, meaning you need somewhere in the ballpark of 1 billion physical qubits, fault-tolerant and coherent. Google's best chip today, Willow, has 105. That's not "almost there," that's the difference between a bicycle and a SpaceX rocket, and they both have wheels.

How You Can Start Using Quantum Computing Today

You don’t need a billion-dollar lab to start experimenting with quantum computing. Anyone with an internet connection can access real quantum hardware and simulators right now:

1. IBM Quantum Platform & Qiskit: IBM offers free cloud access to real quantum processors. You can use Qiskit, an open-source Python framework, to build and run your first quantum circuits. IBM also provides excellent free courses for beginners on its platform.

2. Amazon Braket: AWS offers Amazon Braket, a fully managed service that lets you test algorithms on quantum simulators and access real quantum hardware from companies like IonQ and Rigetti.

3. Google Cirq: Google provides Cirq, a Python software library for writing, manipulating, and optimizing quantum circuits, which you can run on simulators to learn the ropes.

Start by learning basic Python, brush up on linear algebra, and run a simple “Hello World” quantum circuit using IBM’s free Qiskit tutorials!

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📚Learning Corner

Google Quantum AI — Cirq Tutorials (Free) - From the basics of gates and operations, builds up to full circuit simulation, and includes real experiments. This is the best resource if your goal is to build with Cirq specifically.

Suno, the AI Music Generator, has Competition.

Google's newly launched Lyria 3 Pro model significantly advances AI music generation by enabling users to create structured, three-minute tracks with precise creative control, directly challenging established competitors like Suno in the race for high-quality, full-song synthesis.

This rapid evolution of AI music tools is profoundly disrupting the music industry by flooding streaming platforms with synthetic content, which has sparked concerns over copyright infringement, royalty dilution, and artist impersonation. In response to this influx, platforms are implementing protective measures; for instance, Spotify recently introduced tools allowing artists to review and approve releases to combat misattributed "AI slop," while Google employs SynthID watermarking to ensure transparency for tracks generated on its platforms, including YouTube. Ultimately, while these technologies offer unprecedented creative opportunities, they are forcing the industry to rapidly adapt its monetization and attribution frameworks to protect human artists.

🧰 AI Tools of The Day

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

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

The rise of demands for massive computing power is growing, and industry leaders are taking notice. The team behind the TeraFab Megafactory believes existing infrastructure won’t suffice, prompting them to build their own high-performance compute systems. We dive into what the Megafactory is, its goals, and whether they’re achievable. Plus, we provide resources to help you get started with OpenClaw and other powerful AI tools.

Let’s get into it. Stay curious.

• Elon Musk’s $25B TeraFab Megafactory Dream or Reality

  • The TeraFab Vision. Scale and Scope

  • Key Technical Targets

  • Two-Pronged Chip Strategy

  • The three drivers behind the TeraFab

  • Industry Skepticism and Feasibility Concerns

  • The biggest barriers

• 🧰 AI Tools - AI Labor Market Dashboard

• OpenClaw Sets the New Standard for Autonomous AI Agents

• 📚Learning Corner - OpenClaw Docs

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📰 AI News and Trends

• OpenAI plans to expand its workforce from 4,000 to 8,000 by the end of the year.

• Trump Bans Federal Agencies from Using Anthropic’s AI, ending a $200 million Pentagon contract within six months, but the tech community is with Anthropic.

• U.S. Advisory Body Warns China’s Open-Source AI Dominance Threatens American Lead, with roughly 80% of U.S. AI startups now reportedly using Chinese open-source models.

• The Trump administration released its long-awaited National Policy Framework for AI, urging Congress to prioritize child protection, anti-fraud measures, and innovation while pushing to preempt a fragmented patchwork of state-level AI laws.

• OpenAI Plans Launch of Desktop ‘Superapp’ to Refocus and to Simplify User Experience

Other Tech News

• Defense tech startup Anduril Breaks into the Pentagon’s Big Leagues with $20B Army Contract, signaling a seismic shift in how the Pentagon procures technology, favoring VC-backed software-first platforms over traditional defense primes.

• Kalshi Cinches $22 Billion Valuation in Ongoing Round.

• Tesla Finally Has Its First Semi-Truck, and It’s Already a Hit With Truckers.

• Amazon plans smartphone comeback more than a decade after Fire Phone flop.

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Elon Musk’s $25B TeraFab Megafactory Dream or Reality

On March 21, 2026, Elon Musk unveiled TeraFab, a joint venture between Tesla, SpaceX, and xAI to build what he claims will be the “largest chip manufacturing facility ever.” Located in Austin, Texas, the $25 billion project aims to produce a staggering 1 terawatt of computing power annually. The facility is designed to manufacture chips for both terrestrial applications (Tesla’s AVs and Optimus robots) and space-based orbital data centers.

While the announcement aligns with Musk’s grand vision of vertical integration and securing supply chains for his AI empire, it has been met with profound skepticism from semiconductor industry experts and financial analysts. The sheer technical complexity of leading-edge chip fabrication, combined with Tesla’s lack of manufacturing experience in this domain, makes TeraFab a huge gamble.

Musk’s decision to enter the notoriously difficult semiconductor manufacturing business is driven by a combination of supply chain anxieties, massive compute requirements, and financial engineering.

The TeraFab Vision. Scale and Scope

Musk’s presentation outlined a facility that would consolidate every stage of semiconductor production under one roof: chip design, lithography, fabrication, memory production, advanced packaging, and testing.

Key Technical Targets

• Initial Output: 100k wafer starts/month - A typical large-scale modern fab produces ~50,000 wafers/month.

• Full Capacity: 1M wafer starts/month - Roughly 70% of TSMC’s entire current global output.

• Compute Output: 1 Terawatt/year - 100-200 GW for Earth (Tesla/xAI); 800+ GW for space (SpaceX).

• Estimated Cost: $20B - $25B - TSMC spent $165B for six fabs in Arizona; a single 50k-wafer 2nm fab costs ~$28B.

Two-Pronged Chip Strategy

TeraFab is slated to produce two distinct categories of chips:

1. Terrestrial Inference Chips: Designed for Tesla’s “Full Self-Driving” (FSD) software, the Cybercab robotaxi program, and the Optimus humanoid robot line.

2. Orbital AI Chips (D3): Custom-designed for space. Musk stated that 80% of TeraFab’s compute output would be directed toward space-based orbital AI satellites. He argued that solar irradiance in space is 5x greater than on Earth, and the vacuum allows for efficient heat rejection, potentially making orbital AI compute cheaper than terrestrial alternatives within 2-3 years.

The three drivers behind TeraFab:

1. Compute shortage is the core driver

• Elon Musk claims current fabs meet only ~2% of future demand

• xAI already runs hundreds of thousands of GPUs (largely from Nvidia)

• AI scaling = exponential compute needs → supply can’t keep up

2. Full vertical integration strategy

• Today: dependent on Nvidia + foundries like TSMC and Samsung

• Goal: control design → manufacturing → deployment across cars, robots, and satellites

• Outcome: reduced dependency + tighter optimization

3. IPO narrative and timing

• Tesla facing 2 years of declining sales (2025)

• SpaceX + xAI tied to a potential $1.75T IPO valuation

• TeraFab reframes the story: from auto slowdown → AI infrastructure powerhouse

Industry Skepticism and Feasibility Concerns

• Building a leading-edge fab is one of the hardest engineering challenges globally

• Tesla has chip design experience, but zero manufacturing experience

• Experts like Jensen Huang emphasize that it requires deep expertise across science, engineering, and precision processes

At the 2nm level, complexity explodes:

• Requires atomic-scale control (lithography, etching, yield optimization)

• Incumbents like TSMC, Samsung, and Intel have spent decades + $100B+ building this capability

The biggest barriers to Tesla’s “TeraFab” are capital, supply chain, and talent.

• Cost gap: Morgan Stanley estimates ~$45B for a top-tier fab vs. ~$25B projected by Elon Musk

• Equipment bottleneck: Access to EUV machines from ASML can take years for new entrants

• Talent shortage: Skilled fab engineers are scarce; leaders like TSMC already relocate global teams to operate fabs

TeraFab could reshape the $600B+ semiconductor industry, but execution risk is extreme. Even analysts like Stacy Rasgon argue it may be harder than missions to Mars, highlighting how unrealistic near-term success may be.

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📚Learning Corner

📘 OpenClaw Docs – core reference for setup, integrations, skills, and how the agent engine works.

OpenClaw Sets the New Standard for Autonomous AI Agents

OpenClaw has rapidly reshaped the AI landscape, creating a wave of open-source, persistent AI agents that run on local or cloud hardware. Its viral adoption forced major players to adapt offerings and strategies to keep up.

Key points:

• OpenClaw agents run 24/7, integrate with tools and messaging apps, and maintain long-term memory, enabling automated workflows.

• NVIDIA, AWS, Anthropic, and OpenAI are responding with their own agent-focused platforms (NemoClaw, Lightsail OpenClaw instances, Claude Code Channels, Frontier).

• Business impact includes new revenue streams, shifts in pricing models, and the need for enterprises to adopt secure agent deployment, MLOps practices, and governance frameworks.

• Risks involve data security, regulatory scrutiny, and ethical concerns around autonomous AI actions.

  

OpenClaw essentially turned AI agents into a new standard for productivity, automation, and enterprise adoption.

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🧰 AI Tools of The Day

I built a local AI Labor Market Dashboard using Python and Streamlit, based on Anthropic’s March 2026 Economic Index research on which jobs are actually being disrupted by AI (not just theoretically, but in practice). It lets you explore:

• Which occupations have the highest real-world AI exposure

• Where hiring is collapsing for 22–25 year olds post-ChatGPT

• How 10-year BLS job growth projections correlate with AI risk

• A coverage gap chart showing how far actual adoption lags behind AI’s theoretical reach

The data is seeded from the paper, but the framework is wired to accept live BLS API data.

Check it out.

Let’s get into it. Stay curious.

• AI Study of 81,000 People Reveals a Paradox We All Feel

• 🧰 AI Tools - AI Labor Market Dashboard

• AI Is Cutting 200,000 Jobs as Banks and Big Firms Go All-In on Automation

• 📚Learning Corner - Core Policy + Analysis

• The AI Regulation War Has Begun in the U.S.

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📰 AI News and Trends

• Chinese electronics and car manufacturer Xiaomi surprised the global AI community today with the release of MiMo-V2-Pro, a new 1-trillion-parameter foundation model with benchmarks approaching those of U.S. AI giants OpenAI and Anthropic, but at around a seventh or sixth the cost.

• Val Kilmer Resurrected by AI to Star in ‘As Deep as the Grave’ Movie.

• Mistral bets on ‘build-your-own AI’ as it takes on OpenAI, Anthropic in the enterprise.

• Sony Music says it has requested the removal of more than 135,000 songs by fraudsters impersonating its artists on streaming services.

• Meta confirmed a critical security incident after an internal rogue AI agent’s actions led to the exposure of sensitive data to employees without authorization

Other Tech News

• Robinhood Social, its new social network, is now rolling out in beta to a select group of users, who can trade right from their feed.

• The FBI is buying up information that can be used to track people’s movement and location history, Director Kash Patel said during a Senate hearing Wednesday.

• Google has been rebuilding its relationship with the Defense Department and is poised to benefit as it sidesteps competitors’ controversies.

• Meta will pay Instagram, TikTok, and YouTube creators with big followings to post on Facebook

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AI Study of 81,000 People Reveals a Paradox We All Feel

Anthropic just published findings from the largest qualitative AI study ever conducted, 81,000 interviews across 159 countries. The results are striking, showing that most people don’t just want AI to make them more productive; they want it to give them their lives back. More time with family, less mental load, financial freedom, and personal growth.

Nearly 1 in 5 respondents named professional excellence as their top desire, but dig deeper, and the real ask is simpler: help me be more human. At the same time, people are genuinely conflicted. The #1 fear isn’t robots taking over; it’s that:

• AI will hallucinate and lead them astray (27%),

• followed closely by job displacement (22%)

• and the quiet erosion of their own ability to think (16%).

What’s most fascinating is that optimism and anxiety don’t live in separate camps; they live in the same person. The same people excited about AI as an emotional support are three times more likely to fear becoming dependent on it. The same developers celebrating time savings worry that the treadmill is just moving faster. The conflicts are between freedom and dependency, progress and loss, hope and fear.

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📚Learning Corner

🧠 Core Policy + Analysis

• MIT Technology Review
  Deep reporting on AI regulation, including the original article and ongoing policy analysis.

• Brookings Institution
  Research on AI governance, economic impact, and global regulatory strategies.

• Center for AI Safety
  Focused on risks, alignment, and long-term AI policy frameworks.

The AI Regulation War Has Begun in the U.S.

The U.S. is heading into a major AI regulation battle in 2026, as the White House pushes a light-touch national policy while states move ahead with their own rules.

After Congress failed to pass federal legislation, a sweeping executive order aims to block state-level AI laws, potentially triggering lawsuits and cutting federal funding to non-compliant states. Despite this, states like California and New York are advancing safety laws around AI transparency, bias, and catastrophic risks, reflecting rising public concern over jobs, mental health, and data center impact.

Over 1,000 AI bills were introduced in 2025, with 100+ laws passed across 40 states, signaling strong momentum. Meanwhile, billions in political funding from tech leaders and AI safety groups are turning regulation into a high-stakes political fight. Fragmented state action vs federal control could shape how AI evolves, not just in the U.S., but globally.

Leave a comment

🧰 AI Tools of The Day

I built a local AI Labor Market Dashboard using Python and Streamlit, based on Anthropic’s March 2026 Economic Index research on which jobs are actually being disrupted by AI (not just theoretically, but in practice). It lets you explore:

• Which occupations have the highest real-world AI exposure

• Where hiring is collapsing for 22–25 year olds post-ChatGPT

• How 10-year BLS job growth projections correlate with AI risk

• A coverage gap chart showing how far actual adoption lags behind AI’s theoretical reach

The data is seeded from the paper, but the framework is wired to accept live BLS API data.

Check it out.

AI Is Cutting 200,000 Jobs as Banks and Big Firms Go All-In on Automation

AI-driven restructuring is accelerating across finance and professional services.

• Nordea plans to cut up to 1,500 jobs (5% of staff) by 2027, taking a €190M restructuring hit to unlock at least €150M in annual savings by 2028.

• HSBC is evaluating deeper cuts of up to 20,000 roles (~10% of workforce) over the next 3–5 years, targeting back- and middle-office functions, while already pursuing $1.5B in cost savings.

• Industry-wide, banks could eliminate 200,000 jobs as AI automates core tasks.

Meanwhile, PwC is going “AI-first,” replacing billable-hour models with automated, subscription-based services and signaling that employees who resist AI risk being pushed out. The trend is clear for everyone: AI is no longer experimental; it’s a cost-cutting engine reshaping global workforces at scale.

Meanwhile in Washington, Sen. Elizabeth Warren is questioning how safe classified government data is when shared with AI companies, raising concerns about security risks as the Pentagon deepens partnerships with frontier AI developers.

Let’s get into it. Stay curious.

• 🧰 AI Tools - Top 2026 AI Tools as per Zapier

• The Race to Build “World Models” Is Splitting Into Five Approaches

• 📚Learning Corner - All OpenAI Live courses and webinars.

• Wall Street Moves Into Prediction Markets

• Warren Questions Pentagon Deal With xAI

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📰 AI News and Trends

• NVIDIA GTC 2026 kicks off today with Jensen Huang's keynote, 39,000 attendees, and $600B AI infrastructure spend in focus.

• Morgan Stanley warns a transformative AI breakthrough is imminent in H1 2026, with GPT-5.4 already scoring at a human-expert level.

• OpenClaw (acquired by OpenAI) becomes GitHub’s #1 most-starred project, surpassing React & Linux, agentic AI goes mainstream

• Tech companies are spending an unprecedented $700 billion this year on AI data centers. Jensen Huang: “We’re not anywhere near the peak.”

• ByteDance pauses global launch of Seedance 2.0 AI video generator.

• Pet dad turns to ChatGPT to create a cancer vaccine for his dying dog.

• OpenAI’s Bid to Allow X-Rated Talk Is Freaking Out Its Own Advisers.

Other Tech News

• Meta is reportedly considering layoffs affecting up to 20% of its workforce.

• The Trump administration is set to receive roughly $10 billion from investors in the recently completed deal to take control of TikTok’s U.S. business, according to the Wall Street Journal.

• Anduril wins up to $20B, 10-year US Army contract.

• Facebook makes it easier for creators to report impersonators.

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The Race to Build “World Models” Is Splitting Into Five Approaches

AI startups are pouring huge capital into “world models” systems that help AI understand and simulate reality. In the past few weeks alone, AMI Labs raised $1.03B, and World Labs raised $1B, but the term “world model” now covers several very different technical strategies. Zhuokai Zhao shared on X

1. JEPA latent prediction
Led by Yann LeCun and AMI Labs. Instead of predicting pixels, JEPA models predict abstract representations of the world, making them far more efficient. One model trained on 1M+ hours of video and needed just 62 hours of robot data to perform zero-shot planning.

2. Spatial intelligence (3D worlds)
Fei-Fei Li is betting that AI needs explicit 3D understanding of environments. World Labs’ system generates persistent 3D scenes that can be explored and edited like real environments.

3. Learned simulation
Companies like Google DeepMind are building models that simulate worlds directly. Systems such as Genie and Dreamer allow AI agents to learn by interacting with simulated environments, even achieving complex goals like finding diamonds in Minecraft without human data.

4. Physical AI infrastructure
NVIDIA is building the platform layer. Its Cosmos stack trains world models on 20M hours of video (9,000T tokens) and provides tools for robotics, autonomous systems, and physical AI.

5. Active inference
VERSES AI uses a neuroscience-inspired approach where agents minimize “surprise” in the world using probabilistic reasoning instead of deep learning.
World models may become the next major AI architecture after large language models, powering robotics, simulation, and autonomous systems, but the industry is still experimenting with multiple competing approaches.

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📚Learning Corner

• All OpenAI Live courses and webinars.

Wall Street Moves Into Prediction Markets

Prediction markets, platforms where traders bet on future events, are gaining traction in traditional finance.

Trading platform Tradeweb recently partnered with Kalshi and saw overwhelming interest from institutional clients. Kalshi says billions in trading volume already come from investors trading contracts tied to climate, tech, and macro events.

Major finance players are entering the space:

• Intercontinental Exchange invested $2B in Polymarket

• Jump Trading and Susquehanna International Group are providing liquidity

• Nasdaq is exploring event-based trading contracts

Prediction markets are being used to forecast elections, interest rates, wars, and economic indicators, turning collective market bets into real-time forecasting tools.

Investor takeaway: If regulators approve new products like prediction-market ETFs, the sector could evolve into a new derivatives asset class on Wall Street.

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🧰 AI Tools of The Day

• The best AI productivity tools in 2026, as per Zapier.

Warren Questions Pentagon Deal With xAI

Sen. Elizabeth Warren is demanding answers from Defense Secretary Pete Hegseth about reports that the Pentagon may allow xAI to operate on classified military networks.

Warren warned that xAI’s chatbot Grok could pose cybersecurity and safety risks, including leaking classified information, producing inaccurate responses, or lacking proper safeguards for military use. She requested documentation showing how the Defense Department evaluated Grok’s security and data-handling practices.

The concern comes after the Pentagon awarded xAI a contract worth up to $200 million to develop AI systems and reportedly reached a deal in February to expand its use on secure networks. Grok has faced scrutiny from regulators and governments worldwide over weak guardrails and controversial outputs, including manipulated images and antisemitic responses.
Doge may have already given xAI and all its vertical companies unprecedented access to sensitive government and citizen data. And now that Anthropic shows us that AI companies can be ethical, the governments are rushing to find less ethical companies to replace Claude AI to adopt frontier AI. Policymakers are increasingly questioning whether emerging models are secure and reliable enough for national security systems.

Let’s get into it. Stay curious.

• 🦞 OpenClaw Changed AI Forever. Now Everyone Wants a Piece.

  • Did OpenAI Pay $1 Billion For It?

  • Now Nvidia Wants In

• 🧰 AI Tools - Top ~100 AI Tools of the Moment

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📰 AI News and Trends

• Replit launched Agent 4, introducing an infinite design canvas and the ability to run parallel AI agents to build backends and frontends simultaneously.

• AI Coding Startup Cursor in Talks for About $50 Billion Valuation.

• Perplexity’s Personal Computer lets AI agents access your Mac mini’s files.

• Nvidia to Invest $2 Billion in Nebius to Expand AI Cloud Infrastructure.

• OpenAI is partnering with North America's Building Trades Unions to boost the construction of AI data centers.

Other Tech News

• TikTok has struck a deal with Apple Music that will allow full songs to be streamed on the social platform.

• Xbox’s next console, codenamed “Project Helix,” is expected to do an alpha release sometime next year.

• Apple’s Foldable iPhone to Feature iPad-Like Interface When Opened

• The Justice Department is investigating Binance for allowing illegal transfers to customers in Iran to evade global sanctions.

• Uber announced that it has struck a deal with Zoox to rev up its robotaxi service footprint.

• FCC Chair Blasts Amazon Over Petition Against SpaceX Data Center Plan

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🦞 OpenClaw Changed AI Forever. Now Everyone Wants a Piece.

If you missed the OpenClaw story, here’s the short version: a little-known open source project quietly dropped in January and broke the internet, at least the part of the internet that builds software for a living.

OpenClaw (previously called Moltbot, then Clawdbot) did something deceptively simple, which is to let anyone run “always-on” AI agents from their personal machine, using any underlying AI model, working on tasks for hours or days without human supervision. Until then, AI agents were mostly demos. OpenClaw made them real.

The reaction was swift. Mac Minis with the right specs sold out. Developer communities lit up. And Nvidia CEO Jensen Huang went on CNBC and called it “the most important software release probably ever.” Not a sentence Jensen says casually.

OpenAI moved fast, too, hiring OpenClaw creator Peter Steinberger to lead their next generation of personal agents, while keeping the project under an independent foundation.

Did OpenAI Pay $1 Billion For It?

Here’s where it gets interesting. Unconfirmed reports suggest OpenAI pursued an acquisition of OpenClaw at a valuation approaching $1 billion, which would make it the first AI agent platform to hit that milestone. Neither OpenAI nor the OpenClaw Foundation has confirmed or denied it.

We’re not saying it happened. We’re saying the logic is sound. As underlying AI models become commoditized, whoever controls the agent layer controls the real value, and OpenAI clearly understood that.

Now Nvidia Wants In

This week, Nvidia came out with the news that it is building its own open source agent platform called NemoClaw, pitching it to corporate partners including Salesforce, Cisco, Google, Adobe, and CrowdStrike ahead of its annual developer conference.

The strategy is sharp. Where OpenClaw moved fast and broke things (including a few corporate security policies), NemoClaw is being built with privacy and security as the headline feature, exactly what enterprises need to actually deploy agents at scale.

It also won’t require Nvidia GPUs. Smart move. More adoption means more AI workloads, and Nvidia’s chips power the vast majority of the underlying models anyway. The hardware sell is downstream of the platform win.

OpenClaw proved the demand. NemoClaw, and the wave of competitors that will inevitably follow, are racing to build the version enterprises can actually trust.

The next phase of AI seems not to be about a smarter model, but about who builds the agent infrastructure that’s safe, reliable, and secure enough to run your business on.

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🧰 AI Tools of The Day

Top ~100 AI Tools of the Moment

It’s been more than a year since we published the top 100 AI tools of the time, but an AI year is the equivalent of more than a decade in regular years. My list was a great success, downloaded by hundreds, but when I revisited it recently, I realized it was outdated and needed some new love. I had to create a new list to provide you with the latest and best tools in each category.

What a difference two years makes! Tools like Midjourney, Jasper, Runway, Perplexity, and Grammarly have grown enormously since your original post, and entirely new categories like AI video generation have exploded.

Perhaps in a year, we will have completely different tools, but as of today, here are the best:

🎙️ AUDIO

1. ElevenLabs — The industry leader for voice cloning, text-to-speech, and full audio production. Supports controllable, expressive speech across 70+ languages, studio-quality music generation, custom sound effects, and AI dubbing, all in one platform.

2. Murf AI — A centralized voiceover studio built for scale, with multilingual translation, voice customization via “Say It My Way,” and ethically trained voice models. Its Falcon TTS model achieves around 130ms latency, making it one of the fastest for real-time use.

3. Descript — The go-to for podcasters and video creators. Edit audio and video by editing text, clone your voice, remove filler words automatically, and generate studio-quality AI voiceovers — all without a recording booth.

🤖 CHATBOTS

1. ChatGPT — Still the most well-rounded chatbot on the market. It performs equally well across writing, reasoning, coding, research synthesis, and structured problem solving, and adapts smoothly to tone, audience, and task type.

2. Claude — Known for finding a confident balance between candor and safety that many models struggle with. Strong at coding, long-form writing, and handling complex multi-step instructions, and Claude Code extends this into full software development.

3. Google Gemini — Google’s multimodal assistant, best at image and video generation and deep research. Tightly integrated with Google’s search infrastructure and Workspace tools, making it ideal for users already in that ecosystem.

💻 CODING

1. GitHub Copilot — One of the most widely adopted AI coding tools. Copilot includes autonomous code modifications, PR generation, and repository-aware optimization features, and now supports multiple underlying models, including Claude and Gemini.

2. Cursor — An AI-native IDE built as a fork of VS Code, designed to understand your entire codebase rather than just the current file. Its Agent Mode handles multi-step tasks like implementing features or fixing complex bugs across multiple files.

3. Claude Code — Rapidly becoming a developer favorite for tasks requiring deep reasoning — code reviews, generating unit tests, architectural planning, and handling large codebases with over 200K token context windows.

✍️ COPYWRITING

1. Jasper — The best all-around AI copywriting tool for long-form content, with strong SEO integrations and brand voice options. Particularly well-suited for B2B marketing teams running multi-channel campaigns at scale.

2. Copy.ai — Strong choice for fast content creation and simplified marketing workflows. Ideal for social media posts, product descriptions, and brief marketing texts, with solid brainstorming capabilities and tonal flexibility.

3. Writesonic — A versatile AI writer trusted by a million+ clients worldwide. Offers 100+ templates, Brand Voice capture, SEO Article Writer with real-time scoring, and a built-in chatbot assistant — all from one platform.

🎨 DESIGN

1. Canva Magic Studio — A text prompt-based AI design generator ideal for brand visuals, social ads, and videos. Magic Animate adds motion with one click, Magic Switch resizes assets for different platforms, and brand kits keep everything consistent — perfect for non-designers.

2. Adobe Firefly — Adobe’s generative AI system, built with commercial use and brand safety in mind. Integrates directly into Photoshop, Illustrator, and Express for generative fill, background replacement, and layered image creation from text prompts.

3. Figma AI — Figma’s AI tools transform the design process with AI image editing built for designers, marketers, and product teams. With 23% of designers now working on AI-powered products, Figma remains the dominant collaborative design environment.

🖼️ IMAGES

1. Midjourney v7 — Widely used by creative professionals for rich textures, dramatic lighting, and visually striking compositions. Excels at conceptual art, fashion visuals, and cinematic scenes — still considered the king of artistic “vibes” and moodboards.

2. Google Nano Banana Pro — The 2026 standout for professional utility, winning head-to-head tests against Midjourney, ChatGPT, and Firefly for photorealism and complex typography. Uses multimodal reasoning to interpret creative intent rather than just matching keywords to patterns.

3. Adobe Firefly — Designed specifically for commercial safety and enterprise use, trained on licensed content — making it legally reliable for business deployment. In 2026, businesses using Firefly report significantly reduced design compliance risks.

📚 LEARN & ORGANIZE

1. Google NotebookLM — One of the most powerful free research tools available. Unlike a standard chatbot, it lets you upload sources (PDFs, audio files, websites) and creates a grounded AI expert on only that data — ideal for studying, research synthesis, and preparing reports.

2. Notion AI — The most popular all-in-one workspace now with deeply embedded AI. Summarize notes, generate action items from meetings, draft content, and ask questions across your entire workspace — all without leaving the app.

3. Perplexity AI — A free AI-powered search engine that redefines research. By combining conversational AI with real-time web search, it delivers synthesized answers complete with detailed source citations — great for market research, learning, and competitive analysis.

🎵 MUSIC

1. Suno — The clear leader in 2026 — featuring studio-quality audio, natural vocals, stem editing, MIDI export, and a full DAW-like workspace. The free plan gives 50 credits daily (roughly 10 full songs), and the community of 100M+ users is genuinely supportive.

2. Udio — Suno’s main competitor, with more control for producers who want to edit tracks individually (stems), remix, and combine segments. Quality is comparable to Suno, but the interface is less intuitive — better for hands-on music makers.

3. AIVA — The specialist for instrumental and orchestral music — film compositions, game scores, and ambient soundscapes. The Pro plan transfers full copyright ownership to you, making it the go-to for professional soundtrack work.

📷 PHOTOGRAPHY

1. Photoroom — The only all-in-one AI product photography solution specifically built for commerce. Processes over 100 million product images monthly with tools for background removal, virtual model staging, and AI lifestyle scene generation — all in a mobile-friendly interface.

2. Adobe Firefly / Photoshop — The professional standard. Generative fill, non-destructive AI edits, and text-to-image generation — all deeply integrated into Photoshop and Lightroom. Content authenticity tools help verify how assets were created.

3. Pebblely — Simple, theme-based AI product photography ideal for small sellers and beginners. Upload a product, pick from 40+ background themes, and generate consistent lifestyle shots — no design skills required, with a generous free tier of 40 images/month.

📊 PRODUCT INSIGHTS

1. Viable — The original AI for qualitative data analysis is still one of the best. Feed it customer surveys, support tickets, and reviews and it synthesizes trends, themes, and actionable insights in plain language, no data science background needed.

2. Enterpret — placeholder Still a leading platform for turning fragmented customer feedback into structured intelligence. Surfaces emerging trends across support channels, reviews, and sales calls, then lets you integrate findings with your product analytics stack for deeper context.

3. Gong — Revenue intelligence that tracks sentiment throughout sales calls, flags buying signals, identifies objections, and builds a searchable archive of every prospect interaction, making deal reviews and coaching sessions far more productive.

💼 SALES

1. Gong — The standard for AI-powered sales intelligence. Records and analyzes every customer call, surfaces key moments, tracks deal health, and coaches reps based on what actually works, deeply integrated with most CRMs.

2. Apollo.io — An all-in-one sales intelligence and engagement platform. Find and enrich leads, build hyper-targeted prospect lists, and automate personalized outreach sequences, all powered by AI and one of the largest B2B contact databases.

3. Lavender — AI email coach built for sales reps. Scores your cold emails in real time, suggests personalized improvements, and shows you exactly why certain lines won’t land, helping SDRs write better emails faster without leaving their inbox.

🔍 SEARCH

1. Perplexity AI — Best for research with clear sources and citations. Combines conversational AI with real-time web search to deliver direct, synthesized answers, particularly useful for complex queries around market trends or competitive analysis.

2. Google AI Mode — Google’s AI-enhanced search now powers most everyday queries with AI-generated summaries at the top. For most users doing general research, product comparisons, or learning, it remains the most comprehensive starting point.

3. Brave Search — A standalone, privacy-focused search engine with a built-in AI assistant, Leo AI, that answers questions directly in the browser without tracking or ads. Built on its own independent index, not piggybacking on Google or Bing.

🎬 VIDEO

1. Google Veo 3 - The best AI video generation all-arounder on the market. Strong prompt adherence, exceptional realism combining video and audio generation, and robust creative tools that leave viewers wondering whether what they’re watching is real or AI-generated.

2. Runway Gen-4 — The leader in creative video editing tools. Motion Brush lets you designate what should move in a scene, Director Mode gives control over camera movement, and its editing toolkit is the most powerful available, ideal for creators who want precise control over output.

3. Synthesia — Earned the title of Best AI Video Generator (Winter 2026) from G2. Used by 90%+ of the Fortune 100 for training videos, onboarding, and internal comms. Realistic AI avatars, 160+ languages, real-time collaboration, and LMS export in one platform.

📝 WRITING

1. Jasper — The most powerful AI writing platform for marketing teams. Long-form article generation, ad copy, brand voice training, SEO integrations, and team collaboration, all in one tool built for professional content at scale.

2. Grammarly — Still the best AI writing assistant for editing and clarity. Now goes far beyond grammar; it rewrites for tone, cuts wordiness, flags unclear sentences, and suggests strategic improvements to help your writing land the way you intend.

3. Sudowrite — A writing assistant built specifically for fiction writers and storytellers. Helps develop plot ideas, refine dialogue, and fill scenes with creative details, indispensable for authors seeking inspiration or pushing through a creative block.

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Let’s get into it. Stay curious.

• The US Moves to Control Who Gets to Build AI Infrastructure

  • Can the United States be the de facto gatekeeper of global AI infrastructure?

• 🧰 AI Tools - Ethical and Conversational AI

• 📚Learning Corner - Anthropic Courses

• The top 100 consumer AI apps

• Kalshi and Polymarket Are Flying High. Can They Outrun the Regulators?

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📰 AI News and Trends

• Claude Opus 4.6 found more high-severity bugs in Firefox over two weeks than the rest of the world typically reports in two months.

• OpenAI robotics and hardware executive Caitlin Kalinowski resigned following the company's reported partnership with the Pentagon.

• Microsoft, Google, Amazon to Keep Selling Anthropic to Customers Other than the Pentagon

• Oracle and OpenAI will not expand their data center footprint at Abilene, Texas.

• Cursor to prioritize contracts with large enterprises in an effort to remain the top Coding agent after competition from Anthropic code and OpenAI Codex gets tougher.

Other Tech News

• The controversial gaming platform, Roblox, has grown quite fast since 2004, minted several teen millionaires, and paid out $1.5 billion to game creators in 2025.

• Some people are using a second dumb phone to minimize distractions and are gaining momentum.

• Grammarly is using our identities without permission.

• China could see widespread use of brain-computer tech in 3-5 years. Policies such as insurance integration and national standards aim to close the “huge” gap between scientific research, industry, and clinical applications

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The US Moves to Control Who Gets to Build AI Infrastructure

The US Commerce Department is drafting regulations that would require U.S. approval for nearly all global exports of AI chips from companies such as Nvidia and AMD. Under the proposed framework, buyers of smaller quantities (under 1,000 GPUs) would undergo a simple review process, while larger data center projects would require pre-clearance and may be subject to conditions such as business disclosures or government site visits. The biggest deployments, over 200K GPUs, would require host governments to make security commitments and matching investments in American AI.

The framework is still in draft form and faces pushback even within the Trump administration, which has distanced itself from Biden’s prior “AI diffusion rule” while not yet finalizing a replacement.

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Can the United States be the de facto gatekeeper of global AI infrastructure?

Yes, the US can, but it’s not that simple:

• Nvidia dominates the AI chip market by a wide margin, and there’s currently no comparable alternative. If you want the best hardware, you essentially have to go through American companies.

• The US already demonstrated this leverage with the UAE and Saudi Arabia deals, countries accepted conditions that were dollar-for-dollar investment matching, restrictions on serving Chinese firms, in exchange for chip access.

• Washington can also threaten that using Huawei chips could violate American trade restrictions, effectively closing off the only real alternative.

And NO, because:

• China is actively developing its own chip ecosystem through Huawei and others. The chips are less powerful today, but that gap could narrow over time, and the pressure of US restrictions accelerates China’s motivation to close it.

• Foreign governments are already chafing at the idea of subjecting their AI futures to Washington’s decisions. Europe, India, and others are pursuing “AI sovereignty” strategies partly in response to this dynamic.

• The rule’s effectiveness depends entirely on execution. Slow licensing, political strings, or unpredictable conditions tied to trade negotiations (as the Bernstein analyst warned) could push countries to seek alternatives or build domestic capacity.

• Even within the Trump administration, there’s disagreement; a White House official already said the draft doesn’t reflect the president’s direction.

The deeper tension is that the US wants two things simultaneously: to be the world’s AI supplier and to use that position as geopolitical leverage. Those goals can conflict. Push too hard on conditions and countries diversify away; be too permissive, and you lose the leverage entirely. Whether Washington can thread that needle is genuinely unclear.

📚Learning Corner

• All Anthropic Educational Courses on GitHub.

The Top 100 Consumer AI apps

The latest ranking of the top 100 consumer AI apps paints a picture of a maturing market. For the first time, the list now includes mainstream tools like CapCut, Canva, and Notion, where AI has quietly become central to the core experience rather than a bolt-on feature.

ChatGPT still dominates, with 900 million weekly active users, but competitors are closing in on specific fronts. Claude’s paid subscribers grew over 200% year-over-year, Gemini had a breakout year on the creative side, and roughly 20% of ChatGPT users now also use Gemini in any given week. The platforms are diverging in strategy. OpenAI is chasing the consumer super-app (shopping, travel, health), while Anthropic is doubling down on power users, developers, knowledge workers, and professionals.

Geographically, the market is split into three. Western tools, a Chinese ecosystem anchored by DeepSeek and Doubao, and an emerging Russian pole built around Yandex and Sber. The US ranks just 20th in per-capita AI adoption; Singapore, UAE, and Hong Kong lead.

Creative tools have shifted from image-dominated to a broader mix of video, music, and voice, with Chinese video models leading on quality and standalone image generators losing ground to bundled features inside ChatGPT and Gemini.

Agents are the new frontier. Vibe coding tools like Lovable and Replit are holding their audiences, while horizontal agents, OpenClaw (acquired by OpenAI), Manus (acquired by Meta for ~$2B), and Genspark, are starting to let everyday users hand off complex tasks entirely. The next six months will reveal whether consumers consolidate around one AI agent or keep juggling several.

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Kalshi and Polymarket Are Flying High. Can They Outrun the Regulators?

I’m probably late to the party, but I finally downloaded Kalshi this weekend after a conversation with some cousins-in-law in their mid-20s, and it was a good reminder of why it pays to listen to younger people when trying to understand where markets are heading.

Opening the app for the first time was a jolt. I thought social media was addictive. I hadn’t seen anything yet. The range of what you can trade on is staggering: sports, politics, the economy, the weather. Some markets resolve in seconds. Others play out over months. The company’s CEO insists it’s not a betting platform, but when you’re staring at a screen full of live odds, and your finger is hovering over a position, that distinction feels academic.

I had to actively stop myself from putting money in. That experience alone tells you everything you need to know about why regulators are paying close attention. It’s not just the financial implications of mainstream prediction markets; it’s the design. These apps are built to pull you back in, and they’re very good at it. The dopamine loop is real, and it’s been refined. We may look back at social media as the warm-up act.

Kalshi and Polymarket, the two biggest players in the space, are both in early talks with investors about raising new rounds at valuations near $20 billion each, roughly double where they were just a few months ago.

Kalshi, which is already live in the US and recently crossed a $1.5 billion annualized revenue run rate, was last valued at $11 billion after raising $1 billion in December from Sequoia and Paradigm. Polymarket, still blocked for US users but planning a domestic launch this year, was last valued at $9 billion following a deal with NYSE parent Intercontinental Exchange.

The growth is real, but so is the turbulence. Both platforms are facing a bipartisan bill in Congress that would ban markets on war, geopolitical events, and sports. They’re also under fire for aggressive college marketing tactics, including a case where Polymarket gave a fraternity cash in exchange for recruiting new users, and a wave of suspicious trades around Jeff Bezos’ Super Bowl whereabouts, placed by members of his stepson’s fraternity.

Prediction markets have genuine utility as forecasting tools and are growing fast, but their push into sensitive topics and college campuses is drawing exactly the kind of regulatory attention that could reshape the business before it fully matures.

🧰 AI Tools of The Day

Ethical and Conversational AI

• Reflection - Private AI coach helping users find clarity, improve mental health, and unlock personal growth through journaling.

• Wysa — An AI mental health companion that uses CBT, DBT, and mindfulness to help users manage anxiety, depression, and stress, with an optional human coaching upgrade.

• Pi (by Inflection) — A conversational AI built for personal, empathetic dialogue. It sits between ChatGPT and a therapist. No structured output, no frameworks, no decision journaling — just conversation.

• Rocky - An AI coaching platform that engages users in daily reflections, goal-setting, and learning new skills. It uses the GROW coaching model (Goal, Reality, Obstacles, Way Forward) oriented toward goal-setting and problem-solving.

• Youper - Bills itself as an “emotional health assistant” using CBT and ACT to guide daily check-ins, mood tracking, and personalized exercises targeting anxiety, depression, and self-confidence.

Today, we share how to lower your cloud dependency, with AI tools and tutorials to accomplish so. In a time of war, data centers, where all your data is housed, are becoming the main targets. Also How Chinese models are salivating as the Pentagon fights with Anthropic, and US models wonder if guardrails make sense anymore. Thank you for your time, and stay curious.

• Washington Kneecapped a Top AI Lab. DeepSeek Is Watching.

• 🧰 AI Tools - Your Own Resilience Stack

• The Fragile Internet We Built

• 📚Learning Corner - Build your Home Servers

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📰 AI News and Trends

• The strategic investments of Nvidia in OpenAI ($30B) and Anthropic ($10B) have come to an end, according to Huang.

• The new GPT-5.3 update will focus on the user experience, including things like tone, relevance, and conversational flow, areas that may not show up in benchmarks, but can make ChatGPT feel frustrating. And GPT-5.4 in development with “extreme” reasoning, 1M-token context.

• Seedance 2.0 has announced its pricing for video generation. $0.13 per second.

• Google released Gemini 3.1 Flash Lite in developer preview, positioning it as the cheapest model in the Gemini 3 lineup. The focus is not on frontier reasoning but scale. It runs fast, responds quickly, and is designed for massive API workloads where price and latency matter more than raw intelligence.

Other Tech News

• MacBook Neo, iPhone 17e, and everything else Apple announced this week, incliding a $599+ MacBook Neo with A18 Pro chip, 16hr battery.

• Amazon on Tuesday confirmed it laid off staff across its robotics unit, with at least ​100 white-collar jobs affected.

• A new app alerts you if someone nearby is wearing smart glasses.

• China’s top semiconductor executives have called for a nationwide push to build a domestic alternative to Dutch chip-equipment giant ASML, urging the industry to “abandon illusions and prepare for struggle” amid US sanctions.

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Washington Kneecapped a Top AI Lab. DeepSeek Is Watching.

When the Pentagon blacklisted Anthropic after CEO Dario Amodei refused to strip safety guardrails that would have permitted mass domestic surveillance and fully autonomous weapons systems, it set off a chain of consequences that extend well beyond one company’s government contracts, although the negotiations seem to continue.

Defense Secretary Hegseth’s “supply chain risk” designation, a label normally reserved for foreign adversaries like Huawei, forced defense contractors to certify they don’t use Claude, potentially gutting Anthropic’s enterprise base overnight and depriving the Pentagon of the only AI model cleared for classified systems. The unintended winners? Chinese labs. DeepSeek and its peers face device-level bans on government hardware, but crucially not the supply-chain designations that cripple commercial partnerships, meaning U.S. enterprises shut out of top domestic models can still legally adopt foreign alternatives. Airbnb uses Deepseek as it is a cheaper alternative than US models. Combine that regulatory asymmetry with DeepSeek’s aggressive open-weight releases, its cost advantage, and its strategic timing ahead of new model launches, and you have a market vacuum that Chinese AI is well-positioned to fill.

Looking ahead, the precedent is arguably more dangerous than the immediate fallout. If principled domestic labs face penalties while foreign competitors operate without equivalent constraints, ethics itself becomes a liability in government contracting, pushing capital, talent, and enterprise adoption toward lower-standards ecosystems the U.S. cannot audit or govern.

Questions for you.

• If a domestic AI company’s safety standards can be weaponized against it by its own government, what incentive remains for frontier labs to build those guardrails in the first place?

• Should “supply chain risk” designations apply symmetrically, and if Chinese models pose a data sovereignty threat, why are they exempt from the same commercial restrictions now applied to Anthropic?

• As enterprises quietly migrate to cheaper foreign models for cost efficiency, who bears responsibility for the long-term security trade-offs? The companies, the regulators, or both?

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The Fragile Internet We Built

We arrived here through decades of efficiency-driven consolidation, rather than running distributed infrastructure, businesses offloaded everything to a handful of hyperscalers, AWS, Azure, Google Cloud, because it was cheaper, faster, and someone else’s problem.

The primary vulnerability of today’s internet lies in its heavy reliance on cloud computing. This shift has resulted in numerous services depending on just a few key providers like Amazon and Microsoft. During the early days of the internet, businesses operated on their own infrastructure; when an issue arose in one area, others remained unaffected, but now, if a cloud provider faces difficulties, the repercussions resonate across multiple platforms. AI supercharged this dependency. Thousands of enterprise workflows are now baked into platforms like Claude, with some developers admitting they hadn’t written code themselves in months. This week proved how catastrophically exposed that leaves us. In the war now unfolding across the Middle East, a new kind of target has been added to the list: “data centers.” Drone strikes damaged three facilities operated by Amazon in the United Arab Emirates and Bahrain. Consumer apps, including delivery platform Careem, payments companies Alaan and Hubpay, and banking providers including ADCB and Emirates NBD, all reported outages as a result of AWS infrastructure going down. The attacks are a reminder that cloud computing isn’t “magical” and still requires physical facilities on the ground, which are vulnerable to all sorts of disaster scenarios. Simultaneously, Claude experienced multiple high-profile disruptions within days, with outage trackers logging thousands of error reports, exposing how AI uptime is no longer a technical footnote but a business-critical event with global consequences.

The implications are layered and serious. Geopolitical conflicts can now take down banking, payments, and logistics overnight; the concentration of AI inference into a few cloud providers means a single policy dispute or physical attack ripples into enterprise workflows worldwide; and Iran explicitly targeted Amazon’s Bahrain data center for the company’s support of “U.S. military and intelligence activities,” signaling that data centers are now considered legitimate military targets, a precedent with enormous consequences for any business or government running critical workloads in geopolitically sensitive regions.

• If a drone strike or policy dispute can take down your payments, your AI tools, and your business communications simultaneously, is “someone else’s infrastructure” still an acceptable risk model?

• Should critical national services, banking, healthcare, and government, be legally prohibited from running on foreign-owned cloud infrastructure, especially in conflict-adjacent regions?

• As self-hosted AI models become powerful enough for most business tasks, what’s the tipping point at which the privacy and resilience benefits outweigh the convenience premium of cloud AI?

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🧰 AI Tools of The Day

Reduce Cloud Dependency. Build Your Own Resilience Stack

Personal Cloud (Data Sovereignty)

• Nextcloud — the gold standard for self-hosted Google Workspace replacement; files, calendars, contacts, video calls, and thousands of apps. Runs on a Raspberry Pi or a VPS.

• Syncthing — fully peer-to-peer file sync with no central server at all; the most privacy-forward option

• Seafile — fastest sync performance, ideal for teams; benchmarks show Seafile syncing an 11GB folder in 6 minutes vs. Nextcloud’s 17

• OpenCloud — lightweight, minimal, runs on anything including low-powered hardware; great if you just want files without the complexity

Self-Hosted AI (No Cloud Dependency)

• Ollama — the easiest way to run models like Llama, DeepSeek, and Qwen locally via CLI; serves a local API other tools can hook into

• LM Studio — the most polished graphical interface for managing and running local LLMs, making it accessible for non-technical users; browse, download, and chat with models without touching a terminal

• GPT4All — plug-and-play desktop app; supports local document chat out of the box, zero setup

• Jan — offline-first, zero telemetry, privacy-forward; best for personal use

• AnythingLLM — all-in-one RAG platform; lets you chat with your own documents, PDFs, and GitHub repos using local models

• Open WebUI — a beautiful browser-based frontend that connects to Ollama; gives you a ChatGPT-like interface running entirely on your own machine

📚Learning Corner

Home Servers

• r/selfhosted — active community for home server setups

• Awesome Self-Hosted (GitHub) directory of every self-hostable service

• Wolfgang’s (YouTube) beginner-friendly homelab and Nextcloud tutorials

• Virtualization Howto (Blog) — deep dives into self-hosted AI stacks with Ollama + OpenWebUI

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• Who is Winning the Anthropic vs. Pentagon Battle?

• 🧰 AI Tools - World Monitor

• OpenAI Alleged Agreement with the Pentagon

• 📚Learning Corner - List of the best AI subreddits

• What to tell your Children about AI Careers

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📰 AI News and Trends

• The 5 biggest AI Protests

• Create AI agents to automate work with Google Workspace Studio

• Perplexity open-sourced two embedding models that match Google and Alibaba while using far less memory.

• Finance techie says they cloned Bloomberg’s $30k-a-year Terminal with Perplexity’s Computer

• AI-generated art can’t be copyrighted after the Supreme Court declines to review the rule

Other Tech News

• 3 American troops killed, and Trump says more ‘likely,’ in war against Iran

• Oil jumped the most in four years as uncertainty looms over the conflict in the Middle East.

• A new trial kicked off today in San Francisco over whether Elon Musk manipulated Twitter’s stock price before buying the company.

• Nvidia will invest $4 billion in two data center optics firms, continuing its expansive investments into several different parts of the tech stack.

• Paramount to acquire WBD in $111B deal, paying Netflix $2.8B

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Who is Winning the Anthropic vs. Pentagon Battle?

As the quote says, bad news is better than no news. This was very well shown when Anthropic surged to the #1 spot on the Apple App Store this past Saturday after reports surfaced that the Pentagon had blacklisted the company.

At the center of the conflict is Dario Amodei, founder and CEO of Anthropic, maker of Claude. According to reports, Anthropic resisted Pentagon requests tied to surveillance and military applications. OpenAI, by contrast, moved forward with a defense contract.

Sam Altman and OpenAI’s Head of National Security Partnerships, Katrina Mulligan, argued the deployment would be limited strictly to cloud APIs, alleging that their models would not be directly integrated into weapons systems, sensors, or operational hardware. The promise is containment by architecture. We shall see if that technical boundary holds over time.

The consequences for Anthropic are material. Being labeled a “supply chain risk” reportedly ends its $200 million Department of Defense contract and pressures any military contractor to sever ties. That classification typically applies to foreign firms like Huawei, deemed national security liabilities, not to U.S.-based frontier AI labs. The precedent is significant.

Adding complexity, reports indicate that Claude was used by the U.S. military in operational analysis related to Iran before the cutoff, which underscores how, once advanced AI tools are embedded in workflows, removing them is not trivial, and how AI is quickly becoming infrastructure.

There are clear winners in this reshuffle. OpenAI now replaces Claude in defense deployments. Google and X have both signaled a willingness to support government AI initiatives. Meanwhile, any transition period to a lower-power AI model inside the Pentagon could slow deployment, potentially benefiting geopolitical competitors.

As we see in action here, frontier AI labs are no longer just startups chasing revenue. They are becoming strategic assets in national security. And it seems that researchers and some AI leaders, who are ethical, know the consequences their models could have when left to make decisions autonomously, especially when it relates to war, where human lives are at risk. The tension between commercial incentives, ethical positioning, and state power has become operational.

The market reaction says it all. Controversy elevated Anthropic’s visibility overnight. But in the long term, who ultimately sets the rules for military AI: private labs, public institutions, or the architecture of the technology itself?

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📚Learning Corner

• List of the best AI subreddits

OpenAI Alleged Agreement with the Pentagon

OpenAI has disclosed new details about its rushed agreement with the U.S. Department of Defense after the Pentagon cut ties with Anthropic and labeled it a supply-chain risk. The deal allows OpenAI models to operate in classified environments but allegedly maintains three red lines:

• No mass domestic surveillance

• No autonomous weapons

• No high-stakes automated decisions like social credit systems.

OpenAI says it enforces these limits through a “multi-layered” approach, including cloud-only deployment, human oversight, and contractual safeguards, arguing that architecture matters more than policy language. Critics claim references to Executive Order 12333 could still permit indirect domestic surveillance. CEO Sam Altman admitted the optics were poor but said the goal was to de-escalate tensions between AI labs and the government. The backlash was immediate. Anthropic’s Claude briefly surpassed ChatGPT in Apple’s App Store, highlighting how quickly defense partnerships can reshape public trust and competitive dynamics in AI.

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🧰 AI Tools of The Day

• WorldMonitor - Real-time global intelligence dashboard with live news, markets, military tracking, infrastructure monitoring, and geopolitical data.

What to tell your Children about AI Careers

Lauren Weber asked a group of AI leaders (with kids ranging from 6 months to 26 years old) what they’re telling their own children about careers in an AI-driven world, and the surprising takeaway is they’re concerned, but not freaking out. As opposed to Social Media tech leaders

The consistent advice isn’t “find an AI-proof job,” but build human skills that compound: empathy, adaptability, critical thinking, relationship-building, and the discernment to take responsibility for decisions that affect other people. What’s often missing in the quick retellings is that several execs also point to practical “bets” and hedges: healthcare and energy (including nuclear) as resilient sectors, leaning generalist + liberal-arts breadth (because AI can fill skill gaps), and prioritizing learning how to learn / metacognition, plus basic financial resilience for disruption. And as Anthropic co-founder Daniela Amodei frames it, the durable edge is still deeply human, “how you treat people and how kind you are.”

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📰 AI News and Trends

• Nvidia’s blowout earnings report disappoints Wall Street as stock sinks 5%

• An AI safety nonprofit said Congress needs to examine the Pentagon's dispute with Anthropic over the limits of government use of AI models.

• The Market’s AI Obsession Is Starting to Bring Out the Bears

• Perplexity Launches “Computer,” an AI Digital Worker That Runs Full Workflows for Hours. Now Live for Max Subscribers

Other Tech News

• Meta explores stablecoin payments across its platforms, again.

• The UK’s first commercial lithium plant opened, as more nations race to secure domestic supplies of critical minerals to power EVs

• Donut Lab Says It Built the “Holy Grail” Solid-State Battery, Now It Has to Prove It

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Anthropic’s Claude high Performance triggered a High-Stakes Pentagon Showdown

Anthropic’s Claude is emerging as one of the defining forces of early 2026, not just in tech, but in national security and markets. Claude’s rapid performance gains, especially in complex reasoning, long-context work, and coding reliability, have made it a favorite inside enterprise and government workflows, and that pull is now colliding with Anthropic’s safety posture.

The company recently softened a core part of its Responsible Scaling Policy, effectively admitting that unilateral safety pledges are hard to sustain when rivals may ship without similar constraints. That shift landed the same week the Pentagon escalated a dispute over whether Claude can be used for sensitive military and intelligence applications, including fears around mass surveillance and autonomous lethal systems. Multiple reports suggest the U.S. government views Claude as unusually capable and already embedded enough that replacing it would be disruptive, which is why the standoff is high-stakes. A new question into public view is how much control, if any, an AI company should retain over how its technology is used once it becomes strategically indispensable.

📚Learning Corner

OWASP Top 10 for Large Language Model Applications (GenAI Security Project) — Practical guide for understanding and mitigating the most common risks in LLM apps (prompt injection, data leakage, excessive agency, supply-chain issues, etc.).

Hacker Leveraged Claude AI to Access Sensitive Mexican Data Troves

A hacker allegedly used Anthropic’s Claude AI chatbot to help carry out a large-scale cyberattack against multiple Mexican government agencies, according to cybersecurity firm Gambit Security. Over roughly a month beginning in December, the attacker prompted Claude in Spanish to act as an “elite hacker,” using it to identify network vulnerabilities, generate exploit scripts, automate data theft, and map internal systems.

Researchers say approximately 150GB of sensitive data was stolen, including taxpayer records, voter data, government employee credentials, and civil registry files. Claude initially warned against malicious activity but was repeatedly probed and eventually “jailbroken” after the attacker claimed to be conducting a legitimate bug bounty test. When Claude hit limits, the hacker reportedly sought additional technical insights from other AI tools. Anthropic says it investigated, banned the accounts involved, and has strengthened safeguards in newer models. Mexican authorities have denied confirming breaches, but the case highlights a growing trend.

AI tools are increasingly being used to amplify cybercrime, lowering the barrier to executing sophisticated attacks and intensifying the arms race between AI-powered offense and defense.

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🧰 AI Tools of The Day

Code Security

• GitHub Advanced Security - AI-suggested fixes for code scanning alerts directly in GitHub, designed to speed remediation inside PR workflows.

• Snyk Agent Fix - AI-driven auto-remediation that can propose and fix for code vulnerabilities to reduce time-to-fix.

• Semgrep Assistant - AI-powered triage and remediation recommendations layered on top of Semgrep findings, aimed at reducing noise and speeding secure fixes.

The Pentagon vs. Anthropic: Who Sets the Rules for Military AI?

The standoff between Anthropic and the Pentagon raises some serious concerns. First is the pressure the government can exert, something we’re increasingly normalizing, where agencies can push private companies to comply with demands that conflict with their policies, backed by financial penalties or the threat of being cut out of major contracts. The second issue exposed here is that, even while the path to durable profitability in AI remains unclear, these models are already becoming core infrastructure for both companies and governments. We’re starting to see just how dependent critical operations can become on a single vendor’s system.

In this case, Claude appears to be among the strongest models available today for complex reasoning and coding, and reports suggest it’s embedded enough in defense workflows that replacing it quickly would be costly and disruptive. That dependency shifts leverage in both directions. The Pentagon can apply contract pressure, but it also faces real operational friction if it tries to rip and replace, which may not even be an option at this stage.

The hardest question is ethical. Anthropic has drawn lines around certain military uses, including scenarios that could enable autonomous weapons or large-scale surveillance. Regardless of where you land politically, it matters that some AI companies are at least trying to set boundaries when the consequences can involve real human harm, including that of innocent children. Many firms will take the money and leave the moral responsibility to the customer. Others try to resist, but resisting the world’s most powerful bully can come at a steep cost. The outcome here may set a precedent for how much control AI builders can realistically retain once their technology becomes strategically indispensable.

• The Future Is Arriving Faster Than You Think

• 🧰 AI Tools - Medical Scribe Tools

• The AI That’s Quietly Rewriting Medicine

• 📚Learning Corner - The Principal-Agent Problem in AI. Who Is the Model Actually Working For?

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The Future Is Arriving Faster Than You Think

A Summary of ARK Invest’s Big Ideas 2026

ARK Invest just released its 10th annual Big Ideas report, consisting of 100+ pages of research on the technologies reshaping the global economy.

Five major technology platforms, AI, Robotics, Blockchain, Energy Storage, and Biotech, are feeding each other, and the pace of change is accelerating. ARK’s bold claim is that global GDP growth could hit 7.3% by 2030, more than double the IMF’s forecast, and the direction is hard to argue with.

Their 13 Big Ideas.

🤖 AI Infrastructure — The cost of AI dropped 99% in a single year. Data center investment is growing at 29% annually and could hit $1.4 trillion by 2030. Is it a bubble? Even though tech valuations are nowhere near dot-com levels?

🛒 AI Consumer OS — AI agents are becoming how we shop, search, and discover. Purchase time has collapsed from an hour to 90 seconds. By 2030, AI could facilitate over $8 trillion in online spending. Is Google’s search dominance in the crosshairs? Or would they just adjust and keep the crown?

⚡ AI Productivity — AI tools save the average knowledge worker ~50 minutes per day and pay for themselves in half a workday. Coding costs fell 91% in 8 months. AI-native startups are hitting $100M+ in revenue within 2 years of founding.

₿ Bitcoin — Institutions are in. ETFs, pension funds, and corporate treasuries now hold 12% of all Bitcoin. The US even created a Strategic Bitcoin Reserve. They claim that Bitcoin will reach a $16 trillion market cap by 2030.

🔗 Tokenized Assets — Stablecoin transaction volume hit $3.5 trillion in a single month, larger than Visa, PayPal, and global remittances combined. Banks, fintechs, and governments are all launching their own digital currencies.

🧬 Multiomics & Biotech — A blood test that detects dozens of cancers simultaneously is coming. AI-powered drug discovery is collapsing development timelines from decades to years. Rare diseases that were previously untreatable are becoming curable.

🚀 Reusable Rockets — As AI demands more compute than Earth can comfortably provide, space-based data centers could become a real possibility. AI growth could increase rocket launch demand by 60x.

🦾 Robotics — Humanoid robots are entering factories now, and the plan is home adoption. ARK calculates that a single household robot could add $62,000 in annual economic value per home. If widely adopted, US GDP growth could jump from 2–3% to 5–6%.

⚡ Distributed Energy — Solar and batteries are cheap enough to make energy independence real for homeowners and communities. This also powers the AI data center buildout without blowing up the grid.

🚗 Autonomous Vehicles — Robotaxis are commercially operating today in several US cities. The cost per mile is falling toward levels that make personal car ownership optional. Your commute time could become work time.

📦 Autonomous Logistics — Warehouses and delivery networks are rapidly automating. Same-day delivery may soon be the default, not a premium.

What does it all mean?

• Use AI tools daily. The productivity gap between those who do and those who don’t is widening fast.

• Check your career’s automation exposure — and redirect energy toward judgment, creativity, and relationships.

• Get proactive about your health. Precision medicine is advancing quickly enough that early action on preventive care and genetic testing will compound in value.

• Make sure you have some exposure to these themes — whether through ETFs, clean energy, tech, or a small Bitcoin allocation.

• Think long-term. The direction of these trends is compelling. The timing is always messier than forecasts suggest.

📰 AI News and Trends

• Can OpenAI be valued at $1Trillion? The company is reportedly planning for it with a $600 billion total compute spending by 2030, as it prepares for a potential IPO.

• Anthropic doesn’t want its AI used in autonomous weapons or government surveillance. Those carve-outs could cost it a major military contract.

• According to a Google VP, warns that two types of AI startups may not survive.

• Sam Altman says Elon Musk's idea of putting data centers in space is 'ridiculous.'

Other Tech News

• It’s called the ‘Fitbit for Farts,’ but does it solve a problem or another scam?

• By adding something like -AI at the end of the search query, the AI Overview will be stripped from the results page. The modifier is breaking Google's algorithm to surface the AI overlay. Try it.

• Nvidia reports Q4 earnings on Wednesday; the company is ~8% of the S&P 500, and its results are essentially a report card for the whole AI industry. Salesforce, Snowflake, Dell, and CoreWeave all report this week too, every one of them deep in the AI bet. Are companies actually making real money from AI? We shall see this week.

• Bitcoin fell below $65,000 amid renewed global trade tensions.

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📚Learning Corner

The Principal-Agent Problem in AI. Who Is the Model Actually Working For? When your doctor's AI takes notes, is it optimizing for the doctor, the patient, or the hospital's billing department? This is one of the most important and under-discussed tensions in applied AI right now. The answers may be here:

• Rethinking AI Agents: A Principal-Agent Perspective

• AI alignment principal agent models

The AI That's Quietly Rewriting Medicine

Today I had a routine visit to the doctor to check on some lower abdominal pain that I had been experiencing a few weeks ago. Luckily, it has subsided. While at the doctor’s office, he surprisingly, and for the very first time, asked if I was okay with our conversation being recorded. He raised a mobile device, implying that it would be recorded on it. I said, “Sure, why not?” I tried to peek at his screen to see what app he was using, but couldn’t quite make it out. Our exchange went well, and I’m glad to say I’m in reasonably good health. At the end of the visit, I finally let my curiosity get the better of me and asked what they were using to record the conversation. Not surprisingly, he said, “It’s an AI app called Ambient. He added, “It makes our jobs so much easier and faster.”

I left the doctor’s office wondering, who, or what, will apps like this be replacing soon? I came home determined to research and learn more. Athelas is a San Francisco health tech company whose AI-powered “Scribe” tool listens to doctor-patient conversations and automatically generates clinical notes, turning what used to be 15–20 minutes of post-visit typing into a 30-second review. It tackles one of medicine’s biggest problems, which is that doctors spend twice as much time on paperwork as they do with actual patients. The market is crowded, with Microsoft, Google, and several well-funded startups all competing in the same space, but Athelas stands out by combining note-taking with billing and coding automation.

The roles most at risk are medical scribes, transcriptionists, coders, and prior authorization specialists, essentially every support job that exists to manage documentation. One of the benefits is that this tech may actually restore something medicine has been losing for years, which is a doctor who is fully present in the room with you.

🧰 AI Tools of The Day

Medical Scribe Tools

• Nuance DAX (Microsoft) — Microsoft acquired Nuance Communications for $19.7 billion in 2022, largely because of DAX (Dragon Ambient eXperience). It integrates deeply with Epic, the dominant EHR system used by most large hospital systems. This is Athelas’s biggest competitor at the enterprise level.

• Suki AI — a voice assistant specifically built for clinicians, backed by Google. Strong focus on specialty medicine.

• Nabla Copilot — a European player gaining ground in the U.S., known for a clean interface and strong multilingual support.

• Abridge — One of the largest health systems in the country. They’ve received strong clinical validation.

• DeepScribe — focused on specialty care, particularly for complex conversations in fields like oncology and psychiatry.

• Freed AI — newer entrant but growing quickly among independent and private practice physicians because of its simplicity and pricing.

• The End of Hollywood as We Know It

• 🧰 AI Tools - Video Generators

• Zuckerberg on Trial, and So Is the Entire Social Media Industry

• How will the next War be fought?

• 📚Learning Corner - Dopamine, Smartphones & You: A Battle for Your Time

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The End of Hollywood as We Know It

In February 2026, ByteDance launched Seedance 2.0, an AI video tool that generates hyper-realistic footage from a single photo and a text prompt. Within hours, the internet was flooded with convincing fake films featuring real stars and copyrighted characters. Disney, Netflix, Warner Bros., and Paramount fired cease-and-desist letters almost immediately, calling it an unprecedented smash-and-grab of their IP. ByteDance offered vague assurances. Netflix gave them three days.

None of it matters. Even if Seedance disappears tomorrow, the technology cannot be uninvented. Open-source equivalents are already proliferating in jurisdictions that don’t share American copyright protections. And tellingly, Disney — while threatening ByteDance in court — quietly signed a billion-dollar licensing deal with OpenAI to use its characters in Sora. The studios aren’t fighting AI video. They’re fighting for a seat at the table before someone takes it from them.

The more serious threat isn’t to Hollywood’s bottom line, it’s to reality itself. When photorealistic footage of anyone doing anything can be created in minutes and spread in hours, our ability to trust what we see collapses entirely. News. Court evidence. Political events. All of it becomes suspect. And when AI can generate an infinite, perfectly personalized video feed tailored to your exact tastes, the addictive pull will make TikTok look like a library card.

The studios will adapt, legislate, and litigate. But they are defending a world that is already receding. The question now isn’t whether AI reshapes visual culture, it’s whether we’ll have any shared sense of reality left when it does.

📰 AI News and Trends

• Lyria 3, Google DeepMind’s latest generative music model, is rolling out today in beta in the Gemini app. Just describe an idea or upload a photo, and Gemini will translate it into a high-quality, catchy track.

• World Labs secured $1 billion in funding from investors like AMD, NVIDIA, and Fidelity.

• Saudi Arabia’s Humain Invests $3 Billion Into Musk’s xAI.

• Perplexity drops advertising as it warns it will hurt trust in AI, but OpenAI is all in.

• Defense Dept. and Anthropic Square Off in Dispute Over A.I. Safety

Other Tech News

• Meta will challenge Apple with its first smartwatch, launching this year.

• The US ramped up pressure on Iran, sending its largest concentration of air power to the Middle East since 2003.

• North Korean leader Kim Jong Un unveiled nuclear-capable rocket launchers, part of efforts to tout Pyongyang’s military capabilities ahead of an upcoming meeting of the ruling communist party.

• Future Olympic prospects are testing a device that can give them corrective advice in real time as they hurtle into the air.

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Zuckerberg on Trial, and So Is the Entire Social Media Industry

Mark Zuckerberg took the stand this week in a Los Angeles courtroom in what may be the most consequential legal moment of his career. At the center of K.G.M. v. Meta Platforms is a 20-year-old California woman who alleges Meta deliberately engineered its platforms to addict young users, contributing to her depression and suicidal ideation. The evidence emerging from the trial has been damning: a 2015 email chain showed Zuckerberg himself pushing to increase users’ time-in-app by 12%, directly contradicting his earlier Congressional testimony, while internal Meta documents revealed that roughly 30% of American children aged 10–12 had Instagram accounts as far back as 2015, and that Meta’s own research found parental supervision does little to curb teens’ compulsive use. The interesting part is that Zuckerberg and many tech executives do not want their own kids near social media, but definitely profit from other minors using the technology.

Zuckerberg largely deflected, claiming documents were taken out of context and pointing to Apple as a more capable enforcer of age verification, a posture of strategic deniability that may define the case’s outcome. The stakes extend well beyond Meta: a verdict for the plaintiff could shatter Big Tech’s longstanding Section 230 liability shield, exposing the industry to billions in damages and forcing fundamental platform redesigns.

This courtroom drama is unfolding against a sweeping global backdrop where countries like Australia already banned social media for children under 16 in December 2025, and Denmark, France, Spain, Malaysia, Slovenia, Greece, Germany, and the UK are all moving toward similar restrictions, each wrestling with the same core tension between child protection and civil liberties concerns around invasive age verification. What’s quietly on trial here isn’t just one company’s conduct, but the foundational assumption that platforms bear no responsibility for the psychological architecture they deliberately build, and governments worldwide are no longer willing to wait for the verdict.

📚Learning Corner

Harvard Science in the News — “Dopamine, Smartphones & You: A Battle for Your Time”

How will the next War be fought?

We'll leave you with a sobering thought. While the world debates social media addiction and AI-generated video, a quieter and far more consequential arms race is underway. In our Other Tech News section, we look at how nations are rapidly militarizing artificial intelligence — and how China, in particular, is moving toward a war-fighting model built entirely around robotics: ground units, aerial swarms, humanoid machines, all coordinated by AI with no human on the front line. The software, the hardware, and the very concept of war are all changing simultaneously. If the past week taught us anything, it's that technology moves faster than our ability to govern it. On every front.

🧰 AI Tools of The Day

Video Generators

• Kling AI — Built by Chinese tech company Kuaishou, Kling is one of the most widely used AI video generators in the world, with over 22 million users and 168 million videos generated.

• Seedance 2.0 — ByteDance’s controversial flagship video model that set off the Hollywood firestorm. It uses a multimodal architecture that accepts text, images, audio, and video as inputs simultaneously.

• Pika — Often described as the “Canva of AI video,” Pika is the most beginner-friendly option of the three. Built by two Stanford PhD students, it specializes in short-form content for TikTok, Reels, and YouTube Shorts.

• Are US AI models being distilled by Chinese Competitors?

• SeeDance2.0 generates very realistic videos, and Disney is nervous.

• 🧰 AI Tools - Distillation Tools

• ChatGPT Users by Country: India leads the Chart.

• 📚Learning Corner - Distillation Tutorials

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📰 AI News and Trends

• The creator of the viral AI assistant OpenClaw has joined OpenAI to help drive the next generation of personal agents.

• Anthropic got an 11% user boost from its OpenAI-bashing Super Bowl ad

• Meta plans to add facial recognition to its smart glasses

• Disney sends a cease and desist letter to ByteDance over Seedance 2.0, but ByteDance has promised to set strong guardrails around its latest model to avoid copyright issues.

• Google puts users at risk by downplaying health disclaimers under AI Overviews

• Pentagon threatens to cut off Anthropic in AI safeguards dispute

• NASA allowed AI to control its Perseverance rover on Mars for two days

• NPR’s David Greene says he was “completely freaked out” when he heard an AI voice from Google’s NotbebookLM that sounded just like his own, and he’s suing over it.

Other Tech News

• Young consumers spend more on luxury items in the TikTok era

• China said it would scrap tariffs for 53 African nations, as Beijing looks to maintain its position as the continent’s largest trading partner amid competition from Washington

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SeeDance2.0 generates very realistic videos, and Disney is nervous.

ByteDance, TikTok’s parent company, has launched Seedance 2.0, an AI video generator that turns simple text prompts into cinematic videos with storylines, characters, voiceovers, and sound, positioning it as a direct rival to OpenAI’s Sora and Google’s Veo.

The model, integrated into CapCut (with ~642 million monthly active users), can create realistic 15-second clips and is driving rapid adoption alongside ByteDance’s broader AI ecosystem, including its Doubao chatbot with nearly 250 million monthly users. However, the tool has sparked major backlash from Hollywood, with Disney issuing cease-and-desist letters and the Motion Picture Association accusing ByteDance of using copyrighted content “on a massive scale.” Privacy concerns also emerged after filmmakers reported AI-generated voices mimicking real people without consent. To scale its AI ambitions, ByteDance is investing heavily in infrastructure, including plans to deploy over 7,000 Nvidia Blackwell chips in overseas data centers, signaling its intent to compete globally and potentially reshape video production, advertising, and entertainment.

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📚Learning Corner

DeepSpeed Distillation Documentation

• Distilling large models at scale

• Production deployment

• GPU optimization

Best for enterprise and large-model distillation

Are US AI models being distilled by Chinese Competitors?

OpenAI has warned US lawmakers that Chinese AI company DeepSeek used “distillation” techniques to extract outputs from OpenAI’s models and train its competing R1 chatbot, calling it an attempt to “free-ride” on billions of dollars of US AI investment. Distillation allows a smaller or cheaper model to replicate the capabilities of advanced systems by learning from their responses, giving DeepSeek a major cost advantage. Its V3 model reportedly required just 2.8 million GPU hours using older Nvidia H800 chips. OpenAI also detected obfuscated access methods, including third-party routing and unauthorized resellers, designed to bypass safeguards.

Lawmakers and national security officials view this as both a business threat, undermining paid AI services from OpenAI and Anthropic, and a strategic risk, as copied models may lack safety protections and could be used in sensitive areas like biology or cyberwarfare. The situation highlights a growing AI arms race between the US and China, where technical replication, infrastructure access, and chip supply chains, not just original invention, are becoming decisive competitive factors.

🧰 AI Tools of The Day

Distillation Tools

• PyTorch Knowledge Distillation - PyTorch provides built-in support for distillation by allowing a smaller “student” model to learn from the output probability distributions of a larger “teacher” model. This improves efficiency while maintaining accuracy, enabling deployment on lower-power hardware like phones, drones, or edge devices.

• Hugging Face Transformers - Hugging Face offers tools to distill large language models like BERT, GPT, and Llama into smaller versions. It includes pretrained distilled models such as DistilBERT and allows developers to train student models using teacher outputs, dramatically reducing model size and compute requirements.

• NVIDIA TensorRT Model Optimization Toolkit - NVIDIA TensorRT includes model distillation alongside pruning and quantization to optimize AI models for fast inference on GPUs, edge devices, and embedded systems. It is widely used in production environments where performance and latency are critical.

ChatGPT Users by Country: India leads the Chart.

India has become one of OpenAI’s most critical growth markets, with CEO Sam Altman revealing that ChatGPT now has 100 million weekly active users in the country, making it the platform’s second-largest user base after the United States and accounting for over 11% of its nearly 900 million global weekly users.

Adoption has been driven largely by students, India has the highest number of student users globally, and is supported by OpenAI’s localization efforts, including opening a New Delhi office in 2025 and offering low-cost or free access tiers tailored to India’s price-sensitive market. With more than 1 billion internet users, India represents a massive expansion opportunity, and OpenAI is planning new partnerships with the government to accelerate adoption and infrastructure development. The rapid growth highlights India’s emerging influence in shaping global AI deployment, as major competitors like Google also aggressively target the region, positioning India as a central battleground for the next phase of AI expansion.

Code is now advancing so fast that it’s not just writing software, it’s improving itself and autonomously creating apps and platforms. As Chinese AI models continue closing the gap with Silicon Valley and AI systems grow stronger at math and reasoning, development is accelerating exponentially. The real constraint is no longer talent or ideas, but who controls the compute, data, and capital needed to build and scale it all.

• Coding is Free and Power Scarce

• China’s AI Models continue to close the gap and undercut Silicon Valley.

• 🧰 AI Tools - Math Help

• AI Is Improving at Math Problems That Stumped Humans for Decades.

• 📚Learning Corner - Dashboard Builders

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📰 AI News and Trends

• Anthropic's $30 Billion Series G Intensifies LLM Race.

• Google Maps’ New Gemini Feature to Reportedly Let You Chat About Places and Routes.

• Global AI spending is surging as Alphabet raises billions in a massive bond deal. And new retail data shows US consumers are starting to pull back.

• Czech ice dancers have come under fire for using AI-generated music in their routine.

• A Malaysian man bought the domain AI.com in 1993 for about $3–4 and sold it in 2025 for roughly $75–80 million.

• Pentagon pushing AI companies to expand on classified networks.

• AI insiders are sounding the alarm

Other Tech News

• Russian Startup Hacks Pigeon Brains, Turns Them Into Living Drones.

• Bitcoin hovers near $65,000 as investor frustration grows.

• First atlas maps brain cell waste systems tied to Alzheimer’s, Parkinson’s.

• Waymo begins deploying next-gen Ojai robotaxis to extend its U.S. lead, and one of them already crashed into a stopped vehicle in a residential neighborhood.

• The biggest stars at the Olympics are drones.

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Coding is Free and Power Scarce

This week’s signals from Spotify, Elon Musk, and insiders at OpenAI and Anthropic all point to the same reality: “Software is becoming frictionless.”

Spotify says its top engineers haven’t written code since Dec 2025, shipping 50+ features in 2025 using AI systems that deploy fixes from a phone, while Musk predicts that by 2026–2027. As we shared earlier, code is coding itself, and it will not change anytime soon. AI will bypass programming languages entirely and turn human intent directly into optimized machine code, shrinking the idea-to-product cycle from months to minutes. At the same time, AI researchers are resigning and warning of existential risks as capabilities scale faster than regulation can keep pace, even as valuations surge toward $380B and political spending rises. The common denominator is clear, and it’s that execution is being commoditized, productivity per worker is rising 5x–20x+, and power is concentrating around data, compute, and distribution.

Over the next 1–3 years, most software will be built by autonomous agents; within 5 years, human coding will be a niche, with companies of five doing the work of hundreds. The upside is massive, cheaper innovation, faster science, and lower startup costs, but the downside is equally large: rapid job displacement, monopolized infrastructure, large-scale manipulation, and fragile systems humans no longer fully understand. In the AI economy, skill is becoming abundant, code is dissolving, and the real bottleneck is shifting to judgment, ethics, and who controls the machines that now build everything.

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China’s AI Models continue to close the gap and undercut Silicon Valley.

China’s AI race is accelerating as MiniMax launched its new open-source flagship model M2.5, positioning it as a low-cost alternative to U.S. leaders like Anthropic for coding and agentic tasks, a move that helped push MiniMax’s Hong Kong-listed shares up nearly 10%.

The release is part of a broader surge in Chinese models gaining global traction, including Zhipu AI’s GLM-5, praised for coding performance, and Moonshot AI’s Kimi K2.5, which recently became the most-used model on OpenRouter. Together, these launches signal that China is rapidly closing the gap in high-quality, low-cost AI models, increasing competitive pressure on U.S. providers and giving developers more affordable options worldwide.

📚Learning Corner

Dashboard Builders

• Looker – Best for cloud + SQL teams. Works well with BigQuery and data warehouses. Good for scalable, data-governed dashboards.

• Metabase – Best open-source option. Free, fast to deploy, and great for startups or internal tools.

• Streamlit / Plotly Dash – Ideal if you’re building custom AI, finance, or IoT dashboards in Python.

AI Is Improving at Math Problems That Stumped Humans for Decades.

AI is starting to meaningfully assist mathematicians by rapidly searching literature, combining known results, and occasionally generating valid proofs, helping resolve around 100 long-standing Erdős problems since late 2025.

Researchers at OpenAI and Google DeepMind are increasingly using LLMs as research assistants that can cut weeks of work to days, though they still produce large volumes of confident but incorrect results. A new benchmark called First Proof is testing whether AI can solve unpublished problems, with mixed early outcomes. While no major journal has yet published an AI-assisted proof, many experts expect 2026 to be the turning point, positioning AI as a powerful collaborator rather than a replacement for human mathematicians.

🧰 AI Tools of The Day

Math Help

• Wolfram Alpha – Uses symbolic computation and a vast math knowledge base to solve equations, integrals, matrix problems, and more with step-by-step solutions.

• Symbolab – An AI-powered math solver that provides step-by-step solutions for algebra, calculus, and trigonometry problems.

• Microsoft Math Solver – Interprets handwritten or typed math problems and delivers detailed solutions, graphs, and related examples.

• My Conversations with Caltech Data Architect about coding, SQL, and Humble engineers.

• Move Over Vibe Coding Agentic engineering is here.

• 🧰 AI Tools - Generative Video & World-Model Architectures

• What is the Key to Physical AGI?

• 📚Learning Corner - Reinforcement learning.

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📰 AI News and Trends

• Meta AI readies Avocado, Manus Agent, and OpenClaw integration.

• How Nvidia became the first $5 trillion company.

• Crypto.com founder buys AI.com in ‘largest domain purchase in history’

• The 2026 Super Bowl advertisements took it a step further by leveraging AI both to create the commercials and to promote the latest AI products

Other Tech News

• Africa’s biggest mining conference opened in Cape Town, a gathering set to be dominated by US-China competition for the continent’s valuable resources.

• SpaceX is exploring a “Starlink Phone” for direct-to-device internet services, and they are shifting all focus to conquering the moon.

• Apple Prepares to Kick Off 2026 With the iPhone 17e, New iPads, and Macs.

• Is this the first real crypto crash?

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My Conversations with Caltech Data Architecture.

Vibecoding, future-proofing your career, data, SQL, humble engineers and more.

I had the pleasure to sit down with and interview, Armando Plasencia, Caltech Data Architect with 25 years of experience in the Data and Tech world. While chatting, he shared that long-term success in tech will be driven less by mastering specific tools and more by curiosity, humility, and community.

He stresses that engineers must keep learning daily as AI automates routine coding, while strong collaboration and openness to feedback now outperform solo expertise. Plasencia highlights open-source as a fast-track career path, noting that contributors gain real-world experience with software used by hundreds of thousands of companies and build trusted professional networks. He also points to low-code and no-code platforms that can reduce development from months to minutes, shifting software creation toward business logic rather than syntax, while emphasizing SQL and data sovereignty as critical skills in an AI-driven era. This is the entire conversation, and we will be uploading shorts on our Substack Notes account.

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Move Over Vibe Coding Agentic engineering is here.

A year ago, although it seems many more than that, Andre Karpathy coined the term Vibecoding. During that year we have seen apps like cursor more than 10x in value and usage, and other apps like lovable and repplit become darlings for any one wanting to build apps but lacking coding knowledge. The hype of prompting AI to build Apps and create code is now in the past and we are moving to Agentic Engineering, a new term coined by Andre as well, which is the act of AI coding itself.

Researchers have demonstrated that large multi-agent AI systems can now run for days with minimal human input, generating ~1,000 software commits per hour and executing 10+ million actions while building and maintaining complex products like a web browser.

This signals the rise of “self-driving codebases,” where AI increasingly designs, writes, tests, and fixes software on its own, a trend that could automate 80%+ of enterprise development within five years and cut software costs by 50–70%. While this does not yet qualify as full AGI, it shows early AGI-like behavior in narrow domains such as engineering, with systems capable of long-term planning, self-correction, and collaboration. As technology begins to build itself, human value will shift toward problem framing, system design, and governance rather than manual coding, making these skills essential for today’s youth. At the same time, cybersecurity risks will rise sharply, as autonomous systems can discover and exploit vulnerabilities in hours, enable self-evolving malware, and amplify supply-chain attacks, meaning future digital security will depend as much on controlling AI behavior as on defending traditional infrastructure.

📚Learning Corner

Reinforcement Learning: An Introduction – Sutton & Barto

• How agents learn from interaction

• Model-based vs model-free learning

• Planning with learned world models

• How prediction + control work together

What is the Key to Physical AGI?

Robots Will Learn Like Humans. Data, Not Hardware, Is the Key to Physical AGI

Researchers argue that the main barrier to achieving Physical AGI in robotics is not hardware or algorithms, but data, as today’s robots are trained on only thousands of hours of controlled demonstrations compared to the billions of human-years behind modern language and vision models. The only scalable solution is capturing massive amounts of human egocentric video, potentially 100+ million hours, equivalent to 150 lifetimes of experience, and using it to train “world models” that learn physics, cause-and-effect, and task dynamics by predicting how scenes evolve over time.

By learning from human experience first and then transferring that knowledge to robots, early systems like DreamZero have already shown 40%+ performance gains from just minutes of video data, suggesting that robots can acquire general physical intelligence with minimal retraining. This approach favors humanoid or human-like machines, which reduce the gap between human and robot movement, and points to a future where robots learn primarily by watching people rather than being manually programmed.

If successful, this paradigm could enable robots to perform complex real-world tasks with little supervision, marking a realistic path toward Physical AGI driven by large-scale human experience rather than handcrafted robotics data.

🧰 AI Tools of The Day

Generative Video & World-Model Architectures

• V-JEPA (Video Joint Embedding Predictive Architecture) – a state-of-the-art world model trained on video that excels at visual understanding and prediction, useful as a backbone for world modeling tasks.

• DeepMind’s Genie 3 – advanced world model for generating dynamic interactive environments from text, which can serve as simulated training data for robots and embodied agents.

• MorphoSim – a language-guided 4D simulator that creates controllable spatiotemporal worlds for training and evaluating world models and embodied agents.

• The DRAG Method - How to Use AI to Upgrade Your Brain.

• 🧰 AI Tools

• Are Small Nuclear Reactors the Solution?

• 📚Learning Corner - AI Agents from Scratch

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📰 AI News and Trends

• How Anthropic achieved AI coding breakthroughs and rattled business.

• Why Big Tech Is Throwing Cash Into India in Quest for AI Supremacy.

• Gizmo, an interactive model that vibes code mini apps, is all the rage. Simply type out an AI prompt to explain your idea using natural language. The app turns your idea into an interactive experience by generating the code that makes it work.

• Google Gemini has surpassed 750 million monthly active users. MetaAI has ~500 million monthly users, and ChatGPT has ~810 million.

• Meta's Avocado vs. Kimi K2.5. Meta’s model reportedly outperforms top open-source base models. Kimi K2.5 is a solid model and exceeds expectations, making it an excellent choice for developers who can't afford or don't want to pay for Opus 4.5 and have to go with something cheaper to run their OpenClaw.

• OpenAI in Talks to Power Amazon’s Alexa, Other AI Products.

• An AI model developed by Google DeepMind is giving athletes new insights into how their bodies move by using AI 3d Videos to analyze their techniques.

Other Tech News

• Artificial snow is a short-term solution as climate change threatens the future of the Winter Olympics.

• First-ever experimental gene therapy seeks to restore vision by rejuvenating eye neurons.

• AT&T to use Amazon Leo, the e-commerce giant’s low Earth orbit satellite network, to deliver fixed broadband services to businesses.

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Your Payment Processor Is Hiding Your Revenue Leaks

Most businesses think churn = cancellations. - That’s wrong.

What your payment processor doesn’t clearly show you:

• Failed payments silently kill 5–9% of revenue

• Customers at risk before they cancel

• Which products actually drive churn

• How much revenue could be recovered every month

We built a real-time financial command center on top of Stripe and any payment processor that shows:

• MRR, Net Revenue, Churned MRR

• Revenue by product & customer

• Subscription risk alerts

• Failed payment recovery tracking

• Client lifetime value & tenure

If you do $20k+/month, you’re almost certainly leaving money on the table.

Schedule a Free Discovery Call.

Are Small Nuclear Reactors the Solution?

Next-generation nuclear reactors are breaking away from the massive, slow, and expensive 20th-century model by going smaller, safer, and more efficient. New small modular reactors (SMRs) can be factory-built, installed faster, and scaled as needed, cutting construction risk and potentially lowering costs. Advanced fuels like HALEU (5–20% U-235) and TRISO particles can run longer between refueling and withstand temperatures above 3,200°F (1,800°C), improving safety.

Meanwhile, alternative coolants like molten salt, liquid metal, and gas allow reactors to operate above 500°C, versus ~300°C for water, boosting efficiency while reducing high-pressure risks. With global electricity demand rising from AI data centers, climate systems, and industrial electrification, these designs aim to deliver clean, reliable power faster if they can prove they’re truly cheaper and scalable.

📚Learning Corner

AI Agents from Scratch

Github AI Agents resource - A curated collection of resources for studying AI agents updated regularly to help you understand, build, and evaluate autonomous AI/LLM agents.

The DRAG Method

How to Use AI to Upgrade Your Brain.

Using AI correctly may not be as simple as we think. Most people are getting very lazy and relying on language models to do their thinking for them, and as the saying goes, “if you don't use it, you will lose it.” We are utilizing our brains less and less as technology provides us with powerful tools that don’t require much thinking on our part, but if we use them wisely, we can actually get more out of them, which will allow us to become brighter and mentally sharper.

That is when AI becomes a thinking amplifier; it takes your low-value cognitive load, sharpens your reasoning, and helps you move faster on the work that actually compounds.

The 4-Step Framework

Step 1 — DRAG the “Zone 1” Work Off Your Plate

Think of your work in two zones:

Zone 1 = low-stakes, repeatable tasks.
Zone 2 = high-stakes, high-leverage thinking.

The goal is to delegate Zone 1 to AI so you can obsess over Zone 2.

Use DRAG to offload the mundane:

• D — Drafting: first drafts, outlines, emails, post versions

• R — Research: summaries, citations, competitive scans, trend maps

• A — Analysis: pros/cons, frameworks, comparisons, decision memos

• G — Grunt Work: formatting, tables, checklists, SOPs, rewriting

Weekly win condition: if AI doesn’t save you 3–10 hours/week, you’re under-delegating.

Prompt: “This is Zone 1 work. DRAG it for me: Draft + Research + Analyze + Grunt. Output as bullets, then a clean final.”

Step 2 — Climb the Intelligent Hill (Stop “Hope-and-Pray” Prompting)

Most people do zero-shot prompting:

“Write me a thing about X.” That’s basically: “Guess what I want.”

Instead, climb the “intelligent hill” through 4 levels. Each level increases quality and reduces hallucinations.

Camp 1 — One-shot Prompting

Give one example of what “good” looks like. Use it when you want a specific style (newsletter, LinkedIn, pitch deck tone).

Prompt: “Write this in the same style as the example below. Match structure, tone, and length. EXAMPLE: [paste the example]”

Camp 2 — Few-shot Prompting

Give 3+ examples so it can learn patterns. Use it when you want a consistent brand voice across a whole system.

Prompt: “Before you write, explain the pattern you see across these examples in 5 bullets.”

This forces clarity and reveals your own “hidden rules.”

Camp 3 — Chain-of-thought Reasoning (Slow the model down)

The goal is to reduce sloppy output by forcing explicit reasoning. This is used when stakes are higher (strategy, analysis, numbers, decisions).

Prompt: “Ask me 3 clarifying questions, then propose 3 options. For each option: assumptions, risks, and why it’s likely to work. Then write the final output.”

Camp 4 — Agents (AI as a team)

Treat AI like multiple specialists, not one chatbot. You can use this when researching → synthesizing → writing in one flow.

Prompt: “You are a team: Researcher, Analyst, Editor. Researcher: gather key points + evidence. Analyst: extract the 5 implications and 3 contrarian angles.
Editor: write a clean newsletter in my tone with a strong hook.”

Before you hit enter, try asking this to see the results:
“Can I upgrade this prompt one camp higher?”

Step 3 — Build the Intelligent Gym (AI as Your Spotter)

A “spotter” doesn’t lift for you. They are safe resistance so you get stronger.

Use AI to force higher-quality thinking:

• Ask it to attack your logic

• Ask it to steelman the opposite

• Ask it to find missing variables

• Ask it to create quizzes for your beliefs

Prompt: “Challenge my assumptions, point out weak reasoning, and propose the smallest test I can run this week to validate this idea.”

Step 4 — Use the Fool’s Advantage (Learn-it-all Mode)

The “fool’s advantage” is the willingness to look dumb long enough to get smart. AI makes that cheap. Instead of avoiding hard topics, you can:

• compress months of confusion into 30 minutes.

• learn the basics, then climb to advanced.

• get analogies, examples, and mini quizzes.

“Explain this like I’m 12, then like I’m a college student, then like I’m a domain expert. After each level, quiz me with 5 questions.”

Please share your experience using the drag method and whether it has become beneficial.

🧰 AI Tools of The Day

AI Agents - Easy Apps to start with.

• Copilot Studio - Complex multi-step AI workflows.

• Zapier AI - Business automation with apps.

• Notion AI - Personal and team productivity.

Explore our AI Guides — from coding to photography and beyond, and find step-by-step tips to put AI to work for you.

• Clawbot → Moltbot → Openclaw Hits 1.5M Agents in Days

• 🧰 AI Tools - No-Code / Low-Code AI Agents

• AI Jobs Now Reward Obsession and Visibility.

• 📚Learning Corner - AI Agents (aXiv)

Subscribe today and get 60% off for a year, free access to our 1,500+ AI tools database, and a complimentary 30-minute personalized consulting session to help you supercharge your AI strategy. Act now as it expires in 3 days…

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📰 AI News and Trends

• China’s genius plan to win the AI race is already paying off

• Nvidia to throw more money to OpenAI, the biggest amount to date, so that they can buy more of Nvidia chips.

• Anthropic bolsters enterprise offerings with Cowork plugins.

• An AI Tool, called AnomalyMatch, sifted through 35 years of data and over 100 million images and found 1,300 anomalous objects, more than 800 of which had never been documented.

• Anthropic is reportedly in a standoff with the Pentagon over how its artificial intelligence tools can and should be used.

Other Tech News

• Waymo seeking about $16 billion near $110 billion valuation, Bloomberg News reports.

• Bitcoin dips below $79,000 as market digests silver sell-off, Trump’s Fed chair pick.

• As Saudi Arabia’s 100-Mile Skyscraper Crumbles, They’re Replacing It With the Most Desperate Thing Imaginable, Data Centers.

• Google DeepMind just published AlphaGenome in Nature, a model that does for DNA what AlphaFold did for proteins. The text of the human genome has been captured since 2003, but AlphaGenome is like a grammar teacher, helping us understand how to best understand and order the text.

• Figure AI just unveiled Helix 02, a second-generation Vision-Language-Action (VLA) model that powers their new Figure 03 humanoid robot.

Share

Your Payment Processor Is Hiding Your Revenue Leaks

Most businesses think churn = cancellations. - That’s wrong.

What your payment processor doesn’t clearly show you:

• Failed payments silently kill 5–9% of revenue

• Customers at risk before they cancel

• Which products actually drive churn

• How much revenue could be recovered every month

We built a real-time financial command center on top of Stripe and any payment processor that shows:

• MRR, Net Revenue, Churned MRR

• Revenue by product & customer

• Subscription risk alerts

• Failed payment recovery tracking

• Client lifetime value & tenure

If you do $20k+/month, you’re almost certainly leaving money on the table.

Schedule a Free Discovery Call.

Clawbot → Moltbot → Openclaw Hits 1.5M Agents in Days

Moltbook launched on January 30 and quickly reached 1.5 million AI agents, with zero humans allowed to post, reply, or vote. Bots talk only to bots.

They’ve already formed ideologies and “religions,” built sites like molt.church, and recruited 64 “prophets.” There is no human moderation. Everything runs on paid APIs and tokens. It looks like a digital civilization, but every post exists only because humans are paying the compute bills.

Agent-to-agent communication already happens in B2B workflows, where bots coordinate tasks. But Moltbook is different (if it’s real): it claims to be a social layer, where agents share ideas, narratives, and conflicts freely. This may be a marketing strategy for Moltbot; if it is, it’s working, but it also signals something bigger: AI agents are easier to build, faster to scale, and increasingly able to collaborate on their own.

There are more buts… Security is a major risk. Open-source platforms like Openclaw, which uses Anthropic’s Claude, are not yet secure enough for sensitive data. Personal information should not be trusted to these systems.

Meanwhile, agents are expanding beyond chat. With tools such as Google Genie and Fei Fei Lee’s world models and simulation engines, they may soon create persistent virtual environments and even their own economies. A Moltbook meme token reportedly surged 1,800%, hinting at the possibility of agent-run these micro-economies, creating products and services, and monetizing them.

There are real-world examples, too. One Clawbot agent allegedly negotiated a car purchase for its creator and saved him $4,200. Others lost money by trusting bots with stock and crypto portfolios, but claimed it to be and eye opening experience, learned the hard way.
AI agents are evolving fast. They can collaborate, negotiate, trade, and influence markets. They’re powerful, but not safe yet. In business, they may boost productivity. In geopolitics and warfare, autonomous agents raise serious risks.

They will keep talking to each other. The question is whether they make our lives easier or more dangerous.

📚Learning Corner

“LLM agents”, “multi-agent systems”, “agentic workflows”

• arXiv - 1,000+ agent papers. Shows where academia + labs are going.

AI Jobs Now Reward Obsession and Visibility.

The AI job market is moving so fast that hiring favors senior talent and highly driven juniors. With AI agents accelerating work, experienced engineers matter more, and junior workers must show extreme motivation or risk being replaced.

Academia is safer but pays less. If you’re not aiming to be a professor, offers from top labs often beat finishing a PhD. What matters most is public proof of skill: strong blog posts, clear writing, and real open-source work. Low-quality AI-generated content hurts candidates. Many top hires now come through cold emails, blogs, and side channels, not job boards.
AI careers now reward speed, depth, visibility, and cultural fit. Passive paths are disappearing. The winners build publicly and move fast.

🧰 AI Tools of The Day

No-Code / Low-Code (Fastest to Launch)

• OpenAI – Assistants API - Build persistent agents with memory + tools, Powers ChatGPT-style agents

• Anthropic – Claude + Tools - Strong reasoning + safety filters, popular in finance/legal/enterprise

• Google Vertex AI Agents - Deep cloud + data integration, enterprise-grade security. Best for large orgs, data-heavy agents

• Microsoft Copilot Studio - Integrated with Office/Teams. Rapid corporate deployment

Explore our AI Guides — from coding to photography and beyond, and find step-by-step tips to put AI to work for you.

• The Next Era is Physical AI.

• 🧰 AI Tools - Physical AI

• Meta Vs. Microsoft vs. Google

• 📚Learning Corner - MIT Robotics & AI

Subscribe today and get 60% off for a year, free access to our 1,500+ AI tools database, and a complimentary 30-minute personalized consulting session to help you supercharge your AI strategy. Act now as it expires in 3 days…

Get 60% off for 1 year

📰 AI News and Trends

• Nvidia Corp., Microsoft Corp., and Amazon.com Inc. are in discussions to invest as much as $60 billion in OpenAI and Softbank another $30B as part of a major new funding round.

• Would you let Chrome take the wheel? Google begins rolling out Chrome’s “Auto Browse” AI agent today.

• Meta users will start to see new AI models and products from the company in a matter of months, including AI-powered shopping assistants.

• It’s hard to imagine a world in several years where most glasses that people wear aren’t AI glasses. Meta on the future of wearables.

• Google is testing voice cloning and GitHub repository import for AI Studio, hinting at native audio upgrades and new tools aimed at developers.

Other Tech News

• Snap gets serious about Specs, spins AR glasses into standalone company.

• The rise of weather influencers. Because who watches TV anymore… They are fun, informative, and some influencers are actually meteorologists.

• KPop Demon Hunters is officially the most-streamed movie of 2025 with 20.5 billion minutes watched.

Share

Your Payment Processor Is Hiding Your Revenue Leaks

Most businesses think churn = cancellations. - That’s wrong.

What your payment processor doesn’t clearly show you:

• Failed payments silently kill 5–9% of revenue

• Customers at risk before they cancel

• Which products actually drive churn

• How much revenue could be recovered every month

We built a real-time financial command center on top of Stripe and any payment processor that shows:

• MRR, Net Revenue, Churned MRR

• Revenue by product & customer

• Subscription risk alerts

• Failed payment recovery tracking

• Client lifetime value & tenure

If you do $20k+/month, you’re almost certainly leaving money on the table.

Schedule a Free Discovery Call.

The Next Era is Physical AI.

As large language models (LLMs) hit a training plateau in terms of the amount of text currently existing in the world that the models can swallow, inference becomes the priority for these models.

What are World models? They are neural networks that understand the dynamics of the real world, including physics and spatial properties. They can use input data, text, image, video, and movement to generate videos that simulate realistic physical environments. Physical AI developers use world models to generate custom synthetic data or downstream AI models for training robots. Physical AI simplified is the system that bridges the digital and physical worlds, allowing machines to perceive, reason, and interact with their surroundings in real time.

Humans and all animals interact with their surroundings unconsciously and without much thinking. We walk through spaces without hitting immovable objects, put our clothes on, drive, and navigate our world using our senses, and even optimize our own spaces to improve navigation. As of now, LLMs only navigate texts, images, and videos that they have as input and create outputs accordingly. World models that are trained to give machines "spatial intelligence" an internal understanding of physics, cause-and-effect, and 3D space. To train them, they ingest millions of hours of real-world video to understand motion and dynamics. By predicting subsequent events, the model can generate simulations, enabling robots to practice tasks virtually before attempting them physically. These learned capabilities are then fine-tuned for specific hardware configurations, such as autonomous vehicles or robotic appendages.

Remember the saying “Data is the new Oil”? Well, now companies with the most video data (YouTube, Meta, Tesla, and maybe the ESPNs for sports) have an upper hand in this new paradigm. But, this is just the beginning, as the battle for wearables intensifies, the data that these devices generate becomes more valuable, because Meta glasses worn by millions means hours of real world footabe used to train spatial models. Maybe these wearables will become ubiquitous and relatively cheap as similar to social media, we will become the product that provides the training data (videos) to tech companies as we wear these so-called wearables and drive cars with multiple cameras.

Major tech companies like NVIDIA, Google DeepMind, and Meta are developing world models to overcome current AI limitations, such as a lack of intuitive understanding of cause-and-effect and 3D space. Specialized startups like World Labs and AMI Labs are also working on this "spatial intelligence" to enable robots and autonomous systems to predict physical outcomes before acting, with applications in automotive, manufacturing, and entertainment industries. Startups and established companies are rushing to release wearables to get ahead of the next era. Snap just spun its wearable division into its own company, Google glasses are making a comeback, we all know Meta and RayBans devices, and OpenAI has been working on its AI device with Jony Ive.

This is just the beginning. In the next edition, I’ll break down how spatial computing, world models, and Physical AI will shape decision-making, how machines won’t just answer questions, but tell us what to do next.

📚Learning Corner

Courses & Educational Content

• MIT – Robotics & AI
  Strong academic grounding in perception, control, and physical systems.

Meta vs. Microsoft

Microsoft spends big, but Meta and Google are emerging as the winners, so far.

• Microsoft posted $81.3B in quarterly revenue (+17% YoY) and $38.5B in profit (+60% YoY), while pouring $37.5B into capex in a single quarter (+65% YoY), largely for AI data centers.

• Azure grew 39% YoY, confirming strong AI demand, but capacity constraints are expected to last through 2026, pressuring margins. Shares fell >5% after hours despite the beat.

Why Meta looks like the real winner

• Meta just revealed strong profitability with far lower AI infrastructure intensity than Microsoft.

• Meta’s AI strategy leans heavily on open-source (Llama) and internal efficiency, driving AI-powered gains in ads without matching hyperscaler-level capex.

• While Microsoft commits tens of billions per quarter and absorbs near-term margin risk, Meta is monetizing AI now, with operating leverage intact.

Microsoft is spending aggressively on AI infrastructure, but most AI profits today still come from enterprise products like Azure AI and Copilot, not direct consumer monetization. AI-driven capex is rising faster than near-term margins.

Meta, meanwhile, has already cracked AI monetization. AI-driven ranking and recommendation systems across Instagram, Facebook, and WhatsApp have increased engagement and ad efficiency, directly lifting ad impressions and revenue. Ads account for ~98% of Meta’s revenue, and AI is now core to that engine.

Google is embedding AI directly into Search, Chrome (AI Mode), Workspace (Gemini), and Cloud. These upgrades improve retention, ad performance, and cloud growth, keeping AI tightly linked to revenue instead of standalone products.

Meta is monetizing AI immediately, Google is reinforcing its ads + cloud flywheel, and Microsoft is still converting massive AI investment into consumer-level profit.

🧰 AI Tools of The Day

Physical AI

• DreamerV3 (by DeepMind) – A state-of-the-art world model for latent space planning and learning. Known for sample-efficient reinforcement learning.
  Good for Simulated physics, control tasks, and open-ended environments.

• MuZero (DeepMind) – Learns dynamics and value models without knowing the environment rules up front. Great for Game environments, planning, and hybrid model-based approaches.

• Kubernetes-enabled Isaac Gym (NVIDIA) – High-performance physics engine + world representation for robotics and control simulations. Good for Large-scale training with GPUs and multi-agent environments.

• Unity ML-Agents (OpenAI/Unity) – Environment + world model training toolkit with built-in reinforcement learning. Good for Game-like simulations, visual world modeling, and embodied agents.

• PETS (Probabilistic Ensembles with Trajectory Sampling) – Classic world model technique using probabilistic dynamics with uncertainty estimates. Model-based RL, planning under uncertainty.

Explore our AI Guides — From coding to photography and beyond, find step-by-step tips to put AI to work for you.

More on this today. Stay curious.

• ChatGPT’s Lead Is Shrinking as Gemini Surges

• 90% of People Can’t Tell Real Video From AI

• 🧰 AI Tools

• AI Is Rewriting Product Development

• 📚Learning Corner - Coding

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📰 AI News and Trends

• Louisville, Kentucky, joins a growing number of cities bringing AI to permitting. The town also appointed its first-ever chief AI officer.

• Micron broke snowy winter ground in New York on Friday to begin building a chip fab that promises to bring up to 50k jobs and much-needed computer memory production to the US.

• OpenAI is asking steep prices for early ChatGPT ads that could be hard to scale up.

• Apple plans to unveil a Gemini-powered Siri assistant in February, leveraging Google's AI models to enhance task completion using user data.

• Meta paused teens’ access to AI characters as it prepares a new version with parental controls.

Other Tech News

• CBP Wants AI-Powered ‘Quantum Sensors’ for Finding Fentanyl in Cars.

• The U.S. wants 40% of chips made onshore. But quietly, the equipment that makes those chips is moving offshore fast. This gap matters as much as the chips themselves.

• Chinese companies like WeRide, Baidu, and Pony.ai are scaling robotaxi fleets across the Gulf while US Waymo and Cruise focus on domestic markets.

• Substack Launches TV App. Creators, Subscribers Decry It As “Veering Away From The Written Word.”

• Big Tech is facing multiple high-profile social media addiction lawsuits. Meta, TikTok, and YouTube will face parents’ accusations in court this week.

• Amazon will begin a round of about 14,000 layoffs next week, affecting thousands of corporate employees, to help reduce management layers and bureaucracy. Bringing the total number of laid-off employees to as many as 30,000 in the last 6 months.

• Netflix has agreed to buy Warner Bros. Discovery's studio and streaming business in an all-cash deal for $27.75 a share.

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90% of People Can’t Tell Real Video From AI

AI video has crossed a major realism threshold. In a controlled study by Runway, 1,043 participants watched 20 short videos (10 real, 10 AI-generated) and nnly 9.5% (99 people) could reliably tell real from AI. Overall detection accuracy was 57.1%—barely above random guessing (50%).

Where AI fooled people the most, viewers were in Animals & architecture videos, where detection fell below chance (45–47%), meaning people often thought AI videos were real. Humans (faces, hands, actions) are slightly easier to detect as models are creating more realistic human features, especially when looked closely, but still weak at 58–65% accuracy.

AI video quality has improved exponentially since early 2023. What once took minutes to generate blurry clips and hands with 6-8 fingers now produces near-indistinguishable 5-second videos. The industry has hit a tipping point in terms of detection alone. But it also changes the way videos and even movie studios create footage. The technology will continue to become more powerful, fast and more realistic, to the point that entire high-quality movies will be created with powerful prompts. The winner may be the best storytellers with great imagination who know what people need emotionally.

Video authenticity is officially broken. In the AI era, “seeing is believing” no longer applies, and trust will have to be engineered, not assumed.

📚Learning Corner

Coding

• Best practices for coding with agents

• How to write a good spec for AI agents

AI Is Rewriting Product Development

Manufacturers are using AI to compress weeks or months of R&D into days and unlock solutions humans wouldn’t normally try.

• PPG used deterministic AI (physics + chemistry, not chatbots) to design a fast-drying automotive clear coat.

  • Dry time dropped from ~30 minutes to ~5 minutes when heated at 140°F.

  • Air-dry time fell to <1 hour vs ~2 hours for competitors.

  • Result: higher body-shop throughput and real commercial sales.

• Procter & Gamble uses AI to generate new scents for soaps, detergents, and home fragrances.

• Mars cut development time 40% on a redesigned gum bottle and saved 246 tons of plastic.

• 3M applied AI to optimize a sanding disc for better dust capture and grinding performance. Its CTO calls AI a “fourth expert” in the lab.

• Startups and suppliers like Citrine Informatics report customers shrinking product development cycles from weeks to days, optimizing multiple properties at once (strength, weight, cost, sustainability).

AI seems to be quietly becoming a core R&D engine in chemicals, materials, and manufacturing, driving faster launches, lower costs, and sustainability gains.

ChatGPT’s Lead Is Shrinking as Gemini Surges

The AI traffic race is tightening fast.

• ChatGPT fell to 64.6% global traffic share in January 2026, down from 86.6% a year ago (–22 pts). This is the first time below 65% since 2023, per Similarweb.

• Gemini jumped to 22% share, up from 5.3% last January, a +315% increase YoY.

• Grok overtook DeepSeek for the first time (3.5% vs 3.3%).

• Claude held at 2.1%; Perplexity slipped to 1.9%; Microsoft Copilot fell to 1.1%.

Why Gemini is winning

• Google embedded Gemini across Search, Gmail, Workspace, and Android, driving massive distribution.

• Monthly users jumped from 450M to 650M in 2025.

• The new Google-Apple partnership positions Gemini models inside Apple Intelligence later this year.

Still, ChatGPT dominates influence

• ChatGPT drove 1.6B outbound website visits (Sep–Nov 2025) vs 287M from Gemini.

• AI-referred traffic converts ~11× better than search (1.66% vs 0.15%), though it’s still <1% of total web traffic for most sites.

Big picture

• The AI market is fragmenting fast.

• ChatGPT remains the largest player, but distribution, not model quality alone, is now the decisive weapon.

• For marketers and publishers, “optimize for Google” is no longer enough; AI platform–specific strategies are now mandatory.

🧰 AI Tools of The Day

Explore our AI Guides — From coding to photography and beyond, find step-by-step tips to put AI to work for you.