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

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

  • The $700 Billion Capex Arms Race

  • Breakdown of Big Tech 2026 Capex Guidance

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

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

• What Are AI Evals and Why Do They Matter?

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

📰 AI News and Trends

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

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

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

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

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

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

Other Tech News

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

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

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

• Emergency First Responders Say Waymos Are Getting Worse

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

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

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

Alphabet’s Q1 2026: The AI Bet Pays Off

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

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

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

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

The $700 Billion Capex Arms Race

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

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

Breakdown of Big Tech 2026 Capex Guidance

• Amazon: ~$200 Billion

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

• Alphabet: $180 Billion – $190 Billion

• Meta: $125 Billion – $145 Billion

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

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

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

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

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

The Bull Case for $900 Billion

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

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

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

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

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

The Valuation Math and IPO Horizon

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

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

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

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

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📚Learning Corner

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

What Are AI Evals and Why Do They Matter?

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

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

Types of AI Evals

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

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

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

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

Why Evals Are Crucial for Everyone

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

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

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

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

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

The Way Forward

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

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

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🧰 AI Tools of The Day

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

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

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

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

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

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

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

  • The Meta-Manus Deal: A Geopolitical Flashpoint

  • The New Era of Investment Restrictions

  • Chinese startups.

  • State-Driven vs. Private-Sector Models

  • The “Dual-Exit” Trap

• 🧰 AI Tools - Top Chinese AI Models

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

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

📰 AI News and Trends

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

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

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

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

Other Tech News

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

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

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

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

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

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

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

The Meta-Manus Deal: A Geopolitical Flashpoint

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

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

The New Era of Investment Restrictions

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

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

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

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

State-Driven vs. Private-Sector Models

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

The Chinese Model: State-Led Innovation

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

The US Model: Private-Sector Dynamism

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

The “Dual-Exit” Trap

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

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

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📚Learning Corner

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

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

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

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

1. High-Profile AI Startups: The Funding Freeze

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

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

2. Strategic Partnerships and Global Alliances

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

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

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

3. Supply Chain and Operational Contracts

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

The Nvidia “H20” Crisis

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

Apple’s “China-Light” Strategy

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

Tesla’s Data Security Tightrope

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

4. The Future of Venture Capital

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

Conclusion: The Decoupling of the AI Ecosystem

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

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🧰 AI Tools of The Day

Top Chinese AI Models

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

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

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

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

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

📊 Quick Comparison

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

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

  • The Orwellian Parallel. Surveillance Capitalism in the Workplace

  • The Hacker Strategy

  • The Broader Trend

  • The Value of Behavioral Data for AI

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

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

  • Why Table Tennis? Why Does This Matter?

  • Where will we be in 5 years

  • The Real Takeaway for You

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

📰 AI News and Trends

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

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

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

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

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

Other Tech News

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

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

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

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

The New Panopticon

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

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

The Orwellian Parallel

Surveillance Capitalism in the Workplace

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

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

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

The Hacker Strategy

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

Keylogging (T1056.001)

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

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

The Broader Trend

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

Liquidating Defunct Startups for Data

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

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

The Rise of “Bossware” and AI Monitoring

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

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

The Value of Behavioral Data for AI

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

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

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

Future Trajectories: Beyond the Keyboard

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

Emerging technologies and future trends include:

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

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

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

Conclusion

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

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🧰 AI Tools of The Day

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

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

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

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

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

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

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

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

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

Last week, we got that moment for physical robotics.

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

Why Table Tennis? Why Does This Matter?

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

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

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

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

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

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

This Didn’t Happen in Isolation

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

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

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

Where Are We in 5 Years?

Compound this trajectory, and the picture gets serious:

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

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

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

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

The Real Takeaway for You

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

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

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

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

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

• 🧰 AI Tools - Autonomous Agents

• Huawei, DeepSeek, and the Big Threat.

• 📚Learning Corner - The Complete Perplexity Mastery Guide

📰 AI News and Trends

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

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

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

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

Other Tech News

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

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

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

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

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

• Gucci-branded Google smart glasses are coming next year

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

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

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

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

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🧰 AI Tools of The Day

Autonomous Agents

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

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

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

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

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Huawei, DeepSeek, and the Big Threat.

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

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

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

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

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

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

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• The Complete Perplexity Mastery Guide

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

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

  • Requirements for Full Compliance

  • Tools of the Trade

  • The Webmaster’s “To-Do List.”

• 🧰 AI Tools - Audit & Guide Compliance

• 📚Learning Corner - Start Building with Anthropic

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

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📰 AI News and Trends

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

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

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

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

Other Tech News

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

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

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

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

How AI Is Fueling a New Wave of ADA Lawsuits Online

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

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

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

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

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

1. Requirements for Full Compliance

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

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

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

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

2. 🧰 AI Tools of The Day

AI Tools to Audit & Guide Compliance

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

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

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

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

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

3. The Webmaster’s “To-Do List.”

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

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

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

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

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

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

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

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• Start Building with Anthropic

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

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

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

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

This is what we have today:

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

  • The 2025 Wearables Boom

  • Meta is running the show

  • Apple gets ready

  • The Android Squad and global rivals

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

  • The Contenders: A Stack, Not a Monopoly

  • The Hype vs. Reality

  • Layoffs, Burnout, and “AI Psychosis.”

  • Who wins the race

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

• 📚Learning Corner - Lovable

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📰 AI News and Trends

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

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

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

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

• Anthropic Pushes Further Into the Office With Claude for Word

• Meta must face a youth addiction lawsuit by Massachusetts

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

• Microsoft Plots New Copilot Features Inspired by OpenClaw

Other Tech News

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

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

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

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

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

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

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

The 2025 Wearables Boom

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

Meta is Running the Show

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

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

Apple gets ready

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

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

The Android Squad and Global Rivals

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

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

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

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

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🧰 AI Tools of The Day

AI-powered mobile app builders.

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

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

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

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

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

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

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

The Contenders: A Stack, Not a Monopoly

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

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

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

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

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

The Hype vs. The Reality

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

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

Layoffs, Burnout, and “AI Psychosis”

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

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

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

Who Wins the Race?

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

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

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

• 🔴 What’s Mythos and what can it do

  • Claude Mythos Preview & Project Glasswing

  • The Emergency Government Response

  • The Core Problem

  • As an Investor

  • What time is it?

• 🧰 AI Tools - The Top 20 AI Tools Playbook

• AI starts reshaping jobs in real time

• 📚Learning Corner - Anthropic courses

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📰 AI News and Trends

• Everyone thinks the Musk vs Altman lawsuit is a billionaire grudge match. Two egos, one grudge, a $150 billion damages number designed for headlines.

• Anthropic weighs building its own AI chips

• Tesla Considers Producing Smaller, Cheaper SUV ala BYD.

• Finally, CoreWeave gets a break after Meta commits to spending an additional $21 billion with the company as AI costs keep rising
  

Other Tech News

• Volkswagen joins the list of car makers ending or slowing down E.V. Production.

• Waymo is sharing data about potholes to select cities in the interest of making streets safer to drive for humans and robots.

• Now, anyone can buy a Unitree Robot on Alibaba

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AI starts reshaping jobs in real time

A new survey from Epoch AI and Ipsos shows AI is already impacting the workforce at scale:

• 20% of full-time U.S. workers say AI has replaced parts of their job

• 15% report new tasks created by AI, but replacement is outpacing augmentation

• 50% of U.S. adults used AI in the past week, signaling rapid adoption

Usage is still shallow:

• 62.5% only use AI for 1–2 quick tasks per day

• Just ~6% are heavy users

A major shift is happening outside companies:

• ~50% of workers use personal or free AI tools, not employer-provided systems

Early signals of what’s next:

• 8% of users already use AI agents (tools that act autonomously)

• Top use cases: search (80%), writing (59%), brainstorming (53%)

Platform share:

• ChatGPT leads with 31% usage

• Google Gemini at 21%

• Microsoft Copilot at 10.5%

Macro impact:

• Goldman Sachs estimates ~16,000 jobs/month are being displaced due to AI

• AI could automate ~25% of total work hours

AI adoption is broad but still early in depth. The bigger signal is that job replacement is already outpacing job creation, and most of it is happening outside formal enterprise controls.

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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.

🔴 What’s Mythos and what can it do

Claude Mythos Preview & Project Glasswing

The Mythos/Glasswing announcement is one of the most consequential AI-security developments of the decade. Anthropic announced Project Glasswing, a new initiative bringing together AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks, with the explicit goal of securing the world’s most critical software.

Claude Mythos Preview is Anthropic’s unreleased frontier AI model that autonomously finds and exploits critical software vulnerabilities at a level surpassing that of all but the most skilled human security experts.

Anthropic used Claude Mythos Preview to identify thousands of zero-day vulnerabilities, previously unknown flaws, in every major operating system and every major web browser, many of them critical, largely autonomously without human steering.

Three documented examples that should make every technologist pause:

1. A 27-year-old vulnerability in OpenBSD - one of the most security-hardened OS systems used in firewalls and critical infrastructure, that allowed an attacker to remotely crash any machine simply by connecting to it.

2. A 16-year-old vulnerability in FFmpeg (used by virtually all video software) in a line of code that automated testing tools had hit five million times without ever catching the problem.

3. Mythos autonomously found and chained together multiple Linux kernel vulnerabilities, allowing escalation from ordinary user access to complete machine control.

On the CyberGym cybersecurity vulnerability reproduction benchmark, Mythos Preview scored 83.1% versus Claude Opus 4.6’s 66.6%, a substantial capability gap even within Anthropic’s own model family.

The Emergency Government Response

Treasury Secretary Scott Bessent and Fed Chair Jerome Powell summoned Wall Street leaders to an urgent meeting at Treasury headquarters in Washington to ensure banks are aware of the risks Mythos poses and are taking defensive precautions.

The bank heads, except Jamie Dimon, were already in Washington for a Financial Services Forum board meeting when the special Mythos-specific session was called.

Anthropic briefed senior U.S. government officials, including CISA and the Center for AI Standards and Innovation, about Mythos’s offensive and defensive cyber applications ahead of the release.

Regulators are now framing AI risk not merely as a technological challenge, but as a potential catalyst for systemic events, meaning a single AI-enabled breach of a major bank could have cascading consequences across the entire financial system.

The Core Problem

This is the most important concept to understand. The same capabilities that make AI models dangerous in the wrong hands make them invaluable for finding and fixing flaws in critical software — and for producing new software with far fewer security bugs from the start.

Anthropic CEO Dario Amodei framed it this way: “The dangers of getting this wrong are obvious, but if we get it right, there is a real opportunity to create a fundamentally more secure internet and world than we had before the advent of AI-powered cyber capabilities.” AI capabilities at this level will proliferate; given the rate of AI progress, it will not be long before such capabilities reach actors who are not committed to deploying them safely.

The Trump administration’s engagement on Mythos comes as Anthropic is simultaneously challenging the Department of Defense over its designation of the company as a supply-chain risk to national security, a classification Anthropic is contesting in court. The DOD has, however, continued to use Claude during the Iran war.

💡 As an Investor

Cyber stocks already slumped when Fortune discovered the draft Anthropic blog post revealing Mythos’s capabilities. But the medium-term thesis flips bullish: AI-driven cybersecurity demand is going to explode. CrowdStrike is literally a Project Glasswing partner. Palo Alto Networks, too, which validates the AI + cybersecurity investment theme hard.

What Time is it?

• Assume zero-days exist in your stack. Mythos just proved decades-old bugs hide in “hardened” systems.

• Get ahead of AI-powered penetration testing tools built on models like this, which will reach SMBs within 12–18 months.

• Anthropic is committing up to $100M in usage credits for Mythos Preview across Project Glasswing, plus $4M in direct donations to open-source security organizations; watch for downstream security tooling to emerge from this.

• Audit your critical vendor stack (hosting, payments, auth) and ask your providers what their AI-threat posture looks like.

Mythos is the clearest proof yet that AI has crossed a threshold; it is now genuinely superhuman at a class of high-stakes tasks. The government’s response signals that regulators understand this isn’t just a tech story; it’s a financial stability and national security story. Can defenders (Project Glasswing) deploy these capabilities faster than bad actors can access similar models through other means?

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📚Learning Corner

Anthropic courses - Courses from Prompting 101 to Agent Building and how to use Claude Code.

Maja Vujinovic is a seasoned global executive and pioneer at the intersection of fintech, blockchain, and AI. She got into mobile payments as early as 2005, building infrastructure across Africa and Latin America, then recognized Bitcoin’s potential in 2010 making her one of the earliest serious operators in the crypto space.

She spent time as CIO of Emerging Tech at General Electric, where she launched GE’s first blockchain initiatives and facilitated what’s considered one of the world’s first trade-finance deals on blockchain, a collaboration with JPMorgan that helped inspire JP Morgan Quorum and blockchain-as-a-service offerings from Microsoft and Amazon.

After GE, she ran OGroup, an early crypto and AI fund and advisory platform partnered with $4B+ in family office capital, investing in and operating 45+ companies. She’s now CEO of Digital Assets at FG Nexus.

A few things worth knowing going into the conversation:

• She brings a rare geopolitical lens to digital assets, having lived in 16 places and worked across 5 continents

• She’s deeply connected in both Wall Street and crypto-native circles, a genuine bridge between the two worlds

• She’s a board member of Civic, Catalan.ai, CoinDesk, and the Longevity Science Foundation, signaling interests that overlap with your own, AI, digital assets, and longevity/biotech

• She’s a law school graduate (Emory), so expect sharp, precise thinking

She speaks regularly at Davos, FII Institute, Blockworks DAS, and the FT Digital Assets Summit.

This is our live conversation with Maja.

Enjoy…

• 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

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

• 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)

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

• 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

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

• 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.

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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.

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?

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

• 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.”

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

• 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.