Washington, D.C. – March 7, 2025 – The Office of the Comptroller of the Currency (OCC), a key regulator for U.S. banks, announced today that banks can now get more involved with cryptocurrencies like Bitcoin. This decision marks a big shift in how banks can handle digital money.
In a new statement, the OCC said banks are allowed to offer custody services for cryptocurrencies. This means they can hold and manage these digital assets for customers, much like they do with regular money or valuables. Banks can also act as “nodes” in blockchain networks—the tech behind cryptocurrencies—which could help verify transactions.
The OCC also loosened some rules around stablecoins, a type of cryptocurrency tied to traditional money like the U.S. dollar. Previously, banks had to prove they could handle the risks of crypto before jumping in. Now, they can start these activities without as many upfront checks, though they still need to follow basic safety rules.
This change reverses some caution put in place after the collapse of FTX, a major crypto company, in 2022. Back then, regulators worried about banks getting too risky with digital money. Today’s update shows a more open attitude, though the OCC stressed that banks must still manage risks carefully and follow the law.
The announcement came on the same day as a White House summit, raising eyebrows about the timing. Some see it as a sign of growing support for crypto in the U.S., while others wonder if banks are ready for the fast-moving world of digital currencies.
For everyday people, this could mean more ways to use crypto through their local bank. For now, it’s up to the banks to decide how—and if—they’ll take the plunge.
In a world where artificial intelligence is advancing at breakneck speed, Alibaba Cloud has just thrown its hat into the ring with a new contender: QwQ-32B. This compact reasoning model is making waves for its impressive performance, rivaling much larger AI systems while being more efficient. But what exactly is QwQ-32B, and why is it causing such a stir in the tech community?
What is QwQ-32B?
QwQ-32B is a reasoning model developed by Alibaba Cloud, designed to tackle complex problems that require logical thinking and step-by-step analysis. With 32 billion parameters, it’s considered compact compared to some behemoth models out there, yet it punches above its weight in terms of performance. Reasoning models like QwQ-32B are specialized AI systems that can think through problems methodically, much like a human would, making them particularly adept at tasks such as solving mathematical equations or writing code.
Built on the foundation of Qwen2.5-32B, Alibaba Cloud’s latest large language model, QwQ-32B leverages the power of Reinforcement Learning (RL). RL is a technique where the model learns by trying different approaches and receiving rewards for correct solutions, similar to how a child learns through play and feedback. This method, when applied to a robust foundation model pre-trained on extensive world knowledge, has proven to be highly effective. In fact, the exceptional performance of QwQ-32B highlights the potential of RL in enhancing AI capabilities.
Stellar Performance Across Benchmarks
To test its mettle, QwQ-32B was put through a series of rigorous benchmarks. Here’s how it performed:
AIME 24: Excelled in mathematical reasoning, showcasing its ability to solve challenging math problems.
Live CodeBench: Demonstrated top-tier coding proficiency, proving its value for developers.
LiveBench: Performed admirably in general evaluation tasks, indicating broad competence.
IFEval: Showed strong instruction-following skills, ensuring it can execute tasks as directed.
BFCL: Highlighted its capabilities in tool and function-calling, a key feature for practical applications.
When stacked against other leading models, such as DeepSeek-R1-Distilled-Qwen-32B and o1-mini, QwQ-32B holds its own, often matching or even surpassing their capabilities despite its smaller size. This is a testament to the effectiveness of the RL techniques employed in its training. Additionally, the model was trained using rewards from a general reward model and rule-based verifiers, which further enhanced its general capabilities. This includes better instruction-following, alignment with human preferences, and improved agent performance.
Agent Capabilities: A Step Beyond Reasoning
What sets QwQ-32B apart is its integration of agent-related capabilities. This means the model can not only think through problems but also interact with its environment, use tools, and adjust its reasoning based on feedback. It’s like giving the AI a toolbox and teaching it how to use each tool effectively. The research team at Alibaba Cloud is even exploring further integration of agents with RL to enable long-horizon reasoning, where the model can plan and execute complex tasks over extended periods. This could be a significant step towards more advanced artificial intelligence.
Open-Source and Accessible to All
Perhaps one of the most exciting aspects of QwQ-32B is that it’s open-source. Available on platforms like Hugging Face and Model Scope under the Apache 2.0 license, it can be freely downloaded and used by anyone. This democratizes access to cutting-edge AI technology, allowing developers, researchers, and enthusiasts to experiment with and build upon this powerful model. The open-source nature of QwQ-32B is a boon for the AI community, fostering innovation and collaboration.
The buzz around QwQ-32B is palpable, with posts on X (formerly Twitter) reflecting public interest and excitement about its capabilities and potential applications. This indicates that the model is not just a technical achievement but also something that captures the imagination of the broader tech community.
A Bright Future for AI
In a field where bigger often seems better, QwQ-32B proves that efficiency and smart design can rival sheer size. As AI continues to evolve, models like QwQ-32B are paving the way for more accessible and powerful tools that can benefit society as a whole. With Alibaba Cloud’s commitment to pushing the boundaries of what’s possible, the future of AI looks brighter than ever.
On March 6, 2025, the President of the United States issued an Executive Order officially establishing the Strategic Bitcoin Reserve (SBR) and the United States Digital Asset Stockpile (USDAS). This landmark decision signals a major shift in the nation’s approach to digital assets, reinforcing Bitcoin’s status as a strategic financial asset while setting the foundation for digital asset management at the federal level.
Why Is the U.S. Creating a Strategic Bitcoin Reserve?
Bitcoin (BTC) has long been referred to as “digital gold” due to its fixed supply of 21 million coins and its strong security. Unlike traditional fiat currencies, Bitcoin cannot be printed or manipulated by central authorities, making it a valuable hedge against inflation and economic uncertainty.
The United States government already holds a significant amount of Bitcoin, mainly through asset forfeitures and law enforcement seizures. However, there has been no structured policy for managing these assets strategically—until now. By consolidating all forfeited BTC into a sovereign Bitcoin reserve, the U.S. aims to:
Strengthen its position in the global digital economy
Enhance financial security by holding Bitcoin as a long-term store of value
Establish Bitcoin as a key national asset alongside gold and other strategic reserves
Key Takeaways from the Executive Order
1. Creation of the Strategic Bitcoin Reserve (SBR)
The Department of the Treasury will oversee the SBR, which will hold all BTC forfeited through criminal or civil proceedings.
Government-held BTC will not be sold; instead, it will be retained as a reserve asset.
Strategies will be developed to acquire additional Bitcoin, as long as they are budget-neutral and do not place additional financial burdens on taxpayers.
2. Establishment of the United States Digital Asset Stockpile (USDAS)
In addition to Bitcoin, other government-seized digital assets (such as Ethereum and stablecoins) will be consolidated into the USDAS.
The Treasury Department will be responsible for managing and safeguarding these assets.
Unlike Bitcoin, these assets may be liquidated under certain circumstances, such as funding law enforcement operations or returning funds to victims of crimes.
3. Prohibitions on Liquidating Government-Held Bitcoin
The Executive Order prohibits the government from selling BTC in the Strategic Bitcoin Reserve unless under specific legal circumstances.
This policy contrasts with previous auctions of seized Bitcoin, where the U.S. government sold off assets at significantly lower prices than their future valuations.
By holding Bitcoin instead of selling it, the U.S. acknowledges Bitcoin’s long-term value as a digital asset.
4. Legal and Investment Evaluation
The Secretary of the Treasury must conduct a comprehensive legal and investment review within 60 days to outline the best management strategies for the SBR and USDAS.
Agencies are required to submit full reports on their current digital asset holdings within 30 days.
How Will This Affect Bitcoin and the Digital Asset Market?
1. Increased Legitimacy for Bitcoin
This move further legitimizes Bitcoin as a strategic financial asset, potentially leading to:
Greater institutional and sovereign investment in BTC
Strengthened global confidence in Bitcoin as a store of value
A potential increase in Bitcoin’s price due to long-term government retention
2. Potential Ripple Effects on Global Bitcoin Policy
As the first major government to establish a dedicated Bitcoin reserve, the U.S. could set a precedent for other nations to follow. This may lead to:
More governments adding Bitcoin to their national reserves
Increased global competition for acquiring BTC
Accelerated adoption of Bitcoin as a reserve currency
3. Bitcoin Reserves as a Global Game Theory Strategy
From a game theory perspective, the establishment of a U.S. Bitcoin reserve places pressure on other nations to follow suit. If Bitcoin continues to appreciate in value, any country that delays adopting a strategic reserve will be at a disadvantage compared to those that act swiftly. This creates a Nash equilibrium scenario, where rational actors (governments) must also accumulate Bitcoin to avoid economic and geopolitical disadvantages.
Nations that fail to establish reserves risk:
Losing influence in the emerging Bitcoin-based financial system
Facing competitive disadvantages in international trade if Bitcoin becomes a major reserve asset
Allowing their adversaries to gain a first-mover advantage in digital asset accumulation
Historically, early adopters of transformative financial assets—such as gold reserves in the 19th century or the U.S. dollar’s global dominance after World War II—gained significant economic and strategic power. The same dynamic could unfold with Bitcoin, leading to an inevitable cascade where more countries begin stockpiling BTC as a matter of national security and financial stability.
4. Shift in U.S. Crypto Regulations
The creation of a formalized digital asset policy suggests the U.S. government is moving toward a more structured regulatory framework for crypto assets. Future implications may include:
Stricter compliance measures for digital asset exchanges and custodians
New tax policies and reporting requirements for crypto holdings
Potential future policies governing CBDCs (Central Bank Digital Currencies)
A Historic Moment for Bitcoin
The establishment of the Strategic Bitcoin Reserve is a monumental step in the evolution of Bitcoin’s role in global finance. By recognizing Bitcoin as a critical financial and strategic asset, the U.S. government is signaling its commitment to digital asset adoption and economic innovation.
As the game theory dynamics unfold, other nations will be forced to establish their own Bitcoin reserves or risk falling behind in the digital economy. This decision could significantly impact Bitcoin’s long-term valuation, financial stability, and global adoption. As governments, institutions, and investors react to this historic policy shift, the future of Bitcoin has never looked brighter.
Diffusion Language Models (LLMs) represent a significant departure from traditional autoregressive LLMs, offering a novel approach to text generation. Inspired by the success of diffusion models in image and video generation, these LLMs leverage a “coarse-to-fine” process to produce text, potentially unlocking new levels of speed, efficiency, and reasoning capabilities.
The Core Mechanism: Noising and Denoising
At the heart of diffusion LLMs lies the concept of gradually adding noise to data (in this case, text) until it becomes pure noise, and then reversing this process to reconstruct the original data. This process, known as denoising, involves iteratively refining an initially noisy text representation.
Unlike autoregressive models that generate text token by token, diffusion LLMs generate the entire output in a preliminary, noisy form and then iteratively refine it. This parallel generation process is a key factor in their speed advantage.
Advantages and Potential
Enhanced Speed and Efficiency: By generating text in parallel and iteratively refining it, diffusion LLMs can achieve significantly faster inference speeds compared to autoregressive models. This translates to reduced latency and lower computational costs.
Improved Reasoning and Error Correction: The iterative refinement process allows diffusion LLMs to revisit and correct errors, potentially leading to better reasoning and fewer hallucinations. The ability to consider the entire output at each step, rather than just the preceding tokens, may also enhance their ability to structure coherent and logical responses.
Controllable Generation: The iterative denoising process offers greater control over the generated output. Users can potentially guide the refinement process to achieve specific stylistic or semantic goals.
Applications: The unique characteristics of diffusion LLMs make them well-suited for a wide range of applications, including:
Code generation, where speed and accuracy are crucial.
Dialogue systems and chatbots, where low latency is essential for a natural user experience.
Creative writing and content generation, where controllable generation can be leveraged to produce high-quality and personalized content.
Edge device applications, where computational efficiency is vital.
Potential for better overall output: Because the model can consider the entire output during the refining process, it has the potential to produce higher quality and more logically sound outputs.
Challenges and Future Directions
While diffusion LLMs hold great promise, they also face challenges. Research is ongoing to optimize the denoising process, improve the quality of generated text, and develop effective training strategies. As the field progresses, we can expect to see further advancements in the architecture and capabilities of diffusion LLMs.
If you’ve been scrolling through tech conversations on X recently, you might have spotted John Rush’s thread about the Model Context Protocol (MCP). Shared on March 6, 2025, Rush (@johnrushx, post ID: 1897655569101779201) breaks down why MCP is stealing the spotlight in the AI world—and trust me, it’s not just for tech nerds. Whether you’re a developer, an AI enthusiast, or someone who just wants smarter tools, MCP is set to revolutionize how AI connects with the world. Let’s dive into this protocol, explore its potential, and have some fun along the way!
Picture this: Your favorite AI chatbot, like Claude, isn’t just chatting with you—it’s also pulling data from Gmail, checking the weather, or editing code on GitHub, all in real time, without you needing to jump through hoops. That’s the magic of the Model Context Protocol, or MCP, an open standard launched by Anthropic in November 2024.
MCP is a universal framework that lets AI tools—think chatbots, AI agents, and integrated development environments (IDEs)—connect seamlessly with external systems like Google Drive, Slack, local databases, and cloud storage. John Rush’s X post includes a slick diagram showing AI tools linking to MCP servers, which then bridge to the internet, cloud services, and your personal files. It’s like building a superhighway for AI, letting it zip between systems without getting bogged down in custom coding.
In short, MCP is the Rosetta Stone for AI integration, enabling secure, two-way communication between AI and the tools we use every day. It’s not just a technical upgrade—it’s a game-changer for productivity and innovation.
Why MCP Is a Big Deal: The Pre-MCP Struggle vs. the MCP Revolution
Before MCP, connecting an AI tool to an external system was a developer’s nightmare. Imagine you have 1,000 AI tools (like chatbots or code generators) and 1,000 external tools (like Gmail or GitHub). To make them talk, you’d need to write custom code for each connection via APIs—resulting in a mind-boggling 1 million hard-coded integrations. That’s not just inefficient; it’s a logistical black hole that slows down progress and invites errors.
Then came MCP, and everything changed. As John Rush explains in his X thread, MCP is a standardized protocol that requires just one implementation per AI tool and one per external system. With 10,000 AI tools and 10,000 external tools, that drops the number of connections from 100 million to a mere 20,000. It’s like trading in a clunky old bicycle for a sleek, supersonic jet—suddenly, development becomes faster, simpler, and scalable.
This leap isn’t just technical; it’s transformative. MCP slashes complexity, reduces maintenance headaches, and lets developers focus on building amazing features instead of wrestling with integrations. It’s no wonder Rush calls it “a huge deal”—and he’s absolutely right.
How Does MCP Work? A Fun Look Under the Hood
For the tech-savvy readers, let’s geek out a bit. MCP operates on a client-server architecture that’s as straightforward as it is powerful:
MCP Clients: These are your AI tools—chatbots, IDEs, or AI agents—that want to access data or perform actions in external systems.
MCP Servers: These are the external tools or systems (like Google Drive, Slack, or a local database) that provide the data or functionality AI needs.
The protocol can run on both cloud and local computers, making it incredibly flexible. Developers can set up an MCP server to expose their data or build an MCP client to connect AI tools to those servers. This modular design ensures secure, efficient communication, letting AI tools tap into real-time data without the need for complex, bespoke integrations.
Rush’s X thread includes dazzling demos that bring this to life. For instance, Claude’s desktop app can take a screenshot of a website and convert it to HTML using an MCP server—all you need is a URL. Or picture an AI IDE connecting to GitHub to create a repository and submit a pull request with a simple chat command. It’s like giving your AI X-ray vision and super-speed!
MCP in Action: Real-World Examples That Blow Minds
John Rush’s X thread doesn’t stop at theory—it dives into practical applications that make MCP exciting for everyone. Here are a few jaw-dropping examples:
Claude’s Website Wizardry: Want to analyze a webpage? With MCP, you give Claude a URL, and it uses an MCP server to snap a screenshot and convert it to HTML. No manual screenshots, no hassle—just pure AI magic.
Supercharged AI IDEs: MCP turbocharges AI-powered IDEs, letting them connect directly to GitHub. Your AI can create a new repo, write code, and submit pull requests—all through a chat interface. It’s like having a coding sidekick that never sleeps.
Chatting with Databases: Need to query or update a local database? MCP lets Claude or other AI tools “talk” to your database, making data management as easy as sending a text message.
Slack Superpowers: Connect your AI assistant to Slack via MCP, and it can manage notifications, draft messages, or pull project updates—all with seamless integration.
These examples show how MCP isn’t just for developers—it’s for anyone who wants smarter, more connected AI tools. It’s transforming workflows in software development, business operations, and beyond, making productivity feel effortless and fun.
Why Non-Tech Users Should Get Excited About MCP
You don’t need to be a coder to love MCP. For everyday users, this protocol means AI tools that feel like intuitive, context-aware helpers. Imagine asking your AI to check the weather while drafting an email—thanks to MCP, it can pull data from a weather app and Gmail simultaneously, all in one smooth conversation. Or picture your AI organizing files in Google Drive or summarizing Slack chats, all without you lifting a finger.
MCP’s simplicity lets developers build user-friendly features, so AI tools feel less like clunky software and more like personal assistants. It’s the future of human-AI collaboration, and it’s arriving faster than a speeding bullet!
The Bigger Picture: MCP’s Role in the AI Revolution of 2025
MCP isn’t just a standalone innovation—it’s part of the AI explosion of 2025. As AI tools evolve at warp speed, interoperability is the key to unlocking their full potential. Anthropic’s decision to open-source MCP has sparked a wildfire of adoption, with companies like Block, Apollo, Zed, Replit, Codeium, and Sourcegraph already integrating it into their platforms.
At events like the AI Engineer Summit, experts are raving about how standardized protocols like MCP can drive innovation while tackling challenges like security, privacy, and scalability. John Rush’s X thread taps into this buzz, showing how MCP fits into the broader push for AI tools that can “talk” to each other and the systems we rely on daily. It’s a peek into a future where AI isn’t isolated but interconnected, adaptive, and endlessly useful.
Getting Started with MCP: Resources for Developers
If you’re a developer eager to explore MCP, there’s a goldmine of resources waiting for you. Start here:
Anthropic’s Official Documentation: Head to www.anthropic.com to dive into MCP’s architecture, implementation, and best practices.
DEV Community Articles: Tech communities are buzzing with tutorials and case studies on using MCP in AI projects.
Workshops and Demos: Check out John Rush’s links in his X thread for in-depth workshops and live demos that walk you through MCP’s real-world applications.
Whether you’re building AI agents, enhancing IDEs, or connecting business tools, MCP offers a scalable, efficient framework to future-proof your projects. As Rush suggests, understanding MCP now could give you a leg up in the fast-paced AI landscape.
Challenges and the Future of MCP
No technology is flawless, and MCP has room to grow. Some developers have noted gaps, like the need for better tooling for environment variable sharing, tool descriptions for large language models (LLMs), or a formal protocol RFC (Request for Comments). As Anthropic and the community refine MCP—potentially adding features like remote server support—it’s on track to become the ultimate standard for AI integration.
Security and privacy are also critical. With MCP enabling two-way connections, ensuring data protection will be paramount. But with Anthropic’s commitment to open-source collaboration and input from industry leaders, MCP is well-positioned to address these challenges and evolve into an even more powerful tool.
Why MCP Is the Hottest Topic in AI for 2025
John Rush’s X post captures the excitement around MCP, and it’s easy to see why. This protocol isn’t just a technical breakthrough—it’s a cultural shift in how we approach AI integration. By simplifying connections, boosting interoperability, and enabling real-world applications, MCP is paving the way for a future where AI tools work smarter, not harder.
Whether you’re a developer dreaming of seamless integrations or a non-tech user craving more intuitive AI, MCP is a protocol worth watching. As the AI revolution of 2025 unfolds, MCP could be the key to unlocking the next generation of intelligent, connected tools. So, stay curious, check out the demos, and get ready for a tech transformation that’s as thrilling as it is transformative!
Let’s dive into the DeepSeek Terms of Use and Privacy Policy, updated as of January 20 and February 14, 2025, respectively, and figure out if this is just standard tech company stuff or something that raises red flags. DeepSeek, a Chinese AI company run by Hangzhou DeepSeek Artificial Intelligence Co., Ltd. and its affiliates, offers generative AI services—think chatbots and text generation tools. Their legal docs outline how you can use their services and what they do with your data. But is it par for the course, or does it feel sketchy? Let’s break it down in plain English and weigh the vibes.
What’s DeepSeek Offering?
DeepSeek’s services let you interact with AI models that churn out text, code, or tables based on what you type in (your “Inputs”). You get responses (called “Outputs”), and the company uses big neural networks trained on tons of data to make this happen. They’re upfront that the tech’s always evolving, so they might tweak, add, or kill off features as they go. They also promise to keep things secure and stable—at least as much as other companies do—and let you complain or give feedback if something’s off.
Normal or Sketchy?
This part’s pretty standard. Most tech companies, especially in AI, have similar setups: you input stuff, they spit out answers, and they reserve the right to change things. The “we’ll keep it secure” promise is boilerplate—vague but typical. Nothing screams sketchy here; it’s just how these platforms roll.
Signing Up and Your Account
You need an account to use DeepSeek, and they want your email or a third-party login (like Google). They say it’s for adults, and if you’re under 18, you need a guardian’s okay. You’ve got to give real info, keep your password safe, and not hand your account to anyone else. If you lose it or someone hacks it, you can ask for help, but you’re on the hook for anything done under your name. You can delete your account, but they might hang onto some data if the law says so.
Normal or Sketchy?
Totally normal. Every app from Netflix to X has account rules like this—real info, no sharing, your fault if it gets compromised. The “we keep data after you delete” bit is standard too; laws often force companies to hold onto stuff for compliance. No red flags yet.
What You Can and Can’t Do
Here’s where they lay down the law: you get a basic right to use the service, but they can yank it anytime. You can’t use it to make hateful, illegal, or creepy stuff—like threats, porn, or fake celebrity accounts (unless it’s labeled parody). No hacking, no stealing their code, no reselling their service. If you share AI-generated content, you’ve got to check it’s true and tag it as AI-made. They can scan your inputs and outputs to make sure you’re playing nice.
Normal or Sketchy?
This is par for the course. Every platform has a “don’t be a jerk” list—X, YouTube, you name it. The “we can revoke access” part is standard; it’s their service, their rules. Checking your content isn’t weird either—AI companies like OpenAI do it to avoid legal headaches. The “label it as AI” rule is newer but popping up more as fake content worries grow. Nothing sketchy; it’s just them covering their butts.
Your Inputs and Outputs
You own what you type in and what the AI spits out, and you can use it however—personal projects, research, even training other AI (cool, right?). But they might use your inputs and outputs to tweak their system, promising to scramble it so no one knows it’s yours. They warn the outputs might be wrong, so don’t bet your life on them—especially for big stuff like legal or medical advice.
Normal or Sketchy?
Mostly normal, with a twist. Letting you own outputs and use them freely is generous—some AI companies (looking at you, certain competitors) claim rights to what their models make. Using your data to improve their AI is standard; Google and others do it too, with the same “we’ll anonymize it” line. The “outputs might suck” disclaimer is everywhere in AI—nobody wants to get sued over a bad answer. The twist? They’re based in China, and data laws there can be murky. Not sketchy on its face, but the location might make you squint.
Who Owns the Tech?
DeepSeek owns all their code, models, and branding. You can’t use their logos or try to copy their tech without permission. Simple enough.
Normal or Sketchy?
Bog-standard. Every company guards its intellectual property like this. No surprises, no sketchiness.
If Something Goes Wrong
If you think they’re ripping off your ideas or breaking rules, you can complain via email or their site. They’ll look into it. If you break their rules, they can warn you, limit your account, or ban you—no notice required. They’re not liable if the service flops or gives you bunk info, and you’ve got to cover their back if your screw-up costs them money.
Normal or Sketchy?
Normal, if a bit harsh. The “we can ban you anytime” clause is in every terms of service—X has it, so does every game app. The “we’re not responsible” and “you pay if you mess up” bits are classic corporate shields. It’s not cuddly, but it’s not sketchy—just self-protective.
Privacy Stuff
They collect your account details, what you type, your device info, and rough location (via IP). They use it to run the service, improve their AI, and keep things safe. They might share it with their team, service providers (like payment processors), or cops if the law demands it. You’ve got rights to see, fix, or delete your data, but it’s stored in China, and they don’t take kids under 14.
Normal or Sketchy?
Mostly normal, with a catch. Data collection and sharing are what every tech company does—X grabs your IP and tweets, Google slurps everything. Rights to access or delete are standard, especially with privacy laws like GDPR influencing global norms. The China storage is the catch—data there can be subject to government snooping under laws like the National Intelligence Law. Not sketchy by design, but it’s a wild card depending on your trust level.
Legal Fine Print
Chinese law governs everything, and disputes go to a court near their HQ in Hangzhou. They can update the terms anytime, and if you keep using the service, you’re cool with it.
Normal or Sketchy?
Normal-ish. Picking their home turf for law and courts is typical—X uses U.S. law, others pick wherever they’re based. The “we can change terms” bit is everywhere too. The China angle might feel off if you’re outside that system, but it’s not inherently sketchy—just inconvenient.
The Verdict
DeepSeek’s terms and privacy rules are mostly par for the course. They’re doing what every AI and tech company does: setting rules, grabbing data, dodging liability, and keeping their tech theirs. The “you own outputs” part is a nice perk, and the content rules align with industry norms as AI gets more regulated. The sketchy vibes creep in with the China factor—data storage and legal oversight there aren’t as transparent as, say, the U.S. or EU. If you’re chill with that, it’s standard fare. If not, it might feel off. Your call, but it’s not a screaming red flag—just a “hmm, okay” moment.
In a nearly 100-minute address to a joint session of Congress on Tuesday night, President Donald J. Trump, speaking for the first time before lawmakers since his return to the White House in January, laid out an ambitious agenda to “usher in the greatest and most successful era” in U.S. history. Delivered six weeks after his inauguration, the speech blended triumphant rhetoric with policy specifics, reflecting the administration’s aggressive start—marked by nearly 100 executive orders and over 400 executive actions.
Addressing a chamber divided along partisan lines, Trump touted his landslide victory in the November 5, 2024, election—winning all seven swing states, 312 Electoral College votes, and the popular vote—calling it a “mandate like has not been seen in many decades.” He highlighted early achievements, including a drastic reduction in illegal border crossings following a national emergency declaration and military deployment to the southern border, which he credited to his administration’s swift action.
The speech was punctuated by moments of drama, including an interruption by Representative Al Green (D-Texas) and others, prompting Speaker Mike Johnson (R-La.) to order the sergeant-at-arms to restore order. Green was escorted out, underscoring the contentious atmosphere as Democrats largely remained seated while Republicans frequently rose to applaud.
Policy Priorities: Economy, Borders, and Culture
Trump emphasized economic revitalization, announcing plans to combat inflation through energy expansion—“drill, baby, drill”—and the creation of a Department of Government Efficiency led by Elon Musk to cut wasteful spending. He cited examples of terminated programs, such as $22 billion for housing illegal immigrants and $8 million for “transgender mice,” drawing applause from supporters and skepticism from critics.
On immigration, Trump doubled down on border security, signing the Laken Riley Act to detain dangerous criminal aliens and renaming a Texas wildlife refuge after Jocelyn Nungaray, a 12-year-old murder victim, as a tribute to victims of crimes by undocumented immigrants. He also announced a historic deportation operation, surpassing even Dwight Eisenhower’s record, and thanked Mexican authorities for handing over 29 cartel leaders amid tariff pressures.
Culturally, Trump positioned his administration as a counter to “woke” policies, banning critical race theory and transgender ideology from schools, ending diversity, equity, and inclusion programs, and affirming “only two genders: male and female.” He introduced a “gold card” initiative, offering citizenship to wealthy job-creators for $5 million, aiming to reduce national debt while contrasting this with the deportation of criminals.
Global Ambitions and National Security
Internationally, Trump promised to reclaim the Panama Canal, citing violations of its transfer agreement, and invited Greenland to join the U.S. for security reasons. He claimed progress toward peace in Ukraine, reading a letter from President Volodymyr Zelensky expressing readiness to negotiate, and signaled Russia’s willingness to talk, though specifics remained vague. He also announced the capture of an ISIS terrorist linked to the 2021 Abbey Gate bombing in Afghanistan, earning bipartisan nods.
Domestically, Trump proposed a missile defense shield, a revitalized shipbuilding industry, and reciprocal tariffs starting April 2 to counter trade imbalances with nations like China and India. He celebrated recent investments—$1.7 trillion from companies like Apple and Taiwan Semiconductor—attributing them to his election win and tariff threats.
Personal Stories and Emotional Appeals
The address featured poignant guest appearances, a Trump hallmark. He honored Payton McNabb, a high school athlete injured by a transgender competitor, and vowed to ban men from women’s sports. He recognized Corey Comperatore’s family, killed during a July 13, 2024, rally shooting in Butler, Pennsylvania, crediting divine intervention for his own survival that day. Other guests included a steelworker, a Border Patrol agent, and a cancer-stricken teen named DJ Daniel, whom Trump named an honorary Secret Service agent.
Partisan Divide and a Call for Unity
Despite appeals for bipartisanship—“Let’s work together and truly make America great again”—Trump’s sharp critiques of Democrats, whom he accused of never applauding even historic achievements, highlighted the gulf. His mention of beating George Washington as the most successful first-month president drew laughs from allies and eye-rolls from opponents.
As of March 5, 2025, reactions are pouring in. Supporters hail the speech as a bold blueprint for renewal, while critics question the feasibility of promises like balancing the budget—unachieved in 24 years—and decry the rollback of progressive policies. With Congress set to debate his funding requests and tax cuts, the coming months will test whether Trump’s “golden age” vision can bridge America’s divides or deepen them.
In a wide-ranging interview on The Rubin Report with host Dave Rubin, premiered on March 2, 2025, entrepreneur and investor Peter Thiel offered his insights into the evolving political landscape of Silicon Valley, the growing influence of tech figures in politics, and the challenges facing science, education, and artificial intelligence (AI). The discussion, which garnered 88,466 views within days of its release, featured Thiel reflecting on the 2024 U.S. presidential election, the decline of elite institutions, and the role of his company, Palantir Technologies, in shaping modern governance and security.
Silicon Valley’s Political Realignment
Thiel, a co-founder of PayPal and an early backer of President Donald Trump, highlighted what he described as a “miraculous” shift in Silicon Valley’s political leanings. He noted that Trump’s 2024 victory, alongside Vice President JD Vance, defied the expectations of demographic determinism—a theory suggesting voting patterns are rigidly tied to race, gender, or age. “Millions of people had to change their minds,” Thiel said, attributing the shift to a rejection of identity politics and a renewed openness to rational arguments. He pointed to the influence of tech luminaries like Elon Musk and David Sacks, both former PayPal colleagues, who have increasingly aligned with conservative priorities.
Thiel traced his own contrarian stance to 2016, when supporting Trump was seen as an outlier move in Silicon Valley. He suggested that regulatory pressure from left-leaning governments historically pushed Big Tech toward progressive policies, but a backlash against “woke” culture and political correctness has since spurred a realignment. He cited Musk’s evolution from a liberal-leaning Tesla advocate to a vocal Trump supporter as emblematic of this trend, driven in part by frustration with overbearing regulation and failed progressive policies.
The Decline of Elite Credentialism
A significant portion of the conversation focused on the diminishing prestige of elite universities, particularly within the Democratic Party. Thiel observed that while Republicans like Trump (University of Pennsylvania) and Vance (Yale Law School) still tout their Ivy League credentials, Democrats have moved away from such markers of meritocracy. He contrasted past leaders like Bill Clinton (Yale Law) and Barack Obama (Harvard Law) with more recent figures like Kamala Harris and Tim Walz, arguing that the party has transitioned “from smart to dumb,” favoring populist appeal over intellectual elitism.
Thiel singled out Harvard as a symbol of this decline, describing it as an institution that once shaped political elites but now churns out “robots” ill-equipped for critical thinking. He recounted speaking at Yale in September 2024, where he found classes less rigorous than high school coursework, suggesting a broader rot in higher education. Despite their massive endowments—Harvard’s stands at $50 billion—Thiel likened universities to cities rather than companies, arguing they can persist in dysfunction far longer than a failing business due to entrenched network effects.
Science, Skepticism, and Stagnation
Thiel expressed deep skepticism about the state of modern science, asserting that it has become more about securing government funding than achieving breakthroughs. He referenced the resignations of Harvard President Claudine Gay (accused of plagiarism) and Stanford President Marc Tessier-Lavigne (implicated in fraudulent dementia research) as evidence of pervasive corruption. “Most of these people are not scientists,” he claimed, describing academia as a “stagnant scientific enterprise” hindered by hyper-specialization, peer review consensus, and a lack of genuine debate.
He argued that scientific discourse has tilted toward excessive dogmatism, stifling skepticism on topics like climate change, COVID-19 origins, and vaccine efficacy. Thiel advocated for a “wholesale reevaluation” of science, suggesting that fields like string theory and cancer research have promised progress for decades without delivering. He posited that exposing this stagnation could undermine universities’ credibility, particularly if their strongest claims—scientific excellence—are proven hollow.
Palantir’s Role and Philosophy
When asked about Palantir, the data analytics company he co-founded in 2003, Thiel offered a poetic analogy, likening it to a “seeing stone” from The Lord of the Rings—a powerful tool for understanding the world, originally intended for good. Palantir was born out of a post-9/11 mission to enhance security while minimizing civil liberty violations, a response to what Thiel saw as the heavy-handed, low-tech solutions of the Patriot Act era. Today, the company works with Western governments and militaries to sift through data and improve resource coordination.
Thiel emphasized Palantir’s dual role: empowering governments while constraining overreach through transparency. He speculated that the National Security Agency (NSA) resisted adopting Palantir’s software early on, not just due to a “not invented here” bias, but because it would have created a trackable record of actions, limiting unaccountable excesses like those tied to the FISA courts. “It’s a constraint on government action,” he said, suggesting that such accountability could deter future abuses.
Accountability Without Revenge
Addressing the Trump administration’s priorities, Thiel proposed a “Truth and Reconciliation Commission” modeled on post-apartheid South Africa to investigate recent government overreach—such as the FISA process and COVID-19 policies—without resorting to mass arrests. “We need transparency into what exactly was going on in the sausage-making factory,” he said, arguing that exposing figures like Anthony Fauci and the architects of the Russia collusion narrative would discourage future misconduct. He contrasted this with the left’s focus on historical grievances, urging a focus on the “recent past” instead.
AI and the Future
On AI, Thiel balanced optimism with caution. He acknowledged existential risks like killer robots and bioweapons but warned against overregulation, citing proposals like “global compute governance” as a path to totalitarian control. He framed AI as a critical test: progress is essential to avoid societal stagnation, yet unchecked development could amplify dangers. “It’s up to humans,” he concluded, rejecting both extreme optimism and pessimism in favor of agency-driven solutions.
Wrapping Up
Thiel’s conversation with Rubin painted a picture of a tech visionary cautiously hopeful about America’s trajectory under Trump’s second term. From Silicon Valley’s political awakening to the decline of elite institutions and the promise of technological innovation, he sees an opportunity for renewal—if human agency prevails. As Rubin titled the episode “Gray Pilled Peter Thiel,” Thiel’s blend of skepticism and possibility underscores his belief that the future, while uncertain, remains ours to shape.
In a significant leap forward for quantum computing, a team of researchers in China has unveiled Zuchongzhi 3.0, a 105-qubit superconducting quantum computer prototype. This groundbreaking processor has demonstrated its exceptional capabilities by performing a task considered virtually impossible for even the most powerful classical supercomputers.
Quantum Computational Advantage
The concept of quantum computational advantage, also known as quantum supremacy, signifies a pivotal milestone where a quantum computer can solve problems beyond the reach of classical computers. In 2019, Google claimed to have achieved this milestone with their Sycamore processor. Since then, the race has been on to develop even more powerful quantum computers, with China’s Zuchongzhi processors emerging as strong contenders.
Zuchongzhi 3.0’s Superiority
Zuchongzhi 3.0 boasts high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62%, and 99.18%, respectively. To demonstrate its superior performance, the researchers conducted experiments with an 83-qubit, 32-cycle random circuit sampling task. Zuchongzhi 3.0 completed this task in a matter of seconds, while it is estimated to take the most powerful classical supercomputer, Frontier, approximately 6.4 x 10^9 years to replicate the same task.
Random Circuit Sampling
Random circuit sampling has become a critical benchmark for demonstrating quantum computational advantage. It involves applying a series of random quantum gates to create quantum states, followed by measuring the results. This process is computationally very expensive for classical computers, especially as the number of qubits and cycles increases.
A New Benchmark
Zuchongzhi 3.0’s success in performing large-scale random circuit sampling marks a significant advancement in quantum computing. It pushes the boundaries of quantum computational advantage, setting a new benchmark that surpasses Google’s previous achievements with Sycamore.
Implications and Future Directions
This breakthrough has far-reaching implications for the future of quantum computing. It not only highlights the rapid progress in quantum hardware but also paves the way for tackling complex real-world problems using quantum computers. Potential applications include optimization, machine learning, drug discovery, and materials science.
Zuchongzhi 3.0’s success represents a major step towards a new era where quantum computers play an essential role in scientific discovery and technological innovation. As quantum computers continue to evolve, we can expect even more groundbreaking achievements that will reshape our understanding of the world and unlock new possibilities for the future.
For nearly a century, Gross Domestic Product (GDP) has stood as the unrivalled titan of economic measurement, a numerical shorthand for a nation’s strength and success. Born in the 1930s amid the chaos of the Great Depression, it was the brainchild of economist Simon Kuznets, who crafted it to help a struggling United States quantify its economic output. At the time, it was revolutionary—a clear, unified way to tally the value of goods and services produced within a country’s borders. Factories roared, assembly lines hummed, and GDP offered a vital pulse of industrial might. Today, however, this once-innovative metric feels like an artifact unearthed from a bygone era. The world has transformed—into a tapestry of digital networks, service-driven economies, and urgent ecological limits—yet GDP remains stubbornly rooted in its industrial origins. Its flaws are no longer mere quirks; they are profound disconnects that demand we reconsider what prosperity means in the 21st century.
A Tool Forged in a Different Age
GDP’s story begins in 1934, when Kuznets presented it to the U.S. Congress as a way to grasp the scale of the Depression’s devastation. It was a pragmatic response to a specific need: measuring production in an economy dominated by tangible outputs—steel, coal, automobiles, and textiles. The metric’s genius lay in its simplicity: add up everything bought and sold in the marketplace, and you had a gauge of economic health. Kuznets himself was clear-eyed about its limits, warning that it was never meant to capture the full scope of human welfare. “The welfare of a nation,” he wrote, “can scarcely be inferred from a measurement of national income.” Yet his caution was sidelined as GDP took on a life of its own. By the mid-20th century, it had become the global yardstick of progress, fueling post-World War II recovery efforts and shaping the rivalry of the Cold War. Nations flaunted their GDP figures like medals, and for a time, it worked—because the world it measured was still one of smokestacks and assembly lines.
That world no longer exists. The industrial age has given way to a reality where intangible forces—knowledge, data, services, and sustainability—drive human advancement. GDP, however, remains a prisoner of its past, a metric designed for a landscape of physical production that has largely faded. Its historical roots explain its rise, but they also expose why it feels so out of touch today.
The Modern Economy’s Invisible Wealth
Step into 2025, and the global economy is a vastly different beast. In advanced nations, services—think healthcare, software development, education, and tourism—account for over 70% of economic activity, dwarfing manufacturing’s share. Unlike a car or a ton of wheat, the value of a therapy session or a streaming subscription is slippery, often undervalued by GDP’s rigid focus on market transactions. Then there’s the digital revolution, which has upended traditional notions of wealth entirely. Giants like Google, Meta, and Wikipedia power modern life—billions navigate their platforms daily—yet their free-to-use models barely register in GDP. A teenager coding an app in their bedroom or a volunteer editing an open-source encyclopedia contributes immense societal value, but GDP sees nothing. This is a metric forged for an age of steel, not silicon.
Even within traditional sectors, GDP’s lens is myopic. Consider automation: as robots replace workers, productivity might climb, boosting GDP, but the human cost—job losses, community upheaval—goes unrecorded. Or take the gig economy, where millions cobble together livelihoods from freelance work. Their hustle fuels innovation, yet its precariousness escapes GDP’s notice. The metric’s obsession with output ignores the texture of how wealth is created and who benefits from it, leaving us with a hollow picture of progress.
The Costs GDP Refuses to Count
Beyond its struggles with modern economies, GDP’s gravest sin is what it omits. It’s a machine that counts ceaselessly but sees selectively. Income inequality is a stark example: GDP can trumpet record growth while wages stagnate for most, funneling riches to an elite few. In the U.S., the top 1% now hold more wealth than the entire middle class, yet GDP offers no hint of this chasm. Similarly, environmental destruction slips through its cracks. Logging a forest or pumping oil spikes GDP, but the loss of ecosystems, clean air, or biodiversity? Invisible. Absurdly, disasters can inflate GDP—think of the 2010 Deepwater Horizon spill, where cleanup costs added billions to the tally—while proactive stewardship, like rewilding land, earns no credit. This perverse logic turns a blind eye to the planet’s breaking points, a flaw that feels unforgivable in an era of climate reckoning.
Then there’s the silent backbone of society: unpaid labor. The parent raising a child, the neighbor tending a community garden, the caregiver nursing an elder—these acts sustain us all, yet GDP dismisses them as economically irrelevant. Studies estimate that if unpaid household work were monetized, it could add trillions to global economies. In failing to see this, GDP not only undervalues half the population—disproportionately women—but also the very foundation of human resilience. It’s a relic that measures motion without meaning, tallying transactions while ignoring life itself.
Searching for a Truer Compass
The cracks in GDP have sparked a quest for alternatives, each vying to redefine what we value. The Genuine Progress Indicator (GPI) takes a stab at balance, starting with GDP but subtracting costs like pollution and crime while adding benefits like volunteerism and equitable wealth distribution. It’s a messy, imperfect fix, but it at least tries to see the bigger picture. The Human Development Index (HDI), used by the United Nations, pivots to well-being, blending income with life expectancy and education to track how economies serve people, not just markets. Bhutan’s Gross National Happiness (GNH) goes further, weaving in cultural vitality, mental health, and ecological harmony—an ambitious, if subjective, rethink of progress. None of these have dethroned GDP’s global reign; their complexity and lack of universality make them tough to scale. But their existence signals a hunger for something truer, a metric that doesn’t just count the past but guides us toward a sustainable future.
The Stubborn Giant and the Road Ahead
Why does GDP endure despite its obsolescence? Its staying power lies in its clarity and consistency. Central banks tweak interest rates based on it, governments craft budgets around it, and international bodies like the IMF rank nations by it. A country’s GDP still carries swagger—China’s rise or America’s dominance owes much to those headline numbers. Abandoning it outright risks chaos; no replacement has the infrastructure or consensus to take its place. Yet this inertia is a double-edged sword. Chasing GDP growth can trap us in a cycle of short-term wins—bulldozing forests, burning fossil fuels—while the long-term costs pile up unseen. In a world grappling with climate collapse, AI disruption, and social fractures, leaning on a 1930s relic feels like navigating a spaceship with a sextant.
The path forward isn’t to topple GDP but to demote it—to treat it as one tool among many, not the sole arbiter of success. Pair it with GPI’s nuance, HDI’s humanity, or even experimental dashboards that track carbon footprints and mental health. Simon Kuznets saw this coming: he knew his creation was a partial measure, never the full story. Nearly a century later, we’re still catching up to that insight. GDP’s legacy as a groundbreaking metric is secure, but its reign as the lone king of prosperity must end. The world has outgrown it—not just in years, but in complexity, ambition, and need. It’s time to honor its service and let it share the stage with measures that see what it cannot: the messy, vital heartbeat of life in 2025 and beyond.
