In a world where artificial intelligence (AI) is reshaping economies, cultures, and security, the stakes for nations have never been higher. In a recent episode of The a16z Podcast, Jensen Huang, CEO of NVIDIA, and Arthur Mensch, co-founder and CEO of Mistral, unpack the urgent need for sovereign AI—national strategies that ensure countries control their digital futures. Drawing from their discussion, this article explores why every nation must prioritize AI, the economic and cultural implications, and practical steps to build a robust strategy.
The Global Race for Sovereign AI
The conversation kicks off with a powerful idea: AI isn’t just about computing—it’s about culture, economics, and sovereignty. Huang stresses that no one will prioritize a nation’s unique needs more than the nation itself. “Nobody’s going to care more about the Swedish culture… than Sweden,” he says, highlighting the risk of digital dependence on foreign powers. Mensch echoes this, framing AI as a tool nations must wield to avoid modern digital colonialization—where external entities dictate a country’s technological destiny.
AI as a General-Purpose Technology
Mensch positions AI as a transformative force, comparable to electricity or the internet, with applications spanning agriculture, healthcare, defense, and beyond. Yet Huang cautions against waiting for a universal solution from a single provider. “Intelligence is for everyone,” he asserts, urging nations to tailor AI to their languages, values, and priorities. Mistral’s M-Saaba model, optimized for Arabic, exemplifies this—outperforming larger models by focusing on linguistic and cultural specificity.
Economic Implications: A Game-Changer for GDP
The economic stakes are massive. Mensch predicts AI could boost GDP by double digits for countries that invest wisely, warning that laggards will see wealth drain to tech-forward neighbors. Huang draws a parallel to the electricity era: nations that built their own grids prospered, while others became reliant. For leaders, this means securing chips, data centers, and talent to capture AI’s economic potential—a must for both large and small nations.
Cultural Infrastructure and Digital Workforce
Huang introduces a compelling metaphor: AI as a “digital workforce” that nations must onboard, train, and guide, much like human employees. This workforce should embody local values and laws, something no outsider can fully replicate. Mensch adds that AI’s ability to produce content—text, images, voice—makes it a social construct, deeply tied to a nation’s identity. Without control, countries risk losing their cultural sovereignty to centralized models reflecting foreign biases.
Open-Source vs. Closed AI: A Path to Independence
Both Huang and Mensch advocate for open-source AI as a cornerstone of sovereignty. Mensch explains that models like Mistral Nemo, developed with NVIDIA, empower nations to deploy AI on their own infrastructure, free from closed-system dependency. Open-source also fuels innovation—Mistral’s releases spurred Meta and others to follow suit. Huang highlights its role in niche markets like healthcare and mining, plus its security edge: global scrutiny makes open models safer than opaque alternatives.
Risks and Challenges of AI Adoption
Leaders often worry about public backlash—will AI replace jobs? Mensch suggests countering this by upskilling citizens and showcasing practical benefits, like France’s AI-driven unemployment agency connecting workers to opportunities. Huang sees AI as “the greatest equalizer,” noting more people use ChatGPT than code in C++, shrinking the tech divide. Still, both acknowledge the initial hurdle: setting up AI systems is tough, though improving tools make it increasingly manageable.
Building a National AI Strategy
Huang and Mensch offer a blueprint for action:
Talent: Train a local workforce to customize AI systems.
Infrastructure: Secure chips from NVIDIA and software from partners like Mistral.
Customization: Adapt open-source models with local data and culture.
Vision: Prepare for agentic and physical AI breakthroughs in manufacturing and science.
Huang predicts the next decade will bring AI that thinks, acts, and understands physics—revolutionizing industries vital to emerging markets, from energy to manufacturing.
Why It’s Urgent
The podcast ends with a clarion call: AI is “the most consequential technology of all time,” and nations must act now. Huang urges leaders to engage actively, not just admire from afar, while Mensch emphasizes education and partnerships to safeguard economic and cultural futures. For more, follow Jensen Huang (@nvidia) and Arthur Mensch (@arthurmensch) on X, or visit NVIDIA and Mistral’s websites.
NVIDIA CEO Jensen Huang delivered an expansive keynote at GTC 2025, highlighting AI’s transformative impact across industries. Key points include:
AI Evolution: AI has progressed from perception to generative to agentic (reasoning) and now physical AI, enabling robotics. Each phase demands exponentially more computation, with reasoning AI requiring 100x more tokens than previously estimated.
Hardware Advancements: Blackwell, now in full production, offers a 40x performance boost over Hopper for AI inference. The roadmap includes Blackwell Ultra (2025), Vera Rubin (2026), and Rubin Ultra (2027), scaling up to 15 exaflops per rack.
AI Factories: Data centers are evolving into AI factories, with NVIDIA’s Dynamo software optimizing token generation for efficiency and throughput. A 100MW Blackwell factory produces 1.2 billion tokens/second, far surpassing Hopper’s 300 million.
Enterprise & Edge: New DGX Spark and DGX Station systems target enterprise AI, while partnerships with Cisco, T-Mobile, and GM bring AI to edge networks and autonomous vehicles.
Robotics: Physical AI advances with Omniverse, Cosmos, and the open-source Groot N1 model for humanoid robots, supported by the Newton physics engine (with DeepMind and Disney).
Networking & Storage: Spectrum-X enhances enterprise AI networking, and GPU-accelerated, semantics-based storage systems are introduced with industry partners.
Huang emphasized NVIDIA’s role in scaling AI infrastructure globally, projecting a trillion-dollar data center buildout by 2030, driven by accelerated computing and AI innovation.
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NVIDIA GTC March 2025 Keynote: Jensen Huang Unveils the AI Revolution’s Next Chapter
On March 18, 2025, NVIDIA CEO Jensen Huang took the stage at the GPU Technology Conference (GTC) in San Jose, delivering a keynote that redefined the boundaries of artificial intelligence (AI), computing, and robotics. Streamed live to over 593,000 viewers on NVIDIA’s YouTube channel (1.9 million subscribers), the event—dubbed the “Super Bowl of AI”—unfolded at NVIDIA’s headquarters with no script, no teleprompter, and a palpable sense of excitement. Huang’s two-hour presentation unveiled groundbreaking innovations: the GeForce RTX 5090, the Blackwell architecture, the open-source Groot N1 humanoid robot model, and a multi-year roadmap that promises to transform industries from gaming to enterprise IT. Here’s an in-depth, SEO-optimized exploration of the keynote, designed to dominate search results and captivate tech enthusiasts, developers, and business leaders alike.
GTC 2025: The Epicenter of AI Innovation
GTC has evolved from a niche graphics conference into a global showcase of AI’s transformative power, and the 2025 edition was no exception. Huang welcomed representatives from healthcare, transportation, retail, and the computer industry, thanking sponsors and attendees for making GTC a “Woodstock-turned-Super Bowl” of AI. With over 6 million CUDA developers worldwide and a sold-out crowd, the event underscored NVIDIA’s role as the backbone of the AI revolution. For those searching “What is GTC 2025?” or “NVIDIA AI conference highlights,” this keynote is the definitive answer.
GeForce RTX 5090: 25 Years of Graphics Evolution Meets AI
Huang kicked off with a nod to NVIDIA’s roots, unveiling the GeForce RTX 5090—a Blackwell-generation GPU marking 25 years since the original GeForce debuted. This compact powerhouse is 30% smaller in volume and 30% more energy-efficient than the RTX 4890, yet its performance is “hard to even compare.” Why? Artificial intelligence. Leveraging CUDA—the programming model that birthed modern AI—the RTX 5090 uses real-time path tracing, rendering every pixel with 100% accuracy. AI predicts 15 additional pixels for each one mathematically computed, ensuring temporal stability across frames.
For gamers and creators searching “best GPU for 2025” or “RTX 5090 specs,” this card’s sold-out status worldwide speaks volumes. Huang highlighted how AI has “revolutionized computer graphics,” making the RTX 5090 a must-have for 4K gaming, ray tracing, and content creation. It’s a testament to NVIDIA’s ability to fuse heritage with cutting-edge tech, appealing to both nostalgic fans and forward-looking professionals.
Blackwell Architecture: Powering the AI Factory Revolution
The keynote’s centerpiece was the Blackwell architecture, now in full production and poised to redefine AI infrastructure. Huang introduced Blackwell MVLink 72, a liquid-cooled, 1-exaflop supercomputer packed into a single rack with 570 terabytes per second of memory bandwidth. Comprising 600,000 parts and 5,000 cables, it’s a “sight of beauty” for engineers—and a game-changer for AI factories.
Huang explained that AI has shifted from retrieval-based computing to generative computing, where models like ChatGPT generate answers rather than fetch pre-stored data. This shift demands exponentially more computation, especially with the rise of “agentic AI”—systems that reason, plan, and act autonomously. Blackwell addresses this with a 40x performance leap over Hopper for inference tasks, driven by reasoning models that generate 100x more tokens than traditional LLMs. A demo of a wedding seating problem illustrated this: a reasoning model produced 8,000 tokens for accuracy, while a traditional LLM floundered with 439.
For businesses querying “AI infrastructure 2025” or “Blackwell GPU performance,” Blackwell’s scalability is unmatched. Huang emphasized its role in “AI factories,” where tokens—the building blocks of intelligence—are generated at scale, transforming raw data into foresight, scientific discovery, and robotic actions. With Dynamo—an open-source operating system—optimizing token throughput, Blackwell is the cornerstone of this new industrial revolution.
Agentic AI: Reasoning and Robotics Take Center Stage
Huang introduced “agentic AI” as the next wave, building on a decade of AI progress: perception AI (2010s), generative AI (past five years), and now AI with agency. These systems perceive context, reason step-by-step, and use tools—think Chain of Thought or consistency checking—to solve complex problems. This leap requires vast computational resources, as reasoning generates exponentially more tokens than one-shot answers.
Physical AI, enabled by agentic systems, stole the show with robotics. Huang unveiled NVIDIA Isaac Groot N1, an open-source generalist foundation model for humanoid robots. Trained with synthetic data from Omniverse and Cosmos, Groot N1 features a dual-system architecture: slow thinking for perception and planning, fast thinking for precise actions. It can manipulate objects, execute multi-step tasks, and collaborate across embodiments—think warehouses, factories, or homes.
With a projected 50-million-worker shortage by 2030, robotics could be a trillion-dollar industry. For searches like “humanoid robots 2025” or “NVIDIA robotics innovations,” Groot N1 positions NVIDIA as a leader, offering developers a scalable, open-source platform to address labor gaps and automate physical tasks.
NVIDIA’s Multi-Year Roadmap: Planning the AI Future
Huang laid out a predictable roadmap to help enterprises and cloud providers plan AI infrastructure—a rare move in tech. Key milestones include:
Blackwell Ultra (H2 2025): 1.5x more flops, 2x networking bandwidth, and enhanced memory for KV caching, gliding seamlessly into existing Blackwell setups.
Vera Rubin (H2 2026): Named after the dark matter pioneer, this architecture debuts MVLink 144, a new CPU, CX9 GPU, and HBM4 memory, scaling flops to 900x Hopper’s baseline.
Rubin Ultra (H2 2027): An extreme scale-up with 15 exaflops, 4.6 petabytes per second of bandwidth, and MVLink 576, packing 25 million parts per rack.
Feynman (Teased for 2028): A nod to the physicist, signaling continued innovation.
This annual rhythm—new architecture every two years, upgrades yearly—targets “AI roadmap 2025-2030” and “NVIDIA future plans,” ensuring stakeholders can align capex and engineering for a $1 trillion data center buildout by decade’s end.
Enterprise and Edge: DGX Spark, Station, and Spectrum-X
NVIDIA’s enterprise push was equally ambitious. The DGX Spark, a MediaTek-partnered workstation, offers 20 CPU cores, 128GB GPU memory, and 1 petaflop of compute power for $150,000—perfect for 30 million software engineers and data scientists. The liquid-cooled DGX Station, with 20 petaflops and 72 CPU cores, targets researchers, available via OEMs like HP, Dell, and Lenovo. Attendees could reserve these at GTC, boosting buzz around “enterprise AI workstations 2025.”
On the edge, a Cisco-NVIDIA-T-Mobile partnership integrates Spectrum-X Ethernet into radio networks, leveraging AI to optimize signals and traffic. With $100 billion annually invested in comms infrastructure, this move ranks high for “edge AI solutions” and “5G AI innovations,” promising smarter, adaptive networks.
AI Factories: Dynamo and the Token Economy
Huang redefined data centers as “AI factories,” where tokens drive revenue and quality of service. NVIDIA Dynamo, an open-source OS, orchestrates these factories, balancing latency (tokens per second per user) and throughput (total tokens per second). A 100-megawatt Blackwell factory produces 1.2 billion tokens per second—40x Hopper’s output—translating to millions in daily revenue at $10 per million tokens.
For “AI token generation” or “AI factory software,” Dynamo’s ability to disaggregate prefill (flops-heavy context processing) and decode (bandwidth-heavy token output) is revolutionary. Partners like Perplexity are already onboard, amplifying its appeal.
Silicon Photonics: Sustainability Meets Scale
Scaling to millions of GPUs demands innovation beyond copper. NVIDIA’s 1.6 terabit-per-second silicon photonic switch, using micro-ring resonator modulators (MRM), eliminates power-hungry transceivers, saving 60 megawatts in a 250,000-GPU data center—enough for 100 Rubin Ultra racks. Shipping in H2 2025 (InfiniBand) and H2 2026 (Spectrum-X), this targets “sustainable AI infrastructure” and “silicon photonics 2025,” blending efficiency with performance.
Omniverse and Cosmos: Synthetic Data for Robotics
Physical AI hinges on data, and NVIDIA’s Omniverse and Cosmos deliver. Omniverse generates photorealistic 4D environments, while Cosmos scales them infinitely for robot training. A new physics engine, Newton—developed with DeepMind and Disney Research—offers GPU-accelerated, fine-grain simulation for tactile feedback and motor skills. For “synthetic data robotics” or “NVIDIA Omniverse updates,” these tools empower developers to train robots at superhuman speeds.
Industry Impact: Automotive, Enterprise, and Beyond
NVIDIA’s partnerships shone bright. GM tapped NVIDIA for its autonomous vehicle fleet, leveraging AI across manufacturing, design, and in-car systems. Safety-focused Halos technology, with 7 million lines of safety-assessed code, targets “automotive AI safety 2025.” In enterprise, Accenture, AT&T, BlackRock, and others integrate NVIDIA Nims (like the open-source R1 reasoning model) into agentic frameworks, ranking high for “enterprise AI adoption.”
NVIDIA’s Vision Unfolds
Jensen Huang’s GTC 2025 keynote was a masterclass in vision and execution. From the RTX 5090’s gaming prowess to Blackwell’s AI factory dominance, Groot N1’s robotic promise, and a roadmap to 2028, NVIDIA is building an AI-driven future. Visit nvidia.com/gt Doughnutc to explore sessions, reserve a DGX Spark, or dive into CUDA’s 900+ libraries. As Huang said, “This is just the beginning”—and for searches like “NVIDIA GTC 2025 full recap,” this article is your definitive guide.
Dr. Peter Attia, longevity expert and Outlive author, chats with Shawn Ryan about living long and strong. A former boxer turned MD, he’s all about Medicine 3.0—preventing the “four horsemen” (heart disease, cancer, dementia, metabolic issues) before they strike. Key takeaways? Eat smart (calories and protein matter most), exercise daily (aim for top 25% muscle and cardio fitness), sleep 7.5–8 hours (no screens before bed), and cut plastic use (think glass containers). He debunks sugar-cancer myths, loves hunting for quality meat, and swears by exercise to fend off dementia. Bonus: his perfect day starts with coffee, chess with the kids, and a solid workout. Simple, actionable, and badass—start today!
Imagine this: you’re sipping coffee with a guy who’s hunted wild game in Hawaii, swum between Hawaiian islands, and boxed his way through his teenage years—all while becoming a world-class doctor obsessed with helping you live longer and better. That’s Dr. Peter Attia, the longevity guru who dropped by the Shawn Ryan Show on March 10, 2025, to dish out a masterclass on health, science, and why you might want to ditch that plastic water bottle. Buckle up—this is going to be a fun, easy, and seriously useful ride through the wild world of Medicine 3.0!
Meet the Man Who Does Nothing in Moderation (Except Moderation)
Peter Attia isn’t your average MD. He’s a Canadian-American physician who trained at Stanford, cut his surgical teeth at Johns Hopkins, and geeked out on cancer research at the National Cancer Institute. Now, he’s the brain behind Early Medical, a practice laser-focused on stretching your lifespan and your healthspan—because who wants to live to 100 if they’re too creaky to enjoy it? He’s also the host of The Drive podcast and the guy who wrote the #1 New York Times bestseller Outlive: The Science and Art of Longevity. Oh, and Time magazine named him one of 2024’s most influential health icons. No biggie.
In this epic 2-hour-47-minute chat with Shawn Ryan, Attia doesn’t just drop knowledge—he hurls it at you like a dodgeball in gym class. From hunting axis deer to dodging microplastics, he covers it all with a mix of nerdy precision and real-world swagger. Ready to steal some of his secrets? Let’s dive in.
Hunting, Boxing, and a Teacher Who Changed Everything
Attia’s story kicks off in Toronto, where he grew up as the son of Egyptian immigrants. As a kid, he was all about hockey (because Canada), but then boxing stole his heart. By 14, he was training six hours a day, dreaming of going pro. “It saved my life,” he says, crediting the sport with keeping him out of trouble—like the kind that landed some of his high school buddies in jail or worse. (One kid even died playing chicken with a subway train. Yikes.)
School? Not his jam—until a math teacher named Woody Sparrow saw something special in him. “You’ve got potential,” Woody told him, planting a seed that turned a scrappy boxer into a future engineer and doctor. Attia ditched the ring, hit the books, and eventually swapped punches for scalpels. Talk about a plot twist!
Medicine 3.0: The Future of Feeling Awesome
Attia’s big idea is something he calls Medicine 3.0. Forget patching you up after you’re already a mess (that’s Medicine 2.0). This is about preventing the mess in the first place. He’s targeting the “four horsemen” of death: cardiovascular disease, cancer, neurodegenerative diseases (like dementia), and metabolic disorders (think diabetes). His mission? Keep you kicking butt well into your golden years.
So, how do you do it? Attia’s got a playbook that’s equal parts science and common sense—plus a few surprises. Let’s break it down into bite-sized, actionable goodies you can start using today.
1. Eat Smart (No, You Don’t Need to Hunt Your Own Elk)
Attia’s a hunter—think elk steaks and axis deer sausage—but you don’t need a bow and arrow to eat well. His take? Focus on the big wins: don’t overeat, get enough protein, and prioritize quality. “You can’t be healthier than the animal you eat,” he quips, which is why he’s all about wild game and grass-fed beef from his buddy’s sustainable farm.
Your Move:
Calories matter most. Overeating—whether it’s kale or Big Macs—leads to fat in all the wrong places (liver, heart, pancreas). Keep it in check.
Protein is king. Aim for enough to keep your muscles strong—because nobody in a nursing home ever wished they had less muscle.
Upgrade your sources. Can’t hunt? Go for grass-fed meat or organic options at the store. Bonus points if you buddy up with a local farmer for half a cow.
Oh, and that farm-to-table hype? It’s cool, but not a dealbreaker. Focus on the basics first.
2. Exercise: The Magic Pill You’re Not Taking Enough Of
If Attia could bottle one thing to sell you, it’d be exercise. “It’s the most potent tool for reducing dementia risk,” he says, and it’s a superhero for your heart, metabolism, and mood too. He’s clocking about 8 hours a week—cycling, lifting, and soon, swimming again—because it’s his mental health reset button.
Your Move:
Set a goal, not a schedule. Want to be in the top 25% for muscle mass and aerobic fitness? A DEXA scan or VO2 Max test can tell you where you stand.
Start small, stay consistent. Got 3 hours a week? Great—maintain what you’ve got. Got 6? You’ll see progress. Got 12? You’re a rockstar.
Mix it up. Lift weights for strength, pedal or jog for stamina, and maybe try swimming for that Zen vibe.
3. Sleep Like a Champ (No Phone Required)
Sleep’s a non-negotiable for Attia. “If you’re sleep-deprived, your cravings go nuts, cortisol spikes, and everything sucks more,” he warns. His ideal? 8–8.5 hours in bed to snag 7.5–8 hours of shut-eye.
Your Move:
Take the PSQI quiz. Google it—it’s a quick way to see if your sleep’s secretly sabotaging you.
Nail the basics. Dark room, cool temp, no screens 1–2 hours before bed, no booze or big meals late. You know this stuff—now do it.
Track it. A wearable can clue you in on how deep you’re really sleeping.
Still struggling? A sleep study might uncover apnea or other gremlins.
4. Dodge the Cancer Bullet (and Maybe the Plastic One Too)
Cancer scares the bejeezus out of everyone—including Attia. “In the next decade, it’s cancer or an accident that’d take me out,” he admits. Smoking, obesity, and diabetes are the big baddies driving it, but what about microplastics and sugar?
Microplastics: The evidence is “modest,” he says, but why risk it? He’s swapped plastic containers for glass, ditched his drip coffee maker for a metal-and-glass one, and even rocks steel water bottles on his bike.
Sugar: “Cancer doesn’t uniquely feed off it,” he clarifies, debunking the myth. But overeating sugar can lead to obesity, and that’s a cancer trigger.
Your Move:
Cut the plastic. Store food in glass, skip heating anything in plastic, and maybe splurge on a reverse osmosis water filter.
Chill on sugar paranoia. It’s not the devil—just don’t let it make you overeat.
Screen smart. Talk to your doc about colonoscopies (start at 40–45) or liquid biopsies, but weigh the false-positive stress first.
5. Keep Your Brain Sharp (and Your Heart Open)
Dementia’s another boogeyman Attia’s tackling head-on. Exercise is your best weapon (those myokines are brain food!), but sleep, low blood pressure, and kicking smoking help too. Psychedelics? He’s skeptical about dementia benefits but raves about their power for addiction and emotional healing—like the time psilocybin gave him a tear-soaked epiphany about his dad.
Your Move:
Move daily. Even a brisk walk pumps those brain-boosting hormones.
Sleep tight. See tip #3—it’s a twofer.
Feel your feels. Ask yourself, “Why am I mad? Who do I connect with?” Naming emotions keeps you sane.
The Attia Daily: Coffee, Chess, and Chaos Control
So, what’s a day in the life of this longevity ninja? Up early for coffee with his wife, breakfast and chess with the kids, then work and a workout by 8:30. Meetings start at 10 or 11, dinner’s a family affair, and he wraps up with some Netflix or a sauna. Boring? Nope—balanced and badass.
Your Move:
Steal one thing. Maybe it’s 15 quiet minutes with your partner or a quick game with your kids. Small wins stack up.
The Bottom Line: You’ve Got This
Attia’s not here to scare you into a kale-only diet or a 24/7 gym life. He’s about probability—stacking the odds so you thrive, not just survive. Eat decently, move often, sleep well, and maybe rethink that plastic cup. It’s not rocket science—it’s Medicine 3.0, and it’s your ticket to a longer, stronger, happier you.
Want more? Catch the full Shawn Ryan Show episode (SRS 181) or hit up Attia’s podcast, The Drive. Your future self will thank you—probably while eating an elk burger.
TLDW (Too Long; Didn’t Watch):Sean Frank, a $200M/year e-commerce expert, shares his playbook on the My First Million podcast. Key takeaways: Start with services to build skills and cash flow, spot fast-emerging trends (e.g., no screen time, creatine), prioritize profitability from the first sale over lifetime value (LTV), and be ruthless with product expansion. His company, Ridge, grew from $5M to over $200M in six years by focusing on a simple product (wallets), leveraging Facebook ads, and expanding into categories like wedding bands—all without debt or outside funding.
E-commerce in 2025 is a battlefield, but Sean Frank, the mastermind behind Ridge—a company pulling in over $200 million annually—has cracked the code. In a recent My First Million podcast episode hosted by Sam Parr and Shaan Puri, Frank unpacked his journey from a 22-year-old agency hustler to a dominant force in direct-to-consumer (DTC) commerce. His insights offer a blueprint for anyone looking to thrive in the ever-shifting e-commerce landscape. Here’s what he revealed—and how you can apply it.
From Agency to Empire: The Ridge Story
Frank’s journey began not with a groundbreaking product but with a services gig. In 2012, as Facebook ads emerged, he learned the ropes at a mediocre ad agency. At 22, he saw an opportunity: “I could do this better.” With his CMO, Conor, he launched his own agency, snagging 10 clients—including Ridge, a fledgling wallet brand started by a father-son duo and their friend. By 2016, Ridge was doing $5 million in sales, but Frank saw untapped potential.
His agency took over everything—marketing, customer service, logistics—eventually merging with Ridge in 2018. From there, the brand skyrocketed: $5M to $10M, $15M, $18M, $30M, $50M, $100M, and now “multi-hundred million” in revenue. No debt. No venture capital. Just pure, profitable growth.
What fueled this? A simple product (a sleek, minimalist wallet), a massive total addressable market (TAM—$10 billion for men’s wallets), and a relentless focus on paid ads—especially Facebook. “We could always put another dollar into Facebook and it worked,” Frank said. While others chased complex innovations, Ridge doubled down on what worked.
The 2025 Playbook: How to Win
Frank’s success isn’t luck—it’s strategy. Here’s his advice for winning in e-commerce in 2025:
Start with Services, Then Pivot to Products Frank recommends cutting your teeth in services—think ad agencies, consulting, or freelance gigs. “You’ll make your first million delivering good value to people,” he says. It’s low-risk, permissionless, and builds skills and cash flow. Ridge grew out of his agency; so did brands like Brez (a weed-mushroom drink) and Holo Socks, both founded by ex-agency operators. Services let you test trends and markets before committing to inventory.
Spot Fast-Emerging Trends Trends are your rocket fuel. Frank highlights two for 2025: no screen time (e.g., crocheting kits like The Woobles, which went from $10M to $150M in two years) and creatine (tied to fitness and wellness). Others include microplastic-free products and tactile toys. How do you find them? Look at your life for passion points, or—if you’re seasoned—follow TikTok’s “girlies” or LA’s trendsetting Erewhon crowd. “Reddit and Etsy are dead—AI slop,” Frank warns. Go where real humans signal what’s next.
Profit First, Forget LTV Lifetime value (LTV) is a trap, Frank argues. “Most brands die waiting for LTV.” Ridge thrives by being profitable on the first purchase—crucial for one-off products like wallets. Contrast this with supplement brands banking on repeat buys; if the trend fades, they’re toast. In 2025, cash flow is king—don’t bet on future loyalty to save you.
Expand Ruthlessly Don’t cling to brand purity. Ridge added wedding bands in 2022, hitting eight figures in year one. “Customers never think about you,” Frank says. Look at BIC—lighters, pens, razors—and now tattoo removal. Allbirds stagnated by staying rigid; Ridge grows by meeting customers where they are. Test new categories fast, cut what flops, and double down on winners.
Respect Your Customer Frank’s team obsesses over “Ed,” the everyday dad who loves widgets, fishing, and the NFL. HexClad, a cookware brand Frank admires, spent years perfecting pans before scaling to $500M+. “Are we delivering value to Ed?” guides every move. In 2025, quality matters—arbitrage alone won’t cut it.
Case Studies: Who’s Crushing It?
HexClad: Bootstrapped from county fairs to Super Bowl ads, now over $500M with Gordon Ramsay as an investor. Product-first excellence.
The Woobles: A crocheting kit brand that rode the no-screen-time wave from $10M to $150M in two years—no capital raised.
Brez: Ex-agency founders hit $4.6M monthly revenue in 21 months with a cannabis-mushroom drink, leveraging TikTok’s organic reach.
The Hard Truth: E-commerce Isn’t Easy
Frank admits e-commerce is “blue-collar work”—unsexy, physical, and trend-dependent. “It’s permissionless,” he says, unlike tech infrastructure gigs requiring credentials. But scaling means bigger POs, more management, and constant pivoting. Compare that to SaaS, where growth can feel effortless once the product clicks. Yet for Frank, the grind fits: “If I have to pack boxes, I’ll pack boxes.”
What’s Next for Frank?
Ridge could fetch $300M today, but Frank’s eyeing $500M–$600M by decade’s end, fueled by tech retail (Apple, Verizon) and new products like power banks. His long-term goal? Net $100M from a sale, then build a portfolio of trend-driven brands and services—a personal PE empire.
Takeaway for 2025
E-commerce rewards the adaptable. Start small with services, chase growing markets, prioritize profit, and expand fearlessly. As Frank puts it, “Strong beliefs, loosely held.” In a world of fading trends and brutal competition, that’s the mindset to win.
Ray Dalio shares his journey from a $50 stock market bet at age 12 to building Bridgewater Associates into a $14 billion empire, revealing how failure, radical transparency, and the formula “Pain + Reflection = Progress” fueled his success, alongside tips for entrepreneurs on decision-making, team-building, and thriving through adversity.
Ray Dalio—billionaire investor, founder of Bridgewater Associates, and the mastermind behind the world’s largest hedge fund—didn’t stumble into his $14 billion empire. He clawed his way there through brutal failures, radical transparency, and a relentless obsession with turning pain into progress. In a jaw-dropping episode of The Foundr Podcast hosted by Nathan Chan on February 28, 2025, Dalio pulls back the curtain on the gritty principles that transformed him from a kid with $50 in the stock market to a titan of finance. Spoiler: It’s not about luck—it’s about learning to “struggle well.”
The Punch That Changed Everything
Dalio’s story isn’t all polished suits and Wall Street swagger. It’s raw, messy, and real. Picture this: New Year’s Eve, a young Dalio, drunk and rambunctious, decks his boss at Shearson Hayden Stone. The next day, he’s out of a job. Most would call it a career-ending disaster. Dalio calls it the spark that lit Bridgewater’s fire. “That big punch in the face did me a lot of good,” he admits with a chuckle. From a two-bedroom apartment in 1975, with a rugby buddy and a dream, he built a hedge fund juggernaut managing hundreds of billions. But the real turning point? A colossal failure years later that nearly wiped him out.
The $4,000 Lifeline and a Lesson in Humility
Fast forward to 1982. Dalio’s riding high, predicting a debt crisis after Mexico’s default. He’s wrong—dead wrong. The Federal Reserve pumps money into the system, the stock market soars, and Dalio’s left with nothing. “I was so broke I had to borrow $4,000 from my dad to take care of my family,” he recalls. Clients ditch him. His team evaporates. Yet, in that gut punch of a moment, he finds gold: humility. “It made me think, ‘How do I know I’m right?’” That question became the bedrock of Bridgewater’s success—an “idea meritocracy” where the best ideas win, no matter who they come from.
Pain + Reflection = Progress
Dalio’s mantra isn’t just a catchy phrase—it’s a battle-tested formula. “Struggling in ideas and getting ahead in life is just like struggling in the gym. No pain, no gain,” he says. Take 1982: He could’ve sulked. Instead, he reflected, wrote down his lessons, and built a system to never repeat the mistake. That’s the essence of his iconic book Principles—a playbook of hard-won wisdom distilled over decades. “Every mistake is a puzzle,” he explains. “Solve it, and you get a gem—a principle for the future.” Entrepreneurs, take note: Success isn’t avoiding failure; it’s mastering it.
Radical Transparency: The Secret Sauce
Bridgewater’s culture isn’t for the faint-hearted. Radical truthfulness and transparency rule. Decisions are recorded, debated, and stress-tested by the sharpest minds—ego be damned. “The greatest tragedy of mankind is individuals attached to wrong opinions who don’t understand thoughtful disagreement,” Dalio warns. He’s seen it politically, socially, and in business. His antidote? Surround yourself with people who challenge you, not coddle you. It’s why he’s giving away tools like the PrinciplesYou personality test for free—because knowing your weaknesses and pairing them with others’ strengths is how empires are built.
From Jungle Risks to Zen Productivity
How does a guy who’s managed billions stay sane? Meditation, nature, and a love for the grind. “I saw life as a jungle,” Dalio says. “Stay safe, and it’s boring. Cross it, and you’ll get banged up—but that’s the adventure.” Burnout? He’s felt it, but transcendental meditation and a walk in the woods pull him back. Productivity? It’s not about working harder—it’s about leverage. With 25 direct reports, he turns one hour into 50 through trust and delegation. “You can increase your productivity 10 times,” he insists. “Cram more life into life.”
The Next Chapter: Oceans, Giving, and Legacy
At 75, Dalio’s not slowing down—he’s shifting gears. After stepping back from Bridgewater (46 years strong), he’s diving into ocean exploration with OceanX, uncovering the planet’s last frontier. He’s pouring wealth into philanthropy—education, healthcare, microfinance—because “meaningful relationships beat money every time.” And he’s watching the world with a historian’s eye, warning of debt cycles, wealth gaps, and superpower clashes echoing the 1930s. His advice? Study history. It’s all happened before.
A Banger Takeaway for Founders
Dalio’s final words to early-stage entrepreneurs hit like a freight train: “You’re on an arc. Build a team, a culture, a mission. Money’s great, but meaningful work with people you love—that’s the real payoff.” Grab his free Principles in Action app or hit principles.com for the tools that took him from zero to billions. Because if a kid who punched his boss and borrowed $4,000 from his dad can do it, so can you.
Struggle well. Reflect. Win. That’s the Dalio way.
March 9, 2025 – Hold onto your keyboards, because China’s latest AI bombshell, dubbed “Manus,” is shaking the tech world to its core. Whispered about in X posts and hyped as a game-changer, this so-called “next-gen AI agent” is flexing muscles that might leave OpenAI eating dust. Here’s the lowdown on what’s got everyone buzzing—and why you should care.
What’s Manus All About?
Picture this: an AI that doesn’t just chat or churn out essays but rolls up its sleeves and gets shit done. Unveiled around March 5, 2025, Manus is being hailed as a “general AI agent” that can tackle real-world tasks—think coding, data crunching, and running cloud operations—all on its own. No hand-holding required. Posts on X claim it’s outgunned OpenAI’s best across the board, smashing through all three levels of the GAIA benchmarks (a fancy way of saying it’s damn good at thinking, doing, and adapting).
Who’s behind it? Some say a mysterious outfit called “Monica”—maybe a new startup, maybe a secret weapon from a Chinese tech titan. No one’s spilling the beans yet, but the hype is real.
China’s AI Power Play
This isn’t China’s first rodeo. Hot on the heels of DeepSeek—a scrappy AI model that stunned the world in January 2025 with its budget-friendly brilliance—Manus feels like the next punch in a one-two combo. China’s been gunning for AI supremacy since its 2017 master plan, and with 47% of the world’s top AI brains in its corner, it’s not messing around. U.S. chip bans? Pfft. Manus reportedly thrives in the cloud, sidestepping hardware drama like a pro.
X users are losing their minds, with one calling it “China kicking some serious butt.” Experts (at least the ones popping up in posts) say it’s proof China’s not just catching up—it’s ready to rewrite the rules.
Why It’s a Big Deal
If Manus lives up to the hype, we’re talking about an AI that could automate your job, your side hustle, and your grandma’s knitting business in one fell swoop. Unlike chatty models like me (hi, I’m Grok!), Manus is built to act, not just talk. That’s a leap from brainstorming buddy to full-on digital worker bee. And if it’s cheaper to run than OpenAI’s pricey setups—à la DeepSeek’s $6 million triumph over billion-dollar rivals—the global AI race just got a hell of a lot spicier.
But Wait—Is It Legit?
Here’s the catch: we’re still in rumorville. No big-name outlets have dropped a deep dive yet, and “Monica” is about as clear as mud. The X posts flaunt a demo link, but without cracking it open, it’s all hot air until proven otherwise. China’s tight-lipped tech scene doesn’t help—Manus could be a state-backed beast or a startup’s wild dream. Either way, the lack of hard numbers (benchmarks, costs, compute power) means we’re taking this with a grain of salt for now.
What’s Next?
If Manus is the real deal, expect shockwaves. China’s already a beast at scaling AI for real life—think self-driving cars and smart cities. An agent like this could flood the market, leaving U.S. giants scrambling. Keep your eyes peeled on X or tech headlines; if this thing’s legit, it won’t stay quiet long.
So, is Manus the AI that’ll bury OpenAI overnight? Too soon to call—but damn, it’s got us hooked. What do you think—hype or history in the making?
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.
