TL;DR
One X post asking long-married couples for their best advice blew up to 7,400+ replies. The clear winners: remove divorce as an option, put God first, forgive daily, never stop laughing, and keep choosing each other when it’s hard.
Top 10 Real Takeaways from Couples Married 25–58 Years
Divorce is never an option (mentioned in ~25% of replies)
Put God/Jesus at the absolute center
Forgive fast and never keep score
Never speak badly about your spouse to anyone
Love is a daily decision, not just a feeling
Keep dating – date nights are sacred even after 40+ years
Pray together every single day
Never go to bed angry + zero name-calling ever
Lower expectations and serve without keeping score
Marry someone who makes you laugh – humor is the glue
The Funniest Replies (Most Liked)
Here are some of the top-performing answers that perfectly capture the vibe of the thread:
Modern culture sells “soulmates + constant fireworks.” These 40–50+ year couples are unanimously saying the opposite: marry a good person, burn the exit door, decide every morning to love and serve them, and outlast the hard seasons together.
The couples who make it the longest aren’t the luckiest or the most “in love” – they’re the ones who simply refused to quit when it stopped being easy.
In a rare and revealing discussion on November 25, 2025, Ilya Sutskever sat down with Dwarkesh Patel to discuss the strategy behind his new company, Safe Superintelligence (SSI), and the fundamental shifts occurring in the field of AI.
TL;DW
Ilya Sutskever argues we have moved from the “Age of Scaling” (2020–2025) back to the “Age of Research.” While current models ace difficult benchmarks, they suffer from “jaggedness” and fail at basic generalization where humans excel. SSI is betting on finding a new technical paradigm—beyond just adding more compute to pre-training—to unlock true superintelligence, with a timeline estimated between 5 to 20 years.
Key Takeaways
The End of the Scaling Era: Scaling “sucked the air out of the room” for years. While compute is still vital, we have reached a point where simply adding more data/compute to the current recipe yields diminishing returns. We need new ideas.
The “Jaggedness” of AI: Models can solve PhD-level physics problems but fail to fix a simple coding bug without introducing a new one. This disconnect proves current generalization is fundamentally flawed compared to human learning.
SSI’s “Straight Shot” Strategy: Unlike competitors racing to release incremental products, SSI aims to stay private and focus purely on R&D until they crack safe superintelligence, though Ilya admits some incremental release may be necessary to demonstrate power to the public.
The 5-20 Year Timeline: Ilya predicts it will take 5 to 20 years to achieve a system that can learn as efficiently as a human and subsequently become superintelligent.
Neuralink++ as Equilibrium: In the very long run, to maintain relevance in a world of superintelligence, Ilya suggests humans may need to merge with AI (e.g., “Neuralink++”) to fully understand and participate in the AI’s decision-making.
Detailed Summary
1. The Generalization Gap: Humans vs. Models
A core theme of the conversation was the concept of generalization. Ilya highlighted a paradox: AI models are superhuman at “competitive programming” (because they’ve seen every problem exists) but lack the “it factor” to function as reliable engineers. He used the analogy of a student who memorizes 10,000 problems versus one who understands the underlying principles with only 100 hours of study. Current AIs are the former; they don’t actually learn the way humans do.
He pointed out that human robustness—like a teenager learning to drive in 10 hours—relies on a “value function” (often driven by emotion) that current Reinforcement Learning (RL) paradigms fail to capture efficiently.
2. From Scaling Back to Research
Ilya categorized the history of modern AI into eras:
2012–2020: The Age of Research (Discovery of AlexNet, Transformers).
2020–2025: The Age of Scaling (The consensus that “bigger is better”).
2025 Onwards: The New Age of Research.
He argues that pre-training data is finite and we are hitting the limits of what the current “recipe” can do. The industry is now “scaling RL,” but without a fundamental breakthrough in how models learn and generalize, we won’t reach AGI. SSI is positioning itself to find that missing breakthrough.
3. Alignment and “Caring for Sentient Life”
When discussing safety, Ilya moved away from complex RLHF mechanics to a more philosophical “North Star.” He believes the safest path is to build an AI that has a robust, baked-in drive to “care for sentient life.”
He theorizes that it might be easier to align an AI to care about all sentient beings (rather than just humans) because the AI itself will eventually be sentient. He draws parallels to human evolution: just as evolution hard-coded social desires and empathy into our biology, we must find the equivalent “mathematical” way to hard-code this care into superintelligence.
4. The Future of SSI
Safe Superintelligence (SSI) is explicitly an “Age of Research” company. They are not interested in the “rat race” of releasing slightly better chatbots every few months. Ilya’s vision is to insulate the team from market pressures to focus on the “straight shot” to superintelligence. However, he conceded that demonstrating the AI’s power incrementally might be necessary to wake the world (and governments) up to the reality of what is coming.
Thoughts and Analysis
This interview marks a significant shift in the narrative of the AI frontier. For the last five years, the dominant strategy has been “scale is all you need.” For the godfather of modern AI to explicitly declare that era over—and that we are missing a fundamental piece of the puzzle regarding generalization—is a massive signal.
Ilya seems to be betting that the current crop of LLMs, while impressive, are essentially “memorization engines” rather than “reasoning engines.” His focus on the sample efficiency of human learning (how little data we need to learn a new skill) suggests that SSI is looking for a new architecture or training paradigm that mimics biological learning more closely than the brute-force statistical correlation of today’s Transformers.
Finally, his comment on Neuralink++ is striking. It suggests that in his view, the “alignment problem” might technically be unsolvable in a traditional sense (humans controlling gods), and the only stable long-term outcome is the merger of biological and digital intelligence.
On November 24, 2025, President Trump signed an Executive Order launching “The Genesis Mission.” This initiative aims to centralize federal data and high-performance computing under the Department of Energy to create a massive AI platform. Likened to the World War II Manhattan Project, its goal is to accelerate scientific discovery in critical fields like nuclear energy, biotechnology, and advanced manufacturing.
Key Takeaways
The “Manhattan Project” of AI: The Administration frames this as a historic national effort comparable in urgency to the project that built the atomic bomb, aimed now at global technology dominance.
Department of Energy Leads: The Secretary of Energy will oversee the mission, leveraging National Labs and supercomputing infrastructure.
The “Platform”: A new “American Science and Security Platform” will be built to host AI agents, foundation models, and secure federal datasets.
Six Core Challenges: The mission initially focuses on advanced manufacturing, biotechnology, critical materials, nuclear energy, quantum information science, and semiconductors.
Data is the Fuel: The order prioritizes unlocking the “world’s largest collection” of federal scientific datasets to train these new AI models.
Detailed Summary of the Executive Order
The Executive Order, titled Launching the Genesis Mission, establishes a coordinated national effort to harness Artificial Intelligence for scientific breakthroughs. Here is how the directive breaks down:
1. Purpose and Ambition
The order asserts that America is currently in a race for global technology dominance in AI. To win this race, the Administration is launching the “Genesis Mission,” described as a dedicated effort to unleash a new age of AI-accelerated innovation. The explicit goal is to secure energy dominance, strengthen national security, and multiply the return on taxpayer investment in R&D.
2. The American Science and Security Platform
The core mechanism of this mission is the creation of the American Science and Security Platform. This infrastructure will provide:
Compute: Secure cloud-based AI environments and DOE national lab supercomputers.
AI Agents: Autonomous agents designed to test hypotheses, automate research workflows, and explore design spaces.
Data: Access to proprietary, federally curated, and open scientific datasets, as well as synthetic data generated by DOE resources.
3. Timeline and Milestones
The Secretary of Energy is on a tight schedule to operationalize this vision:
90 Days: Identify all available federal computing and storage resources.
120 Days: Select initial data/model assets and develop a cybersecurity plan for incorporating data from outside the federal government.
270 Days: Demonstrate an “initial operating capability” of the Platform for at least one national challenge.
4. Targeted Scientific Domains
The mission is not open-ended; it focuses on specific high-impact areas. Within 60 days, the Secretary must submit a list of at least 20 challenges, spanning priority domains including Biotechnology, Nuclear Fission and Fusion, Quantum Information Science, and Semiconductors.
5. Public-Private and International Collaboration
While led by the DOE, the mission explicitly calls for bringing together “brilliant American scientists” from universities and pioneering businesses. The Secretary is tasked with developing standardized frameworks for IP ownership, licensing, and trade-secret protections to encourage private sector participation.
Analysis and Thoughts
“The Genesis Mission will… multiply the return on taxpayer investment into research and development.”
The Data Sovereignty Play
The most significant aspect of this order is the recognition of federal datasets as a strategic asset. By explicitly mentioning the “world’s largest collection of such datasets” developed over decades, the Administration is leveraging an asset that private companies cannot easily duplicate. This suggests a shift toward “Sovereign AI” where the government doesn’t just regulate AI, but builds the foundational models for science.
Hardware over Software
Placing this under the Department of Energy (DOE) rather than the National Science Foundation (NSF) or Commerce is a strategic signal. The DOE owns the National Labs (like Oak Ridge and Lawrence Livermore) and the world’s fastest supercomputers. This indicates the Administration views this as a heavy-infrastructure challenge—requiring massive energy and compute—rather than just a software problem.
The “Manhattan Project” Framing
Invoking the Manhattan Project sets an incredibly high bar. That project resulted in a singular, world-changing weapon. The Genesis Mission aims for a broader diffusion of “AI agents” to automate research. The success of this mission will depend heavily on the integration mentioned in Section 2—getting academic, private, and classified federal systems to talk to each other without compromising security.
The Energy Component
It is notable that nuclear fission and fusion are highlighted as specific challenges. AI is notoriously energy-hungry. By tasking the DOE with solving energy problems using AI, the mission creates a feedback loop: better AI designs better power plants, which power better AI.
Important Disclaimer: Watermarks like Google’s SynthID (embedded in images generated by Nano Banana Pro or Gemini’s image tools) exist to promote transparency and responsible use of AI-generated content. Removing them may violate Google’s terms of service, copyright laws, or platform policies — especially if the image isn’t yours or is used commercially without permission. This tutorial is for educational and personal fair-use purposes only. Always respect intellectual property rights and consider legitimate alternatives (e.g., Google’s paid Ultra plan for watermark-free exports). Proceed at your own risk.
TL;DR
You can remove the visible Gemini/Nano Banana watermark (the little sparkle/diamond logo) completely for free using Adobe Express’s crop or AI Remove/Spot Healing tool. The invisible SynthID watermark cannot be fully removed with free tools — only diluted slightly through editing/exporting. The whole process takes 5–15 minutes per image.
Key Takeaways
Visible watermark → easily removed with cropping or Adobe Express free “Remove object” / Spot Healing tool
Invisible SynthID → not fully removable without paid/specialized tools; editing only reduces detection confidence a little
Adobe Express free tier works perfectly for this and lets you export without its own watermark if you avoid premium assets
Always keep the original file and disclose AI origin when sharing
Better long-term solution: pay for Gemini Ultra / Nano Banana Pro to get clean exports natively
Detailed Step-by-Step Tutorial
Step 1: Get Your Nano Banana Image
Open Gemini (web or app) → Nano Banana
Generate your image
Download it (free tier includes visible watermark)
Step 2: Open Free Adobe Express
Go to adobe.com/express → Sign in with free Adobe account → “Start for free”
Step 3: Quickest Method – Crop It Out
Upload your image
Use the Crop tool → drag to exclude the bottom-right corner watermark
Apply → Done (perfect for most images)
Step 4: Remove Visible Watermark with AI (When Cropping Isn’t Possible)
In the left panel → Quick Actions → “Remove object” (or search “remove”)
Brush over the Gemini sparkle logo
AI automatically fills the area with surrounding pixels
Repeat or use Clone Stamp if needed
Step 5: Export Without Adobe Watermark
Click Download
Choose PNG or JPG
If it tries to add Adobe watermark → you probably used a premium template/element → undo and use only free assets, or toggle watermark off in settings
Free basic edits export clean 99% of the time
Step 6: Verify
Zoom in → no visible logo
Optional: upload to Hive Moderation or ask Gemini “Is this AI-generated?” → invisible SynthID usually still detectable
Alternative Free Tools if Adobe Express Is Acting Up
WatermarkRemover.io (4 free removals/day)
Photopea.com (web Photoshop clone)
Photoshop Express mobile app (free Spot Heal)
GIMP (desktop, fully free)
My Thoughts on This Whole Thing
Google adding both visible and invisible watermarks is actually a good move for transparency — the problem is they lock clean exports behind the priciest Ultra tier. For hobbyists and educators who just want to use a nice AI image in a presentation or blog without an ugly logo in the corner, having to pay $20+/month feels excessive.
Adobe Express giving us a powerful, free “Remove object” tool essentially hands everyone a workaround for the visible mark, which is why this method works so well right now. The invisible SynthID is much harder to defeat without specialized (often paid or legally gray) tools, so for most practical purposes, the images are still identifiable as AI-generated — which keeps the transparency promise somewhat intact.
Ethically, I’m fine with individuals cleaning up images they generated themselves for personal or clearly disclosed use. The line gets crossed when people start stripping watermarks to pass off AI art as human-made photography or to sell commercially without disclosure.
Long-term, I hope Google adds a middle-tier plan that removes the visible logo (keep SynthID) for a few bucks a month — that would solve 95% of the frustration without undermining their transparency goals.
When the co-founder of Creative Artists Agency (CAA) sits down with David Senra, the host of the Founders podcast, you don’t just get industry gossip—you get a masterclass in agency, psychology, and relentless ambition. Michael Ovitz, often cited as the most powerful man in Hollywood during the 1980s and 90s, shared the playbook he used to revolutionize the entertainment industry.
From his early days in the mailroom to orchestrating the sale of Columbia Pictures to Sony, Ovitz’s career is a testament to the power of information and relationships. Below is a breakdown of his conversation with David Senra, including key takeaways and a detailed summary of their discussion.
TL;DW
Michael Ovitz argues that success is driven by “frame of reference”—the accumulation of experiences that allows you to instinctively spot quality and talent. He emphasizes that fear is the enemy of business, that you must relentlessly study history to leverage it in the present, and that true salesmanship often involves “punching without punching”—selling without ever explicitly asking for the sale.
Key Takeaways
Build a “Frame of Reference”: You cannot spot excellence if you haven’t seen it before. Ovitz believes in consuming vast amounts of information—art, culture, business history—to build a mental database that allows for instant pattern recognition.
Information is Leverage: As a mailroom trainee, Ovitz showed up at 6:30 AM (hours before anyone else) to read the agency’s private files. This gave him an encyclopedic knowledge of the business that his peers lacked.
The “No Guardrails” Mindset: Creativity in business means refusing to accept arbitrary boundaries. As Ovitz famously states, “I’ve never seen a guardrail I don’t try to jump”.
Punching Without Punching: The highest form of sales is demonstrated by David Rockefeller, who raised millions for MoMA without ever asking Ovitz for a dime. He simply built a relationship and shared a vision until Ovitz wanted to contribute.
Radical Transparency creates Loyalty: At CAA, Ovitz instituted a rule of “no lying.” If an agent didn’t know an answer, they had to say “I don’t know” and follow up later. This created trust in an industry famous for dishonesty.
Detailed Summary
1. The Mailroom Strategy: Outworking the Competition
Ovitz’s career began in the mailroom at William Morris. Realizing he had no nepotistic connections in a relationship-driven town, he decided to differentiate himself through pure knowledge. While the other trainees arrived at 9:00 AM, Ovitz arrived at 6:30 AM.
He read the correspondence of the top agents, learning the history of the industry. This allowed him to speak the language of the older generation of filmmakers. When he later met legendary directors, he could discuss their obscure influences (like Frank Capra or Howard Hawks) because he had done the reading. He noted that he wasn’t necessarily smarter than the Ivy League trainees, but he eradicated them by outworking them.
2. The “Frame of Reference”
A recurring theme in the interview is the “frame of reference.” Ovitz explains that his ability to spot talent—whether it was a young Wolfgang Puck in a parking lot restaurant or the chef Nobu Matsuhisa—came from constantly scanning the world for excellence.
He creates a “personal AI” in his brain by consuming hundreds of images of art, reading widely, and meeting people. This creates a benchmark. When he met Nobu, he knew the chef was special not just because the food was good, but because Nobu “filled the room” with a sensei-like presence.
3. The Coca-Cola Deal and The $3 Million Check
One of the most tactical examples of Ovitz’s negotiation style involved Coca-Cola. CAA took over Coke’s advertising, employing film directors to make commercials—a move the industry mocked. When Coke sent CAA a check for $3 million to cover the cost of a specific commercial, Ovitz sent it back voided.
He told them the commercial only cost $30,000 (having been made on an Apple IIe computer). He refused to let the client overpay for the production, which established immense trust. He then told them, “You’re not going to overpay for commercials, but you got to pay us.” This move allowed him to negotiate a much higher fee for the agency’s intellectual property and strategy rather than just production margins.
4. Lessons from Mentors: Rockefeller and Morita
Ovitz collected mentors as aggressively as he collected art. Two stand out:
David Rockefeller: Ovitz learned the art of the “soft sell.” Rockefeller invited Ovitz to join the MoMA board and spent hours discussing art and architecture, never bringing up money. By the end, Ovitz wrote a larger check than he ever intended, purely out of respect for Rockefeller’s integrity and vision.
Akio Morita (Sony): Ovitz admired Morita’s courage to disrupt his own business. Morita taught him the value of “thinking big”—not just building a company, but changing the perception of a nation (Japan). Ovitz also recounted how Morita hired his harshest critic, Norio Ohga, because he valued an honest “mirror” over a “yes man”.
5. The Friendship with Michael Crichton
Ovitz speaks touchingly of his 30-year friendship with author Michael Crichton. He describes Crichton as possessing a unique work ethic: he wouldn’t write every day, but when a deadline approached, he would write 20 hours a day for months. Crichton wrote Jurassic Park in a five-month burst of intensity. The biggest lesson Ovitz took from Crichton was “curiosity about everything”.
Some Thoughts
What stands out most in this interview is the bridge Ovitz builds between the “old world” of Hollywood and the “new world” of Silicon Valley. He speaks about Marc Andreessen and Ben Horowitz with the same reverence he holds for Paul Newman or Martin Scorsese.
Ovitz’s philosophy is ultimately one of input/output. He treats his brain like a machine learning model—if you feed it high-quality data (art, history, business biographies), it will output high-quality decisions (spotting Nobu, packaging Jurassic Park). In an age of algorithmic curation, Ovitz represents the value of manual curation—going to the library, reading the files, and seeing the world with your own eyes.
As he told Senra regarding his relentless drive even after achieving wealth: “I’ve never seen a guardrail I don’t try to jump”. For entrepreneurs, that is the only way to operate.
As of November 2025, X has rolled out a significant transparency update. The platform now displays every account’s detected base country directly on their profile. This new feature, nestled under the “About this account” section, is designed to verify the authenticity of users and combat misinformation.
Whether you want to verify a source’s location or adjust your own privacy settings, here is everything you need to know about the new location display.
How to View Someone’s Location on X
You do not need to follow a user to see their location data. It is visible to everyone by default. Here is how to access it in just three taps:
Open the user’s profile page.
Tap the “Joined” date located at the top of their profile (next to the calendar icon).
Look for the line reading “Account based in [Country].”
What the Location Status Means
X uses IP activity to detect location, but users also have manual control over how specific this information is. Here is a breakdown of what you might see on a profile:
Displayed Text
What It Means
United States / India / etc.
This is the default setting. X has detected the precise country based on recent IP activity.
North America / Europe / etc.
The user has manually changed their settings to show a broader “Region” rather than a specific country.
“Country or region may not be accurate”
X has detected active VPN or proxy usage. This warning is automatically appended to the profile.
Location hidden
The user has opted out of the feature entirely. Note: Profiles hiding this info may be flagged or highlighted by the platform.
How to Change or Hide Your Own Location
If you are concerned about privacy, you can alter how—or if—your location is displayed. The feature is currently rolling out globally and is expected to be fully enforced within weeks.
To change your settings, follow this path:
Settings & Privacy → Privacy and safety → About your account → Account location display
From there, you can select one of three options:
Country: The default, precise view.
Region: A broader, less specific view.
Hide entirely: Removes the data from your profile.
Live from The Venetian: The Besties break down the Epstein file release, the massive margins of Tether, the Michael Burry vs. Nvidia debate, and a masterclass in risk with Alan Keating.
In this special live episode recorded during the F1 weekend in Las Vegas, the “Besties” (Chamath Palihapitiya, Jason Calacanis, David Sacks, and David Friedberg) reunite in person. The agenda is packed: political intrigue surrounding Jeffrey Epstein, the financial dominance of stablecoins, technical debates on AI chip accounting, and high-stakes poker strategy.
TL;DR: Executive Summary
The US government has voted nearly unanimously to release the Epstein files, leading the hosts to speculate that the lack of leaks points to intelligence agency involvement rather than political dirt on Donald Trump. Chamath details a meeting with Tether CEO Paolo Ardoino, revealing a business holding over $100 billion in US Treasuries with profit margins potentially exceeding 95%. The group then debates Michael Burry’s short position on Nvidia, with Friedberg defending the “useful life” of AI chips under GAAP accounting. Finally, poker legend Alan Keating joins to discuss “soul reading” opponents and mastering fear in high-stakes games.
Key Takeaways
The Epstein Intelligence Theory: The hosts argue that if the files contained damaging information on Donald Trump, it would have been leaked during the Biden administration. The prevailing theory discussed is that Epstein may have been an intelligence asset (CIA/Mossad/Russia), explaining the long-standing secrecy.
Tether is a Financial Juggernaut: Tether holds approximately $135 billion in US Treasuries and operates with roughly 100 employees. Chamath estimates the business runs at 95%+ margins, effectively exporting US dollar stability to developing nations while capturing massive interest yields.
Nvidia vs. Michael Burry: “The Big Short” investor Michael Burry is shorting the sector, arguing tech companies are “cooking the books” by depreciating AI chips over 6 years when they become obsolete in 3. Friedberg counters that chips retain a “useful life” for inference and background tasks long after they are no longer top-of-the-line.
Google Gemini 3: Google has regained the lead on LLM benchmarks with Gemini 3. The conversation highlights a shift toward proprietary silicon (TPUs) and a fragmented chip market, posing a potential long-term risk to Nvidia’s dominance.
The “Oppenheimer” Moment: David Friedberg reveals he decided to return as CEO of Oho after watching the movie Oppenheimer, realizing he needed to be an active operator rather than a passive board member.
Detailed Episode Breakdown
1. The Epstein Files Release
In a stunning bipartisan move, the House and Senate voted nearly unanimously to release the Epstein files. The Besties analyzed why this is happening now. Sacks and Chamath suggested that because Epstein was the “most investigated human on earth,” any compromising information regarding Trump would likely have been weaponized politically by now.
The discussion pivoted to the source of Epstein’s wealth. Chamath noted Epstein managed money for billionaires and charged inexplicable fees for “tax advice”—such as a documented $168 million payment from Apollo’s Leon Black. The hosts speculated that Epstein likely functioned as a spy or asset for intelligence agencies, which would explain the protective layer surrounding the files for so long.
2. Tether and the Stablecoin Boom
Chamath shared insights from a dinner with Tether CEO Paolo Ardoino. Tether’s financials are staggering: approximately $135 billion in US Treasuries and billions more in Bitcoin and gold.
The hosts discussed the utility of stablecoins in high-inflation economies, where locals use USDT to preserve purchasing power. Because Tether earns the interest on the backing treasuries (rather than passing it to the coin holder), and operates with a lean team, the company generates billions in pure profit. Sacks noted that future US regulations might eventually force stablecoin issuers to share that yield with users, but for now, it remains one of the most profitable business models in the world.
3. Accounting Corner: Is Nvidia Overvalued?
Michael Burry is shorting the semiconductor sector, claiming companies are inflating earnings by depreciating Nvidia chips over 6 years despite rapid technological obsolescence.
Friedberg launched a segment dubbed “Accounting Corner” to rebut this. He explained that under GAAP standards, an asset’s useful life is determined by its ability to generate revenue, not just its technological superiority. Even if an H100 chip isn’t the fastest on the market in year 4, it can still run inference models or handle lower-priority compute tasks, justifying the longer depreciation schedule. Chamath added that tech giants monitor “output tokens” closely; if a chip wasn’t profitable, they would simply turn it off.
4. Poker Strategy with Alan Keating
The episode concluded with Alan Keating, a high-stakes poker player famous for his loose, aggressive style. Keating explained his philosophy, which relies less on “solvers” (GTO strategy) and more on “soul reading”—navigating the fear and psychology of the table.
He broke down a famous hand where he beat Doug Polk with a 4-2 offsuit, explaining that he sensed fear in Polk’s betting patterns on the turn. Keating described his approach as finding “beauty in the chaos” and dragging opponents into “deep water” where they are uncomfortable and prone to errors.
Editorial Thoughts
This episode marked a distinct shift in the podcast’s tone regarding crypto, moving from general skepticism to a recognition of the sheer scale and utility of stablecoins like Tether. The “Accounting Corner” segment, while technical, provided critical context for investors trying to value the AI stack—suggesting the AI boom has more fundamental accounting support than bears like Burry believe. Finally, the live format from Las Vegas brought a looser, more energetic dynamic to the conversation, highlighting the chemistry that makes the show work.
In a recent discussion on the Uncapped podcast with Jack Altman, Robinhood co-founder and CEO Vlad Tenev opened up about the company’s transition from a trading platform to a “financial super app.” Tenev discussed the explosion of prediction markets, the role of AI in creating “vibe trading,” and his vision for tokenizing private assets to help retail investors capture value earlier.
TL;DR
Robinhood is aggressively expanding beyond simple stock trading. Vlad Tenev highlights three major frontiers: the rise of prediction markets as “truth machines,” the use of AI to create autonomous “vibe trading” experiences, and the tokenization of private assets to allow everyday investors access to companies like SpaceX or OpenAI before they go public.
Key Takeaways
From App to Ecosystem: Robinhood no longer views itself merely as a trading platform but as a “financial home” and super app, encompassing banking, credit cards, and retirement accounts.
Prediction Markets are Booming: Tenev views prediction markets not just as speculation, but as “truth machines” that offer cleaner data than traditional polling or media. Robinhood’s volume in this sector has seen massive growth.
“Vibe Trading”: Tenev coined the term “vibe trading” to describe a future where AI agents manage a user’s portfolio based on high-level intent, risk tolerance, and personal goals rather than manual trade execution.
Solving the Private Equity Gap: Tenev argues that the biggest inequity in modern markets is that value now accrues in private markets (e.g., SpaceX, OpenAI) rather than public ones. He believes tokenization is the solution to give retail investors access.
Generational Shifts: Contrary to stereotypes, Gen Z is opening retirement accounts as early as 19 years old, signaling a shift toward financial conservatism compared to millennials.
Detailed Summary
The Evolution of the Brokerage
Tenev traces the history of the online brokerage from the deregulation of commissions in 1975 (the “Mayday” event that birthed Charles Schwab) to the mobile-first revolution led by Robinhood. While early digital brokers like E-Trade catered to Gen X, Robinhood capitalized on two shifts: the ubiquity of mobile phones and the infrastructure changes brought by high-frequency trading, which lowered costs enough to offer commission-free trading.
Today, Robinhood generates over a billion dollars in revenue across multiple business lines, aiming to be the primary financial institution for its users.
Prediction Markets: The “Truth Machines”
One of the fastest-growing segments for the company is prediction markets. Tenev notes that the 2024 Presidential Election was a “Big Bang” moment for the industry, validating these markets as superior forecasting tools compared to traditional polls.
He argues that because participants have “skin in the game,” prediction markets filter out noise and bias, acting as “truth machines.” Beyond politics, this is expanding into sports and entertainment, which Tenev views as an inevitability in an economy where AI automates traditional labor.
Tokenization and Private Markets
Tenev expressed deep concern regarding where economic value is created today versus thirty years ago. When Microsoft and Apple went public, they were valued in the low billions, allowing public market investors to capture the majority of their growth. Today, companies like SpaceX or OpenAI may reach trillion-dollar valuations while still private, shutting out retail investors.
His solution is tokenization. Similar to how stablecoins operate, Tenev envisions a structure where private securities are held in a “bucket” while tokens representing them trade freely 24/7 on a blockchain. This would democratize access to private equity, a move he sees as the eventual end-state of capital markets.
AI and the Era of “Vibe Trading”
Robinhood is heavily integrating AI into its operations, achieving high deflection rates in customer support and increased coding output from engineering. However, the consumer-facing future is what Tenev calls “Vibe Trading.”
In this model, the user interface shifts from manual execution to intent-based directives. A user might tell an AI agent their risk appetite, long-term goals, and interests, and the agent—acting as a “financial home”—executes the strategy. Tenev believes this will also solve mundane friction points, such as AI agents automatically handling the paperwork to switch bank accounts.
Thoughts on the Interview
Vlad Tenev’s commentary suggests a significant pivot in Robinhood’s brand identity. Originally seen as the disruptor that “gamified” trading, the company is now positioning itself as the mature “financial super app” for a generation that is aging into wealth.
The most compelling insight is the focus on tokenization. Tenev correctly identifies that the “public market” is no longer the primary engine of wealth creation for early-stage innovative companies. If Robinhood can successfully navigate the regulatory hurdles to tokenize private equity (essentially breaking down the walls of the accredited investor requirements via technology), they wouldn’t just be a brokerage; they would fundamentally alter the structure of modern capitalism.
Furthermore, the concept of “Vibe Trading” aligns with the broader tech trend of “agentic AI.” It moves the user value proposition from “we give you the tools to do it yourself” to “we have the intelligence to do it for you,” which may appeal to a broader demographic than active traders.
November 20, 2025 – NVIDIA just delivered the most dominant quarter in the history of tech and told the world the next one will be even bigger. The market is partying like it’s 2021.
TL;DR
Revenue $57.01B (+62% YoY, beat by ~$1.8–2B)
Data Center $51.2B (+66% YoY, +$10B sequentially) – now 90% of total revenue
GAAP EPS $1.30 (+67% YoY)
Q4 guidance $65B (±2%) – obliterates street $61.98B (some buyside whispers were $75B → Jensen sandbagging again)
Blackwell sales “off the charts”, cloud GPUs completely sold out for the foreseeable future
CFO Colette Kress confirmed ≈$500B Blackwell + Rubin revenue visibility 2025–2026 (analysts now calling it $500B pipeline through FY2027)
Gross margin 73.6% (tiny miss due to Blackwell ramp costs), guided back to 75.0% next quarter
Free cash flow $22.1B in a single quarter
Top 4 customers = 61% of revenue (22% / 15% / 13% / 11%) – concentration risk is real but demand makes it a feature
Stock ripped +5.5% after-hours → +$220B+ market cap in minutes, lifting entire AI complex
Key Takeaways
Demand is not slowing — it’s compounding. Jensen: “Compute demand keeps accelerating and compounding across training and inference — each growing exponentially. We’ve entered the virtuous cycle of AI.”
Blackwell ramp is unprecedented – already the majority of new Data Center mix, sold out for months, driving the entire $10B sequential jump
Gaming ($4.3B) and Automotive ($592M) missed estimates → literally nobody cares when Data Center grew $10B in one quarter
Customer concentration: Four hyperscalers = 61% of revenue. Everyone knows who they are. Everyone also knows they can’t build without NVIDIA
Margins dipped to 73.6% only because of Blackwell complexity/HBM costs – guided 75% next quarter, street relieved
Balance sheet is absurd: $60.6B cash + $22.1B quarterly FCF. Berkshire is only ~$320B ahead
Physical AI multi-trillion opportunity already “multi-billion” today
Detailed Summary
NVIDIA printed $57.01 billion in a single quarter — a number larger than the entire annual revenue of 99% of public companies. Data Center alone did $51.2 billion (+66% YoY, +$10 billion sequentially). Let that sink in.
Blackwell is not “ramping” — it’s exploding. It is already the majority of new Data Center revenue and cloud providers are in a literal bidding war for every wafer. Jensen was blunt: “Blackwell sales are off the charts, and cloud GPUs are sold out.”
Yes, Gaming and Automotive missed estimates (who cares), Pro Visualization crushed it (+56% YoY), but the only number that matters is the $500 billion in confirmed Blackwell + Rubin orders the company can already see through calendar 2026 (Bloomberg Intelligence now calling it $500B pipeline through fiscal 2027).
China export restrictions? Effectively $0 impact in guidance. The rest of the planet is making up for it and then some — sovereign AI factories, enterprises, everyone is building.
Networking (Spectrum-X + InfiniBand) up ~162% YoY to $8.2B+ — the hidden monster line item nobody talks about.
Market & Analyst Reaction
Initial spike was +4%, then kept climbing → closed extended trading up ~5.5%, adding north of $220 billion in market cap. Entire AI food chain ripping: CoreWeave +4%, Nebius +4%, AMD +2%, Micron +2%, Broadcom +2%, Super Micro +8%.
Goldman Sachs (James Schneider) first note post-earnings:
“Strong quarter with upside to guidance should provide relief for the stock… We expect the stock to trade higher following a stronger quarter and guidance relative to the Street.”
X was pure euphoria last night – here are some of the top posts (all >5K likes):
https://x.com/KobeissiLetter/status/1991255966235419112 ← +$205B market cap meme
https://x.com/amitisinvesting/status/1991263435493974047 ← Full breakdown thread
https://x.com/FromValue/status/1991275128439123451 ← “NVIDIA just printed more FCF than most companies make in revenue”
My Thoughts
This was a “relief rally on steroids”. Anyone still waiting for the AI capex slowdown just got obliterated. The $500 billion visibility isn’t hopium — it’s what they can already see in purchase orders.
The moat is now impenetrable: CUDA + NVLink + Spectrum-X + Grace CPU + Blackwell/Rubin roadmap = Microsoft Windows-level lock-in for the AI era.
At ~44× forward earnings the stock looks expensive until you realize the base case is now ~$260–280B annual revenue run-rate by late 2026. That puts the multiple in the low 20s. That is no longer the bull case — that’s the new floor.
The Christmas rally is officially back on. NVIDIA just saved it.
Bill Ackman’s Pershing Square Capital Management just released a 28-page investor presentation urging the Trump administration to immediately (1) deem the Treasury’s Senior Preferred Stock repaid, (2) exercise the 79.9% warrants, and (3) relist Fannie Mae (FNMA) and Freddie Mac (FMCC) on the NYSE — all while keeping the GSEs in conservatorship. They claim this can be done before the end of November 2025 and would instantly value the U.S. taxpayer’s stake at over $300 billion without disrupting mortgage affordability.
Key Takeaways
Fannie & Freddie OTC shares have already more than doubled in 2025 on Trump administration statements.
The three-step plan (repay SPS → exercise warrants → NYSE relisting) can be executed immediately by Treasury and FHFA.
Post-relisting, Treasury would own 79.9% of two NYSE-listed companies worth a combined ~$387 billion (Pershing estimate).
Taxpayers have already received $301 billion in dividends — $25 billion more than required under the original 10% deal.
Pershing strongly opposes any conversion of Senior Preferred into common — calls it value-destructive and legally risky.
Relisting unlocks massive institutional buying (many funds are barred from OTC stocks) and fulfills Trump’s campaign promise timing.
Conservatorship continues for years, giving the administration runway to finalize capital rules, backstop structure, and governance.
Detailed Summary of the Pershing Square Presentation (November 2025)
In a presentation titled “Promises Made, Promises Kept”, Pershing Square lays out a politically and financially attractive path for the second Trump administration to deliver on its GSE reform pledges without raising mortgage rates or rushing a full privatization.
The core argument: the government has already been fully repaid (and then some) via $301 billion of dividends since 2008. The Obama-era 2012 “Net Worth Sweep” was paused under Mnuchin, but never fully reversed. Pershing says a simple letter agreement between Treasury and FHFA can officially retire the Senior Preferred Stock today.
Once the SPS is gone, Treasury can exercise its long-held warrants for 79.9% of the common stock at essentially zero cost. The GSEs already meet every NYSE listing requirement (market cap, float, share price, shareholder count, etc.). FHFA can approve relisting while keeping full conservatorship powers intact — no change to operations, no new capital raises, no dividend payments to juniors until fully recapitalized.
Total taxpayer value: >$310 billion (plus junior preferred)
They explicitly reject the idea of converting Senior Preferred into common, warning it would trigger new litigation, force government consolidation onto the federal balance sheet, and slash valuations by 27–56% depending on the multiple the market would assign to a company that wiped out private shareholders.
My Thoughts
This is classic Ackman: aggressive, detailed, and perfectly timed to influence policy while he has a massive economic interest (Pershing owns large common positions in both GSEs). The beauty of the proposal is that it is genuinely low-risk from a mortgage-market standpoint and gives the administration an instant “win” before Thanksgiving 2025.
The politics line up perfectly: Trump gets to post on Truth Social that he turned two “bailed-out” companies into a $300 billion+ taxpayer windfall, keeps 30-year mortgage rates stable (or even lower), and still retains total control to shape the final exit over the next three years.
If Treasury and FHFA actually follow the three steps before November 30, 2025, the OTC-to-NYSE pop could be one of the largest wealth-transfer events in market history — and almost entirely to existing common shareholders (retail + hedge funds that held on since 2008).
Watch for any joint Treasury/FHFA announcement or letter agreement in the next two weeks. That will be the trigger.
Disclosure: Like Pershing Square, the author may have direct or indirect exposure to FNMA/FMCC securities.
