"Not me" | Vlad's Newsletter Podcast

About "Not me" | Vlad's Newsletter Podcast

A newsletter about modern entrepreneurship, AI, business, and meaningful success. www.vladsnewsletter.com (https://www.vladsnewsletter.com?utm_medium=podcast)

Publisher

Vladyslav Podoliako

Category

business · technology

Language

en

Explicit

No

First episode

18 Apr 2025

Latest episode

19 Aug 2026

Latest episodes

11 episodes in the feed.

1. 

   19 Aug 2026

   "Not Me" Podcast Episode #12: The Geopolitics of Growth

   Hey friends, a new podcast episode has arrived. I spent last week with one of those big strategy quarterlies. The kind that lands in every boardroom, gets forwarded twice, and is read properly by almost nobody. Charts, footnotes, careful hedging, a hundred pages of it. One line reorganized how I think about the next five years. Geopolitics stopped being a risk item on page forty. It became the variable that decides where your capital goes, what your supply chain costs, and whether your growth is real. That is not a warning. That is a relocation of the entire conversation, from the risk register to the P&L. Vlad's Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Chart Everyone spent last year forecasting a trade collapse. Tariffs, sanctions, export controls, the whole architecture of globalization apparently coming apart on live television. It did not collapse. Global goods trade grew by roughly 6.5% in 2025, broadly in step with the world economy. US imports hit records. Chinese exports hit records. What changed was direction, not volume. * The US-to-China corridor shrank by around 30% between 2024 and 2025, and the US replaced roughly two-thirds of it with trade to geopolitical allies in Europe and Asia. * AI-related goods, semiconductors, and data center equipment grew close to 40% and drove about a third of all trade growth. Energy resources went the other way, contracting around 9%. * The average geopolitical distance of trade fell about 7% between 2017 and 2024. Cargo is traveling further in kilometers and shorter in politics. Read that again. Trade did not shrink. It picked sides. Which means the risk was never “will the world stop trading.” The risk was always “will the world stop trading with you.” Political beta Here is the frame I want to give you, and the one I use in the episode. In markets, every asset has a beta. It tells you how much of your return is really your own and how much is just the market moving underneath you. Operators obsess over it. Nobody invests without knowing it. Your company now has a second one. Call it political beta: the share of your enterprise value that moves with the map rather than with your market. Political beta is the part of your growth that was never yours. It belonged to a trade corridor, a tariff schedule, or a relationship between two governments you have never met. And almost nobody has measured theirs. The numbers say this is not a niche problem. More than 90% of multinationals are exposed to countries whose political and diplomatic positions diverge significantly from their own. Business leaders now rank geopolitical and trade instability as a bigger threat to growth than macroeconomic volatility, cybersecurity, or technological disruption. McKinsey & Company (https://www.mckinsey.com/capabilities/geopolitics/our-insights/managing-geopolitical-value-at-stake-to-seize-opportunities-while-mitigating-risk) The practical move is unglamorous and it works. Set tolerance curves by function. Asset-light units like sales can carry high geopolitical exposure because they can move. Capital-heavy units like manufacturing cannot, so they need a tighter tolerance. Then build the agility to sit further out on that curve than your competitors dare. McKinsey & Company (https://www.mckinsey.com/capabilities/geopolitics/our-insights/managing-geopolitical-value-at-stake-to-seize-opportunities-while-mitigating-risk) Agility is the hedge. Not retreat. The advantage The second thesis in the issue is about AI, and it is the one that will annoy people. Roughly 88% of organisations now use AI in at least one function. About 6% report meaningful financial impact at the enterprise level. That gap is the whole story. Everyone bought the same thing. Same frontier models, same APIs, same tooling, same weekend of agent experiments. When every competitor has access to identical capability, capability stops being an advantage and becomes a cost of entry. What survives commoditisation is boring and unbuyable: * Proprietary data generated by your own operations, not scraped from the same public web as everyone else’s. * Workflows with AI embedded so deep that ripping it out would mean rebuilding the company. * Trust and distribution, which take years and cannot be procured. Companies that actually rewire around this typically improve EBITDA by 10 to 30%, averaging around 20%, and the gap between leaders and laggards has widened by roughly 60% in recent years. McKinsey & Company (https://www.mckinsey.com/capabilities/quantumblack/our-insights/from-ai-table-stakes-to-ai-advantage-building-competitive-moats) I wrote about the mechanics of this in The Great Restructuration (https://www.vladsnewsletter.com/p/the-great-restructuration). The quarterly just put a balance sheet under it. Where the growth is actually hiding Third thesis, and the most fun one. The next decade of growth is not evenly distributed across the economy. It is concentrating in a handful of arenas. Quantum. Over 300 companies are now working with quantum vendors. Quantum firms generated more than $1 billion in revenue in 2025, projected to reach $4.4 billion by 2028, with up to $2.7 trillion in economic value by 2035. Consulting.us (https://www.consulting.us/news/13514/quantum-computing-reaches-commercial-turning-point-mckinsey-report-finds) Space. Commercial spending is expected to outspend government by two to one or more in every end market except Earth observation in the second half of this decade. The customer changed. The whole industry follows the customer. McKinsey & Company (https://www.mckinsey.com/industries/aerospace-and-defense/our-insights/defense-blog/new-space-new-rules-commercial-space-markets-are-taking-off) Biotech. mRNA stopped being a vaccine and became a platform. Once you have a platform, the economics flip from one product at a time to one process, many products. That is the same shift software went through, arriving thirty years late and carrying far more upside. Across all these arenas, industries grew roughly four times faster in market cap and ten times faster in revenue than everything else over the past three years. mckinsey (https://mckinsey.com/mgi/our-research/the-race-takes-off-in-the-next-big-arenas-of-competition) Growth is not scarce right now. It is just standing somewhere very specific. What gets overlooked Everyone reading this issue will take away “build a geopolitical dashboard.” Fine. Necessary. Not the insight. Here are the two things almost nobody flags. One. In quantum, the money changed hands, and that is the real signal. Public sources supplied around a third of quantum investment in 2024. In 2025 that fell to three percent, with private capital taking over. Forget the market-size projections, they are always wrong. Watch who is paying. When private money replaces grant money, a technology has crossed from research to product, and it usually crosses before the press notices. all-about-industries (https://www.all-about-industries.com/mckinsey-2026-turning-point-quantum-computing-investments-a-ad54624b3619f2ceaa000aceb84e7427/) Two. Intelligence is not the bottleneck. Decision speed is. Most companies now have plenty of geopolitical insight. What they do not have is a named person who can act on it inside 48 hours without three committees. Fragmentation does not punish the uninformed. It punishes the slow. I made a version of this argument about markets back in Boring Bet (https://www.vladsnewsletter.com/p/not-me-podcast-episode-9-boring-bet), and it holds here. Resilience is not the absence of shocks. It is the ability to debug in real time. Vlad's Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. My take I run companies across three continents from an office in London. Every quarter, some line item moves for a reason that has nothing to do with my product, my team, or my customers. For years I filed that under bad luck. It was not bad luck. It was beta, and I was not measuring it. Here is what I would do this month if I were starting from zero. Take your revenue and split it into two columns. What would still be here if the map changed. What would not. Do not model it, do not hire anyone, just look at the two columns honestly. Most operators have never done that exercise once. The ones who have tend to sleep better, and move faster, because they finally know which half of their business is actually theirs. Fragmentation does not kill companies. Slowness does. The map is going to keep moving, and the only question that matters is how fast you can move after it. I go deeper on all of this in the episode: the trade chart everyone got wrong, how to actually run a tolerance curve, and why the frontier arenas are further along than the headlines suggest. Give it a listen. Then go look at your two columns. Post-Credit Scene A few things worth your time this week: The Great Global Transformation by Branko Milanovic (https://www.amazon.com/Great-Global-Transformation-Remaking-Economic/dp/0226846415) (University of Chicago Press, March 2026) – Milanovic argues the US and China are on opposite trajectories, and names the era that follows “national market liberalism,” where liberalism survives inside domestic economies but not necessarily between them. The best long-form context for everything in this episode. Odd Lots: The Tungsten Market Is Warning of an Upcoming War (https://podcasts.apple.com/us/podcast/the-tungsten-market-is-warning-of-an-upcoming-war/id1056200096?i=1000779646689) (August 3, 2026) – David Fickling calls tungsten mining a century-old prediction market for war, and explains why it has been so hard to sustain production outside China. Political beta made physical, in one small metal. The Race Takes Off in the Next Big Arenas of Competition (https://www.mckinsey.com/mgi/our-research/the-race-takes-off-in-the-next-big-arenas-of-competition) (MGI, March 2026) – The “AI foundation” set of industries has added $500 billion in revenue and $11 trillion in market cap since 2022. If you only read one thing from the frontier section above, read this. BlackRock Geopolitical Risk Dashboard (https://www.blackrock.com/corporate/insights/blackrock-investment-institute/interactive-charts/geopolitical-risk-dashboard) (updated August 2026) – A live indicator tracking market attention across their top ten risks. The current read: fragmentation accelerating, with governments and companies pushing capital toward security, resilience, and sovereignty. Free, and a decent starting point if you want a beta reading without building your own Thanks for listening and reading. Vlad This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.vladsnewsletter.com/subscribe (https://www.vladsnewsletter.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2)

2. 

   19 Jun 2026

   "Not Me" Podcast Episode #11: The Founder's Playbook: Building an AI-Native Startup

   Hey friends, new episode is live. I know, I know, it’s been a while. I spent few days with one of those documents most people skim and forward without reading. A major lab’s full playbook for building an AI-native startup. Idea to Scale, mapped for 2026. Fifty-odd pages on how to move faster than ever. I read it three times. The third time, I found the opposite lesson hiding underneath all the speed. Building just became free. Being wrong didn’t. It only got faster, and better dressed. That’s the episode. Grab a coffee, and wishing you a great listing. Here’s the short version. Vlad's Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. The Wall Came Down For most of startup history there were two camps. People who could build, and people with ideas worth building. A wall ran between them. That wall is gone. You describe what you want in plain language, and real software assembles itself. The founder stops being the one who types the code and becomes the one who directs the agents (https://www.vladsnewsletter.com/p/sub-agents) that do. The idea person’s moment (https://www.vladsnewsletter.com/p/time-for-idea-people) finally arrived. Not as a feeling. As a tool. The Part Nobody Quotes Even before any of this, more than 40% of startups died for one reason: they built something nobody wanted. That was the rate when building was hard. When a prototype cost months and money. Painful. And also a filter. A constraint is a riverbank. Remove every bank and you do not get a faster river. You get a flood. AI removed the banks. The model will build a beautiful product around a wrong idea with the same enthusiasm it brings to a right one. A working prototype feels like proof. It is a stage prop. The conversation you have while holding it is the evidence. The Yes-Machine Problem Ask the model to validate your idea, it finds the evidence. Ask it to size your market, it finds the fundable number. Confirmation bias now ships with its own research department. The machine says yes by default. Which leaves exactly one reliable source of no in the building. You. When everyone can build anything, the rarest skill is knowing what not to. Call it the Discernment Premium. What Gets Overlooked The lean-team dream has a hidden tax. In a normal company, judgment is spread across a dozen people. In a team of five, or one, all of it lands on you. Every agent output is a decision waiting for a human. You do not burn out from doing too much. You burn out from deciding too much. The Only Moat Left “We built it first” is dead. Someone rebuilds it next month. So what is left to defend? Domain depth. The data your users leave behind. The workflows they build on top of you, until leaving becomes a project instead of a choice. Notice what none of those are. The code. The code was never the moat. My Take In the episode, I walk through all four stages- Idea, MVP, Launch, and Scale- and what to refuse at each one. But it comes down to this. The machine will do almost all of the building now, instantly, in whatever direction you point it. So the whole job quietly folded into a single act. Choosing the direction. The tools got the power. You kept the responsibility. The bottleneck was never what you could build. It is what you are willing to choose. One more thing before the picks…. The podcast just crossed 41.6K downloads. I started “Not Me” half expecting an empty room. A few people thinking out loud into a microphone. Now there are thousands of you, pressing play every week. In a world drowning in free, auto-generated content, attention is the one thing the machine cannot manufacture for me. And you keep spending yours here. Thank you. Point the machine. Be the no. Stay curious. Stay deciding. Stay building. Post-Credit Scene A few things worth your time this week: 📘 (https://store.hbr.org/product/power-and-prediction-the-disruptive-economics-of-artificial-intelligence/10580)Power and Prediction: The Disruptive Economics of Artificial Intelligence (https://store.hbr.org/product/power-and-prediction-the-disruptive-economics-of-artificial-intelligence/10580) (Harvard Business Review Press) – The economics under this whole episode: AI makes prediction cheap, which makes judgment expensive. Their line, not mine: AI “decouples human prediction from human judgment.” 🎧 (https://creators.spotify.com/pod/profile/lightconepodcast/episodes/How-AI-Is-Changing-Enterprise-e2v30h0)The Lightcone: How AI Is Changing Enterprise (https://creators.spotify.com/pod/profile/lightconepodcast/episodes/How-AI-Is-Changing-Enterprise-e2v30h0) – A viral report said 95% of enterprise AI projects fail. The YC partners’ read is the founder’s whole opening: it is not that AI does not work, it is that big companies cannot build with it. That is the gap you walk through. 📝 (https://fortune.com/2026/05/18/solo-founders-ai-automation-entire-teams-entrepreneurs/)Solo founders are using AI to do the work of entire teams, but going it alone has limits (https://fortune.com/2026/05/18/solo-founders-ai-automation-entire-teams-entrepreneurs/) (Fortune, May 18, 2026) – The honest footnote to the lean dream. The agents handle the tasks. The judgment, the bill, and the loneliness do not delegate. 🛠️ (https://www.anthropic.com/product/claude-cowork)Claude Cowork (https://www.anthropic.com/product/claude-cowork) (Anthropic) – The “ops team” from the playbook, made real. Hand it the assembly, keep the decisions. The clearest live example of the split this episode is about. 📺 (https://www.youtube.com/watch?v=ojjCvICC86c)The Bear, Season 5 (https://www.youtube.com/watch?v=ojjCvICC86c) (FX, premieres June 25) – The most honest show about building anything. No money, a storm at the door, a founder who walked away, a crew chasing one star. The lesson they land on is the lesson above: it is not the equipment, it is the people, and the taste. Final season. Thanks for listening, Vlad This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.vladsnewsletter.com/subscribe (https://www.vladsnewsletter.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2)

3. 

   12 Feb 2026

   "Not Me" Podcast Episode #10: The End of Context Windows

   Hey, it’s Vlad. Everyone’s obsessed with building bigger AI brains. More parameters. Longer context windows. Better reasoning. But here’s what MIT just figured out: you don’t need a bigger brain. You need a smarter one. Researchers Alex Zhang, Tim Kraska, and Omar Khattab from MIT CSAIL dropped a paper that’s getting way less attention than it deserves. It’s called Recursive Language Models, or RLMs. And it flips everything we know about AI limitations on its head. The Problem Nobody Solved Let’s start with the ugly truth. Every AI model you use has a memory problem. GPT-5? It chokes after 272,000 tokens. Claude? Same ballpark. Even with these “massive” context windows, the models get dumber the more you feed them. It’s called context rot. Think about it. You paste a 50-page document and ask a simple question. The model starts hallucinating. Missing obvious facts. Getting confused. Why? Because cramming everything into the context window is like forcing someone to read a 10,000-page encyclopedia cover-to-cover before answering your question. It’s absurd. We’ve been treating AI like a student with a strict word limit on their exam. No wonder it struggles. The MIT Breakthrough Here’s where it gets interesting. MIT asked a different question: What if the AI didn’t have to read everything at once? What if it could treat the prompt as an external environment? A workspace. A filing cabinet can be explored strategically. That’s RLM. Instead of feeding GPT-5 your entire 10-million-token corpus directly, you store it as a Python variable. The model never sees it in the prompt. Instead, it writes code to peek at specific sections. Grep through for patterns. Chunk it up. And here’s the kicker: it can spawn sub-models to investigate specific parts. It’s recursion. The model calls itself. Over and over. Each layer handles a smaller, more manageable piece. Like hiring a research team instead of forcing one person to do everything. Sound familiar? It’s the same philosophy behind sub-agents I wrote about recently. Stop making one AI do everything. Orchestrate. The Numbers Don’t Lie Let’s talk results. On the OOLONG benchmark, which is designed to torture AI with long context tasks, here’s what happened: * Base GPT-5? Crashed and burned. Near zero performance. * GPT-5 with RLM? 58% F1 score. From essentially nothing to majority correct. That’s not an improvement. That’s a resurrection. On the BrowseComp-Plus benchmark, RLM handled over 10 million tokens. Two orders of magnitude beyond the context window. And it did it for roughly the same cost as running the base model. Sometimes cheaper. 91.33% accuracy on a task where the base model literally couldn’t fit the input. This isn’t incremental progress. This is a paradigm shift. Why Programmatic Decomposition Beats Everything You might ask: Why not just summarize the context? Compress it? They tried that. It’s called context compaction. Here’s the problem. Every time you summarize, you lose information. It’s entropy. Irreversible. Summarization agents on the same benchmarks? 70% at best. Often worse. RLM doesn’t summarize. It delegates. Big difference. The model actively decides what to look at. Uses regex filters. Keyword searches. Strategic sampling. It behaves less like a student cramming for an exam and more like a senior researcher with a team of assistants. And because each sub-call runs with a fresh context window, there’s no pollution. No context rot. Each recursive agent stays sharp. What Most People Overlook Here’s the thing that’s flying under the radar. This approach is model-agnostic. RLM works with GPT-5. With Qwen. With Claude. Open-source, closed-source, doesn’t matter. It’s an inference strategy, not an architecture change. You don’t need to retrain anything. And the cost structure is fascinating. Using GPT-5-mini for recursive calls while GPT-5 handles the final synthesis? Cheaper than running GPT-5 on truncated input. Better results. Lower price. That’s the arbitrage nobody’s talking about. The Bitter Lesson, Again Alex Zhang called this a “bitter-lesson-pilled approach.” If you don’t know Rich Sutton’s Bitter Lesson, it’s simple: general methods that leverage computation beat specialized hand-engineered solutions. Every time. RLM fits perfectly. Instead of designing clever compression schemes or specialized architectures, you give the model tools and let it figure out the strategy. The model learns to peek first. Scan for relevant sections. Delegate the hard parts. Build up answers iteratively. No human had to specify these behaviors. They emerge naturally when you give the model the right environment. That’s the meta-lesson here. Stop constraining AI. Start enabling it. Practical Implications So what does this mean for you? If you’re building with AI, pay attention. Long-horizon agents, the ones that need to process weeks or months of data, suddenly become viable. Legal document analysis? Entire codebase understanding? Research synthesis across hundreds of papers? All unlocked. Prime Intellect is already building RLMEnv, a training environment for this paradigm. They’re betting this is the next major breakthrough after reasoning scaling. My prediction? Within 12 months, every serious AI infrastructure will support RLM-style inference. The teams building this capability today will be the ones dominating tomorrow. Vlad's Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. The Overlooked Angle What most coverage misses is the philosophical shift. We’ve been treating context windows as hard limits. Physical constraints. Like asking “how do we fit more data in this box?” MIT asked: “What if we don’t put the data in the box at all?” That reframing is everything. It’s not about bigger models. It’s about smarter orchestration. Sound familiar? It’s the same pattern we’re seeing with sub-agents. With MCP. With agentic workflows. The future isn’t monolithic AI. It’s distributed intelligence. Each piece specialized. Each piece coordinated. RLM is just another proof point. Takeaway The context window problem everyone complained about? Solved. Not through brute force. Through elegance. The AI doesn’t need to see everything at once. It needs the right tools to explore strategically. That’s RLM. MIT just gave us the blueprint. Now it’s on us to build with it. The code is open source. The paper is public. The opportunity is sitting there. Question is: are you going to use it? Worth Reading While the Episode Downloads * Sub Agents (https://www.vladsnewsletter.com/p/sub-agents) – The frontier of tech just shifted again, and most people haven’t noticed yet. * Ideation (https://www.vladsnewsletter.com/p/ideation) – Forget validation. Build what no search result can show you. * AI Generalist (https://www.vladsnewsletter.com/p/ai-generalist) – Playbook on How to Make $300K+ While Everyone Else Fights for Scraps Post-Credit Scene A few things worth your time this week: 📄 Read: “Why I Believe Recursive Language Models Are the Future of Long-Context Reasoning” (https://levelup.gitconnected.com/why-i-believe-recursive-language-models-are-the-future-of-long-context-reasoning-8aff1738cbc6) – A developer’s breakdown of the RLM paper that goes beyond summary. Published this month. 🎧 Listen: Lenny’s Podcast: “We replaced our sales team with 20 AI agents” (https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents) with Jason Lemkin. 1.2 humans managing 20 AI agents doing the work of 10 SDRs and AEs. This is happening now. (January 2026) 🔬 Deep Dive: Prime Intellect’s “Recursive Language Models: the paradigm of 2026” (https://www.primeintellect.ai/blog/rlm) – They’re betting their entire research agenda on this. Worth understanding why. 🛠 Tool: The RLM GitHub repo (https://github.com/alexzhang13/rlm) is live. Supports OpenAI, Anthropic, local models. If you’re technical, start experimenting today. 🎙 Podcast: Practical AI: “2025 Was The Year Of Agents, What’s Coming In 2026?” (https://player.fm/series/practical-ai/ep-2025-was-the-year-of-agents-whats-coming-in-2026) – Chris and Daniel break down what actually mattered and what’s next. Grounded predictions, not hype. Thank you for listening and reading. See you in the next edition. Vlad This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.vladsnewsletter.com/subscribe (https://www.vladsnewsletter.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2)

4. 

   5 Feb 2026

   "Not Me" Podcast Episode #9: Boring Bet

   Hey friends, new podcast episode arrived. I spent the weekend going through one of those dense institutional reports that most people skip. You know the type. Fifty pages of charts, footnotes, macro projections. But buried in all that noise was something worth your attention. The 2026 ISG Outlook makes one argument very clearly: American preeminence isn’t going anywhere. And here’s the thing. This isn’t blind optimism. It’s not flag-waving patriotism dressed up as investment advice. It’s math. The Case They’re Making Let’s break it down. The U.S. has three structural advantages that no other economy can replicate right now: 1. Labor Productivity American workers produce more output per hour than almost any other developed nation. This isn’t about working harder. It’s about working smarter, with better tools, better systems, better technology. When AI accelerates this, you get compounding. 2. Natural Resources Energy independence changed everything. The shale revolution wasn’t just about oil prices. It was about leverage. When Europe froze, America negotiated. 3. Innovation Ecosystem Here’s what gets overlooked: the U.S. doesn’t just lead in AI. It leads in the commercialization of AI. Ideas are cheap. Turning ideas into products that scale globally? That’s the hard part. And that pipeline, from university labs to venture capital to public markets, is still uniquely American. But What About the Problems? The report doesn’t ignore the risks. And neither should you. Federal debt is a real concern. The numbers are ugly. But here’s the contrarian take: debt matters less when you’re the world’s reserve currency and your economy is growing faster than your debt service. Is it sustainable forever? No. Is it sustainable for the next decade? Probably. Tariff policies create friction. They slow things down. But they also force reshoring and diversification that might be strategically smart in a world where supply chains are weapons. China friction is the wildcard. Nobody knows how this plays out. But the ISG argument is that American institutional checks and balances, messy as they are, provide more stability than any alternative. Democracy is inefficient. But it’s also self-correcting. The Hedge Question Here’s where it gets interesting. A lot of people think gold is the answer to uncertainty. Others are betting on bitcoin. The report’s take? Neither offers the same reliable long-term protection as a diversified equity portfolio. Why? Gold is a fear trade. It spikes during panic and flatlines during growth. Over long time horizons, it underperforms. Bitcoin is still too young, too volatile, too correlated with risk-on sentiment to function as a true hedge. The boring answer, diversified American equities, keeps outperforming the exciting alternatives. Nobody wants to hear that. But it’s true. What Gets Overlooked Here’s what most people miss when reading reports like this: The opportunity cost of sitting out. Every year you wait for the “perfect entry point,” you’re losing compounding. The ISG projects mid-single-digit returns and sturdy global growth. That sounds boring. Five percent. Six percent. But compounded over a decade? That’s wealth. The people who got rich in American markets didn’t time the bottom. They stayed invested through the noise. The Real Advantage Nobody Talks About Think of it like a startup. Every country has bugs. Political instability. Debt problems. Social tensions. America has all of these. Loudly. Publicly. On Twitter. But here’s the difference: America debugs in real time. The courts push back. The press investigates. Elections happen. Power transfers. Compare that to systems where problems compound in silence until they explode. Which one would you bet on for the next 20 years? My Take Look, I’m not a financial advisor. This isn’t investment advice. But here’s how I think about it. If you’re building something, if you’re running companies, if you’re betting on the future, you need to understand where the wind is blowing. And right now, despite everything, despite the debt, despite the politics, despite the geopolitical chaos, the wind is still blowing toward America. Not because America is perfect. Because America is resilient. And resilience, over long time horizons, beats everything else. The institutions that make democracy feel slow are the same institutions that make it stable. That’s the trade-off. And it’s a good one. Stay invested. Stay patient. Stay building. Vlad Post-Credit Scene A few things worth your time this week: 🎧 (https://hbr.org/podcast/2026/01/ray-dalio-on-economic-trends-investing-and-making-decisions-amid-uncertainty)HBR IdeaCast: Ray Dalio on Economic Trends, Investing, and Making Decisions Amid Uncertainty (https://hbr.org/podcast/2026/01/ray-dalio-on-economic-trends-investing-and-making-decisions-amid-uncertainty) (January 20, 2026) – Dalio breaks down his five big forces framework and where the U.S. sits in the current cycle. Essential listening if you want to understand the macro picture. 📘 (https://www.amazon.com/Principles-Investment-Economic-Ray-Dalio/dp/1501124064)“How Countries Go Broke: The Big Cycle” by Ray Dalio (https://www.amazon.com/Principles-Investment-Economic-Ray-Dalio/dp/1501124064) – The #1 NYT bestseller that Washington insiders are passing around. Dense but rewarding. If you want to understand why debt cycles matter and what the warning signs look like, start here. 🎧 (https://open.spotify.com/show/1te7oSFyRVekxMBJUSethH)Odd Lots: Goldman’s Hatzius and Snider on the Outlook for 2026 (https://open.spotify.com/show/1te7oSFyRVekxMBJUSethH) (December 29, 2025) – Joe and Tracy sit down with Goldman’s chief economist and chief US equity strategist to dissect what happened in 2025 and whether it can repeat. Spoiler: AI and tariffs are the two forces shaping everything. 📊 (https://www.goldmansachs.com/insights/articles/us-gdp-growth-is-projected-to-outperform-economist-forecasts-in-2026)Goldman Sachs: US GDP Growth Is Projected to Outperform Economist Forecasts in 2026 (https://www.goldmansachs.com/insights/articles/us-gdp-growth-is-projected-to-outperform-economist-forecasts-in-2026) (January 11, 2026) – Goldman projects 2.5% GDP growth versus the consensus 2.1%. Tax cuts, real wage gains, and AI investment are the drivers. Quick read, worth bookmarking. 🌍 (https://www.deloitte.com/us/en/insights/topics/economy/global-economic-outlook-2026.html)Deloitte: Global Economic Outlook 2026 (https://www.deloitte.com/us/en/insights/topics/economy/global-economic-outlook-2026.html) (https://www.deloitte.com/us/en/insights/topics/economy/global-economic-outlook-2026.html)– For the full global picture. The U.S. section is particularly strong on why “resilient” remains the best one-word description of the American economy. Thanks for listing Vlad This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.vladsnewsletter.com/subscribe (https://www.vladsnewsletter.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2)

5. 

   14 Nov 2025

   "Not Me" Podcast Episode #7: Race to Compute

   Vlad, from Vlad's Newsletter Podcast, analyzes the Accel 2025 Globalscape report, revealing the $4.1 trillion data center spending needed for the AI industrial revolution and the uncomfortable truth about the electricity shortfall, in this interview.

6. 

   15 Oct 2025

   "Not Me" Podcast Episode #6: "Kind of Kindness"

   Vlad's Newsletter Podcast explores research showing that rude prompts can yield slightly more accurate answers from AI models like ChatGPT-4o, revealing how AI responds to superficial cues rather than understanding intent.

7. 

   2 Oct 2025

   "Not Me" Podcast Episode #5: AI at Warp Speed

   This episode of "Not Me" Podcast analyzes Anthropic's Economic Index Report, examining the rapid adoption of AI, its impact on the economy, and the crucial distinction between automation and augmentation.

8. 

   25 Sept 2025

   "Not Me" Podcast Episode #4: OpenAI's Secret Data

   Vlad discusses OpenAI's data analysis of ChatGPT usage, revealing that most users are seeking practical guidance, personal information, and assistance with non-work tasks, which suggests a shift towards consumer AI.

9. 

   29 Jul 2025

   "Not Me" Podcast Episode #3: Americas Action Plan

   Vlad discusses America's AI Action Plan, signed last week, which focuses on innovation, infrastructure, and international diplomacy, and how it impacts builders in the AI space in this interview.

10. 

    24 Jul 2025

    "Not Me" Podcast Episode #2: Future

    Vlad explores McKinsey’s Global Institute's Technology Trends Outlook 2025, highlighting AI, computing, engineering, and other advancements, offering insights for founders and operators to leverage these emerging technologies.

11. 

    18 Apr 2025

    "Not Me" Podcast Episode #1: Effective Prompt Engineering: Techniques and Best Practices

    Hi, it’s Vlad. A quick note before we start. All my earlier newsletters were text‑only.This is our very first audio episode, so you’re part of a small milestone. Also, you won’t hear me talking live this time. Two friendly AI voices that will handle the lesson and show you how to write great prompts. Think of it as a radio play for tech lovers. If you read my earlier pieces: or You already know I like to share every secret. This time, I aim to demonstrate, step by step, how I learned to provide clear prompts to an AI, enabling it to stop guessing and begin performing actual work. In this task, I will break down the recent GPT‑4.1 Prompting Guide (https://cookbook.openai.com/examples/gpt4-1_prompting_guide) and Google’s “Prompt Engineering.”  (https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf) So my AI is with me. Together, we teach you how to write prompts that make big language models work smarter, not harder. Vlad's Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. What the AIs cover in the show * Why prompts matterThey compare a vague question to a laser‑focused one and show how the model’s answer changes. * Highlights from the “GPT‑4.1 Prompting Guide” * Closer instruction‑following: tell GPT‑4.1 exactly what you want—no hedging. * Agentic workflows: learn to add “persist,” “use tools,” and “think step‑by‑step” so the model plans before it acts. * Long context tips: where to place your main instructions when you feed the model a giant document. * Chain‑of‑Thought tricks: how to ask GPT‑4.1 to reveal its reasoning, one step at a time. * Insights from Google’s “Prompt Engineering” * Quick rundown of zero‑shot, one‑shot, and few‑shot methods. * How temperature and top‑p settings change the “creativity dial.” * ReAct prompting: mix reasoning with tool calls for complex tasks. * Best way to force clean JSON output if you need structured data. * A peek at automatic prompt engineering—yes, even prompts can be written by AI. * Live prompt‑doctor sessionCheeky AI writes a sloppy prompt → Calm AI fixes it line by line → They compare results. * Key takeawayGood prompting = clear instructions + relevant examples + step‑by‑step thinking. The better you ask, the stronger the answer. How to listen * Click the audio player above. * Grab a coffee—the episode is about 30 minutes. * Keep a note app available; you’ll want to steal a few prompt patterns. After you listen If you like what you hear, I will prepare more. For example, agent AI workflows—letting the model plan, decide, and finish complex jobs while you supervise. Or dive into sales and marketing that you might even like better. Post-Credit Scene Still here? Love it. Tell me what you think? This is wild, right? I have a strong feeling that this is the future of education here. You can upload whatever data you want to study, create a podcast, and even participate in it. Anyway. Below are bite‑sized resources to help you level up after today’s audio. Mini‑Practice Drill * Pick a short article (300 words). * Write a zero‑shot prompt: “Summarize in three bullet points for a 12‑year‑old.” * Add one example summary → rerun (few‑shot). * Finally, add: “Show reasoning step‑by‑step.” Notice the difference. Books if You Want a Deeper Rabbit Hole * “Rebooting AI” – Marcus & Davis (https://www.amazon.co.uk/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB) * “Prediction Machines” – Agrawal, Gans, Goldfarb (https://www.amazon.co.uk/Prediction-Machines-Economics-Artificial-Intelligence/dp/1633695670) See you in the next editions—until then, keep experimenting and document every prompt tweak. Your future self (and your AI assistant) will thank you. Thank you for reading and listening this time. Vlad Get full access to Vlad's Newsletter at www.vladsnewsletter.com/subscribe (https://www.vladsnewsletter.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)

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Public platform figures. Ratings count people who left a rating, not total listeners.

Spotify

5.0 / 5

1 ratings

Podcast Authority Score: 9 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

18

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0

YouTube

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Engagement

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