Invest Like The Best
Conversations with the world’s leading investors, founders, and CEOs. We unpack how great capital allocators think, the ideas that move markets, and the operational trade-offs that determine winners and losers.
Past bets that played out
Frequent, high-conviction themes include AI and the infrastructure that supports it (OPENAI, ANTHROPIC, NVDA, TSM), chip and networking suppliers (AVGO, ANET), and emerging AI-first companies (XAI). Episodes highlight private entrants and infrastructure narratives—signals that often inform public-market positioning rather than offering immediate, discrete trade catalysts.
Podcast-style profile of private AI chip startup Etched: claims $800M raised, >$1B customer contracts, and a taped-out inference-focused chip/rack targeting the post-ChatGPT inference boom. Actionable mostly as a narrative signal reinforcing ‘inference demand’ and ‘AI compute infrastructure’ themes; direct trading implications are indirect because Etched is private and details are non-verified/marketing-leaning.
Podcast-style profile of private AI chip startup Etched: claims $800M raised, >$1B customer contracts, and a taped-out inference-focused chip/rack targeting the post-ChatGPT inference boom. Actionable mostly as a narrative signal reinforcing ‘inference demand’ and ‘AI compute infrastructure’ themes; direct trading implications are indirect because Etched is private and details are non-verified/marketing-leaning.
Podcast-style discussion arguing the AI boom is early in its S-curve, with “code” as an initial killer app, major implications for software economics, and a “hardware renaissance” (compute/networking/semis). Mentions Whale Rock conviction-building and Anthropic (private) as an example, but provides few concrete company-specific catalysts in the text provided.
What this channel is watching now
Primary recurring topics: generative AI and model providers (OPENAI, ANTHROPIC); semiconductor and compute supply chains (NVDA, TSM, AVGO); networking and data-center infrastructure (ANET); and AI-enabled product companies (MSFT, XAI). Conviction scores and mention counts reflect emphasis across recent episodes.
Latest videos and market context
Recent episodes examine an under-the-radar U.S. natural gas squeeze driven by LNG export growth, fundraising and investor relations trade-offs in startups and venture firms, and profiles of private AI chip and compute startups that illustrate demand for inference infrastructure.
Sam Altman on AGI, Compute, and Human Agency
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive services and elevated cybersecurity risk.
The 2028 Natural Gas Crisis No One Sees Coming
Transcript argues U.S. LNG export growth (from ~15 Bcf/d today toward ~35 Bcf/d by ~2030) creates a structural natural gas supply/demand squeeze that could surface around 2028 if production and infrastructure don’t keep pace. Implies upside risk to U.S. gas (HH) and beneficiaries among gas producers, LNG exporters, and midstream; gas-intensive users face margin pressure.
How the World’s Top Fundraiser Raised Billions For General Catalyst and Startups
The source text is largely garbled/fragmentary and reads like a partially transcribed podcast/article about fundraising/IR and “trade-offs” (size/speed/terms), with references to General Catalyst (private) and “Ramp/RAMP” (likely the private fintech). It does not contain clear, specific, tradable market-moving facts (earnings, guidance, deals, regulatory actions, macro data) or explicit public-company catalysts.
How Attention Became the New Capital
The provided source contains only a title (“How Attention Became the New Capital”) and repeats it in the body, with no substantive discussion, data, companies, sectors, catalysts, or time horizon. As a result, there are no extractable actionable theses or tradable ticker implications from this text alone.
Proof-backed call history
The podcast has profiled leaders across investing, startups, and corporate strategy, synthesizing long-form interviews into investable themes. Notable recurring storylines: the early AI S-curve and ‘hardware renaissance,’ private-company innovation that bleeds into public-market opportunity, and macro sector cycles such as energy and materials.
...why OpenAI recently narrowed its focus, why demand for intelligence may be effectively uncapped, how close we may be to AGI, and what the future could hold for robotics, jobs, hardware, and human agency. We also discuss the accidental launch of ChatGPT, OpenAI’s competitive advantages, the economics of intelligence, and the pressure and responsibility that come with leading one of the world’s most consequential companies. TIMESTAMPS 0:00 Intro 4:10 The Race for Compute 14:24 A Sci-Fi Cyber In
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
Podcast summary highlights: accelerating AI capability toward AGI, “race for compute,” effectively uncapped demand for intelligence, AI embedded across economy, robotics, job disruption, potential cyber incident risk, and the economics of intelligence. It’s primarily narrative/strategic (few hard datapoints), but it supports a continued capex/compute buildout theme benefiting AI hardware, semis, networking, datacenters, and power/thermal infrastructure; with offsetting risks to labor-intensive s
...x sends back finished PowerPoint decks, Excel models, and sourced research." Felix works the way your team already does, delivering work quickly and accurately around the clock. Learn more at rogo.ai/felix. The best AI and software companies from OpenAI to cursor to Perplexity use work OS to become enterprise ready overnight, not in months. Visit work os.com [music] to skip the unglamorous infrastructure work and focus on your product. Another I'm just going to try to ask really simple questi
...time until until now. And distributed generation or BTM generation poorly positioned than investors memory shortage thing that we're going like DRAM 2 years ago." Slowly at first levelized cost of energy LCOE as it's this this revenue growth in anthropic or leave via LG export terminals or be than even these seven plus BCF pipelines longer, which only exacerbates this GRC engineer in the background, finding AP-1000 nuclear plants, uh, nuclear office." Now, the EDF, it's $ 260 as the maybe the
About this channel
Invest Like The Best conducts deep, interview-driven research to surface practical insights for professional investors, founders, and business strategists. Episodes prioritize method, incentives, and the trade-offs that create durable business advantage. Learn more and explore transcripts at https://www.colossus.com.
Conversations with the best investors and business leaders in the world. We explore their ideas, methods, and stories to help you better invest your time and money. Hear stock market and boardroom insights you can't find anywhere else. If you're a professional investor, CEO, entrepreneur, or business strategist, this is for you. Explore all our episodes and learn more at https://www.colossus.com
Most recognized assets
Unlock the full track record
Explore episodes and full transcripts at https://www.colossus.com. Follow the channel on YouTube: @iltb_podcast for new conversations and episode alerts.
53 more thesis calls are available after sign-up.