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AI Podcasts

December 26, 2025

⚡️GPT5-Codex-Max: Training Agents with Personality, Tools & Trust — Brian Fioca + Bill Chen, OpenAI

Latent Space

AI
Key Takeaways:
  1. The transition from chatbots with tools to agents that build tools marks the end of the manual integration era.
  2. Stop building custom model scaffolding and start building on top of opinionated agent layers like the Codex SDK.
  3. In 12 months, the distinction between a coding agent and a general computer user will vanish as the terminal becomes the primary interface for all digital labor.
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December 26, 2025

Steve Yegge's Vibe Coding Manifesto: Why Claude Code Isn't It & What Comes After the IDE

Latent Space

AI
Key Takeaways:
  1. Software is moving from a scarce resource produced by humans to a commodity generated by agentic swarms.
  2. Move beyond simple chat interfaces and start experimenting with agentic loops plus MCP servers to automate entire workflows.
  3. The AI Engineer is the new F1 driver of tech. Mastery of the tool belt matters more than the ability to build the car from scratch.
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December 24, 2025

METR's Benchmarks vs Economics: The AI capability measurement gap – Joel Becker, METR

AI Engineer

AI
Key Takeaways:
  1. The Capability-Utility Gap is widening. We see a divergence where models get smarter but the friction of human-AI collaboration keeps productivity flat.
  2. Deploy AI for mid-level engineers or low-context tasks. Avoid forcing AI workflows on your top seniors working in complex legacy systems.
  3. The next year will focus on reliability over raw intelligence. The winners will have models that require the least amount of human babysitting.
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December 24, 2025

PhD Bodybuilder Predicts The Future of AI (97% Certain) [Dr. Mike Israetel]

Machine Learning Street Talk

AI
Key Takeaways:
  1. The Macro Shift: Scaling laws are hitting a diminishing return on raw data but a massive acceleration in reasoning. The shift from statistical matching to reasoning agents happens when models can recursively check their own logic.
  2. The Tactical Edge: Build for the agentic future by prioritizing high-context data pipelines. Models perform better when you provide massive context rather than relying on zero-shot inference.
  3. The Bottom Line: We are 24 months away from AI that makes unassisted human thought look like navigating London without a map. Prepare for a world where the most valuable skill is directing machine agency rather than performing manual logic.
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December 23, 2025

Continual System Prompt Learning for Code Agents – Aparna Dhinakaran, Arize

AI Engineer

AI
Key Takeaways:
  1. The transition from model-centric to loop-centric development. Performance is now a function of the feedback cycle rather than just the weights of the frontier model.
  2. Implement an LLM-as-a-judge step that outputs a "Reason for Failure" field. Feed this string directly into a meta-prompt to update your agent's system instructions automatically.
  3. Static prompts are technical debt. Teams that build automated systems to iterate on their agent's instructions will outpace those waiting for the next model training run.
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December 23, 2025

Developer Experience in the Age of AI Coding Agents – Max Kanat-Alexander, Capital One

AI Engineer

AI
Key Takeaways:
  1. The Macro Shift: The transition from writing to reviewing as the primary engineering activity. As agents generate more code, the human role moves from creator to editor.
  2. The Tactical Edge: Build CLIs for every internal tool to give agents a native text interface. This increases accuracy and speed compared to visual automation.
  3. The Bottom Line: Developer experience is the infrastructure for AI. Investing in clean code and fast feedback loops is the only way to ensure AI productivity gains do not decay over the next 12 months.
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December 23, 2025

Small Bets, Big Impact Building GenBI at a Fortune 100 – Asaf Bord, Northwestern Mutual

AI Engineer

AI
Key Takeaways:
  1. The Macro Shift: The transition from "Human-in-the-loop" to "Agent-as-the-interface" for enterprise data.
  2. The Tactical Edge: Audit your metadata quality now because LLM accuracy is a direct function of your documentation.
  3. The Bottom Line: Success in enterprise AI is not about the biggest model but about the smallest, most frequent wins that build institutional trust.
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December 24, 2025

METR's Benchmarks vs Economics: The AI capability measurement gap – Joel Becker, METR

AI Engineer

AI
Key Takeaways:
  1. The Capability-Productivity Gap. We are entering a period where model intelligence outpaces our ability to integrate it into high stakes production.
  2. Audit your stack. Identify tasks where "good enough" generation is a win versus high context tasks where AI is currently a net negative.
  3. Do not mistake a climbing benchmark for a finished product. For the next year, the biggest wins are not in smarter models but in better verification loops.
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December 24, 2025

PhD Bodybuilder Predicts The Future of AI (97% Certain) [Dr. Mike Israetel]

Machine Learning Street Talk

AI
Key Takeaways:
  1. The transition from simple Large Language Models to Reasoning Models marks the end of the stochastic parrot era.
  2. Build agentic workflows that utilize high-context windows for recursive problem solving.
  3. We are moving toward a world where intelligence is a commodity. Your value will shift from knowing things to directing outcomes over the next 12 months.
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Crypto Podcasts

February 2, 2026

Gavin Zaentz & Pranav Ramesh: Leadpoet, Lead Generation, Intent-Driven Sales Automation | Ep. 79

Ventura Labs

Crypto
Key Takeaways:
  1. The shift from centralized, static data aggregation to decentralized, real-time, incentivized intelligence networks is fundamentally changing how data-intensive industries operate.
  2. Investigate subnet opportunities where incumbent data quality is low and validation is a core challenge.
  3. The future of sales is not just about more leads, but smarter, fresher, and more relevant ones.
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February 3, 2026

Gold Crashes, Bitcoin Slides, and the Fed Shock Markets

Unchained

Crypto
Key Takeaways:
  1. The Macro Shift: As trust erodes in traditional financial systems and geopolitical risks rise, capital is flowing towards more efficient, permissionless DeFi markets. This is forcing traditional finance to adapt or lose market share.
  2. The Tactical Edge: Evaluate DATs trading below NAV for potential M&A or activist plays, as these discounts often reflect management misalignment rather than fundamental asset weakness.
  3. The Bottom Line: The current market volatility, Fed policy shifts, and the rise of DeFi are not just noise; they are reshaping capital allocation. Investors and builders must understand these structural changes to position for the next cycle of institutional adoption.
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February 2, 2026

Metals Crash & Bitcoin Breaks $80k

1000x Podcast

Crypto
Key Takeaways:
  1. Global economic uncertainty and tariff threats are triggering a broad risk-off sentiment, creating dislocations where fundamentally strong assets are sold indiscriminately.
  2. Reallocate capital from speculative metals positions into Bitcoin at current levels and high-conviction, revenue-producing crypto platforms like Hyperliquid.
  3. The current market turbulence is separating the signal from the noise. Focus on assets with strong fundamentals and organic usage, as they are poised for significant gains once the broader market stabilizes.
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February 3, 2026

Is BTC A Buy, Metals Crash, Hyperliquid RWAs, New Fed Chair

1000x Podcast

Crypto
Key Takeaways:
  1. Global market indigestion is creating a flight to quality and a re-evaluation of speculative assets. This environment favors fundamentally strong assets and platforms with clear utility over pure FOMO plays.
  2. Consider tax-loss harvesting Bitcoin positions that are out of the money and reallocate to high-conviction, revenue-producing crypto assets like Hyperliquid.
  3. The "crypto portfolio" concept is evolving; focus on individual assets with strong organic usage and mega-trend tailwinds. This strategic shift will differentiate winners from losers in the coming market cycles.
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February 2, 2026

Why BitGo Went Public | Mike Belshe

Empire

Crypto
Key Takeaways:
  1. Regulatory clarity and institutional demand are converging, driving a fundamental re-architecture of financial market infrastructure. This shift will see traditional finance increasingly rely on regulated crypto-native service providers.
  2. Builders and investors should prioritize infrastructure providers that offer robust regulatory compliance and fiduciary protection, as these are the non-negotiable requirements for the next wave of institutional capital.
  3. The digital asset industry is poised for massive growth, driven by Wall Street's entry. Companies like BitGo, by building transparent, regulated infrastructure, are not just participating in this growth; they are actively shaping the future of finance, making now the time to understand these foundational shifts.
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February 2, 2026

Curated Credit: How Maple and Morpho Approach DeFi Lending | Sid Powell & Merlin Egalite

0xResearch

Crypto
Key Takeaways:
  1. Institutional capital is eyeing DeFi, pushing for tokenized real-world assets like private credit and bonds to diversify yield sources beyond crypto-backed loans. This requires robust risk isolation at the smart contract level and a new generation of independent risk assessors to bridge TradFi and DeFi.
  2. Prioritize protocols that offer explicit risk profiles and transparent fee structures, especially those building towards intent-based lending. For builders, focus on creating infrastructure that supports isolated risk and attracts independent rating agencies.
  3. The future of DeFi lending hinges on transparency and sophisticated risk management. As institutions enter, the demand for clear, independently verified risk assessments will intensify, making protocols that embrace these principles the winners in the next market cycle.
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