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AI Podcasts

January 8, 2026

AI, markets, and power: A conversation with Paul Krugman

Azeem Azhar

AI
Key Takeaways:
  1. Capital is replacing labor as the primary driver of productivity.
  2. Prioritize investments in incumbents with massive distribution or lean startups that swap payroll for compute.
  3. The US remains the primary engine of growth but the internal divide between tech hubs and the hinterland will widen as AI concentrates wealth.
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January 9, 2026

Artificial Analysis: The Independent LLM Analysis House — with George Cameron and Micah Hill-Smith

Latent Space

AI
Key Takeaways:
  1. The decoupling of parameter count from active compute via sparsity means intelligence is becoming a software optimization problem as much as a hardware one.
  2. Audit your agentic workflows for turn efficiency rather than just cost per token.
  3. In a world of infinite tokens, the winner is the one who can verify the truth the fastest.
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January 7, 2026

Marc Andreessen's 2026 Outlook: AI Timelines, US vs. China, and The Price of AI

a16z

AI
Key Takeaways:
  1. The transition from "adding machines" to "human cognition" models is an 80-year correction finally hitting the vertical part of the S-curve.
  2. Prioritize application-specific models that backward-integrate into the stack.
  3. AI is a physical and digital build-out that will define the next decade of global power.
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January 6, 2026

Who Controls AI's Future? The Battle for GPU Access | CoreWeave SVP

Weights & Biases

AI
Key Takeaways:
  1. The transition from general-purpose compute to specialized AI infrastructure mirrors the rise of Snowflake in the data era.
  2. Audit your current cloud spend to identify where generalist latency is throttling your GPU goodput.
  3. Performance bars move every two years. If your infrastructure isn't purpose-built for AI today, you will be priced out of the market tomorrow.
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January 5, 2026

Welcome to AIE CODE - Jed Borovik, Google DeepMind

AI Engineer

AI
Key Takeaways:
  1. The Macro Pivot: The transition from LLMs as chat interfaces to LLMs as logic engines. As models move from text prediction to logic execution, the value moves from the model itself to the verification systems surrounding it.
  2. The Tactical Edge: Audit the stack. Prioritize the integration of agentic coding tools like Jules to shorten the feedback loop between ideation and deployment.
  3. The Bottom Line: Code is the only medium where AI can self-correct and scale without human intervention. The next 12 months will be defined by who can turn raw model power into reliable, self-healing code.
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January 5, 2026

Claude Agent SDK [Full Workshop] — Thariq Shihipar, Anthropic

AI Engineer

AI
Key Takeaways:
  1. Moving from "Model-as-a-Service" to "Environment-as-a-Service" where the harness matters as much as the weights.
  2. Replace your bespoke API tools with a single bash tool. Use a well-structured file system.
  3. The next year belongs to builders who stop treating LLMs as chatbots. They will treat them as system administrators.
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January 6, 2026

Building durable Agents with Workflow DevKit & AI SDK - Peter Wielander, Vercel

AI Engineer

AI
Key Takeaways:
  1. The Macro Transition: We are moving from "fire-and-forget" prompts to durable execution environments where state is as important as the model itself.
  2. The Tactical Edge: Wrap your existing tool calls in the `useStep` function to gain instant retry logic and execution history.
  3. The Bottom Line: Reliability is the primary moat in the agent market. Builders who adopt durable workflows will move to production while others are still debugging local scripts.
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January 6, 2026

Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize

AI Engineer

AI
Key Takeaways:
  1. The move from manual prompt engineering to automated prompt learning. As models become commodities, the proprietary loop that refines them becomes the moat.
  2. Implement a Train-Test Split for your prompts. Use a subset of failure data to generate new rules and validate them against a separate holdout set to ensure the logic holds.
  3. Reliability is the only metric that matters for agent adoption. If you are not using a feedback loop to update your system instructions, you are building on sand.
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January 6, 2026

The Netflix Culture Code That Changed Entertainment Forever | Reed Hastings Interview

Invest Like The Best

AI
Key Takeaways:
  1. The move from industrial management to creative inspiration. As AI automates routine tasks, the only remaining value is high-variance human creativity.
  2. Apply the Keeper Test today. Ask your leads which team members they would fight for and provide generous exits for the rest to reset your talent bar.
  3. Scaling doesn't require more rules. It requires better people. If you can maintain talent density, you can run fast while your competitors choke on their own handbooks.
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Crypto Podcasts

January 13, 2026

Playing the Right Games: Why Scores Quietly Replace Meaning

Bankless

Crypto
Key Takeaways:
  1. The Legibility Crisis: Global systems demand data which creates a gap between measurable outputs and actual value.
  2. Audit Your Scoreboards: List the metrics you track and identify the true beneficiary of that data.
  3. The Bottom Line: Success belongs to those who use high-scale systems without letting the numbers dictate their internal worth.
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January 13, 2026

Why Venezuela Likely Doesn't Have a $60 Billion Bitcoin Stash

Unchained

Crypto
Key Takeaways:
  1. Monetary Sovereignty Migration. When states weaponize the financial system, capital migrates to censorship-resistant stablecoin layers.
  2. Monitor Remittance Corridors. Watch for the growth of non-custodial stablecoin wallets in high-inflation regions as a leading indicator for broader DeFi adoption.
  3. The Venezuelan story proves that while state-led crypto projects fail, the utility of Bitcoin and stablecoins is a permanent fixture in the global south.
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January 12, 2026

Marc Graczyk: Numinous, Bittensor Subnet 6, AI Forecasting Agents, Polymarket Predictions | Ep. 78

Ventura Labs

Crypto
Key Takeaways:
  1. Verifiable intelligence is replacing black-box predictions. As AI agents become the primary participants in prediction markets, the value moves from the prediction itself to the verifiable logic behind it.
  2. Integrate real-time news APIs like Darch to give agents a qualitative edge over pure quant models.
  3. Forecasting is the ultimate utility for LLMs. If Numinous succeeds, Bittensor becomes the world's most accurate, explainable source of truth for investors and researchers.
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January 13, 2026

How Claude Code is Changing the World with Nick Emmons

The Rollup

Crypto
Key Takeaways:
  1. The transition from human-centric interfaces to agent-first protocols. As agents become the primary users, the internet will be rebuilt around machine-readable data and crypto-native payment rails.
  2. Integrate Model Context Protocol (MCP) servers into your workflow immediately. Use parallel Claude instances to act as both programmer and reviewer to bypass context window degradation.
  3. Software is no longer a product: it is a utility. Over the next year, the winners will be those who control the data graphs and the distribution channels, not the ones writing the code.
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January 12, 2026

Claude Opus 4.5’s Breakout Moment & Investing in 2026 with Qiao Wang

Empire

Crypto
Key Takeaways:
  1. The Macro Pivot: Proprietary data and enterprise switching costs are the only walls left standing as AI commoditizes the act of writing code.
  2. The Tactical Edge: Build internal tools using natural language agents to automate specific, low-volume workflows that third-party vendors ignore.
  3. The Bottom Line: The billion-dollar company with a single employee is no longer a fantasy; it is a mathematical certainty for those who master the prompt over the next twelve months.
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January 13, 2026

How Claude Code is Changing the World with Nick Emmons

The Rollup

Crypto
Key Takeaways:
  1. The migration from human-centric interfaces to agent-first protocols where software is a temporary utility rather than a permanent product.
  2. Use Git and MCP servers to give your agents a persistent memory and toolset, allowing them to work autonomously through complex loops.
  3. Software is no longer the prize; it is the commodity. Your value in the next year depends on how well you direct the agents that build it.
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