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AI Podcasts

January 5, 2026

Welcome to AIE CODE - Jed Borovik, Google DeepMind

AI Engineer

AI
Key Takeaways:
  1. The transition from general-purpose LLMs to specialized coding agents that operate on the entire codebase rather than isolated snippets.
  2. Audit your current stack for agentic readiness. Prioritize tools that integrate with Gemini 3 or similar high-reasoning models to automate repetitive pull requests.
  3. Code is the substrate of the digital world. If you control the means of AI code generation, you control the speed of innovation for every other industry.
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January 5, 2026

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

AI Engineer

AI
Key Takeaways:
  1. The industry is moving from "Chat-as-Interface" to "Computer-as-Interface" where models operate directly on file systems.
  2. Replace your complex list of fifty tools with a single Bash tool and a secure sandbox.
  3. The winners in the agent race will not build the best prompts. They will build the best infrastructure for models to execute code.
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December 31, 2025

Infinity, Paradoxes, Gödel Incompleteness & the Mathematical Multiverse | Lex Fridman Podcast #488

Lex Fridman

AI
Key Takeaways:
  1. The move from a singular "Universe" view to a "Multiverse" perspective mirrors the transition from centralized monoliths to fragmented, interoperable ecosystems.
  2. Build systems that fail gracefully when hitting Gödelian limits.
  3. Truth is a vast ocean while proof is a small boat. Your roadmap must account for the reality that your system will eventually encounter truths it cannot verify.
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December 31, 2025

AI in 2026: 3 Predictions For What’s To Come (a16z Big Ideas)

a16z

AI
Key Takeaways:
  1. The Macro Pivot: Outcome-Based Intelligence. We are moving from AI as a Service to Results as a Service where software value is tied to revenue generation rather than seat licenses.
  2. The Tactical Edge: Verticalize the Data. Build in sectors with non-public outcome data to create a compounding moat that resists commoditization by foundation models.
  3. The winners of 2026 will be those who use AI to solve core human needs for connection and discovery while building defensible, data-rich business models.
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December 31, 2025

AutoGrad Changed Everything (Not Transformers) [Dr. Jeff Beck]

Machine Learning Street Talk

AI
Key Takeaways:
  1. The Macro Transition: Moving from "Big Model" monoliths to "Lots of Little Models" where distributed Bayesian assets represent specific physical objects.
  2. The Tactical Edge: Prioritize "Object-Centered" architectures that track uncertainty. This allows robots to "phone a friend" when encountering novel data.
  3. The LLM era is hitting a wall of implicit representation. The next 12 months belong to those building explicit, causal world models grounded in physics rather than language.
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December 31, 2025

[State of Post-Training] From GPT-4.1 to 5.1: RLVR, Agent & Token Efficiency — Josh McGrath, OpenAI

Latent Space

AI
Key Takeaways:
  1. The move from "bigger is better" to "smarter is cheaper" as token efficiency becomes the primary metric for agentic success.
  2. Prioritize building on models that demonstrate high performance on "graph walk" evals to ensure your long-context applications actually work.
  3. Utilitarian and efficient models that prioritize task completion over cheery personality will dominate the developer market.
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December 31, 2025

[State of Evals] LMArena's $100M Vision — Anastasios Angelopoulos, LMArena

Latent Space

AI
Key Takeaways:
  1. The Macro Trend: The transition from static benchmarks to live human-in-the-loop evaluation. As models saturate fixed tests, the only remaining signal is subjective human preference at scale.
  2. The Tactical Edge: Monitor secret model drops on Arena to spot frontier capabilities before official releases. This provides a lead time advantage for builders choosing their tech stack.
  3. The Bottom Line: Arena is the new kingmaker. If you are building AI products, their expert-tier data is the most reliable map for navigating the frontier.
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December 31, 2025

[State of Context Engineering] Agentic RAG, Context Rot, MCP, Subagents — Nina Lopatina, Contextual

Latent Space

AI
Key Takeaways:
  1. The move from small models to medium models (15B to 70B) suggests that reasoning capability is outstripping the desire for low-latency edge deployment.
  2. Implement instruction-following re-rankers to prune your context window. This prevents the model from getting confused by irrelevant data.
  3. Stop building toys. The next year belongs to those who can build full agentic systems that handle billions of tokens without losing the plot.
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December 31, 2025

[NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL — Kevin Wang et al, Princeton

Latent Space

AI
Key Takeaways:
  1. The wall between RL and self-supervised learning is crumbling, leading to a unified "representation-first" approach to AI.
  2. Swap your reward-heavy objectives for contrastive representation learning to access deeper, more stable architectures.
  3. If you aren't planning for RL models with 100x the current depth, you're building for the past.
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Crypto Podcasts

December 29, 2025

Investing Trends for 2026: DeFi, Tokenization, Capital Formation, Speculation & AI

Bankless

Crypto
Key Takeaways:
  1. The move from human-centric trading to an agent-led economy where programmable money is the native substrate.
  2. Prioritize startups building verticalized tokenization for high-yield exogenous assets rather than generalized service providers.
  3. Crypto is becoming the invisible backend for global finance. Over the next year, the winners will be those who hide the blockchain while using its efficiency to crush traditional margins.
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December 28, 2025

Getting To The Bottom Of Quantum w/ Rearden

The Gwart Show

Crypto
Key Takeaways:
  1. The Macro Transition: Cryptographic security is moving from static models to active systems that must anticipate both classical and quantum breakthroughs.
  2. The Tactical Edge: Audit your UTXOs to ensure no address reuse and keep your Xpubs strictly offline.
  3. The Bottom Line: Quantum risk is a long tail event that serves as a catalyst for necessary Bitcoin upgrades like OP_CAT and BIP 360.
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December 28, 2025

Getting To The Bottom Of Quantum w/ Rearden

The Gwart Show

Crypto
Key Takeaways:
  1. The Macro Shift: Technical reality is decoupled from venture capital hype.
  2. The Tactical Edge: Use hashed addresses and run a node.
  3. The Bottom Line: Quantum is an engineering hurdle rather than an existential crisis for the next decade.
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December 28, 2025

What Can DeFi Users Actually Do on Canton Network Today?

The DCo Podcast

Crypto
Key Takeaways:
  1. The Macro Shift: Institutional Migration. As large-scale capital seeks on-chain efficiency, it will gravitate toward networks that offer privacy as a default.
  2. The Tactical Edge: Monitor Infrastructure. Track the rollout of Canton-native stablecoins to identify when the liquidity floodgates open for professional traders.
  3. The Bottom Line: Canton is building for the "Quiet Money." If you are looking for the next dog coin, look elsewhere, but if you want to see how the global financial system actually moves on-chain, this is the network to watch over the next year.
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December 26, 2025

2025 Year in Review

Bell Curve

Crypto
Key Takeaways:
  1. The transition from "Infra-as-an-Asset" to "Infra-as-a-Service" means valuations will now track real cash flows rather than speculative multiples.
  2. Prioritize protocols that pivot to B2B strategies or vertical integration.
  3. The next 12 months will reward those who build for users rather than for the "crypto-native" echo chamber.
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December 26, 2025

Keith Singery & Garrett Oetken: TAO.com Wallet, Bittensor, TAO Flow, Governance, Subnets | Ep. 77

Ventura Labs

Crypto
Key Takeaways:
  1. The Macro Transition: Capital is migrating from passive staking to active participation in specific intelligence commodities.
  2. The Tactical Edge: Audit the founders behind subnets before swapping tokens.
  3. The Bottom Line: Bittensor is becoming a modular AI stack where the value lies in the integration of specialized subnets rather than isolated performance.
See full notes