Token Drop

DaaX.ai

A weekly, unscripted conversation from the DaaX team on the most interesting developments in AI. Sunil Baliga and Sajjad Khazipura (DaaX Co-Founders), along with Sam Pooni (DaaX Architect) and the occasional guest, explore, discuss, and debate new AI research, news, and real-world use cases. Built for developers and business leaders who want perspectives from experienced AI practitioners. All opinions expressed on this podcast are those of the panelists and do not necessarily reflect the views of their employers.

  1. -2 дн.

    Domain Layers, Not Better Models

    Vinod Khosla says raw ChatGPT gets medical triage wrong 20–30% of the time — and that layering a domain system on top of the same model drives the error rate to zero. Half of that is investor shorthand. The other half is the most important architectural argument in enterprise AI right now. Sunil Baliga, Sajjad Khazipura, and Sam Pooni pull the two apart. Why the failure is structural rather than a training gap: the reward function rewards producing an answer, not a correct one, and fluent English isn't backed by provenance. Why RLHF and distillation can't close it — the cognitive surface is too large to cover every domain and every phrasing variant. And why the domain layer, not the model, is the asset that compounds: every builder can rent the same frontier model, so a product that's a model plus a prompt is a margin waiting to be compressed. Also covered: the progression from loop engineering to harness engineering to grounded knowledge, whether error can ever mathematically reach zero, the intent problem (is natural language even the right way to express what a user wants?), dark data in defense and finance, and why flash trading firms have quietly been running neuro-symbolic architectures for years. Full transcript: https://daax.ai/podcast/episode-21-domain-layers-not-better-models Vinod Khosla on YouTube & X as referenced in this episode https://www.youtube.com/shorts/k32VuZjbQls?app=desktop&ra=m https://x.com/vkhosla/status/2036453452641923496 CHAPTERS 0:00 Vinod Khosla's claim: does domain AI take error to zero? 1:41 Why build on top of a frontier model at all? 2:23 A patient, not a benchmark: the diabetic ketoacidosis case 4:05 Why LLMs behave this way 6:15 The reward function rewards answering, not being right 7:17 From loop engineering to harness engineering 8:07 Grounding answers: the outboard knowledge engine 8:51 Deterministic NLP generation as an alternative 9:38 Would better training (RLHF, distillation) fix it? 11:40 The model is a commodity; the domain layer is not 12:28 Why the domain layer is slow to build — and defensible 14:09 Can the error rate ever actually reach zero? 15:49 What LLMs don't capture: experience 17:00 Human-in-the-loop use cases vs. autonomous ones 18:32 The intent problem: is natural language even the right input? 19:37 Natural language vs. domain-specific languages 21:03 Dark data: why defense and finance are different 24:39 Bloomberg's abandoned LLM and neuro-symbolic trading 27:44 Wrap-up: converting silent errors into caught errors

  2. 15 авг.

    How Knowledge Graphs Handle Time: Event Graphs, Scene Graphs, and Reification Explained

    Time isn't a timestamp you attach to a node — time is change, and most knowledge graphs were never designed to track it. Ontologist and knowledge graph architect Kurt Cagle joins Sunil Baliga, Sajjad Khazipura, and Sam Pooni to walk through the architecture that results when you take time seriously: a declarative knowledge graph for what's constant, an append-only event graph capturing what changed and why, and a scene graph holding the moving "now." Also covered: RDF 1.2 and reification as a way to attach provenance and confidence to any assertion, why information should essentially never be deleted from a graph, and why most enterprise ontology initiatives fail for a reason that has nothing to do with technology — you can't get people to agree on the definition of "customer." Guest: Kurt Cagle — Ontologist, Knowledge Graph Architect, Editor-in-Chief of The Cagle Report https://www.linkedin.com/in/kurtcagle/ Full transcript: https://daax.ai/podcast/episode-20-how-knowledge-graphs-handle-time Chapters (00:00) Introducing Kurt Cagle (01:33) Why time breaks knowledge graphs (03:17) Ontology vs. JSON vs. relational (05:53) Recording every transition (07:24) The event graph (09:40) The scene graph: the moving "now" (11:01) The open world assumption (13:01) Graph vs. graph database (14:30) The six-tuple knowledge unit (16:32) RDF 1.2 and reification (19:57) Is anything ever deleted? (22:29) Newton vs. Einstein: rescoping truth (25:29) The ontology fight in boardrooms (29:12) Why ontology projects fail (31:01) Reasoning is several processes (33:35) Becoming a defensive philosopher (34:41) AI as an epistemological engine

  3. 8 авг.

    The LLM Is the Reader, Not the Library: Where Should Knowledge Actually Live?

    This episode examines a deceptively simple framing: the LLM is not the library, but the reader. Trained knowledge lives in the model's weights — distributed, entangled, and impossible to audit or edit — while new knowledge must live outside it. The panel traces what that split means for RAG, fine-tuning, knowledge editing, and why hallucination is a structural consequence of how neural networks store facts, not a bug to be patched.

  4. 1 авг.

    Small Language Models: Great Translators, Terrible Knowledge Stores

    This episode examines the pitch behind domain-specific small language models — and why training one on proprietary data doesn't eliminate the two problems that plague every language model: knowledge changes (forcing costly retraining) and hallucination. The panel argues SLMs earn their place not as knowledge stores, but as narrow, testable translators sitting in front of a knowledge graph.

  5. 25 июл.

    What Is Knowledge? Why Humans Can Guess but Agents Can’t

    Ranjit Singh Pabla of Stack Overflow joins Token Drop to unpack a deceptively simple question: what is knowledge, and why is serving it to an AI agent fundamentally harder than serving it to a human? The discussion moves from tacit knowledge and memory to trust scores, timestamping, and the frontier of "business as code.” Ranjit Linkedin https://www.linkedin.com/in/ranjitsinghpabla/

  6. 18 июл.

    Context Engineering vs. Prompt Engineering: What Actually Drives a Good AI Answer

    This episode examines the shift from prompt engineering to context engineering — and why that shift is architectural, not semantic. Prompt engineering optimizes the wording of a single, stateless request; context engineering treats the entire input window — instructions, retrieved knowledge, memory, and tool outputs — as a systematic design problem with measurable tradeoffs in cost, precision, and failure mode.

  7. 11 июл.

    The Invisible Layer: Why "Connective Tissue" — Not the Model — Is What Kills AI Projects at Scale

    Six months after the demo wowed executives, the AI pilot quietly stalls — and the model is rarely to blame. Theo Forbath, founder of Above the Model and former Accenture MD, joins Token Drop to unpack the invisible "connective tissue" layer most companies underbuild: shared context, working and long-term memory, tool contracts, evaluation frameworks, and observability. The panel walks through a real insurance-claim-adjudication case where hallucinations cascaded across independent agents, and breaks down five sharp questions for spotting a workflow dressed up as "orchestration." They close on a harder question: if AI increasingly handles the routine cases junior specialists used to learn on, who's left with real expertise when the toughest cases get escalated ten years from now — and what does the next generation of AI-fluent talent actually need to know? Theo's Linkedin https://www.linkedin.com/in/theoforbath/ Theo's Substack https://abovethemodel.substack.com

  8. 4 июл.

    Algorithms Are Logarithmic, AI Is Quadratic — The Hidden Cost of Long Context Windows

    "Algorithms are logarithmic, AI is quadratic.” That one line — from a friend at a party — is the key to understanding why long context windows get so expensive, so fast. This episode examines the quadratic scaling problem behind standard transformer attention. As the number of tokens grows, every token may need to interact with every other token—so doubling the context length can roughly quadruple the attention computation. The panel discusses what this means for million-token context windows, latency, memory use, cost, and answer quality. They also explore advertised versus effective context, the “lost in the middle” problem, and when retrieval-based approaches may be more practical than placing an entire codebase, contract collection, or document library into a single prompt.

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A weekly, unscripted conversation from the DaaX team on the most interesting developments in AI. Sunil Baliga and Sajjad Khazipura (DaaX Co-Founders), along with Sam Pooni (DaaX Architect) and the occasional guest, explore, discuss, and debate new AI research, news, and real-world use cases. Built for developers and business leaders who want perspectives from experienced AI practitioners. All opinions expressed on this podcast are those of the panelists and do not necessarily reflect the views of their employers.

Информация

  • Автор
    DaaX.ai
  • Годы выхода
    2026
  • Выпуски
    21
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © ℗2026 DaaX Technologies Inc.
  • Сайт подкаста
    Token Drop