# Delphina — Full Content Index

> Delphina is the AI-managed context layer for messy enterprise data. It captures, validates, and maintains the business context your best analysts carry in their heads, then grounds every AI agent that touches your data in that foundation — so teams get data agents, workflows, and data apps they can actually trust.

The foundation is a context layer that **builds itself, validates itself, and flags what it doesn't know** (Ingest → Refine → Validate → Evolve). On top sit best-in-class data agents, end-to-end workflows, and shareable data apps. See [The data context trap](https://delphina.ai/blog/the-data-context-trap) for the failure mode this solves.

This file lists every page on delphina.ai for LLM context. For a concise overview, see [llms.txt](https://delphina.ai/llms.txt).

## Core pages

- [Homepage](https://delphina.ai/): The data platform for AI-native teams
- [Our Story](https://delphina.ai/our-story): Team, mission, and how we work
- [Integrations](https://delphina.ai/integrations): Warehouses, BI tools, dbt, and MCP-compatible connectors
- [Security](https://delphina.ai/security): SOC 2 Type II + HIPAA compliance
- [Docs](https://docs.delphina.ai): API reference, integration guides, and product documentation
- [Book a Demo](https://delphina.ai/book-a-demo): Schedule with the Delphina team

## Case Studies

- [Substack](https://delphina.ai/case-studies/substack): How Substack uses Delphina
- [Basecamp Franchising](https://delphina.ai/case-studies/basecamp-franchising): How Basecamp Franchising uses Delphina

## Recently published (in-depth buyer guides and comparisons)
- [Best AI data analyst tools in 2026: a head-to-head comparison](https://delphina.ai/recently-published/best-ai-data-analyst-tools-2026) (May 1, 2026) — The honest 2026 buyer's guide to AI data analyst tools. Compares Delphina, Hex, Omni, WisdomAI, ThoughtSpot, and Tellius across context, accuracy, and deployment.
- [Delphina vs WisdomAI: the enterprise AI data analyst showdown](https://delphina.ai/recently-published/delphina-vs-wisdomai) (May 12, 2026) — Delphina vs WisdomAI — an honest head-to-head comparison of two enterprise AI data analyst platforms. Architecture, accuracy, deployment, and when each is the better fit.
- [Delphina vs Hex: the AI-native analytics platform showdown](https://delphina.ai/recently-published/delphina-vs-hex) (May 17, 2026) — Delphina vs Hex — an honest head-to-head comparison of two AI-native analytics platforms. Architecture, accuracy, primary user, and where each one is actually the better fit.
- [Delphina vs Omni: context layer vs semantic layer for AI analytics](https://delphina.ai/recently-published/delphina-vs-omni) (May 25, 2026) — Delphina vs Omni — an honest head-to-head comparison of two AI analytics platforms. Context layer vs semantic layer, primary user, deployment, and when each is the better fit.
- [Why do AI data agents hallucinate, and how does a context layer fix it?](https://delphina.ai/recently-published/why-ai-data-agents-hallucinate-context-layer) (May 30, 2026) — AI data agents hallucinate because the model doesn't know how your business works. A context layer — broader than a semantic layer, distinct from RAG and vector databases, the data-specific version of a company brain — is the architecture that closes the gap.
- [What your data warehouse alone can't tell an AI agent](https://delphina.ai/recently-published/what-your-warehouse-cant-tell-an-ai-agent) (May 31, 2026) — Seven kinds of context your warehouse doesn't carry — and why an AI agent that only sees your schema is set up to hallucinate on real enterprise questions.
- [What data quality problems break AI data agents?](https://delphina.ai/recently-published/data-quality-problems-ai-data-agents) (Jun 4, 2026) — Five concrete data quality problems that cause AI data agents to confidently return wrong answers — entity duplication, broken joins, inconsistent categorical values, time mismatches, and silent pipeline failures — and what changes when a context layer handles each one.
- [What is a company brain? (And why your AI agents need one)](https://delphina.ai/recently-published/what-is-a-company-brain) (Jun 14, 2026) — A company brain is the central, AI-grounded store of an organization's institutional knowledge. For data specifically — warehouses, metrics, dashboards, and SQL — it's the architecture that makes AI agents accurate on real enterprise questions.
- [How do I write evals for AI data agents?](https://delphina.ai/recently-published/how-to-write-evals-for-ai-data-agents) (Jun 17, 2026) — Evals for AI data agents are test cases that prove the agent produces correct answers on real production data. Here's how to write your first ones, how to avoid eval circularity, and what a continuous eval loop looks like in practice.

## Blog
- [Why Gen AI will transform the workflows of data science and analytics](https://delphina.ai/blog/why-genai-will-transform-workflows) (Dec 13, 2023) — Data science is transformational — it leaves an impact like a crater: profound and enduring. But getting business results from data is still way too hard.
- [The five breaking points for data and AI in the business](https://delphina.ai/blog/breaking-points-for-ml) (Jan 10, 2024) — Deep diving into a question we get all the time from senior leaders: where do data and AI initiatives go wrong?
- [Who should own data and AI?](https://delphina.ai/blog/who-should-own-machine-learning) (Jan 25, 2024) — Today we dive into an uncomfortable question: who owns data and AI?
- [The costliest mistake in data and AI](https://delphina.ai/blog/costliest-mistake-in-machine-learning) (Feb 13, 2024) — Are you solving the right problems? When you don’t get the problem framing right, everything that comes next is a waste.
- [The paradox of data and AI – what leaders need to know](https://delphina.ai/blog/paradox-of-machine-learning) (Feb 28, 2024) — For all the automation it promises, making data and AI work happen is deeply manual. Leaders need a realistic view of what it takes to build data and AI products that deliver value — and how to ensure their teams are actually doing that work.
- [The six most painstaking steps in data work](https://delphina.ai/blog/painstaking-steps-in-machine-learning) (Mar 14, 2024) — If you aren’t involved in the day-to-day work of data and AI, you may assume data scientists spend their time fine-tuning transformer models and performing PhD-level math. Dive in to learn the truth.
- [The seven personas of data and AI](https://delphina.ai/blog/personas-of-machine-learning) (Apr 16, 2024) — Behind the scenes, your team is increasingly worried Data and AI are just a Mirage. Explore the SEVEN key personas on data and AI teams, and the unique challenges they each face in navigating the hype-vs-reality gulf of AI adoption.
- [The danger zone in data science](https://delphina.ai/blog/danger-zone-in-data-science) (May 29, 2024) — Unlike many functions, the returns to quality are highly non-linear in data and AI — and mediocre AI is often downright dangerous. Unpack why, how to identify mediocre AI, and what to do about it.
- [Why PhDs whiff the onsite and how to find a diamond in the rough](https://delphina.ai/blog/interviewing-data-scientists) (Jun 20, 2024) — New PhDs can be total amateurs when it comes to the job market. Knowing these candidates will say some silly things — sometimes unintentionally — how can you separate the wheat from the chaff?
- [What advanced analytics teams are doing that you aren’t](https://delphina.ai/blog/advanced-analytics-with-machine-learning) (Aug 1, 2024) — Data and analytics teams perennially face a burning — yet often unspoken — question: what drives high value actions?
- [Why AutoML failed to live up to the hype](https://delphina.ai/blog/why-automl-failed) (Sep 11, 2024) — AutoML promised to revolutionize data science by automating the machine learning process, but it's fallen short. Unpack the limitations of AutoML and why data science teams remain essential in tackling complex problems that extend beyond routine model optimization.
- [Truth, lies, and ROI](https://delphina.ai/blog/testing-philosophy-for-data-science) (Oct 10, 2024) — Discover the art of crafting high-ROI automated tests for fast-paced tech environments. Delphina engineer Thomas Barthelemy shares insights on effective testing strategies, taking a critical look at outdated models, and exploring new approaches for startups and beyond.
- [Our new High Signal podcast](https://delphina.ai/blog/announcing-high-signal-podcast) (Oct 24, 2024) — Discover groundbreaking insights at the crossroads of AI, economics, and intelligent infrastructure with Michael I. Jordan in our inaugural High Signal podcast episode. Join us as we bring together leading voices in data science to help you advance your career and make a tangible impact in the world.
- [5 ways stakeholders stall out critical data and AI initiatives](https://delphina.ai/blog/stakeholders-stall-out-machine-learning) (Dec 17, 2024) — Explore recurring themes that hinder data and AI progress and get actionable strategies for fostering better collaboration and understanding between all parties involved.
- [The greatest minds in data science](https://delphina.ai/blog/the-greatest-minds-in-data-science) (Dec 24, 2024) — Catch up on the latest takes from the greatest minds in data science, as shared in the first seven episodes of the High Signal podcast from Delphina.
- [The paradox of optimism in data science](https://delphina.ai/blog/paradox-of-optimism-in-data-science) (Feb 11, 2025) — Data science leaders must balance belief in the transformative power of data and AI with reality: major data initiatives are risky and take months to move from conception to production. 
- [The vibes about A/B testing are wrong](https://delphina.ai/blog/vibes-about-a-b-testing-are-wrong) (Mar 11, 2025) — Why anti-A/B testing sentiment is running rampant — and when leaders need to rely on taste, not data.
- [The rise of data slop](https://delphina.ai/blog/the-rise-of-data-slop) (May 2, 2025) — With AI tools, even well-meaning employees can generate misleading analytics without realizing it. Here's how to spot — and stop — data slop.
- [The must-listen perspectives on data and AI](https://delphina.ai/blog/must-listen-perspectives) (Jun 20, 2025) — More insights from the greatest minds in data science, now on High Signal
- [What data leaders got right in 2025](https://delphina.ai/blog/what-data-leaders-got-right) (Dec 23, 2025) — Hard-won lessons from this year's essential High Signal episodes
- [We built an AI data agent for the hardest problem in sports — your March Madness bracket](https://delphina.ai/blog/ncaa-bracket-launch) (Mar 17, 2026) — The perfect March Madness bracket has never been filled out. So we built something about it — an AI data agent loaded with 11 seasons of NCAA data and live prediction markets. It's free and we think you should try it.
- [The data context trap](https://delphina.ai/blog/the-data-context-trap) (May 7, 2026) — We've talked to hundreds of teams trying to build their own context layer for AI. Some succeeded. Most hit the same walls. Here's the pattern — and what to do about it.
- [What TK got right about data at Uber](https://delphina.ai/blog/what-tk-got-right-about-data-at-uber) (May 21, 2026) — Travis Kalanick insisted everyone at Uber write SQL. The instinct was right – the technology just wasn't ready. Now it is.

## High Signal podcast

High Signal is Delphina's podcast hosted by Hugo Bowne-Anderson and Duncan Gilchrist, featuring conversations with data science and AI leaders.
- [AI at Planetary Scale: What’s Next for Machine Learning?](https://delphina.ai/podcast/next-evolution-of-ai) (Oct 27, 2023) with Michael Jordan (UC Berkeley)
- [Fooling Yourself Less: The Art of Statistical Thinking in AI](https://delphina.ai/podcast/art-of-statistical-thinking-in-ai) (Nov 1, 2024) with Andrew Gelman (Columbia University)
- [Ramesh Johari on How to Build an Experimentation Machine and Where Most Go Wrong](https://delphina.ai/podcast/ramesh-johari-on-how-to-build-an-experimentation-machine-and-where-most-go-wrong) (Nov 7, 2024) with Ramesh Johari (Stanford University)
- [The Hard Truth About Building AI Systems and What Most Leaders Miss About AI](https://delphina.ai/podcast/gabriel-weintraub-on-the-hard-truth-about-building-ai-systems-and-what-most-leaders-miss-about-ai) (Nov 20, 2024) with Gabriel Weintraub (Stanford Graduate School of Business)
- [Data Science Meets Management: Teamwork, Experimentation, and Decision-Making](https://delphina.ai/podcast/data-science-meets-management) (Dec 1, 2024) with Chiara Farronato (Harvard Business School)
- [What Happens to Data Science in the Age of AI?](https://delphina.ai/podcast/what-happens-to-data-science-in-the-age-of-ai-hilary-mason) (Dec 5, 2024) with Hilary Mason (Hidden Door)
- [What Lies Beyond Machine Learning and AI: Decision Systems and the Future of Data Teams](https://delphina.ai/podcast/chris-wiggins-on-what-lies-beyond-machine-learning-and-ai-decision-systems-and-the-future-of-data-teams) (Dec 19, 2024) with Chris Wiggins (New York Times)
- [From Zero to Scale: Lessons from Airbnb and Beyond](https://delphina.ai/podcast/elena-grewal-on-from-zero-to-scale-building-data-functions-from-airbnb-to-brick-and-mortar) (Jan 9, 2025) with Elena Grewal (Elena's on Orange)
- [Why 90% of Data Science Fails—And How to Fix It](https://delphina.ai/podcast/why-90-of-data-science-fails-and-how-to-fix-it-eric-colson) (Jan 31, 2025) with Eric Colson (Activation Fund)
- [AI Won't Save You But Data Intelligence Will](https://delphina.ai/podcast/ari-kaplan-on-why-ai-wont-save-you-but-data-intelligence-will) (Feb 13, 2025) with Ari Kaplan (Databricks)
- [What Comes After Code? The Role of Engineers in an AI-Driven Future](https://delphina.ai/podcast/what-comes-after-code-the-role-of-engineers-in-an-ai-driven-future) (Feb 27, 2025) with Peter Wang (Anaconda)
- [Your Machine Learning Solves The Wrong Problem](https://delphina.ai/podcast/your-machine-learning-solves-the-wrong-problem) (Mar 13, 2025) with Stefan Wager (Stanford University)
- [The End of Programming As We Know It](https://delphina.ai/podcast/tim-oreilly-on-the-end-of-programming-as-we-know-it) (Mar 27, 2025) with Tim O'Reilly (O'Reilly Media)
- [Why Most Companies Aren't AI Ready](https://delphina.ai/podcast/barr-moses-on-why-most-companies-arent-ai-ready) (Apr 10, 2025) with Barr Moses (Monte Carlo)
- [Why Good Metrics Still Lead to Bad Decisions — and How to Fix It](https://delphina.ai/podcast/good-metrics-bad-decisions-how-to-fix-it) (Apr 24, 2025) with Eoin O'Mahony (Lightspeed Ventures)
- [How Human-Centered AI Actually Gets Built](https://delphina.ai/podcast/fei-fei-on-how-human-centered-ai-actually-gets-built) (May 6, 2025) with Fei-Fei Li (Stanford University)
- [The Incentive Problem in Shipping AI Products — and How to Change It](https://delphina.ai/podcast/roberto-medri-on-the-incentive-problem-in-shipping-ai-products----and-how-to-change-it) (May 29, 2025) with Roberto Medri (Meta)
- [Sudarshan Seshadri on High-Stakes AI Systems and the Cost of Getting It Wrong](https://delphina.ai/podcast/high-stakes-ai-systems-and-the-cost-of-getting-it-wrong) (Jun 19, 2025) with Sudarshan Seshadri (Alto Pharmacy)
- [Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI](https://delphina.ai/podcast/defaults-decisions-and-dynamic-systems-behavioral-science-meets-ai) (Jul 3, 2025) with Lis Costa (Behavioural Insights Team)
- [Daragh Sibley on Incentives, Accountability, and the Data Leader’s Dilemma](https://delphina.ai/podcast/incentives-accountability-and-the-data-leaders-dilemma) (Jul 21, 2025) with Daragh Sibley (Literati)
- [Why Great Data Still Leads to Bad Decisions (And How to Fix It)](https://delphina.ai/podcast/why-great-data-still-leads-to-bad-decisions-and-how-to-fix-it) (Aug 5, 2025) with Amy Edmondson (Harvard Business School)
- [Tomasz Tunguz on Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It)](https://delphina.ai/podcast/why-a-trillion-dollars-of-market-cap-is-up-for-grabs-and-how-ai-teams-will-win-it) (Aug 19, 2025) with Tomasz Tunguz (Theory Ventures)
- [Rebuilding an Airline for the 21st Century: LATAM's Data-Driven Transformation](https://delphina.ai/podcast/andres-bucchi-on-rebuilding-an-airline-for-the-21st-century-latams-data-driven-transformation) (Sep 16, 2025) with Andres Bucchi (LATAM Airlines)
- [Sergey Fogelson on How Data-Driven Growth Redefined a Media Giant](https://delphina.ai/podcast/sergey-fogelson-on-how-data-driven-growth-redefined-a-media-giant) (Oct 1, 2025) with Sergey Fogelson (Televisa Univision)
- [Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts](https://delphina.ai/podcast/vishnu-ram-venkataraman-on-gen-ais-true-cost-why-todays-wins-are-tomorrows-debts) (Oct 16, 2025) with Vishnu Ram Venkataraman (AI leader, ex-Credit Karma and Intuit)
- [Paras Doshi on Why Your Data Team Doesn't Have a Seat at the Table (And How to Earn It)](https://delphina.ai/podcast/paras-doshi-on-why-your-data-team-doesnt-have-a-seat-at-the-table-and-how-to-earn-it) (Oct 29, 2025) with Paras Doshi (Opendoor)
- [From Context Engineering to AI Agent Harnesses: The New Software Discipline](https://delphina.ai/podcast/context-engineering-to-ai-agent-harnesses-the-new-software-discipline) (Nov 13, 2025) with Lance Martin (LangChain)
- [Why AI Adoption Fails: A Behavioral Framework for AI Implementation](https://delphina.ai/podcast/why-ai-adoption-fails-a-behavioral-framework-for-ai-implementation) (Nov 28, 2025) with Elisabeth Costa (Behavioural Insights Team)
- [The AI Paradox: Why Your Data Team’s Workload is About to Explode](https://delphina.ai/podcast/the-ai-paradox-why-your-data-teams-workload-is-about-to-explode) (Dec 11, 2025) with Chris Child (Snowflake)
- [Why Data Governance In Your Org is Broken (And How to Fix It)](https://delphina.ai/podcast/why-data-governance-in-your-org-is-broken-and-how-to-fix-it) (Dec 30, 2025) with Cara Dailey (Early Warning)
- [The Post Coding-Era: What Happens When AI Writes the System?](https://delphina.ai/podcast/the-post-coding-era-what-happens-when-ai-writes-the-system) (Jan 13, 2026) with Nicholas Moy (Google DeepMind)
- [Why Your AI Product Will Be Obsolete in Six Months (And What To Do About It)](https://delphina.ai/podcast/why-your-ai-product-will-be-obsolete-in-six-months-and-what-to-do-about-it) (Jan 27, 2026) with Benn Stancil
- [Duolingo and the Future of Personalized Education with AI](https://delphina.ai/podcast/duolingo-and-the-future-of-personalized-education-with-ai) (Feb 10, 2026) with Bozena Pajak (Duolingo)
- [Beyond Online Experimentation: Generative Software That Optimizes Itself](https://delphina.ai/podcast/beyond-online-experimentation-generative-software-that-optimizes-itself) (Mar 4, 2026) with Martin Tingley (Microsoft)
- [AI and the Judgment Problem in Data Science](https://delphina.ai/podcast/ai-and-the-judgment-problem-in-data-science) (Mar 19, 2026) with Dawn Woodward (LinkedIn)
- [Engineered Intelligence and The Data Science Problem in AI](https://delphina.ai/podcast/engineered-intelligence-and-the-data-science-problem-in-ai) (Apr 2, 2026) with Jordan Morrow (AgileOne)
- [Why AI Won't Fix Your Data Culture, It Will Only Amplify It (And What To Do About It)](https://delphina.ai/podcast/why-ai-wont-fix-your-data-culture-it-will-only-amplify-it-and-what-to-do-about-it) (Apr 16, 2026) with Noah Bruegmann (Data CRT)
- [The 100-Year Lead: What Baseball Teaches Us About the Future of AI](https://delphina.ai/podcast/the-100-year-lead-what-baseball-teaches-us-about-the-future-of-ai) (May 11, 2026) with Chris Fonnesbeck (PyMC Labs)
- [The Economic Reality of AI: Friction, Talent, and the Future of the Firm](https://delphina.ai/podcast/the-economic-reality-of-ai-friction-talent-and-the-future-of-the-firm) (May 25, 2026) with Steve Tadelis (UC Berkeley)
- [The Verification Crisis: Why Trust Is the New Bottleneck in AI](https://delphina.ai/podcast/the-verification-crisis-why-trust-is-the-new-bottleneck-in-ai) (Jun 17, 2026) with Noah Smith (Noahpinion)

## Podcast transcripts (full text)
- [The Verification Crisis: Why Trust Is the New Bottleneck in AI — Transcript](https://delphina.ai/podcast/the-verification-crisis-why-trust-is-the-new-bottleneck-in-ai/transcript)
- [The Economic Reality of AI: Friction, Talent, and the Future of the Firm — Transcript](https://delphina.ai/podcast/the-economic-reality-of-ai-friction-talent-and-the-future-of-the-firm/transcript)
- [The 100-Year Lead: What Baseball Teaches Us About the Future of AI — Transcript](https://delphina.ai/podcast/the-100-year-lead-what-baseball-teaches-us-about-the-future-of-ai/transcript)
- [Why AI Won't Fix Your Data Culture, It Will Only Amplify It (And What To Do About It) — Transcript](https://delphina.ai/podcast/why-ai-wont-fix-your-data-culture-it-will-only-amplify-it-and-what-to-do-about-it/transcript)
- [Engineered Intelligence and The Data Science Problem in AI — Transcript](https://delphina.ai/podcast/engineered-intelligence-and-the-data-science-problem-in-ai/transcript)
- [AI and the Judgment Problem in Data Science — Transcript](https://delphina.ai/podcast/ai-and-the-judgment-problem-in-data-science/transcript)
- [Beyond Online Experimentation: Generative Software That Optimizes Itself — Transcript](https://delphina.ai/podcast/beyond-online-experimentation-generative-software-that-optimizes-itself/transcript)
- [Duolingo and the Future of Personalized Education with AI — Transcript](https://delphina.ai/podcast/duolingo-and-the-future-of-personalized-education-with-ai/transcript)
- [Why Your AI Product Will Be Obsolete in Six Months (And What To Do About It) — Transcript](https://delphina.ai/podcast/why-your-ai-product-will-be-obsolete-in-six-months-and-what-to-do-about-it/transcript)
- [The Post Coding-Era: What Happens When AI Writes the System? — Transcript](https://delphina.ai/podcast/the-post-coding-era-what-happens-when-ai-writes-the-system/transcript)
- [Why Data Governance In Your Org is Broken (And How to Fix It) — Transcript](https://delphina.ai/podcast/why-data-governance-in-your-org-is-broken-and-how-to-fix-it/transcript)
- [The AI Paradox: Why Your Data Team’s Workload is About to Explode — Transcript](https://delphina.ai/podcast/the-ai-paradox-why-your-data-teams-workload-is-about-to-explode/transcript)
- [Why AI Adoption Fails: A Behavioral Framework for AI Implementation — Transcript](https://delphina.ai/podcast/why-ai-adoption-fails-a-behavioral-framework-for-ai-implementation/transcript)
- [From Context Engineering to AI Agent Harnesses: The New Software Discipline — Transcript](https://delphina.ai/podcast/context-engineering-to-ai-agent-harnesses-the-new-software-discipline/transcript)
- [Paras Doshi on Why Your Data Team Doesn't Have a Seat at the Table (And How to Earn It) — Transcript](https://delphina.ai/podcast/paras-doshi-on-why-your-data-team-doesnt-have-a-seat-at-the-table-and-how-to-earn-it/transcript)
- [Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts — Transcript](https://delphina.ai/podcast/vishnu-ram-venkataraman-on-gen-ais-true-cost-why-todays-wins-are-tomorrows-debts/transcript)
- [Sergey Fogelson on How Data-Driven Growth Redefined a Media Giant — Transcript](https://delphina.ai/podcast/sergey-fogelson-on-how-data-driven-growth-redefined-a-media-giant/transcript)
- [Rebuilding an Airline for the 21st Century: LATAM's Data-Driven Transformation — Transcript](https://delphina.ai/podcast/andres-bucchi-on-rebuilding-an-airline-for-the-21st-century-latams-data-driven-transformation/transcript)
- [Tomasz Tunguz on Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It) — Transcript](https://delphina.ai/podcast/why-a-trillion-dollars-of-market-cap-is-up-for-grabs-and-how-ai-teams-will-win-it/transcript)
- [Why Great Data Still Leads to Bad Decisions (And How to Fix It) — Transcript](https://delphina.ai/podcast/why-great-data-still-leads-to-bad-decisions-and-how-to-fix-it/transcript)
- [Daragh Sibley on Incentives, Accountability, and the Data Leader’s Dilemma — Transcript](https://delphina.ai/podcast/incentives-accountability-and-the-data-leaders-dilemma/transcript)
- [Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI — Transcript](https://delphina.ai/podcast/defaults-decisions-and-dynamic-systems-behavioral-science-meets-ai/transcript)
- [Sudarshan Seshadri on High-Stakes AI Systems and the Cost of Getting It Wrong — Transcript](https://delphina.ai/podcast/high-stakes-ai-systems-and-the-cost-of-getting-it-wrong/transcript)
- [The Incentive Problem in Shipping AI Products — and How to Change It — Transcript](https://delphina.ai/podcast/roberto-medri-on-the-incentive-problem-in-shipping-ai-products----and-how-to-change-it/transcript)
- [How Human-Centered AI Actually Gets Built — Transcript](https://delphina.ai/podcast/fei-fei-on-how-human-centered-ai-actually-gets-built/transcript)
- [Why Good Metrics Still Lead to Bad Decisions — and How to Fix It — Transcript](https://delphina.ai/podcast/good-metrics-bad-decisions-how-to-fix-it/transcript)
- [Why Most Companies Aren't AI Ready — Transcript](https://delphina.ai/podcast/barr-moses-on-why-most-companies-arent-ai-ready/transcript)
- [The End of Programming As We Know It — Transcript](https://delphina.ai/podcast/tim-oreilly-on-the-end-of-programming-as-we-know-it/transcript)
- [Your Machine Learning Solves The Wrong Problem — Transcript](https://delphina.ai/podcast/your-machine-learning-solves-the-wrong-problem/transcript)
- [What Comes After Code? The Role of Engineers in an AI-Driven Future — Transcript](https://delphina.ai/podcast/what-comes-after-code-the-role-of-engineers-in-an-ai-driven-future/transcript)
- [AI Won't Save You But Data Intelligence Will — Transcript](https://delphina.ai/podcast/ari-kaplan-on-why-ai-wont-save-you-but-data-intelligence-will/transcript)
- [Why 90% of Data Science Fails—And How to Fix It — Transcript](https://delphina.ai/podcast/why-90-of-data-science-fails-and-how-to-fix-it-eric-colson/transcript)
- [What Lies Beyond Machine Learning and AI: Decision Systems and the Future of Data Teams — Transcript](https://delphina.ai/podcast/chris-wiggins-on-what-lies-beyond-machine-learning-and-ai-decision-systems-and-the-future-of-data-teams/transcript)
- [What Happens to Data Science in the Age of AI? — Transcript](https://delphina.ai/podcast/what-happens-to-data-science-in-the-age-of-ai-hilary-mason/transcript)
- [The Hard Truth About Building AI Systems and What Most Leaders Miss About AI — Transcript](https://delphina.ai/podcast/gabriel-weintraub-on-the-hard-truth-about-building-ai-systems-and-what-most-leaders-miss-about-ai/transcript)
- [Ramesh Johari on How to Build an Experimentation Machine and Where Most Go Wrong — Transcript](https://delphina.ai/podcast/ramesh-johari-on-how-to-build-an-experimentation-machine-and-where-most-go-wrong/transcript)
- [AI at Planetary Scale: What’s Next for Machine Learning? — Transcript](https://delphina.ai/podcast/next-evolution-of-ai/transcript)

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