Future of Data and AI

About Future of Data and AI

Throughout history, we've chased the extraordinary. Today, the spotlight is on AI—a game-changer, redefining human potential, augmenting our capabilities, and fueling creativity. If you're curious about AI and how it is reshaping the world, you're in the place. Our podcast dives deep into the trends and developments in AI and technology, weaving together the past, present, and future. This podcast explores the profound impact of AI on society, through the lens of the most brilliant and inspiring minds in the industry. Learn more about us: https://hubs.la/Q02q-dj20

Publisher

Data Science Dojo

Category

technology

Language

en

Explicit

No

First episode

23 Feb 2024

Latest episode

12 Aug 2026

Latest episodes

13 episodes in the feed.

1. 

   12 Aug 2026

   Dan Maloney on Visual AI, Agentic Document Extraction & Building Trust in Enterprise AI | Episode 11

   OCR has been around for more than 40 years. So why do the world's biggest banks still have thousands of people reading documents by hand? When Dan Maloney became CEO of Landing AI in 2024, as Andrew Ng stepped back from the day-to-day, he was returning to a problem he had first worked on at SAP back in 2001. When he looked closely at it again two decades later, he was struck by how little it had actually moved. Landing AI's mission is to make the world's documents computable. Instead of growing up from OCR and patching its limits with templates and heuristics, Landing AI came at the problem from visual AI, blending purpose-built models, an intelligent router, and agentic reasoning into a single system that reads a document the way a person does. Today that system extracts structured data from the messiest documents enterprises have, the scanned pages, the tables inside tables, the handwritten forms, at accuracy levels they can build on. Before every enterprise had an AI strategy... Before "agentic" became a boardroom word... Before the industry spent a year token maxing... There was a quieter, more stubborn problem: The world's data was trapped in documents, and no one could read it at scale. In this episode of the Future of Data & AI Podcast, Dan Maloney, CEO of Landing AI and a two-decade veteran of enterprise software and AI, joins Raja Iqbal for a grounded conversation about what it actually takes to move visual AI from an impressive demo into production. Dan is candid about where the hype outruns reality, why the model is the smallest part of the equation, and how a company earns the trust of a compliance team, not just an engineering one. What You'll Discover 🔹Why "good enough" accuracy can be as costly as zero accuracy. At billions of documents, a small error rate forces humans back into the loop. Dan explains why the goal is to get so close to 100% that enterprises can stop checking.🔹Why the model is the smallest part of the system. Landing AI blends its own document pre-trained transformers with LLMs only when needed, wrapped in a harness that handles routing, parsing, and extraction. The intelligence is in the system, not the model alone.🔹How Landing AI reads a document the way a person does. Its DPTx models take in layout, tables, figures, signatures, and marginalia, break them down to the block and word level, and hand each piece to the right model downstream.🔹Why document processing is 80% a vision problem. Dan explains how solving the visual complexity first, the columns, the modalities, the spatial structure, turns the rest into a much simpler language problem.🔹How you earn trust from compliance, not just engineering. Visual grounding shows exactly where every answer came from. SaaS, VPC, on-prem, and air-gapped deployments let regulated industries adopt AI without their data ever leaving the building.🔹Why token maxing gave way to a reckoning. Companies loosened the guardrails to see what AI could do, then met the bill. Dan explains what separates spend that changes the game from spend with nothing to show for it.🔹Why you shouldn't boil the ocean. Dan's advice for leaders getting started: pick one core, high-value use case, prove it end to end, and learn the orchestration, retries, and human-in-the-loop decisions that carry over to everything else.🔹Where document intelligence is headed. Dan makes the case that freeing humans from decades of administrative document work is the disruption that unlocks entirely new revenue models and businesses. This Episode Is For 🔹ML and AI engineers building document ingestion and extraction pipelines 🔹Enterprise and data leaders deploying AI in regulated industries 🔹CIOs and CISOs weighing SaaS, VPC, and on-prem trade-offs 🔹Teams trying to measure real ROI on their AI spend 🔹Anyone thinking seriously about visual AI, trust, and scale

2. 

   12 May 2026

   Mark Cavage on Agentic AI, Sandboxing & Enterprise Security | Ep 10

   He helped build the infrastructure that runs the modern internet. First AWS. Then Oracle Cloud Infrastructure. Then Heroku. Then Stripe. Now he's at Docker — and he thinks we're about to need a completely new layer underneath all of it. When Mark Cavage (https://www.linkedin.com/in/mcavage/), President & COO of Docker (https://www.docker.com/), joined the company, the question wasn't whether agents were coming. It was whether the infrastructure underneath them was ready. It wasn't. Before anyone was talking about agentic workloads in production…Before AI tools started writing, running, and deploying their own code…Before CISOs had a framework for reasoning about autonomous systems… There was a simple but uncomfortable realization: Containers were built for immutable, predictable software. Agents want to mutate everything. In this episode of the Future of Data & AI Podcast, Mark Cavage — President & COO of Docker and one of the founding engineers of Oracle Cloud Infrastructure — joins Raja Iqbal for a candid conversation about what the agentic era actually demands from infrastructure. Mark has spent over two decades building the systems that power modern cloud. Through Docker, he's now working on the sandbox layer that lets enterprises deploy agents at scale — without handing over control to a system nobody fully understands yet. This conversation goes beyond the hype. What You'll Discover: Why containers alone aren't enough for the agentic era.Containers were built for immutable software. Agents mutate, write, and act — and Mark explains exactly what breaks, and what Docker built to fix it. What YOLO mode actually means — and why it matters.Agents running without a human in the loop sounds reckless. Mark explains why that's actually the goal, and how the micro VM sandbox makes it safe enough for enterprise. The 1000x risk surface no one is talking about.Every AI-generated pull request, every "authored by Claude" commit, every autonomously deployed dependency is stacking security debt. Mark breaks down what that means for your CISO. Trusted MCP servers and Docker Hardened Images.What they are, why they exist, and why supply chain security for AI tools is about to become one of the most important conversations in enterprise tech. Mark's bets for the next 12 months.CFOs demanding ROI on token spend, the open source project that no one is talking about, and what the future of Agentic AI looks like. This episode is for: ML engineers and DevOps teams building with agents CISOs and security leaders managing a 1000x larger risk surface Platform and infrastructure leads evaluating MCP servers and supply chain security CTOs and engineering leaders figuring out what "agentic" actually means for their org Founders deciding where the next infrastructure layer gets built This isn't a conversation about demos or roadmaps. It's about the infrastructure that agents actually need to run safely, reliably, and at scale — and whether the industry is building it fast enough. If you're deploying agents in production, managing the security conversation, or trying to understand where Docker fits in the agentic stack… this episode is worth your time. Explore all recordings (https://datasciencedojo.com/podcast/)

3. 

   5 Mar 2026

   João Moura on Multi-Agent Systems, Autonomous Workflows & AI Entrepreneurship

   🎙️ Future of Data and AI Podcast: Episode 09 with João Moura He just wanted to automate his own work, but it turned into an AI platform that is now used by top enterprises. How? When João Moura (http://linkedin.com/in/joaomdmoura), founder and CEO of CrewAI (https://crewai.com/), started building AI agents; it wasn’t to launch a business, he just wanted to make his job easier. Starting at Clearbit, he built a set of agents to automate content creation to post more on LinkedIn… and ended up generating up to 600 inbound leads a day. Instead of relying on a single AI tool, João’s multi-agent systems divide big tasks into smaller ones, letting specialized "crew" of agents collaborate to get work done. That experiment evolved into CrewAI — a platform where teams can manage, deploy, and track crews of AI Agents handling routine workflows, all from a single dashboard. Today, CrewAI is redefining how the world’s largest organizations leverage multi-agent systems, from Oracle to the U.S. Department of Defense. Before AI agents became the hottest trend in enterprise tech… Before every company had 50+ use cases mapped out… Before “autonomous workflows” became a boardroom priority… There was a simple but uncomfortable realization: Building agents is easy. Deploying them is hard. In this episode of the Future of Data & AI Podcast, João Moura — CEO of CrewAI and former Director of AI Engineering at Clearbit — joins us for a deep dive into the real state of agentic AI. João has spent over 20 years in software engineering and AI leadership, and through CrewAI, he’s working with major enterprises — including government agencies and global corporations — to move AI agents from proof-of-concept to production. This conversation goes far beyond hype. Why building agents has zero value — unless they see the light of day. João explains why most AI projects stall after POCs and what separates experiments from real business impact. The biggest misconception about agentic AI. AI agents aren’t magical. They require strong engineering discipline, governance, and architecture — sometimes more software engineering than AI. From guardrails to trust. How enterprises handle non-determinism, hallucinations, prompt injection, and MCP security — using layered guardrails, LLM-as-a-judge, role-based access control, and observability. The real bottleneck isn’t intelligence — it’s deployment. Models are already good enough. The hard part is compliance, traceability, monitoring, and integrating with existing enterprise systems. Why simplicity beats over-engineering. João shares a powerful insight: complexity never disappears — you just choose where to put it. Senior engineers understand this. Many teams don’t. How enterprises mature with AI. Organizations typically start with cost savings, move to revenue generation, and eventually unlock innovation they hadn’t imagined before. Open source vs. enterprise AI. Why CrewAI chose an open-source-first strategy — and how the business model works when moving from prototyping to production-grade orchestration. Advice for AI founders. João speaks candidly about entrepreneurship in the AI era — the hype, the competition, investor psychology, and why this may be a once-in-a-generation opportunity. This episode is for: AI engineers building multi-agent systems CIOs and enterprise leaders deploying autonomous workflows Founders launching AI startups Teams struggling to move from POC to production Anyone thinking seriously about trust, governance, and scale in AI This isn’t a conversation about shiny demos. It’s about building AI systems that run reliably, securely, and at scale — in the real world. If you’re working on multi-agent systems, autonomous workflows, enterprise AI infrastructure, or AI entrepreneurship… this episode will change how you think about shipping AI.

4. 

   16 Jan 2026

   Emil Eifrem on Neo4j, Graph Databases, Connected Data & Graph-Native AI

   🎙️ Future of Data and AI Podcast: Episode 08 with Emil EifremBefore graph databases were everywhere…Before knowledge graphs became essential to AI…Before LLMs, embeddings, and RAG entered the conversation…There was one simple, stubborn idea: data makes more sense when you understand the relationships.In this engaging and insightful episode, Emil Eifrem (Co-Founder & CEO | Neo4j) shares the story behind building Neo4j — the graph database platform that quietly reshaped how the world models and reasons over data. Emil takes us through the early days when graph databases felt risky, misunderstood, and years ahead of the market, and explains why connected data mirrors human thinking better than tables, rows, and columns ever could.What you’ll discover:🔹Why relationships became the foundation of Neo4j — and how treating them as first-class citizens changed everything.🔹How property graph models preserve context, making complex systems easier to reason about and analyze.🔹Unexpected ways graph databases are powering fraud detection, recommendations, cybersecurity, and enterprise knowledge.🔹Why knowledge graphs quietly became essential to modern AI — long before most people noticed.🔹How graphs and LLMs work together to ground AI systems in structure, meaning, and explainability.🔹Insights on building deep infrastructure technology with patience, conviction, and long-term vision.This is more than a conversation about AI, graphs, or databases. It’s a look at how intelligence — human or artificial — depends on connections, context, and understanding.📌 If you’ve ever wondered:- Why AI sometimes feels confident but wrong- How machines can reason instead of just predict- Why some technologies take years before the world catches up…this episode will change how you think about data, AI, and connected systems.🔹A must-listen for: data scientists, AI practitioners, knowledge graph enthusiasts, graph database users, enterprise architects, AI researchers, and anyone curious about how connected data powers modern AI.Prepare to walk away inspired.Keywords: Emil Eifrem, Neo4j, graph databases, knowledge graphs, property graph model, AI reasoning, connected data, enterprise AI, graph analytics, fraud detection AI, AI infrastructure, LLM applications, RAG, embeddings, AI explainability, building data platforms, AI strategy, human-like reasoning, data science insights, graph AI, Neo4j interview, AI podcast, Future of Data and AI PodcastFor more episodes: https://www.youtube.com/playlist?list=PL8eNk_zTBST_jMlmiokwBVfS_BqbAt0z2For highlights, check out: https://www.youtube.com/playlist?list=PL8eNk_zTBST-YYNgPcw3rO9Tvn7fEjR4AVisit our podcast page for more info: https://datasciencedojo.com/podcast/

5. 

   5 Dec 2025

   Joshua Starmer on StatQuest, Storytelling, Next-Gen Learning & His Iconic BAMs

   Before StatQuest (https://www.youtube.com/@statquest) became the go-to learning companion for millions of AI and ML practitioners… Before the “BAM! Double BAM! Triple BAM!” became a teaching tool that many learners adore... There was just one guy in a genetics lab, trying desperately to explain his data analysis to coworkers so they didn't think he was working magic. In this deeply personal and inspiring episode, Joshua Starmer (CEO & Founder | StatQuest) shares the real story behind his rise — a journey shaped by strategy, struggle, blunt feedback, and a relentless desire to make complicated ideas simple. What you’ll discover: 🔹How Josh went from helping colleagues in a genetics lab to becoming a renowned educator, treasuring his first 9 views and 2 subscribers as a big win. 🔹How early feedback Josh received as a kid became a quiet spark — motivating him to improve how he explained things and ultimately shaping the teaching style millions now rely on. 🔹How his method for breaking down complex topics with unique tools like his iconic BAM! help make learning lighter and less intimidating. 🔹His thoughts on AI tutors, avatars, and interactive learning and how ethics, bias, and hallucinations relate to next-gen learning. This is more than a conversation about statistics, data science, AI, education, or YouTube. It’s the story of a researcher who never imagined starting a learning platform, yet became one of the most trusted teachers in statistics and machine learning—turning frustration into clarity, confusion into curiosity, and small beginnings into a massive global impact. 📌 If you’ve ever struggled with PCA, logistic regression, K-means clustering, neural networks, or any tricky stats and ML concepts… chances are StatQuest made it click. Now, hear from the creator himself about what goes on behind the scenes. Now you’ll finally understand how he made it click. 🔹A must-listen for: AI/ML learners, data scientists, educators, content creators, self-taught enthusiasts, and anyone who’s faced the fear of “I’m not good at explaining things.”Prepare to walk away inspired — and with a renewed belief that clarity is a superpower anyone can learn.

6. 

   23 May 2025

   Robin Sutara on Responsible AI, Governance, Diversity, and People Behind Data

   Robin Sutara discusses how enterprises practically adopt AI with security and responsibility, focusing on change management, diverse perspectives, and empowering non-technical teams in this interview.

7. 

   20 Jan 2025

   Jay Alammar on RAG, AI Education, and Industry Transformation - Future of AI🎙️

   In this episode, Raja Iqbal welcomes Jay Alammar, a renowned educator, researcher, and visual storyteller in machine learning. Jay shares his fascinating journey into simplifying complex AI concepts through visual storytelling and his passion for making AI education accessible to everyone. Raja and Jay discuss the power of visual learning, the role of intuition in understanding AI, and the challenges and opportunities in enterprise AI adoption. Jay also explores how AI is reshaping industries, the importance of tools like Retrieval-Augmented Generation (RAG), and his experiences at Cohere, where he helps organizations harness the power of large language models for real-world applications. This episode is perfect for anyone curious about the evolving world of AI, practical ways to adopt AI in business, and the importance of education in driving innovation.

8. 

   23 Dec 2024

   The Future of AI - What Will It Mean for Different Industries and the Job Market? | Amr Awadallah (Part 2)

   This episode features an engaging discussion between Raja Iqbal, Founder and CEO of Data Science Dojo, and Amr Awadallah, Founder and CEO of Vectara, the trusted GenAI Platform for All Builders. In this episode, Raja Iqbal sits down with Amr, a successful entrepreneur and a leader in the tech world, to talk about how technology is shaping our lives and work. They discuss how businesses can adapt to the rapid changes brought by new tools, the challenges faced by different industries, and how technology can improve our lives. Amr shares fascinating insights about how AI can help in healthcare and education, making them more accessible, especially in developing countries. He also talks about the skills our kids will need to thrive in the future and how technology is changing everything—from how we work to how we learn. This is a must-watch for anyone curious about how technology changes the world and what it means for the future!

9. 

   16 Sept 2024

   Amr Awadallah's Journey from Tech Giants to Vectara: Data, Cloud Computing, AI, & Entrepreneurship (Part 1)

   This episode features an engaging discussion between Raja Iqbal, Founder and CEO of Data Science Dojo, and Amr Awadallah, Founder and CEO of Vectara, the trusted GenAI Platform for All Builders. Raja sits down with Amr Awadallah, a visionary who has played a key role in shaping the world of technology. From his early days at Microsoft to his leadership roles at VMware and Vectara, Awadallah has been a driving force behind groundbreaking innovations in data, cloud computing, and artificial intelligence. This episode is a must-watch for anyone interested in a comprehensive outlook on AI's current state and future trajectory.

10. 

    21 Jun 2024

    The Future of AI: LLMs, AGI, and Beyond (Part 2)

    This episode features the second part of an engaging discussion between Raja Iqbal, Founder and CEO of Data Science Dojo, and Bob van Luijt, Co-founder and CEO of Weaviate, a prominent open-source vector database in the industry. Raja and Bob trace the evolution of AI over the years, the current LLM landscape, and its outlook for the future. They further dive deep into various LLM concepts such as RAG, fine-tuning, challenges in enterprise adoption, vector search, context windows, the potential of SLMs, generative feedback loop, and more. Lastly, Raja and Bob explore Artificial General Intelligence (AGI) and whether it could be a reality in the near future. This episode is a must watch for anyone interested in a comprehensive outlook on the current state and future trajectory of AI.

11. 

    27 May 2024

    Building a Multi-million Dollar AI Business - AI Founders Reveal Their Success Hacks (Part 1)

    This interview features Raja Iqbal, Co-founder and CEO of Data Science Dojo, engaging in a candid conversation with Bob van Lujit, Co-founder and CEO of Weaviate. Bob shares his journey, from early childhood and a fascination for tech to his entrepreneurial drive which eventually led to launching his own startup at a very young age. They also discuss the importance of standing out and continuously evolving and adapting in a highly competitive landscape, and the potential of AI in businesses. But it's not all sunshine and robots. Bob and Raja also discuss the triumphs and tribulations of leading their own start-ups, fostering a culture that fuels sustainable growth, and the key decisions that can make or break a young company. They also explore how to impress investors and build their trust so that funding isn't a constant worry. Packed with practical advice and valuable insights, this video is a must-watch for aspiring as well as seasoned AI entrepreneurs aiming to make their mark on the industry.

12. 

    27 Mar 2024

    Jerry Liu: Generative AI, LLMs, LlamaIndex, RAG, Entrepreneurship, and Society, with Raja Iqbal

    Are LLMs useful for enterprises? Well, what is the use of a large language model that is trained on trillions of tokens but knows little to nothing about your business. To make LLMs actually useful for enterprises, it is important for them to retrieve company's data effectively. LlamaIndex has been at the forefront of providing such solutions and frameworks to augment LLMs. In this episode, Jerry Liu, Co-founder and CEO of LlamaIndex, joins Raja Iqbal, CEO and Chief Data Scientist at Data Science Dojo, for a deep dive into the intersection of generative AI, data. and entrepreneurship. Jerry walks us through the cutting-edge technologies reshaping the generative AI landscape such as LlamaIndex. He also explores Retrieval Augmented Generation (RAG) and fine-tuning in detail, discussing their benefits, trade-offs, use cases, and enterprise adoption, making these complex tools and topics not just easily understandable but also fascinating. Jerry further ventures into the heart of entrepreneurship, sharing valuable lessons and insights learned along his journey, from navigating his corporate career at tech giants like Apple, Quora, Two Sigma, and Uber, to starting as a founder in the data and AI landscape. Amidst the excitement of innovation, Raja and Jerry also address the potential risks and considerations with generative AI. They raise thought-provoking questions about its impact on society, for instance, whether we're trading critical thinking for convenience. Whether you're a generative AI enthusiast, seasoned entrepreneur, or simply curious about the future, this podcast promises plenty of knowledge and insights for you.

13. 

    23 Feb 2024

    Luis Serrano: Generative AI, Math, Education, Career, Activism and Society, with Raja Iqbal

    In our very first episode, we had the pleasure of chatting with Luis Serrano—one of the top voices in the AI space. Luis Serrano is a technology and science popularizer, researcher, and practitioner and author of the best-selling book Grokking Machine Learning. He is currently the developer relations lead at Cohere, and has previously worked at several tech companies including Google and Apple. He's also the brains behind popular ML courses on platforms like Coursera and Udacity, and the popular YouTube channel Serrano Academy, with over 135K subscribers. In this episode, we unpack Luis's fascinating journey, from his childhood and maths fears to a deep-seated passion for it and all things related, including AI and ML. We explore his career path in detail uncovering the pivotal moments and learnings, as he navigated through big tech players, changing gears from Maths to AI and Quantum AI, and how he ultimately found his true calling. We further venture into the world of AI, exploring its profound impact on education and society—both the positive advancements and the challenges it presents, and how they are reshaping the world and future. And of course, we touch upon the human side of it all—exploring the themes of humanity and empathy and implications for the future. The podcast ends with a fun and engaging rapid-fire round, again packed with bite-sized learning. So tune in, learn and get inspired!

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

3 ratings

Spotify

5.0 / 5

2 ratings

Podcast Authority Score: 18 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

12

Social presence

0

YouTube

0

Engagement

60