We'll explore how vibe coding impacts SRE teams. Attendees will learn how this shift affects reliability and incident response and the challenges it introduces, such as reduced familiarity with codebases among developers and the loss of subject matter expertise. We'll discuss why 'incident vibing' - leveraging automation and AI-driven features to tackle increased incident volume - is crucial. The audience will learn practical strategies for: - Accelerating incident response using AI-generated incident briefings and automated post-mortem drafts. - Streamlining root cause analysis and resolution through AI-powered anomaly detection and contextual data ingestion. - Mitigating the limitations of AI systems, such as hallucinations and a lack of context. Ultimately, this talk is about turning a risk into a competitive advantage. Not only empowering SRE teams to handle the growing challenges of AI-driven development, but also graduate to achieving the elusive 'six nines' of reliability.

Today on the podcast, I interview AI researcher Tony Zhang about some of his recent findings about the effects that fully automated AI has on user decision-making. Tony shares lessons from his recent research study comparing typical recommendation AIs with a “forward-reasoning” approach that nudges users to contribute their own reasoning with process-oriented support that may lead to better outcomes. We’ll look at his two study examples where they provided an AI-enabled interface for pilots tasked with deciding mid-flight the next-best alternate airport to land at, and another scenario asking investors to rebalance an ETF portfolio. The takeaway, taken right from Tony’s research, is that “going forward, we suggest that process-oriented support can be an effective framework to inform the design of both 'traditional' AI-assisted decision-making tools but also GenAI-based tools for thought.” 

Highlights/ Skip to:

Tony Zhang’s background (0:46) Context for the study (4:12) Zhang’s metrics for measuring over-reliance on AI (5:06) Understanding the differences between the two design options that study participants were given  (15:39) How AI-enabled hints appeared for pilots in each version of the UI (17:49) Using AI to help pilots make good decisions faster (20:15) We look at the ETF portfolio rebalancing use case in the study  (27:46) Strategic and tactical findings that Tony took away from his study (30:47) The possibility of commercially viable recommendations based on Tony’s findings (35:40)  Closing thoughts (39:04)

Quotes from Today’s Episode

“I wanted to keep the difference between the [recommendation & forward reasoning versions] very minimal to isolate the effect of the recommendation coming in. So, if I showed you screenshots of those two versions, they would look very, very similar. The only difference that you would immediately see is that the recommendation version is showing numbers 1, 2, and 3 for the recommended airports. These [rankings] are not present in the forward-reasoning one [airports are default sorted nearest to furthest]. This actually is a pretty profound difference in terms of the interaction or the decision-making impact that the AI has. There is this normal flight mode and forward reasoning, so that pilots are already immersed in the system and thinking with the system during normal flight. It changes the process that they are going through while they are working with the AI.” Tony (18:50 - 19:42)

“You would imagine that giving the recommendation makes your decision faster, but actually, the recommendations were not faster than the forward-reasoning one. In the forward-reasoning one, during normal flight, pilots could already prepare and have a good overview of their surroundings, giving them time to adjust to the new situation. Now, in normal flight, they don’t know what might be happening, and then suddenly, a passenger emergency happens. While for the recommendation version, the AI just comes into the situation once you have the emergency, and then you need to do this backward reasoning that we talked about initially.” Tony ( 21:12 - 21:58)

“Imagine reviewing code written by other people. It’s always hard because you had no idea what was going on when it was written. That was the idea behind the forward reasoning. You need to look at how people are working and how you can insert AI in a way that it seamlessly fits and provides some benefit to you while keeping you in your usual thought process. So, the way that I see it is you need to identify where the key pain points actually are in your current decision-making process and try to address those instead of just trying to solve the task entirely for users.” Tony (25:40 - 26:19)

Links

LinkedIn: https://www.linkedin.com/in/zelun-tony-zhang/  Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making: https://arxiv.org/html/2504.03207v1 

Keynote session featuring Amy Kate Boyd, AI Advocacy Lead, Developer Relations at Microsoft.

Keynote presented by Amy Kate Boyd, AI Advocacy Lead, Developer Relations, Microsoft.

Keynote by Microsoft – Amy Kate Boyd, AI Advocacy Lead, Developer Relations, Microsoft

Keynote talk by Amy Kate Boyd, AI Advocacy Lead, Developer Relations, Microsoft.

Today, we’re joined by Hikari Senju, Founder and CEO at Omneky, the generative AI platform built for performance advertising. We talk about:  Top 3 benefits of Gen AI in content marketingHow the market for digital ads is growing due to generative personalization & attribution capabilitiesAds taking on more of the sales functionThe need for thousands of variations of content to drive advertising results (& the dangers of serving the same ad repeatedly)Advertising to a world of digital users

Turn your current tasks into clear, resume-ready bullets that sound more data-focused 👉 Resume Transformer: https://datafairy.io/GMM2DJKWM9 Here's a realization you need to hear: You're a Data Analyst ALREADY. You're not as far away as you think you are. In this episode, I teach you how to reframe your everyday tasks (and mind) to showcase your existing data skills, along with some real-life examples from my bootcamp students! Also, I provide useful tips to enhance your LinkedIn profile and resume to attract more job opportunities! 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 Introduction: You're a Data Analyst already! 02:16 #1: Sales Professionals 03:04 #2: Teachers 03:54 #3: Delivery Drivers 04:40 #4: Physical Therapists 05:24 #5: Parents 06:12 #6: Retail Workers 06:41 #7: Construction Workers 07:26 DataFairy.io Check out these episodes! The ONLY Framework to Become a Data Analyst in 2025 (SPN Method) 👉 https://datacareerpodcast.com/episode/141-the-only-framework-to-become-a-data-analyst-in-2025-spn-method Zero to Data Analyst: Tim Beecher’s Journey from Locksmith to Data 👉 https://datacareerpodcast.com/episode/81-zero-to-data-analyst-tim-beechers-journey-from-locksmith-to-data

This Math Teacher Became a Data Analyst in 50 Days w/ Alex Sanchez 👉 https://datacareerpodcast.com/episode/112-this-math-teacher-became-a-data-analyst-in-50-days-w-alex-sanchez

How This Delivery Driver Became a FAANG Data Analyst (Jen Hawkins) 👉 https://datacareerpodcast.com/episode/154-how-this-delivery-driver-became-a-faang-data-analyst-in-100-days-jen-hawkins

This Physical Therapist Became a Data Analyst AFTER a 20-Year Career (Melody Santos) 👉 https://datacareerpodcast.com/episode/160-she-became-a-data-analyst-after-a-20-year-career-in-physical-therapy-melody-santos

From Construction to Data Analytics (Jordan Temple's Story) 👉 https://datacareerpodcast.com/episode/79-from-construction-to-data-analytics-jordan-temples-story

🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Synthetic data: it's a fascinating topic that sounds like science fiction but is rapidly becoming a practical tool in the data landscape. From machine learning applications to safeguarding privacy, synthetic data offers a compelling alternative to real-world datasets that might be incomplete or unwieldy. With the help of Winston Li, founder of Arima, a startup specializing in synthetic data and marketing mix modelling, we explore how this artificial data is generated, where its strengths truly lie, and the potential pitfalls to watch out for! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

The line between generic AI capabilities and truly transformative business applications often comes down to one thing: your data. While foundation models provide impressive general intelligence, they lack the specialized knowledge needed for domain-specific tasks that drive real business value. But how do you effectively bridge this gap? What's the difference between simply fine-tuning models versus using techniques like retrieval-augmented generation? And with constantly evolving models and technologies, how do you build systems that remain adaptable while still delivering consistent results? Whether you're in retail, healthcare, or transportation, understanding how to properly enrich, annotate, and leverage your proprietary data could be the difference between an AI project that fails and one that fundamentally transforms your business. Wendy Gonzalez is the CEO — and former COO — of Sama, a company leading the way in ethical AI by delivering accurate, human-annotated data while advancing economic opportunity in underserved communities. She joined Sama in 2015 and has been central to scaling both its global operations and its mission-driven business model, which has helped over 65,000 people lift themselves out of poverty through dignified digital work. With over 20 years of experience in the tech and data space, Wendy’s held leadership roles at EY, Capgemini, and Cycle30, where she built and managed high-performing teams across complex, global environments. Her leadership style blends operational excellence with deep purpose — ensuring that innovation doesn’t come at the expense of integrity. Wendy is also a vocal advocate for inclusive AI and sustainable impact, regularly speaking on how companies can balance cutting-edge technology with real-world responsibility. Duncan Curtis is the Senior Vice President of Generative AI at Sama, where he leads the development of AI-powered tools that are shaping the future of data annotation. With a background in product leadership and machine learning, Duncan has spent his career building scalable systems that bridge cutting-edge technology with real-world impact. Before joining Sama, he led teams at companies like Google, where he worked on large-scale personalization systems, and contributed to AI product strategy across multiple sectors. At Sama, he's focused on harnessing the power of generative AI to improve quality, speed, and efficiency — all while keeping human oversight and ethical practices at the core. Duncan brings a unique perspective to the AI space: one that’s grounded in technical expertise, but always oriented toward practical solutions and responsible innovation. In the episode, Richie, Wendy, and Duncan explore the importance of using specialized data with large language models, the role of data enrichment in improving AI accuracy, the balance between automation and human oversight, the significance of responsible AI practices, and much more. Links Mentioned in the Show: SamaConnect with WendyConnect with DuncanCourse: Generative AI ConceptsRelated Episode: Creating High Quality AI Applications with Theresa Parker & Sudhi Balan, Rocket SoftwareRegister for RADAR AI New to DataCamp? Learn on the go...

In this season of the Analytics Engineering podcast, Tristan is digging deep into the world of developer tools and databases. There are few more widely used developer tools than Docker. From its launch back in 2013, Docker has completely changed how developers ship applications.  In this episode, Tristan talks to Solomon Hykes, the founder and creator of Docker. They trace Docker's rise from startup obscurity to becoming foundational infrastructure in modern software development. Solomon explains the technical underpinnings of containerization, the pivotal shift from platform-as-a-service to open-source engine, and why Docker's developer experience was so revolutionary.  The conversation also dives into his next venture Dagger, and how it aims to solve the messy, overlooked workflows of software delivery. Bonus: Solomon shares how AI agents are reshaping how CI/CD gets done and why the next revolution in DevOps might already be here. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

The first half is tracking roughly in line with our trend-like outlook just ahead of the US election last year. However, 2H25 should deliver a very different picture if our forecast is right. Assessing risks ahead of such an anticipated slowing is difficult but this does not stop us from debating. Central banks are leaning dovish in light of the downside growth risks, with the exception of the Fed facing upside tariff-related inflation risks. The Mideast war adds a new supply shock to complicate the forecast even further.

Speakers:

Bruce Kasman

Joseph Lupton

This podcast was recorded on 20 June 2025.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

In this episode, I rant and speculate about why small teams and companies are the future, AI native data architectures, why the future is awesome, and more.

This episode is brought to you by Keboola. Check out their new MCP server at https://www.keboola.com/mcp

In this episode of Data Unchained, we sit down with Malcolm Hawker, former Gartner analyst and Chief Data Officer at Profisee, to expose the real barriers to AI adoption. We explore why Master Data Management (MDM) is the foundation enterprises overlook, how decentralized systems and unstructured data derail governance, and why CDOs must evolve their role or risk irrelevance. This conversation challenges the myth of a single source of truth, breaks down the politics of data ownership, and offers a new vision for aligning data strategy with AI innovation.

AIReadiness #MasterDataManagement #DataGovernance #CDOInsights #EnterpriseAI #DataStrategy #UnstructuredData #DataInfrastructure #DigitalTransformation #AILeadership #DataUnchained #Profisee #MalcolmHawker #MollyPresley #TechInnovation

Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

Ethics of large-scale data collection; Preserving authenticity in user preference signals; Governance and transparency in AI training data usage; Collaborative standards across the AI ecosystem; Building trust in public data pipelines

In this episode, Conor recommends some articles on AI and LLMs. Link to Episode 239 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Socials ADSP: The Podcast: TwitterConor Hoekstra: Twitter | BlueSky | MastodonShow Notes Date Generated: 2025-06-19 Date Released: 2025-06-20 The Real Python Podcast Episode 253My AI Skeptic Friends Are All Nuts - Thomas PtacekI Think I’m Done Thinking About genAI For Now - GlyphAI Changes Everything - Armin RonacherIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8