Podcasts about programmatic

Next in Media spoke with Conor McKenna, partner at Luma Partners, about what's held ad tech back from exploiting the creator economy, and why creators themselves - along with a new competitive dynamic among the tech platforms - may lead the industry in a whole new direction.

If you haven't already signed the electronic petition to STOP Los AlamosNational Laboratory (LANL) plans to vent large quantities of radioactive tritium into theair beginning on or after June 2 nd , 2025, there's still time.Access the petition at actionnetwork.org/petitions/petition-to-deny-lanls-request-to-release-radioactive-tritium-into-the-air The text of the petition is also available atnuclearactive.org

In this episode, we're joined by Augusto Romano, co-founder of Digo, and Anthony Gonzalez from JWP Connatix, to break down how smart video strategies and real partnerships are helping brands connect with the growing U.S. and Hispanic audience. Augusto shares how Digo was built to serve vibrant communities such as  Dominicans, Puerto Ricans, Salvadorans, and more through premium video content. Meanwhile, Anthony shows us how JWP Connatix powers this mission with advanced tech to deliver and monetize video content effectively. Together, they explain how viewer habits are shifting; everyone's watching more video and why publishers and advertisers need to keep up. The solution? Using tools like Private Marketplaces (PMPs) and Demand-Side Platforms (DSPs) to reach Hispanic audiences in brand-safe, culturally relevant ways. But the real secret sauce? Authenticity. Augusto and Anthony talk about why brands need to understand cultural nuances, not just demographics, to truly connect. By building long-term, genuine partnerships and embracing digital transformation, especially among Latin American and Spanish-language media, brands can unlock the full power of the Hispanic market. Tune in for an insider's look at what it takes to create meaningful connections and successful campaigns in today's video-first world.

This week, Mike and Emily dive into the recent positive performance of The Trade Desk, questioning if the earlier concerns about the ad tech market were overblown, and then shift gears to discuss the evolving landscape of search with the rise of sophisticated AI prompting and its implications for information access and optimization.

"I think it will be blunt and arbitrary" - Goodway Group CEO Jay Friedman on what happens if marketers have to slash budget during TariffmageodonNext in Media talked to Goodway Group CEO Jay Friedman about the state of brands' decision making amidst an uncertain economy and a rise in AI automation.  And of course, we talked about cookies and the various court decisions facing Google.

Technology has evolved and driven new consumer behaviors — forcing advertisers to rethink how they can attract new customers, retain loyal ones and what they can do to drive the most impactful campaigns. Commerce and transaction data is one of the last truly intent-driven indicators available. Dr. Mark Grether, SVP of PayPal Ads, joins Campaign to discuss the rise, advancements and value that commerce media provides advertisers in driving truly impactful campaigns. campaignlive.com What we know about advertising, you should know about advertising. Start your 1-month FREE trial to Campaign US.

Send us a textProgrammatic SEO offers a powerful strategy for creating web pages at scale through automation rather than manual creation. This approach uses templates and databases to generate location-specific or variable-based content that would be too time-consuming to create individually.• Airbnb exemplifies programmatic SEO with their location-specific pet-friendly rental pages• Two key elements: page templates that remain consistent and databases that provide variable content• Modern tools like Lpagery allow implementation without coding skills using simple spreadsheets• Building and maintaining a quality database is often the most challenging aspect• Avoid creating low-quality "doorway pages" that could trigger Google penalties• Proper implementation allows efficient scaling while maintaining useful content for users• Best results come from combining programmatic pages with traditional long-form content• Internal linking and site authority are crucial for getting programmatic pages indexedTry Keywords People Use today for free at keywordspeopleuse.com to find the questions people ask online. Contact Edd on Twitter at @channel5 or email podcast@keywordspeopleuse.com with any questions.SEO Is Not That Hard is hosted by Edd Dawson and brought to you by KeywordsPeopleUse.com Help feed the algorithm and leave a review at ratethispodcast.com/seo You can get your free copy of my 101 Quick SEO Tips at: https://seotips.edddawson.com/101-quick-seo-tipsTo get a personal no-obligation demo of how KeywordsPeopleUse could help you boost your SEO and get a 7 day FREE trial of our Standard Plan book a demo with me nowSee Edd's personal site at edddawson.comAsk me a question and get on the show Click here to record a questionFind Edd on Linkedin, Bluesky & TwitterFind KeywordsPeopleUse on Twitter @kwds_ppl_use"Werq" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/

In this episode, we sit down with Bob Regular, CEO of Infolinks, to explore the evolving world of programmatic advertising and the rising importance of curation. Bob walks us through his journey in digital media—from the dial-up days to today's complex ad tech ecosystem—offering valuable insights along the way. We dive into how Supply Path Optimization (SPO) is playing a critical role in improving the connection between advertisers and publishers by enhancing media quality and eliminating unnecessary middlemen. Using the analogy of choosing the best billboard location, Bob explains how digital ad placement is just as strategic, with audience targeting and creative messaging being essential for success. We also touch on the often-overlooked challenges of inconsistent naming conventions and unclear attention metrics, and how curation and SPO can help simplify and improve the overall advertising experience. The conversation wraps up with a closer look at the differences between direct and reseller relationships in ad tech, and how SPO is shifting industry priorities. Bob shares key takeaways from recent training sessions with media holding companies, including the major reduction in non-value-adding players since the adoption of ads.txt. This episode is a must-listen for anyone interested in the future of digital advertising and how we can build stronger, more transparent partnerships in the ecosystem. About Us: We teach historically excluded individuals how to break into programmatic media buying and land their dream jobs. Through our Reach and Frequency® program, an engaged community, and expert coaching, we offer: Programmatic L&D Support: A monthly retainer providing hands-on training, strategy, and troubleshooting for programmatic teams. Book a Discovery Call: https://www.heleneparker.com/workshop/ Programmatic Training & Coaching:   Executive Membership: for the busy mid-level to senior or director-level programmatic ninja looking for a structured, high-impact way to stay ahead of evolving trends, sharpen your optimization skills, and connect with like-minded experts Join Here: https://programmaticdigest14822.ac-page.com/executivemembership      Accelerator Program: A 6-week structured program with live coaching, hands-on DSP exercises, and real-time feedback. Sign Up: https://reachandfrequencycourse.thinkific.com/courses/program     Self-Paced Course: Learn at your own speed with full content access. Enroll Here: https://reachandfrequencycourse.thinkific.com/bundles/the-reach-frequency-full-course   Timestamp: (01:44) Evolution of Digital Media Industry (09:27) Publisher Placement Curation Strategy  (15:07) Challenges in Ad Serving and Curation  (25:18) The Complexity of Ad Tech Pathways  (33:15) Perspectives on Premium Advertising Approach  (38:12) Rebuilding Collaboration in Advertising Industry     Meet Our Guest: Bob Regular  https://www.linkedin.com/in/rregular/  Infolinks Media  http://www.infolinks.com Meet The Team: Hélène Parker - Chief Programmatic Coach https://www.heleneparker.com/  https://www.linkedin.com/in/helene-parker/ Learn Programmatic As a TEAM: https://www.heleneparker.com/workshop/  As a Programmatic Ninja: https://www.heleneparker.com/course/ Programmatic Coaching Newsletter:https://www.heleneparker.com/newsletter/    Programmatic Digest https://www.linkedin.com/company/programmatic-digest-podcast https://www.youtube.com/@programmaticdigest    Manuela Cortes - Co-Host  Programmatic Digest In Espanol  https://www.linkedin.com/in/manuela-cortes-/   Looking for programmatic training/coaching?  Sign up to our Accelerator Program: A 6-week structured program with live coaching, hands-on within DSP(s) exercises, and real-time feedback—perfect for those who thrive on accountability and community, and looking to grow their technical skillset https://reachandfrequencycourse.thinkific.com/courses/program  Self-Paced Course: Full access to course content anytime, allowing independent learners to study at their own speed with complete flexibility. https://reachandfrequencycourse.thinkific.com/bundles/the-reach-frequency-full-course    Join our next workshop by signing up to our waitlist below: https://www.heleneparker.com/waitlist/   

To pinpoint the key principles behind successful omnichannel programmatic DOOH (pDOOH) campaigns, JCDecaux, in partnership with MTM, hosted an interactive online discussion forum bringing together 25 pDOOH experts from DSPs and agencies for an asynchronous group discussion. In this episode of Life in Programmatic DOOH, Mark Halliday, Director of Programmatic at JCDecaux is joined by one of the discussion group participants, Aliki Radley, Group OOH Strategy Director, Publicis Media. Read The Seven Key Factors for Success in pDOOH here: https://heyzine.com/flip-book/c6d5157a43.html Hosted on Acast. See acast.com/privacy for more information.

At NAB Show 2025, Rob Walch, VP of Podcaster Relations for Libsyn, discusses podcasting trends, gear updates, and Libsyn's new faith-based ad channel. He shares insights on monetization strategies, true crime's continued rise, and upcoming platform features for creators. Show Notes: Chapters: 00:07 Opening at NAB Show 2025 01:42 Podcasting Trends and Gear 04:12 Monetization Strategies for Podcasters 06:59 True Crime Podcast Phenomenon 09:45 Libsyn Innovations and Features 11:36 Libsyn's Commitment to Free Speech Support: Become a MacVoices Patron on Patreon http://patreon.com/macvoices Enjoy this episode? Make a one-time donation with PayPal Connect: Web: http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner http://www.twitter.com/macvoices Mastodon: https://mastodon.cloud/@chuckjoiner Facebook: http://www.facebook.com/chuck.joiner MacVoices Page on Facebook: http://www.facebook.com/macvoices/ MacVoices Group on Facebook: http://www.facebook.com/groups/macvoice LinkedIn: https://www.linkedin.com/in/chuckjoiner/ Instagram: https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes Video in iTunes Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss Video: http://www.macvoices.com/rss/macvoicesvideorss

In this episode we discuss 2024 winner of the JCDecaux PROGRAMMATIC Campaign of the Year Award for the campaign Extra’s Gum Cities. The campaign demonstrated successfully using programmatic Out-of-Home to increase brand awareness, improve sentiment, and drive sales growth. Designed to engage Gen Z students, workers, and commuters, Extra’s Gum Cities proved how Out-of-Home connects brands with young audiences in the moments that matter. Joining Gai on the podcast for this episode: Brad Palmer (JCDecaux), Brady Arlt (Group M Nexus), Matt Ridsdale (EssenceMediacom) and Mel Alforque (Mars) See omnystudio.com/listener for privacy information.

Tariff panic, Adalytics fallout, the fate of the open web, and TikTok weirdness.Takeaways:Tariffs & Ad World Uncertainty

With airport passenger numbers back at record-breaking levels, and programmatic DOOH (pDOOH) forecast to account for 16% of OOH ad spend in 2027 (up from ~5% in 2023) now is the perfect opportunity for brands to embrace pDOOH in airport environments. To help better understand the potential of this powerful combination, Mark Halliday, Director of Programmatic at JCDecaux spoke to Albert Jones, Head of Geosophy, Kinetic and Richard Simkins, Commercial and Partnerships Director – Airport, JCDecaux. Hosted on Acast. See acast.com/privacy for more information.

summaryIn this episode, Ashley Monk dives into the world of programmatic advertising, explaining its definition, mechanisms, and best practices. She discusses how programmatic allows for automated buying and selling of digital ads across various platforms, emphasizing the importance of targeting specific demographics and behaviors. The conversation also covers when to effectively use programmatic advertising and when it may not be the best fit, particularly for smaller budgets. The episode concludes with a call for audience engagement and feedback. takeaways Programmatic advertising automates the buying and selling of digital ads. It allows targeting across multiple channels and platforms. Effective programmatic strategies require a clear understanding of the target audience. Programmatic works best for specific demographics and behaviors. The number of touchpoints needed for conversion has increased post-pandemic. Programmatic can be used for both top-of-funnel awareness and bottom-of-funnel retargeting. Smaller budgets may benefit more from high-intent platforms like paid search. Understanding demand-side platforms is crucial for programmatic success. Programmatic can contextualize ads based on user location and behavior. Audience feedback is essential for improving content and engagement.

Programmatic advertising is changing the game for digital marketers! This week, Mark is joined by the media and digital marketing duo, Alyssa Laubacher and Lauren Moses, to discuss what programmatic advertising is, how it works, and why it can be a beneficial tool for marketers. Join Mark, Alyssa, and Lauren for 30-ish as they discuss all things marketing, advertising, and of course … positioning!

To pinpoint the key principles behind successful omnichannel programmatic DOOH (pDOOH) campaigns, JCDecaux, in partnership with MTM, hosted an interactive online discussion forum bringing together 25 pDOOH experts from DSPs and agencies for an asynchronous group discussion. In this episode of Life in Programmatic DOOH, Jon Mundy, Associate Director – Programmatic at JCDecaux is joined by one of the discussion group participants, Rob Handley, Addressable Strategy, Associate Account Director at Kinesso. Read the white paper here: https://heyzine.com/flip-book/c6d5157a43.html Hosted on Acast. See acast.com/privacy for more information.

Trailblazing Women in Ad Tech: Insights from StackAdapt Leaders In this episode, we welcome three inspiring women from StackAdapt who share their journeys and wisdom on navigating the ad tech industry with confidence and curiosity: Anna Grodecka-Grad, Senior Vice President of Global Client Services, discusses balancing technical expertise with strategic business acumen. Lydia Berlacher, Head of Commercial Strategy and Operations, reveals how she filters valuable insights amid distractions. Zeynep Akkalyoncu, Lead Data Scientist, explores how curiosity fuels interdisciplinary collaboration. Key Topics & Takeaways: Empowering Mentorship & Confidence Building (08:35): Anna and Zeynep reflect on mentors who saw their potential before they did, particularly female leaders who provided guidance and opportunities. The Power of Negotiation (17:51): A discussion on self-advocacy, knowing your worth, and strategic negotiation for career growth. Navigating Imposter Syndrome & Personal Growth (26:00): Lydia and Zeynep share strategies for overcoming self-doubt, building a support network, and continuous learning. Personal & Professional Development (32:12): How daily routines enhance focus and growth, plus the importance of setting goals and celebrating achievements. Practicing Gratitude & Self-Care (40:05): Recognizing the women who influence and uplift us, especially as International Women's Day approaches. Join Our Next FREE Workshop (March 27th): Sign Up Here About Us: We teach historically excluded individuals how to break into programmatic media buying and land their dream jobs. Through our Reach and Frequency® program, an engaged community, and expert coaching, we offer: Customized training roadmaps for teams focusing on campaign performance, cross-departmental communication, and revenue growth. Request a sample training roadmap A hybrid model where we activate and train in DSPs. Book a Free Call Timestamped Breakdown: (00:02) Women in Ad Tech Leadership (08:35) Empowering Mentors and Confidence Building (17:51) The Power of Negotiation Skills (26:00) Navigating Imposter Syndrome and Personal Growth (32:12) Personal and Professional Development (36:05) Building Confidence and Achieving Success (40:05) Practicing Gratitude and Self-Care Connect with Our Guests: Anna Grodecka-Grad Zeynep Akkalyoncu Lydia Berlacher   Meet the Team: Hélène Parker - Chief Programmatic Coach, Helene Parker Consulting LLC Website LinkedIn Manuela Cortes - Co-Host, Programmatic Digest in Español LinkedIn   Learn Programmatic: As a Team: Custom Training Workshops As an Individual: Online Course Newsletter: Join on LinkedIn Programmatic Digest: YouTube | LinkedIn   Programmatic Training & Coaching: Accelerator Program: A 6-week structured program with live coaching, hands-on DSP exercises, and real-time feedback. Sign Up Self-Paced Course: Learn at your own speed with full content access. Enroll Here Waitlist for Future Workshops: Join Here

In this Ortho Marketing episode, Dean Steinman is joined by Robert Brill, CEO of Brill Media. They discuss how automation is reshaping digital advertising. They dive into programmatic ads, how they improve targeting and efficiency, and what the future holds for marketers. Whether you're new to the concept or looking to refine your strategy, this episode has valuable insights for you. Tune in!Ready to elevate your practice? Contact us!https://orthomarketing.com/contact-us/ About Robert BrillRobert Brill is the CEO of Brill Media, a top white-label media buying agency that eliminates the guesswork from marketing. Under his leadership, Brill Media has earned recognition from the Inc. 5000 and Financial Times 500 a combined 11 times, establishing it as one of the fastest-growing private companies in the U.S. For more information: https://brillmedia.co

In this Ortho Marketing episode, Dean Steinman is joined by Robert Brill, CEO of Brill Media. They discuss how automation is reshaping digital advertising. They dive into programmatic ads, how they improve targeting and efficiency, and what the future holds for marketers. Whether you're new to the concept or looking to refine your strategy, this episode has valuable insights for you. Tune in!Ready to elevate your practice? Contact us!https://orthomarketing.com/contact-us/ About Robert BrillRobert Brill is the CEO of Brill Media, a top white-label media buying agency that eliminates the guesswork from marketing. Under his leadership, Brill Media has earned recognition from the Inc. 5000 and Financial Times 500 a combined 11 times, establishing it as one of the fastest-growing private companies in the U.S. For more information: https://brillmedia.co

Next in Media spoke with Larry Allen, VP & GM Data & Addressable Enablement at Comcast about the challenge in getting everyone in media to speak the same language when it comes to targeted TV ads. Allen also talked about why he think the TV business needs to ditch identifiers for old school household data, and why he thinks that media companies are ready to work together to broaden the TV ad pie.Takeaways:Addressable TV is Evolving – It's no longer just about traditional cable ad slots. Today, addressable TV spans streaming, connected devices, and multi-screen environments

Ever wondered how to truly connect with your audience and level up your marketing? In this episode, I sit down with Crystal Foote, CEO of Digital Culture Group, to talk about the power of targeted audience growth. Crystal shares her journey—from finding inspiration to taking action—and how she's helping brands reach diverse audiences in a meaningful way.   We dive into why understanding different audience segments is key and why one-size-fits-all marketing just doesn't cut it. Crystal and I chat about using AI and automation to improve targeting while avoiding the pitfalls of oversimplifying audiences. Plus, we explore the impact of smart audio advertising and how it helps brands speak to multicultural households in a more personal way.   We also get real about rejection and resilience in marketing. Crystal shares her take on staying strong, showing proof of your work, and bouncing back from setbacks. We touch on why diversity and representation matter—not just in advertising but in education too—and how seeing yourself in stories can be life-changing.   Crystal's passion and dedication to inclusivity shine through in this episode, making it a must-listen for anyone looking to grow their audience with authenticity and impact. Tune in for great insights, inspiring stories, and practical takeaways to transform your marketing approach! Sign up to our next FREE workshop on March 27th: https://www.heleneparker.com/freeworkshop    About Us: Our mission is to teach historically excluded people how to get started in programmatic media buying and find a dream job.  We do so by providing on-demand lessons via the Reach and Frequency® program (https://reachandfrequencycourse.thinkific.com ), a dope community with like-minded programmatic experts, and live free and paid group coaching.  We can help 2 ways:  Customized a training roadmap for teams of programmatic traders (https://www.heleneparker.com/workshop/ ), adops, customer success, AMs, etc focusing on campaigns performance increase, cross-departmental communication, and revenue growth overall 

Programmatic algorithms optimize for performance, which can leave digital media companies floundering. Inside programmatic's pursuit of “premium.” Plus: an ad tech acquisition forged on matchmaking buy-side and sell-side IDs.

In this episode, we talk about the big mistake many brands make when planning and measuring their ads. Our guest, Chelsey, explains why you can't measure awareness, consideration, and conversions the same way. She breaks it down simply: Awareness ads should be measured by how many people see them (reach, impressions, CPMs). Conversion ads should be measured by results (sales, revenue, ROAS). Chelsey also talks about how brands can estimate the impact of awareness ads on conversions using smart data models. Plus, she shares tools like GA4 and media mix modeling (MMM) that help brands understand which ads are working best. Tune in to learn how to fix your media planning and get better results from your campaigns!

For publishers, digital advertising is a lot like playing craps, says Aditude's Justin Wohl. It's all about tuning out the noise while placing safe bets that work for your monetization strategy.

Season five of the Life in Programmatic DOOH (pDOOH) podcast kicks off with Imogen Nightingale, Senior Consultant at research and strategy agency MTM and Mark Halliday, Director of Programmatic, JCDecaux, discussing the findings from our latest collaborative qualitative research that has identified seven key principles that, when followed, result in successful pDOOH campaigns. Read the white paper here: https://heyzine.com/flip-book/c6d5157a43.html#page/1 Hosted on Acast. See acast.com/privacy for more information.

Next in Creator Media talked with Business Insider media correspondent Lucia Moses about why Netflix is suddenly paying more attention to creators and YouTube, and what this might mean for the future of talent deals and distribution.Moses also weighed in on Amazon's Beast Games, Netflix's ad business and the state of Hollywood.Takeaways:Netflix's Creator Strategy EvolutionNetflix is shifting its approach, recognizing YouTube as both a competitor and a source of creator talent. They aim to bring podcasters onto the platform to revamp talk show content.YouTube's Dominance on TV ScreensYouTube now leads in TV viewing time, surpassing traditional networks. This shift signals a transformation in how audiences consume video content, influencing ad spend and media strategy.The Role of Authenticity in Creator-Led ContentAudiences prefer unpolished, authentic content over heavily produced talk shows. Netflix and others are learning that overproduction can diminish engagement with creator-led shows.Amazon's Aggressive Creator InvestmentsAmazon is pushing creator-led content aggressively, with deals like MrBeast's Beast Games. Legacy studios remain hesitant due to past failed investments in creators.The Rise of Video Podcasting on YouTubeYouTube is actively positioning itself as a home for video podcasts, appealing to Gen Z and advertisers who see potential in habitual, TV-like viewing patterns.Netflix's Ad Tier ExpansionNetflix's ad-supported tier is growing, with over 45–50 million U.S. users. However, it lags behind Disney and Amazon, which have twice the ad-tier audience.Brand Safety Perceptions Are ChangingThe shift in advertiser sentiment suggests that concerns over brand safety on platforms like YouTube are diminishing, making it easier for YouTube to compete for TV ad dollars.Sports Streaming Is Fragmenting the MarketSports fans now face a complex streaming landscape with multiple providers like Disney, Amazon, YouTube, and cable alternatives. Consolidation may be inevitable.Media Industry Mergers & UncertaintyMajor mergers loom in the media and ad industries, with legacy networks being spun off. The uncertain political climate may delay some deals but will likely reshape the industry.Guest: Lucia MosesHost: Mike ShieldsSponsor: VuePlannerProducer: FEL Creative

Next in Media spoke with David Kostman, CEO of Teads (formerly Outbrain) about the company's plans to bring together performance advertising, web video and TV, and move beyond its reputation as haven for 'cheap' ads.  Kostman also talked about how publishers are preparing for more AI-driven search and content discovery, and whether brands are as invested as they should be in news and the open web.Takeaways:Outbrain & Teads: A Game-Changing Merger for the Open InternetThe Outbrain-Teads merger creates a $1.7 billion ad powerhouse, merging native performance and premium video advertising to serve brands across the entire marketing funnel.The Power of Controlled Real Estate & First-Party DataUnlike traditional ad networks, Teeds secures exclusive publisher inventory, ensuring premium ad placement without competing in an auction model.AI & The Future of Digital Advertising OptimizationTeeds is integrating AI-driven predictive analytics for automated media buying and ad optimization, enhancing real-time targeting.CTV Advertising & The Evolution of Small Business ReachTeeds is making a strong push into Connected TV (CTV), with exclusive placements on OEM home screens like LG and Hisense.AI, Content Discovery & The Fight for Quality JournalismWith the rise of AI-generated content, premium publishers are at risk. Teeds is doubling down on supporting quality journalism, ensuring trusted news sites get premium monetization opportunities.

Did you know that adding a simple Code Interpreter took o3 from 9.2% to 32% on FrontierMath? The Latent Space crew is hosting a hack night Feb 11th in San Francisco focused on CodeGen use cases, co-hosted with E2B and Edge AGI; watch E2B's new workshop and RSVP here!We're happy to announce that today's guest Samuel Colvin will be teaching his very first Pydantic AI workshop at the newly announced AI Engineer NYC Workshops day on Feb 22! 25 tickets left.If you're a Python developer, it's very likely that you've heard of Pydantic. Every month, it's downloaded >300,000,000 times, making it one of the top 25 PyPi packages. OpenAI uses it in its SDK for structured outputs, it's at the core of FastAPI, and if you've followed our AI Engineer Summit conference, Jason Liu of Instructor has given two great talks about it: “Pydantic is all you need” and “Pydantic is STILL all you need”. Now, Samuel Colvin has raised $17M from Sequoia to turn Pydantic from an open source project to a full stack AI engineer platform with Logfire, their observability platform, and PydanticAI, their new agent framework.Logfire: bringing OTEL to AIOpenTelemetry recently merged Semantic Conventions for LLM workloads which provides standard definitions to track performance like gen_ai.server.time_per_output_token. In Sam's view at least 80% of new apps being built today have some sort of LLM usage in them, and just like web observability platform got replaced by cloud-first ones in the 2010s, Logfire wants to do the same for AI-first apps. If you're interested in the technical details, Logfire migrated away from Clickhouse to Datafusion for their backend. We spent some time on the importance of picking open source tools you understand and that you can actually contribute to upstream, rather than the more popular ones; listen in ~43:19 for that part.Agents are the killer app for graphsPydantic AI is their attempt at taking a lot of the learnings that LangChain and the other early LLM frameworks had, and putting Python best practices into it. At an API level, it's very similar to the other libraries: you can call LLMs, create agents, do function calling, do evals, etc.They define an “Agent” as a container with a system prompt, tools, structured result, and an LLM. Under the hood, each Agent is now a graph of function calls that can orchestrate multi-step LLM interactions. You can start simple, then move toward fully dynamic graph-based control flow if needed.“We were compelled enough by graphs once we got them right that our agent implementation [...] is now actually a graph under the hood.”Why Graphs?* More natural for complex or multi-step AI workflows.* Easy to visualize and debug with mermaid diagrams.* Potential for distributed runs, or “waiting days” between steps in certain flows.In parallel, you see folks like Emil Eifrem of Neo4j talk about GraphRAG as another place where graphs fit really well in the AI stack, so it might be time for more people to take them seriously.Full Video EpisodeLike and subscribe!Chapters* 00:00:00 Introductions* 00:00:24 Origins of Pydantic* 00:05:28 Pydantic's AI moment * 00:08:05 Why build a new agents framework?* 00:10:17 Overview of Pydantic AI* 00:12:33 Becoming a believer in graphs* 00:24:02 God Model vs Compound AI Systems* 00:28:13 Why not build an LLM gateway?* 00:31:39 Programmatic testing vs live evals* 00:35:51 Using OpenTelemetry for AI traces* 00:43:19 Why they don't use Clickhouse* 00:48:34 Competing in the observability space* 00:50:41 Licensing decisions for Pydantic and LogFire* 00:51:48 Building Pydantic.run* 00:55:24 Marimo and the future of Jupyter notebooks* 00:57:44 London's AI sceneShow Notes* Sam Colvin* Pydantic* Pydantic AI* Logfire* Pydantic.run* Zod* E2B* Arize* Langsmith* Marimo* Prefect* GLA (Google Generative Language API)* OpenTelemetry* Jason Liu* Sebastian Ramirez* Bogomil Balkansky* Hood Chatham* Jeremy Howard* Andrew LambTranscriptAlessio [00:00:03]: Hey, everyone. Welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:12]: Good morning. And today we're very excited to have Sam Colvin join us from Pydantic AI. Welcome. Sam, I heard that Pydantic is all we need. Is that true?Samuel [00:00:24]: I would say you might need Pydantic AI and Logfire as well, but it gets you a long way, that's for sure.Swyx [00:00:29]: Pydantic almost basically needs no introduction. It's almost 300 million downloads in December. And obviously, in the previous podcasts and discussions we've had with Jason Liu, he's been a big fan and promoter of Pydantic and AI.Samuel [00:00:45]: Yeah, it's weird because obviously I didn't create Pydantic originally for uses in AI, it predates LLMs. But it's like we've been lucky that it's been picked up by that community and used so widely.Swyx [00:00:58]: Actually, maybe we'll hear it. Right from you, what is Pydantic and maybe a little bit of the origin story?Samuel [00:01:04]: The best name for it, which is not quite right, is a validation library. And we get some tension around that name because it doesn't just do validation, it will do coercion by default. We now have strict mode, so you can disable that coercion. But by default, if you say you want an integer field and you get in a string of 1, 2, 3, it will convert it to 123 and a bunch of other sensible conversions. And as you can imagine, the semantics around it. Exactly when you convert and when you don't, it's complicated, but because of that, it's more than just validation. Back in 2017, when I first started it, the different thing it was doing was using type hints to define your schema. That was controversial at the time. It was genuinely disapproved of by some people. I think the success of Pydantic and libraries like FastAPI that build on top of it means that today that's no longer controversial in Python. And indeed, lots of other people have copied that route, but yeah, it's a data validation library. It uses type hints for the for the most part and obviously does all the other stuff you want, like serialization on top of that. But yeah, that's the core.Alessio [00:02:06]: Do you have any fun stories on how JSON schemas ended up being kind of like the structure output standard for LLMs? And were you involved in any of these discussions? Because I know OpenAI was, you know, one of the early adopters. So did they reach out to you? Was there kind of like a structure output console in open source that people were talking about or was it just a random?Samuel [00:02:26]: No, very much not. So I originally. Didn't implement JSON schema inside Pydantic and then Sebastian, Sebastian Ramirez, FastAPI came along and like the first I ever heard of him was over a weekend. I got like 50 emails from him or 50 like emails as he was committing to Pydantic, adding JSON schema long pre version one. So the reason it was added was for OpenAPI, which is obviously closely akin to JSON schema. And then, yeah, I don't know why it was JSON that got picked up and used by OpenAI. It was obviously very convenient for us. That's because it meant that not only can you do the validation, but because Pydantic will generate you the JSON schema, it will it kind of can be one source of source of truth for structured outputs and tools.Swyx [00:03:09]: Before we dive in further on the on the AI side of things, something I'm mildly curious about, obviously, there's Zod in JavaScript land. Every now and then there is a new sort of in vogue validation library that that takes over for quite a few years and then maybe like some something else comes along. Is Pydantic? Is it done like the core Pydantic?Samuel [00:03:30]: I've just come off a call where we were redesigning some of the internal bits. There will be a v3 at some point, which will not break people's code half as much as v2 as in v2 was the was the massive rewrite into Rust, but also fixing all the stuff that was broken back from like version zero point something that we didn't fix in v1 because it was a side project. We have plans to move some of the basically store the data in Rust types after validation. Not completely. So we're still working to design the Pythonic version of it, in order for it to be able to convert into Python types. So then if you were doing like validation and then serialization, you would never have to go via a Python type we reckon that can give us somewhere between three and five times another three to five times speed up. That's probably the biggest thing. Also, like changing how easy it is to basically extend Pydantic and define how particular types, like for example, NumPy arrays are validated and serialized. But there's also stuff going on. And for example, Jitter, the JSON library in Rust that does the JSON parsing, has SIMD implementation at the moment only for AMD64. So we can add that. We need to go and add SIMD for other instruction sets. So there's a bunch more we can do on performance. I don't think we're going to go and revolutionize Pydantic, but it's going to continue to get faster, continue, hopefully, to allow people to do more advanced things. We might add a binary format like CBOR for serialization for when you'll just want to put the data into a database and probably load it again from Pydantic. So there are some things that will come along, but for the most part, it should just get faster and cleaner.Alessio [00:05:04]: From a focus perspective, I guess, as a founder too, how did you think about the AI interest rising? And then how do you kind of prioritize, okay, this is worth going into more, and we'll talk about Pydantic AI and all of that. What was maybe your early experience with LLAMP, and when did you figure out, okay, this is something we should take seriously and focus more resources on it?Samuel [00:05:28]: I'll answer that, but I'll answer what I think is a kind of parallel question, which is Pydantic's weird, because Pydantic existed, obviously, before I was starting a company. I was working on it in my spare time, and then beginning of 22, I started working on the rewrite in Rust. And I worked on it full-time for a year and a half, and then once we started the company, people came and joined. And it was a weird project, because that would never go away. You can't get signed off inside a startup. Like, we're going to go off and three engineers are going to work full-on for a year in Python and Rust, writing like 30,000 lines of Rust just to release open-source-free Python library. The result of that has been excellent for us as a company, right? As in, it's made us remain entirely relevant. And it's like, Pydantic is not just used in the SDKs of all of the AI libraries, but I can't say which one, but one of the big foundational model companies, when they upgraded from Pydantic v1 to v2, their number one internal model... The metric of performance is time to first token. That went down by 20%. So you think about all of the actual AI going on inside, and yet at least 20% of the CPU, or at least the latency inside requests was actually Pydantic, which shows like how widely it's used. So we've benefited from doing that work, although it didn't, it would have never have made financial sense in most companies. In answer to your question about like, how do we prioritize AI, I mean, the honest truth is we've spent a lot of the last year and a half building. Good general purpose observability inside LogFire and making Pydantic good for general purpose use cases. And the AI has kind of come to us. Like we just, not that we want to get away from it, but like the appetite, uh, both in Pydantic and in LogFire to go and build with AI is enormous because it kind of makes sense, right? Like if you're starting a new greenfield project in Python today, what's the chance that you're using GenAI 80%, let's say, globally, obviously it's like a hundred percent in California, but even worldwide, it's probably 80%. Yeah. And so everyone needs that stuff. And there's so much yet to be figured out so much like space to do things better in the ecosystem in a way that like to go and implement a database that's better than Postgres is a like Sisyphean task. Whereas building, uh, tools that are better for GenAI than some of the stuff that's about now is not very difficult. Putting the actual models themselves to one side.Alessio [00:07:40]: And then at the same time, then you released Pydantic AI recently, which is, uh, um, you know, agent framework and early on, I would say everybody like, you know, Langchain and like, uh, Pydantic kind of like a first class support, a lot of these frameworks, we're trying to use you to be better. What was the decision behind we should do our own framework? Were there any design decisions that you disagree with any workloads that you think people didn't support? Well,Samuel [00:08:05]: it wasn't so much like design and workflow, although I think there were some, some things we've done differently. Yeah. I think looking in general at the ecosystem of agent frameworks, the engineering quality is far below that of the rest of the Python ecosystem. There's a bunch of stuff that we have learned how to do over the last 20 years of building Python libraries and writing Python code that seems to be abandoned by people when they build agent frameworks. Now I can kind of respect that, particularly in the very first agent frameworks, like Langchain, where they were literally figuring out how to go and do this stuff. It's completely understandable that you would like basically skip some stuff.Samuel [00:08:42]: I'm shocked by the like quality of some of the agent frameworks that have come out recently from like well-respected names, which it just seems to be opportunism and I have little time for that, but like the early ones, like I think they were just figuring out how to do stuff and just as lots of people have learned from Pydantic, we were able to learn a bit from them. I think from like the gap we saw and the thing we were frustrated by was the production readiness. And that means things like type checking, even if type checking makes it hard. Like Pydantic AI, I will put my hand up now and say it has a lot of generics and you need to, it's probably easier to use it if you've written a bit of Rust and you really understand generics, but like, and that is, we're not claiming that that makes it the easiest thing to use in all cases, we think it makes it good for production applications in big systems where type checking is a no-brainer in Python. But there are also a bunch of stuff we've learned from maintaining Pydantic over the years that we've gone and done. So every single example in Pydantic AI's documentation is run on Python. As part of tests and every single print output within an example is checked during tests. So it will always be up to date. And then a bunch of things that, like I say, are standard best practice within the rest of the Python ecosystem, but I'm not followed surprisingly by some AI libraries like coverage, linting, type checking, et cetera, et cetera, where I think these are no-brainers, but like weirdly they're not followed by some of the other libraries.Alessio [00:10:04]: And can you just give an overview of the framework itself? I think there's kind of like the. LLM calling frameworks, there are the multi-agent frameworks, there's the workflow frameworks, like what does Pydantic AI do?Samuel [00:10:17]: I glaze over a bit when I hear all of the different sorts of frameworks, but I like, and I will tell you when I built Pydantic, when I built Logfire and when I built Pydantic AI, my methodology is not to go and like research and review all of the other things. I kind of work out what I want and I go and build it and then feedback comes and we adjust. So the fundamental building block of Pydantic AI is agents. The exact definition of agents and how you want to define them. is obviously ambiguous and our things are probably sort of agent-lit, not that we would want to go and rename them to agent-lit, but like the point is you probably build them together to build something and most people will call an agent. So an agent in our case has, you know, things like a prompt, like system prompt and some tools and a structured return type if you want it, that covers the vast majority of cases. There are situations where you want to go further and the most complex workflows where you want graphs and I resisted graphs for quite a while. I was sort of of the opinion you didn't need them and you could use standard like Python flow control to do all of that stuff. I had a few arguments with people, but I basically came around to, yeah, I can totally see why graphs are useful. But then we have the problem that by default, they're not type safe because if you have a like add edge method where you give the names of two different edges, there's no type checking, right? Even if you go and do some, I'm not, not all the graph libraries are AI specific. So there's a, there's a graph library called, but it allows, it does like a basic runtime type checking. Ironically using Pydantic to try and make up for the fact that like fundamentally that graphs are not typed type safe. Well, I like Pydantic, but it did, that's not a real solution to have to go and run the code to see if it's safe. There's a reason that starting type checking is so powerful. And so we kind of, from a lot of iteration eventually came up with a system of using normally data classes to define nodes where you return the next node you want to call and where we're able to go and introspect the return type of a node to basically build the graph. And so the graph is. Yeah. Inherently type safe. And once we got that right, I, I wasn't, I'm incredibly excited about graphs. I think there's like masses of use cases for them, both in gen AI and other development, but also software's all going to have interact with gen AI, right? It's going to be like web. There's no longer be like a web department in a company is that there's just like all the developers are building for web building with databases. The same is going to be true for gen AI.Alessio [00:12:33]: Yeah. I see on your docs, you call an agent, a container that contains a system prompt function. Tools, structure, result, dependency type model, and then model settings. Are the graphs in your mind, different agents? Are they different prompts for the same agent? What are like the structures in your mind?Samuel [00:12:52]: So we were compelled enough by graphs once we got them right, that we actually merged the PR this morning. That means our agent implementation without changing its API at all is now actually a graph under the hood as it is built using our graph library. So graphs are basically a lower level tool that allow you to build these complex workflows. Our agents are technically one of the many graphs you could go and build. And we just happened to build that one for you because it's a very common, commonplace one. But obviously there are cases where you need more complex workflows where the current agent assumptions don't work. And that's where you can then go and use graphs to build more complex things.Swyx [00:13:29]: You said you were cynical about graphs. What changed your mind specifically?Samuel [00:13:33]: I guess people kept giving me examples of things that they wanted to use graphs for. And my like, yeah, but you could do that in standard flow control in Python became a like less and less compelling argument to me because I've maintained those systems that end up with like spaghetti code. And I could see the appeal of this like structured way of defining the workflow of my code. And it's really neat that like just from your code, just from your type hints, you can get out a mermaid diagram that defines exactly what can go and happen.Swyx [00:14:00]: Right. Yeah. You do have very neat implementation of sort of inferring the graph from type hints, I guess. Yeah. Is what I would call it. Yeah. I think the question always is I have gone back and forth. I used to work at Temporal where we would actually spend a lot of time complaining about graph based workflow solutions like AWS step functions. And we would actually say that we were better because you could use normal control flow that you already knew and worked with. Yours, I guess, is like a little bit of a nice compromise. Like it looks like normal Pythonic code. But you just have to keep in mind what the type hints actually mean. And that's what we do with the quote unquote magic that the graph construction does.Samuel [00:14:42]: Yeah, exactly. And if you look at the internal logic of actually running a graph, it's incredibly simple. It's basically call a node, get a node back, call that node, get a node back, call that node. If you get an end, you're done. We will add in soon support for, well, basically storage so that you can store the state between each node that's run. And then the idea is you can then distribute the graph and run it across computers. And also, I mean, the other weird, the other bit that's really valuable is across time. Because it's all very well if you look at like lots of the graph examples that like Claude will give you. If it gives you an example, it gives you this lovely enormous mermaid chart of like the workflow, for example, managing returns if you're an e-commerce company. But what you realize is some of those lines are literally one function calls another function. And some of those lines are wait six days for the customer to print their like piece of paper and put it in the post. And if you're writing like your demo. Project or your like proof of concept, that's fine because you can just say, and now we call this function. But when you're building when you're in real in real life, that doesn't work. And now how do we manage that concept to basically be able to start somewhere else in the in our code? Well, this graph implementation makes it incredibly easy because you just pass the node that is the start point for carrying on the graph and it continues to run. So it's things like that where I was like, yeah, I can just imagine how things I've done in the past would be fundamentally easier to understand if we had done them with graphs.Swyx [00:16:07]: You say imagine, but like right now, this pedantic AI actually resume, you know, six days later, like you said, or is this just like a theoretical thing we can go someday?Samuel [00:16:16]: I think it's basically Q&A. So there's an AI that's asking the user a question and effectively you then call the CLI again to continue the conversation. And it basically instantiates the node and calls the graph with that node again. Now, we don't have the logic yet for effectively storing state in the database between individual nodes that we're going to add soon. But like the rest of it is basically there.Swyx [00:16:37]: It does make me think that not only are you competing with Langchain now and obviously Instructor, and now you're going into sort of the more like orchestrated things like Airflow, Prefect, Daxter, those guys.Samuel [00:16:52]: Yeah, I mean, we're good friends with the Prefect guys and Temporal have the same investors as us. And I'm sure that my investor Bogomol would not be too happy if I was like, oh, yeah, by the way, as well as trying to take on Datadog. We're also going off and trying to take on Temporal and everyone else doing that. Obviously, we're not doing all of the infrastructure of deploying that right yet, at least. We're, you know, we're just building a Python library. And like what's crazy about our graph implementation is, sure, there's a bit of magic in like introspecting the return type, you know, extracting things from unions, stuff like that. But like the actual calls, as I say, is literally call a function and get back a thing and call that. It's like incredibly simple and therefore easy to maintain. The question is, how useful is it? Well, I don't know yet. I think we have to go and find out. We have a whole. We've had a slew of people joining our Slack over the last few days and saying, tell me how good Pydantic AI is. How good is Pydantic AI versus Langchain? And I refuse to answer. That's your job to go and find that out. Not mine. We built a thing. I'm compelled by it, but I'm obviously biased. The ecosystem will work out what the useful tools are.Swyx [00:17:52]: Bogomol was my board member when I was at Temporal. And I think I think just generally also having been a workflow engine investor and participant in this space, it's a big space. Like everyone needs different functions. I think the one thing that I would say like yours, you know, as a library, you don't have that much control of it over the infrastructure. I do like the idea that each new agents or whatever or unit of work, whatever you call that should spin up in this sort of isolated boundaries. Whereas yours, I think around everything runs in the same process. But you ideally want to sort of spin out its own little container of things.Samuel [00:18:30]: I agree with you a hundred percent. And we will. It would work now. Right. As in theory, you're just like as long as you can serialize the calls to the next node, you just have to all of the different containers basically have to have the same the same code. I mean, I'm super excited about Cloudflare workers running Python and being able to install dependencies. And if Cloudflare could only give me my invitation to the private beta of that, we would be exploring that right now because I'm super excited about that as a like compute level for some of this stuff where exactly what you're saying, basically. You can run everything as an individual. Like worker function and distribute it. And it's resilient to failure, et cetera, et cetera.Swyx [00:19:08]: And it spins up like a thousand instances simultaneously. You know, you want it to be sort of truly serverless at once. Actually, I know we have some Cloudflare friends who are listening, so hopefully they'll get in front of the line. Especially.Samuel [00:19:19]: I was in Cloudflare's office last week shouting at them about other things that frustrate me. I have a love-hate relationship with Cloudflare. Their tech is awesome. But because I use it the whole time, I then get frustrated. So, yeah, I'm sure I will. I will. I will get there soon.Swyx [00:19:32]: There's a side tangent on Cloudflare. Is Python supported at full? I actually wasn't fully aware of what the status of that thing is.Samuel [00:19:39]: Yeah. So Pyodide, which is Python running inside the browser in scripting, is supported now by Cloudflare. They basically, they're having some struggles working out how to manage, ironically, dependencies that have binaries, in particular, Pydantic. Because these workers where you can have thousands of them on a given metal machine, you don't want to have a difference. You basically want to be able to have a share. Shared memory for all the different Pydantic installations, effectively. That's the thing they work out. They're working out. But Hood, who's my friend, who is the primary maintainer of Pyodide, works for Cloudflare. And that's basically what he's doing, is working out how to get Python running on Cloudflare's network.Swyx [00:20:19]: I mean, the nice thing is that your binary is really written in Rust, right? Yeah. Which also compiles the WebAssembly. Yeah. So maybe there's a way that you'd build... You have just a different build of Pydantic and that ships with whatever your distro for Cloudflare workers is.Samuel [00:20:36]: Yes, that's exactly what... So Pyodide has builds for Pydantic Core and for things like NumPy and basically all of the popular binary libraries. Yeah. It's just basic. And you're doing exactly that, right? You're using Rust to compile the WebAssembly and then you're calling that shared library from Python. And it's unbelievably complicated, but it works. Okay.Swyx [00:20:57]: Staying on graphs a little bit more, and then I wanted to go to some of the other features that you have in Pydantic AI. I see in your docs, there are sort of four levels of agents. There's single agents, there's agent delegation, programmatic agent handoff. That seems to be what OpenAI swarms would be like. And then the last one, graph-based control flow. Would you say that those are sort of the mental hierarchy of how these things go?Samuel [00:21:21]: Yeah, roughly. Okay.Swyx [00:21:22]: You had some expression around OpenAI swarms. Well.Samuel [00:21:25]: And indeed, OpenAI have got in touch with me and basically, maybe I'm not supposed to say this, but basically said that Pydantic AI looks like what swarms would become if it was production ready. So, yeah. I mean, like, yeah, which makes sense. Awesome. Yeah. I mean, in fact, it was specifically saying, how can we give people the same feeling that they were getting from swarms that led us to go and implement graphs? Because my, like, just call the next agent with Python code was not a satisfactory answer to people. So it was like, okay, we've got to go and have a better answer for that. It's not like, let us to get to graphs. Yeah.Swyx [00:21:56]: I mean, it's a minimal viable graph in some sense. What are the shapes of graphs that people should know? So the way that I would phrase this is I think Anthropic did a very good public service and also kind of surprisingly influential blog post, I would say, when they wrote Building Effective Agents. We actually have the authors coming to speak at my conference in New York, which I think you're giving a workshop at. Yeah.Samuel [00:22:24]: I'm trying to work it out. But yes, I think so.Swyx [00:22:26]: Tell me if you're not. yeah, I mean, like, that was the first, I think, authoritative view of, like, what kinds of graphs exist in agents and let's give each of them a name so that everyone is on the same page. So I'm just kind of curious if you have community names or top five patterns of graphs.Samuel [00:22:44]: I don't have top five patterns of graphs. I would love to see what people are building with them. But like, it's been it's only been a couple of weeks. And of course, there's a point is that. Because they're relatively unopinionated about what you can go and do with them. They don't suit them. Like, you can go and do lots of lots of things with them, but they don't have the structure to go and have like specific names as much as perhaps like some other systems do. I think what our agents are, which have a name and I can't remember what it is, but this basically system of like, decide what tool to call, go back to the center, decide what tool to call, go back to the center and then exit. One form of graph, which, as I say, like our agents are effectively one implementation of a graph, which is why under the hood they are now using graphs. And it'll be interesting to see over the next few years whether we end up with these like predefined graph names or graph structures or whether it's just like, yep, I built a graph or whether graphs just turn out not to match people's mental image of what they want and die away. We'll see.Swyx [00:23:38]: I think there is always appeal. Every developer eventually gets graph religion and goes, oh, yeah, everything's a graph. And then they probably over rotate and go go too far into graphs. And then they have to learn a whole bunch of DSLs. And then they're like, actually, I didn't need that. I need this. And they scale back a little bit.Samuel [00:23:55]: I'm at the beginning of that process. I'm currently a graph maximalist, although I haven't actually put any into production yet. But yeah.Swyx [00:24:02]: This has a lot of philosophical connections with other work coming out of UC Berkeley on compounding AI systems. I don't know if you know of or care. This is the Gartner world of things where they need some kind of industry terminology to sell it to enterprises. I don't know if you know about any of that.Samuel [00:24:24]: I haven't. I probably should. I should probably do it because I should probably get better at selling to enterprises. But no, no, I don't. Not right now.Swyx [00:24:29]: This is really the argument is that instead of putting everything in one model, you have more control and more maybe observability to if you break everything out into composing little models and changing them together. And obviously, then you need an orchestration framework to do that. Yeah.Samuel [00:24:47]: And it makes complete sense. And one of the things we've seen with agents is they work well when they work well. But when they. Even if you have the observability through log five that you can see what was going on, if you don't have a nice hook point to say, hang on, this is all gone wrong. You have a relatively blunt instrument of basically erroring when you exceed some kind of limit. But like what you need to be able to do is effectively iterate through these runs so that you can have your own control flow where you're like, OK, we've gone too far. And that's where one of the neat things about our graph implementation is you can basically call next in a loop rather than just running the full graph. And therefore, you have this opportunity to to break out of it. But yeah, basically, it's the same point, which is like if you have two bigger unit of work to some extent, whether or not it involves gen AI. But obviously, it's particularly problematic in gen AI. You only find out afterwards when you've spent quite a lot of time and or money when it's gone off and done done the wrong thing.Swyx [00:25:39]: Oh, drop on this. We're not going to resolve this here, but I'll drop this and then we can move on to the next thing. This is the common way that we we developers talk about this. And then the machine learning researchers look at us. And laugh and say, that's cute. And then they just train a bigger model and they wipe us out in the next training run. So I think there's a certain amount of we are fighting the bitter lesson here. We're fighting AGI. And, you know, when AGI arrives, this will all go away. Obviously, on Latent Space, we don't really discuss that because I think AGI is kind of this hand wavy concept that isn't super relevant. But I think we have to respect that. For example, you could do a chain of thoughts with graphs and you could manually orchestrate a nice little graph that does like. Reflect, think about if you need more, more inference time, compute, you know, that's the hot term now. And then think again and, you know, scale that up. Or you could train Strawberry and DeepSeq R1. Right.Samuel [00:26:32]: I saw someone saying recently, oh, they were really optimistic about agents because models are getting faster exponentially. And I like took a certain amount of self-control not to describe that it wasn't exponential. But my main point was. If models are getting faster as quickly as you say they are, then we don't need agents and we don't really need any of these abstraction layers. We can just give our model and, you know, access to the Internet, cross our fingers and hope for the best. Agents, agent frameworks, graphs, all of this stuff is basically making up for the fact that right now the models are not that clever. In the same way that if you're running a customer service business and you have loads of people sitting answering telephones, the less well trained they are, the less that you trust them, the more that you need to give them a script to go through. Whereas, you know, so if you're running a bank and you have lots of customer service people who you don't trust that much, then you tell them exactly what to say. If you're doing high net worth banking, you just employ people who you think are going to be charming to other rich people and set them off to go and have coffee with people. Right. And the same is true of models. The more intelligent they are, the less we need to tell them, like structure what they go and do and constrain the routes in which they take.Swyx [00:27:42]: Yeah. Yeah. Agree with that. So I'm happy to move on. So the other parts of Pydantic AI that are worth commenting on, and this is like my last rant, I promise. So obviously, every framework needs to do its sort of model adapter layer, which is, oh, you can easily swap from OpenAI to Cloud to Grok. You also have, which I didn't know about, Google GLA, which I didn't really know about until I saw this in your docs, which is generative language API. I assume that's AI Studio? Yes.Samuel [00:28:13]: Google don't have good names for it. So Vertex is very clear. That seems to be the API that like some of the things use, although it returns 503 about 20% of the time. So... Vertex? No. Vertex, fine. But the... Oh, oh. GLA. Yeah. Yeah.Swyx [00:28:28]: I agree with that.Samuel [00:28:29]: So we have, again, another example of like, well, I think we go the extra mile in terms of engineering is we run on every commit, at least commit to main, we run tests against the live models. Not lots of tests, but like a handful of them. Oh, okay. And we had a point last week where, yeah, GLA is a little bit better. GLA1 was failing every single run. One of their tests would fail. And we, I think we might even have commented out that one at the moment. So like all of the models fail more often than you might expect, but like that one seems to be particularly likely to fail. But Vertex is the same API, but much more reliable.Swyx [00:29:01]: My rant here is that, you know, versions of this appear in Langchain and every single framework has to have its own little thing, a version of that. I would put to you, and then, you know, this is, this can be agree to disagree. This is not needed in Pydantic AI. I would much rather you adopt a layer like Lite LLM or what's the other one in JavaScript port key. And that's their job. They focus on that one thing and they, they normalize APIs for you. All new models are automatically added and you don't have to duplicate this inside of your framework. So for example, if I wanted to use deep seek, I'm out of luck because Pydantic AI doesn't have deep seek yet.Samuel [00:29:38]: Yeah, it does.Swyx [00:29:39]: Oh, it does. Okay. I'm sorry. But you know what I mean? Should this live in your code or should it live in a layer that's kind of your API gateway that's a defined piece of infrastructure that people have?Samuel [00:29:49]: And I think if a company who are well known, who are respected by everyone had come along and done this at the right time, maybe we should have done it a year and a half ago and said, we're going to be the universal AI layer. That would have been a credible thing to do. I've heard varying reports of Lite LLM is the truth. And it didn't seem to have exactly the type safety that we needed. Also, as I understand it, and again, I haven't looked into it in great detail. Part of their business model is proxying the request through their, through their own system to do the generalization. That would be an enormous put off to an awful lot of people. Honestly, the truth is I don't think it is that much work unifying the model. I get where you're coming from. I kind of see your point. I think the truth is that everyone is centralizing around open AIs. Open AI's API is the one to do. So DeepSeq support that. Grok with OK support that. Ollama also does it. I mean, if there is that library right now, it's more or less the open AI SDK. And it's very high quality. It's well type checked. It uses Pydantic. So I'm biased. But I mean, I think it's pretty well respected anyway.Swyx [00:30:57]: There's different ways to do this. Because also, it's not just about normalizing the APIs. You have to do secret management and all that stuff.Samuel [00:31:05]: Yeah. And there's also. There's Vertex and Bedrock, which to one extent or another, effectively, they host multiple models, but they don't unify the API. But they do unify the auth, as I understand it. Although we're halfway through doing Bedrock. So I don't know about it that well. But they're kind of weird hybrids because they support multiple models. But like I say, the auth is centralized.Swyx [00:31:28]: Yeah, I'm surprised they don't unify the API. That seems like something that I would do. You know, we can discuss all this all day. There's a lot of APIs. I agree.Samuel [00:31:36]: It would be nice if there was a universal one that we didn't have to go and build.Alessio [00:31:39]: And I guess the other side of, you know, routing model and picking models like evals. How do you actually figure out which one you should be using? I know you have one. First of all, you have very good support for mocking in unit tests, which is something that a lot of other frameworks don't do. So, you know, my favorite Ruby library is VCR because it just, you know, it just lets me store the HTTP requests and replay them. That part I'll kind of skip. I think you are busy like this test model. We're like just through Python. You try and figure out what the model might respond without actually calling the model. And then you have the function model where people can kind of customize outputs. Any other fun stories maybe from there? Or is it just what you see is what you get, so to speak?Samuel [00:32:18]: On those two, I think what you see is what you get. On the evals, I think watch this space. I think it's something that like, again, I was somewhat cynical about for some time. Still have my cynicism about some of the well, it's unfortunate that so many different things are called evals. It would be nice if we could agree. What they are and what they're not. But look, I think it's a really important space. I think it's something that we're going to be working on soon, both in Pydantic AI and in LogFire to try and support better because it's like it's an unsolved problem.Alessio [00:32:45]: Yeah, you do say in your doc that anyone who claims to know for sure exactly how your eval should be defined can safely be ignored.Samuel [00:32:52]: We'll delete that sentence when we tell people how to do their evals.Alessio [00:32:56]: Exactly. I was like, we need we need a snapshot of this today. And so let's talk about eval. So there's kind of like the vibe. Yeah. So you have evals, which is what you do when you're building. Right. Because you cannot really like test it that many times to get statistical significance. And then there's the production eval. So you also have LogFire, which is kind of like your observability product, which I tried before. It's very nice. What are some of the learnings you've had from building an observability tool for LEMPs? And yeah, as people think about evals, even like what are the right things to measure? What are like the right number of samples that you need to actually start making decisions?Samuel [00:33:33]: I'm not the best person to answer that is the truth. So I'm not going to come in here and tell you that I think I know the answer on the exact number. I mean, we can do some back of the envelope statistics calculations to work out that like having 30 probably gets you most of the statistical value of having 200 for, you know, by definition, 15% of the work. But the exact like how many examples do you need? For example, that's a much harder question to answer because it's, you know, it's deep within the how models operate in terms of LogFire. One of the reasons we built LogFire the way we have and we allow you to write SQL directly against your data and we're trying to build the like powerful fundamentals of observability is precisely because we know we don't know the answers. And so allowing people to go and innovate on how they're going to consume that stuff and how they're going to process it is we think that's valuable. Because even if we come along and offer you an evals framework on top of LogFire, it won't be right in all regards. And we want people to be able to go and innovate and being able to write their own SQL connected to the API. And effectively query the data like it's a database with SQL allows people to innovate on that stuff. And that's what allows us to do it as well. I mean, we do a bunch of like testing what's possible by basically writing SQL directly against LogFire as any user could. I think the other the other really interesting bit that's going on in observability is OpenTelemetry is centralizing around semantic attributes for GenAI. So it's a relatively new project. A lot of it's still being added at the moment. But basically the idea that like. They unify how both SDKs and or agent frameworks send observability data to to any OpenTelemetry endpoint. And so, again, we can go and having that unification allows us to go and like basically compare different libraries, compare different models much better. That stuff's in a very like early stage of development. One of the things we're going to be working on pretty soon is basically, I suspect, GenAI will be the first agent framework that implements those semantic attributes properly. Because, again, we control and we can say this is important for observability, whereas most of the other agent frameworks are not maintained by people who are trying to do observability. With the exception of Langchain, where they have the observability platform, but they chose not to go down the OpenTelemetry route. So they're like plowing their own furrow. And, you know, they're a lot they're even further away from standardization.Alessio [00:35:51]: Can you maybe just give a quick overview of how OTEL ties into the AI workflows? There's kind of like the question of is, you know, a trace. And a span like a LLM call. Is it the agent? It's kind of like the broader thing you're tracking. How should people think about it?Samuel [00:36:06]: Yeah, so they have a PR that I think may have now been merged from someone at IBM talking about remote agents and trying to support this concept of remote agents within GenAI. I'm not particularly compelled by that because I don't think that like that's actually by any means the common use case. But like, I suppose it's fine for it to be there. The majority of the stuff in OTEL is basically defining how you would instrument. A given call to an LLM. So basically the actual LLM call, what data you would send to your telemetry provider, how you would structure that. Apart from this slightly odd stuff on remote agents, most of the like agent level consideration is not yet implemented in is not yet decided effectively. And so there's a bit of ambiguity. Obviously, what's good about OTEL is you can in the end send whatever attributes you like. But yeah, there's quite a lot of churn in that space and exactly how we store the data. I think that one of the most interesting things, though, is that if you think about observability. Traditionally, it was sure everyone would say our observability data is very important. We must keep it safe. But actually, companies work very hard to basically not have anything that sensitive in their observability data. So if you're a doctor in a hospital and you search for a drug for an STI, the sequel might be sent to the observability provider. But none of the parameters would. It wouldn't have the patient number or their name or the drug. With GenAI, that distinction doesn't exist because it's all just messed up in the text. If you have that same patient asking an LLM how to. What drug they should take or how to stop smoking. You can't extract the PII and not send it to the observability platform. So the sensitivity of the data that's going to end up in observability platforms is going to be like basically different order of magnitude to what's in what you would normally send to Datadog. Of course, you can make a mistake and send someone's password or their card number to Datadog. But that would be seen as a as a like mistake. Whereas in GenAI, a lot of data is going to be sent. And I think that's why companies like Langsmith and are trying hard to offer observability. On prem, because there's a bunch of companies who are happy for Datadog to be cloud hosted, but want self-hosted self-hosting for this observability stuff with GenAI.Alessio [00:38:09]: And are you doing any of that today? Because I know in each of the spans you have like the number of tokens, you have the context, you're just storing everything. And then you're going to offer kind of like a self-hosting for the platform, basically. Yeah. Yeah.Samuel [00:38:23]: So we have scrubbing roughly equivalent to what the other observability platforms have. So if we, you know, if we see password as the key, we won't send the value. But like, like I said, that doesn't really work in GenAI. So we're accepting we're going to have to store a lot of data and then we'll offer self-hosting for those people who can afford it and who need it.Alessio [00:38:42]: And then this is, I think, the first time that most of the workloads performance is depending on a third party. You know, like if you're looking at Datadog data, usually it's your app that is driving the latency and like the memory usage and all of that. Here you're going to have spans that maybe take a long time to perform because the GLA API is not working or because OpenAI is kind of like overwhelmed. Do you do anything there since like the provider is almost like the same across customers? You know, like, are you trying to surface these things for people and say, hey, this was like a very slow span, but actually all customers using OpenAI right now are seeing the same thing. So maybe don't worry about it or.Samuel [00:39:20]: Not yet. We do a few things that people don't generally do in OTA. So we send. We send information at the beginning. At the beginning of a trace as well as sorry, at the beginning of a span, as well as when it finishes. By default, OTA only sends you data when the span finishes. So if you think about a request which might take like 20 seconds, even if some of the intermediate spans finished earlier, you can't basically place them on the page until you get the top level span. And so if you're using standard OTA, you can't show anything until those requests are finished. When those requests are taking a few hundred milliseconds, it doesn't really matter. But when you're doing Gen AI calls or when you're like running a batch job that might take 30 minutes. That like latency of not being able to see the span is like crippling to understanding your application. And so we've we do a bunch of slightly complex stuff to basically send data about a span as it starts, which is closely related. Yeah.Alessio [00:40:09]: Any thoughts on all the other people trying to build on top of OpenTelemetry in different languages, too? There's like the OpenLEmetry project, which doesn't really roll off the tongue. But how do you see the future of these kind of tools? Is everybody going to have to build? Why does everybody want to build? They want to build their own open source observability thing to then sell?Samuel [00:40:29]: I mean, we are not going off and trying to instrument the likes of the OpenAI SDK with the new semantic attributes, because at some point that's going to happen and it's going to live inside OTEL and we might help with it. But we're a tiny team. We don't have time to go and do all of that work. So OpenLEmetry, like interesting project. But I suspect eventually most of those semantic like that instrumentation of the big of the SDKs will live, like I say, inside the main OpenTelemetry report. I suppose. What happens to the agent frameworks? What data you basically need at the framework level to get the context is kind of unclear. I don't think we know the answer yet. But I mean, I was on the, I guess this is kind of semi-public, because I was on the call with the OpenTelemetry call last week talking about GenAI. And there was someone from Arize talking about the challenges they have trying to get OpenTelemetry data out of Langchain, where it's not like natively implemented. And obviously they're having quite a tough time. And I was realizing, hadn't really realized this before, but how lucky we are to primarily be talking about our own agent framework, where we have the control rather than trying to go and instrument other people's.Swyx [00:41:36]: Sorry, I actually didn't know about this semantic conventions thing. It looks like, yeah, it's merged into main OTel. What should people know about this? I had never heard of it before.Samuel [00:41:45]: Yeah, I think it looks like a great start. I think there's some unknowns around how you send the messages that go back and forth, which is kind of the most important part. It's the most important thing of all. And that is moved out of attributes and into OTel events. OTel events in turn are moving from being on a span to being their own top-level API where you send data. So there's a bunch of churn still going on. I'm impressed by how fast the OTel community is moving on this project. I guess they, like everyone else, get that this is important, and it's something that people are crying out to get instrumentation off. So I'm kind of pleasantly surprised at how fast they're moving, but it makes sense.Swyx [00:42:25]: I'm just kind of browsing through the specification. I can already see that this basically bakes in whatever the previous paradigm was. So now they have genai.usage.prompt tokens and genai.usage.completion tokens. And obviously now we have reasoning tokens as well. And then only one form of sampling, which is top-p. You're basically baking in or sort of reifying things that you think are important today, but it's not a super foolproof way of doing this for the future. Yeah.Samuel [00:42:54]: I mean, that's what's neat about OTel is you can always go and send another attribute and that's fine. It's just there are a bunch that are agreed on. But I would say, you know, to come back to your previous point about whether or not we should be relying on one centralized abstraction layer, this stuff is moving so fast that if you start relying on someone else's standard, you risk basically falling behind because you're relying on someone else to keep things up to date.Swyx [00:43:14]: Or you fall behind because you've got other things going on.Samuel [00:43:17]: Yeah, yeah. That's fair. That's fair.Swyx [00:43:19]: Any other observations just about building LogFire, actually? Let's just talk about this. So you announced LogFire. I was kind of only familiar with LogFire because of your Series A announcement. I actually thought you were making a separate company. I remember some amount of confusion with you when that came out. So to be clear, it's Pydantic LogFire and the company is one company that has kind of two products, an open source thing and an observability thing, correct? Yeah. I was just kind of curious, like any learnings building LogFire? So classic question is, do you use ClickHouse? Is this like the standard persistence layer? Any learnings doing that?Samuel [00:43:54]: We don't use ClickHouse. We started building our database with ClickHouse, moved off ClickHouse onto Timescale, which is a Postgres extension to do analytical databases. Wow. And then moved off Timescale onto DataFusion. And we're basically now building, it's DataFusion, but it's kind of our own database. Bogomil is not entirely happy that we went through three databases before we chose one. I'll say that. But like, we've got to the right one in the end. I think we could have realized that Timescale wasn't right. I think ClickHouse. They both taught us a lot and we're in a great place now. But like, yeah, it's been a real journey on the database in particular.Swyx [00:44:28]: Okay. So, you know, as a database nerd, I have to like double click on this, right? So ClickHouse is supposed to be the ideal backend for anything like this. And then moving from ClickHouse to Timescale is another counterintuitive move that I didn't expect because, you know, Timescale is like an extension on top of Postgres. Not super meant for like high volume logging. But like, yeah, tell us those decisions.Samuel [00:44:50]: So at the time, ClickHouse did not have good support for JSON. I was speaking to someone yesterday and said ClickHouse doesn't have good support for JSON and got roundly stepped on because apparently it does now. So they've obviously gone and built their proper JSON support. But like back when we were trying to use it, I guess a year ago or a bit more than a year ago, everything happened to be a map and maps are a pain to try and do like looking up JSON type data. And obviously all these attributes, everything you're talking about there in terms of the GenAI stuff. You can choose to make them top level columns if you want. But the simplest thing is just to put them all into a big JSON pile. And that was a problem with ClickHouse. Also, ClickHouse had some really ugly edge cases like by default, or at least until I complained about it a lot, ClickHouse thought that two nanoseconds was longer than one second because they compared intervals just by the number, not the unit. And I complained about that a lot. And then they caused it to raise an error and just say you have to have the same unit. Then I complained a bit more. And I think as I understand it now, they have some. They convert between units. But like stuff like that, when all you're looking at is when a lot of what you're doing is comparing the duration of spans was really painful. Also things like you can't subtract two date times to get an interval. You have to use the date sub function. But like the fundamental thing is because we want our end users to write SQL, the like quality of the SQL, how easy it is to write, matters way more to us than if you're building like a platform on top where your developers are going to write the SQL. And once it's written and it's working, you don't mind too much. So I think that's like one of the fundamental differences. The other problem that I have with the ClickHouse and Impact Timescale is that like the ultimate architecture, the like snowflake architecture of binary data in object store queried with some kind of cache from nearby. They both have it, but it's closed sourced and you only get it if you go and use their hosted versions. And so even if we had got through all the problems with Timescale or ClickHouse, we would end up like, you know, they would want to be taking their 80% margin. And then we would be wanting to take that would basically leave us less space for margin. Whereas data fusion. Properly open source, all of that same tooling is open source. And for us as a team of people with a lot of Rust expertise, data fusion, which is implemented in Rust, we can literally dive into it and go and change it. So, for example, I found that there were some slowdowns in data fusion's string comparison kernel for doing like string contains. And it's just Rust code. And I could go and rewrite the string comparison kernel to be faster. Or, for example, data fusion, when we started using it, didn't have JSON support. Obviously, as I've said, it's something we can do. It's something we needed. I was able to go and implement that in a weekend using our JSON parser that we built for Pydantic Core. So it's the fact that like data fusion is like for us the perfect mixture of a toolbox to build a database with, not a database. And we can go and implement stuff on top of it in a way that like if you were trying to do that in Postgres or in ClickHouse. I mean, ClickHouse would be easier because it's C++, relatively modern C++. But like as a team of people who are not C++ experts, that's much scarier than data fusion for us.Swyx [00:47:47]: Yeah, that's a beautiful rant.Alessio [00:47:49]: That's funny. Most people don't think they have agency on these projects. They're kind of like, oh, I should use this or I should use that. They're not really like, what should I pick so that I contribute the most back to it? You know, so but I think you obviously have an open source first mindset. So that makes a lot of sense.Samuel [00:48:05]: I think if we were probably better as a startup, a better startup and faster moving and just like headlong determined to get in front of customers as fast as possible, we should have just started with ClickHouse. I hope that long term we're in a better place for having worked with data fusion. We like we're quite engaged now with the data fusion community. Andrew Lam, who maintains data fusion, is an advisor to us. We're in a really good place now. But yeah, it's definitely slowed us down relative to just like building on ClickHouse and moving as fast as we can.Swyx [00:48:34]: OK, we're about to zoom out and do Pydantic run and all the other stuff. But, you know, my last question on LogFire is really, you know, at some point you run out sort of community goodwill just because like, oh, I use Pydantic. I love Pydantic. I'm going to use LogFire. OK, then you start entering the territory of the Datadogs, the Sentrys and the honeycombs. Yeah. So where are you going to really spike here? What differentiator here?Samuel [00:48:59]: I wasn't writing code in 2001, but I'm assuming that there were people talking about like web observability and then web observability stopped being a thing, not because the web stopped being a thing, but because all observability had to do web. If you were talking to people in 2010 or 2012, they would have talked about cloud observability. Now that's not a term because all observability is cloud first. The same is going to happen to gen AI. And so whether or not you're trying to compete with Datadog or with Arise and Langsmith, you've got to do first class. You've got to do general purpose observability with first class support for AI. And as far as I know, we're the only people really trying to do that. I mean, I think Datadog is starting in that direction. And to be honest, I think Datadog is a much like scarier company to compete with than the AI specific observability platforms. Because in my opinion, and I've also heard this from lots of customers, AI specific observability where you don't see everything else going on in your app is not actually that useful. Our hope is that we can build the first general purpose observability platform with first class support for AI. And that we have this open source heritage of putting developer experience first that other companies haven't done. For all I'm a fan of Datadog and what they've done. If you search Datadog logging Python. And you just try as a like a non-observability expert to get something up and running with Datadog and Python. It's not trivial, right? That's something Sentry have done amazingly well. But like there's enormous space in most of observability to do DX better.Alessio [00:50:27]: Since you mentioned Sentry, I'm curious how you thought about licensing and all of that. Obviously, your MIT license, you don't have any rolling license like Sentry has where you can only use an open source, like the one year old version of it. Was that a hard decision?Samuel [00:50:41]: So to be clear, LogFire is co-sourced. So Pydantic and Pydantic AI are MIT licensed and like properly open source. And then LogFire for now is completely closed source. And in fact, the struggles that Sentry have had with licensing and the like weird pushback the community gives when they take something that's closed source and make it source available just meant that we just avoided that whole subject matter. I think the other way to look at it is like in terms of either headcount or revenue or dollars in the bank. The amount of open source we do as a company is we've got to be open source. We're up there with the most prolific open source companies, like I say, per head. And so we didn't feel like we were morally obligated to make LogFire open source. We have Pydantic. Pydantic is a foundational library in Python. That and now Pydantic AI are our contribution to open source. And then LogFire is like openly for profit, right? As in we're not claiming otherwise. We're not sort of trying to walk a line if it's open source. But really, we want to make it hard to deploy. So you probably want to pay us. We're trying to be straight. That it's to pay for. We could change that at some point in the future, but it's not an immediate plan.Alessio [00:51:48]: All right. So the first one I saw this new I don't know if it's like a product you're building the Pydantic that run, which is a Python browser sandbox. What was the inspiration behind that? We talk a lot about code interpreter for lamps. I'm an investor in a company called E2B, which is a code sandbox as a service for remote execution. Yeah. What's the Pydantic that run story?Samuel [00:52:09]: So Pydantic that run is again completely open source. I have no interest in making it into a product. We just needed a sandbox to be able to demo LogFire in particular, but also Pydantic AI. So it doesn't have it yet, but I'm going to add basically a proxy to OpenAI and the other models so that you can run Pydantic AI in the browser. See how it works. Tweak the prompt, et cetera, et cetera. And we'll have some kind of limit per day of what you can spend on it or like what the spend is. The other thing we wanted to b

What if every business problem—whether it’s a lack of cash flow, resources, or room to grow—could be solved by buying another business? And what if the price or your current finances didn’t have to hold you back? On this episode of the Buying Online Business podcast, host Jaryd Krause talks with Tom Shipley, a seasoned entrepreneur, investor, and expert in mergers and acquisitions (M&A). Tom has built brands that generated over $2 billion in sales and raised more than $100 million for business acquisitions. He is also the co-founder and CEO of Ava, a company that specializes in helping businesses grow through acquisitions. In their conversation, Tom shares how he got started in acquisitions, including his first deal, which helped him triple his business in a short time. He explains when acquisitions make sense, when they don’t, and how to avoid deals that might drain a business’s resources. Listeners will also hear about: Simple strategies for financing and structuring deals, even with limited cash. How to avoid overpaying or putting too much strain on a business. Key steps in due diligence to ensure a deal is a good fit. This episode is full of practical tips for anyone who wants to use programmatic M&A to solve problems and grow their business. Episode Highlights 02:00 Tom’s journey before diving into Tech M&A 10:45 Knowing seller’s intention is the key 19:00 Be careful of deals that are too good to be true 28:00 Communication is crucial in deals 40: Where to find Tom? Key Takeaways ➥ Even businesses with limited cash flow can acquire larger companies through financing strategies like seller financing, debt structuring, and rolling over equity. The key is to underwrite the acquired business's cash flow and growth potential, not just the buyer's current financial state. ➥ Understanding why a seller is exiting allows for deal structures that benefit both parties, such as monthly payouts instead of lump sums, offering tax advantages and a smoother transition. ➥ Leaders should seek "force multipliers" like acquisitions and AI to achieve disproportionate results with fewer resources. These tools can give businesses an edge in competitive markets. About The Guest Tom Shipley is a serial entrepreneur, ecommerce investor, strategic advisor, speaker and M&A expert. His brands have generated over $2B in sales, are household names and he's raised more than $100M for acquisitions. Right now Tom is the co-founder & CEO of AVA—Agency Ventures Aggregator—and focuses on programmatic M&A. Connect with Tom Shipley ➥ https://dealconlive.com/ ➥ https://www.linkedin.com/in/t-shipley/ Resource Links ➥ Sell your business to us here: https://buyingonlinebusinesses.com/sell-your-business/ ➥ Buying Online Businesses Website - https://buyingonlinebusinesses.com ➥ Download the Due Diligence Framework: https://buyingonlinebusinesses.com/freeresources/ ➥ Site Ground (Website Hosting) - https://bit.ly/3JBEC1u ➥ Surfer SEO (SEO tool for content writing) - https://bit.ly/3WWMKjM ➥ Convert Kit (Email Software Provider) - https://bit.ly/3o10Xgx

In this insightful episode, Albert Thompson, Managing Director, Digital Innovation at Walton Isaacson (https://www.waltonisaacson.com/), shares his forward-thinking take on where programmatic advertising is headed. He challenges the industry to rethink outdated processes, improve partnerships, and embrace smarter ways of working. Looking ahead to 2025, Albert highlights AI's potential to eliminate inefficiencies, spark creativity, and redefine how brands tell their stories and connect with audiences. Beyond technology, Albert dives into the importance of training—not just for newcomers, but for everyone. He emphasizes that staying curious and continuously learning is the key to staying relevant in a fast-changing, AI-driven world. He shares personal experiences and strategic insights on why companies should prioritize education at all levels to stay ahead of the curve. One of the most exciting parts of the discussion? Albert's take on "agentic AI"—a game-changing concept where AI-powered agents go beyond human capabilities to transform industries. He explores how this shift will impact everything from creative strategy to how brands measure and capture consumer attention. From the evolving role of AI in programmatic advertising to the future of consumer engagement, Albert offers a fresh and engaging perspective on what's next in the digital space. About Us: Our mission is to teach historically excluded people how to get started in programmatic media buying and find a dream job.  We do so by providing on-demand lessons via the Reach and Frequency® program (https://reachandfrequencycourse.thinkific.com ), a dope community with like-minded programmatic experts, and live free and paid group coaching.  We can help 2 ways:  Customized a training roadmap for teams of programmatic traders (https://www.heleneparker.com/workshop/ ), adops, customer success, AMs, etc focusing on campaigns performance increase, cross-departmental communication, and revenue growth overall 

Anna Bager, President and CEO of the Out-of-Home Advertising Association of America (OAAA), discusses the evolving out-of-home (OOH) industry, particularly in the context of digital transformation and programmatic advertising. She shares her journey from Sweden to leading the OAAA, the challenges faced during the pandemic, and the unique advantages of OOH advertising, including its brand safety and effectiveness. Anna emphasizes the importance of cultural adaptation in the U.S. market and highlights the exciting innovations and growth opportunities within the OOH space, while addressing common misconceptions about the industry. Takeaways OOH advertising is evolving with digital transformation. Anna Bager's international experience enriches her leadership. The pandemic presented challenges but also opportunities for OOH. Digital OOH allows for more dynamic and targeted advertising. Programmatic advertising is a key growth driver for OOH. OOH is often overlooked but is a vital part of advertising. Cultural adaptation is crucial for success in the U.S. market. OOH ads are generally perceived as non-intrusive and brand safe. The industry is seeing increased interest and innovation post-pandemic. Misconceptions about OOH advertising complicate its understanding. Chapters 00:00 Introduction to OOH Advertising and Anna Bogger 05:17 Anna's Journey to OAAA Leadership 11:02 Cultural Adaptation in the U.S. Market 12:21 The Evolution of Digital Out of Home 20:24 Programmatic Advertising in OOH 21:35 Exciting Innovations in OOH Advertising 25:12 Common Misconceptions About OOH Advertising Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we dive into the vital role programmatic traders play in driving revenue growth and how sales and account teams can support them. Learn how programmatic traders, through precise targeting, campaign optimization, and KPI-focused monitoring, nurture the business and drive success. We also explore the importance of sales and account teams understanding the ins and outs of a trader's work, as their collaboration is key to ensuring traders can effectively meet their goals and maximize programmatic revenue.  

To close out the year, The Big Story team is posting one of our favorite AdExchanger Talks episodes of 2024. Who better to give her take on programamtic transparency than an ex-FBI agent? Listen to this interview with Sherine Ebadi, Kroll managing director of investigations, who worked on the ANA's transparency report.

Associate Vice President of North America at Lemma, Angelina Marmorato, delves into the dominance of programmatic buying in CTV. Discover how Lemma, a leading omnichannel SSP, revolutionizes supply curation for emerging formats with a privacy-first approach and dynamic creative. Explore how Lemma empowers agencies and brands with unprecedented control over inventory lifecycle and audience targeting, ensuring relevance and precision in advertising. Show NotesConnect With: Angelina Marmorato: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Associate Vice President of North America at Lemma, Angelina Marmorato, delves into the dominance of programmatic buying in CTV. Discover how Lemma, a leading omnichannel SSP, revolutionizes supply curation for emerging formats with a privacy-first approach and dynamic creative. Explore how Lemma empowers agencies and brands with unprecedented control over inventory lifecycle and audience targeting, ensuring relevance and precision in advertising. Show NotesConnect With: Angelina Marmorato: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

If I had 8 days to prep my programmatic campaigns before

Here's what you need to know for today in the business of podcasting: CoHost has 28 podcasting experts' thoughts on 2025, Buzzfeed has sold First We Feast and what that means for M&A, IAB Tech Lab rebrands Seller Defined Audiences, the ANA finds programmatic sinks are slowly being fixed, and EV TV spend is down and it's because of Gen Z preferring digital. Find links to every article mentioned and full coverage here on Sounds Profitable.

Here's what you need to know for today in the business of podcasting: CoHost has 28 podcasting experts' thoughts on 2025, Buzzfeed has sold First We Feast and what that means for M&A, IAB Tech Lab rebrands Seller Defined Audiences, the ANA finds programmatic sinks are slowly being fixed, and EV TV spend is down and it's because of Gen Z preferring digital. Find links to every article mentioned and full coverage here on Sounds Profitable.

Programmatic advertising methods like retargeting can be powerful for pushing interested customers over the line into making a purchase. But the approach can lose potency if the proverbial funnel isn't regularly refilled with new prospective customers. “Over time, in order to compete and continue to grow, you need to expand your funnel. Otherwise you risk to optimize yourself to the ground and run out. If you continue to sharpen a pencil, at some point you run out of pencil,” TripAdvisor's Matteo Balzani said on the latest episode of the Digiday Podcast, which was recorded live during last week's Digiday Programmatic Marketing Summit in Nashville. As senior director of acquisition and retention, it is literally Balzani's job to make sure the travel booking platform does not run out of potential customers. And so he plans to rejigger the company's programmatic strategy in 2025. As the pandemic-era travel restrictions lifted, TripAdvisor found itself in the enviable position of fishing in a barrel. People were desperate to travel again, so all the brand had to do was prod people to book through its platform. “The focus was really on capturing all the pent-up demand that was there,” said Balzani. TripAdvisor still has one eye on capturing that lower-funnel demand, but it is also looking to get in front of potential customers much earlier in their travel-planning processes. To that end, this year the brand tested extending its programmatic buying to mid- and upper-funnel media channels, such as connected TV and podcasts. And heading into next year, it is weighing whether to adopt a media mix model to further inform its full-funnel approach. “What we want to do is to use Q1 and Q2 to figure out what works and what doesn't and make sure we have everything in place. And then based on the results, then we figure out which direction we want to go,” said Balzani.

In this episode of Mostly Marketing with Matt Wilson, Matt is joined by John Costanza, Director of Programmatic at Silverback Advertising for a conversation that explores everything from food and drink etiquette at restaurants to the “Law of Focus” in marketing, based on The 22 Immutable Laws of Marketing. Along the way, they critique cheesy dealership ads, share personal anecdotes (including a Great Dane wreaking havoc on Christmas lights), and lay down some controversial coffee drinking rules.   Mostly Marketing with Matt Wilson is a bi-weekly-ish podcast all about marketing… mostly. You can listen and download on all major podcast platforms, including Apple Podcasts, Google Podcasts, Spotify, and Anchor. 

In this episode of Mostly Marketing with Matt Wilson, Matt teams up with John Costanza, Director of Programmatic at Silverback Advertising, to explore what truly makes ad inventory "good" in programmatic advertising. They dive into the role of The Trade Desk's S&P 500 list, the use of premium versus open inventory, and why context matters in campaigns. From humorous takes on obscure ad placements to real-world insights on connected TV and in-app advertising, this episode offers a fresh look at how to balance ad quality with audience targeting. Perfect for marketers seeking to optimize their programmatic strategy! 

Disruption of linear TV a boon for programmatic Connected TV, or CTV, which refers to everything from movies watched via apps on television to apps offering live TV and traditional DVR capabilities via the Internet, has seen a surge in share of ad spend. This is especially true as more of this connected programming is offered via ad-supported tiers. Programmatic can also bring in more advertisers by making it easier for small and medium sized businesses to buy ad space. AI can drive growth too, allowing businesses to make more effective ad decisions and even helping with campaign content. Jessica Reif Ehrlich also discusses how more targeted CTV ads lead to growth in the format and how there's room for share gains in audio too.   You may also enjoy listening to the Merrill Perspectives podcast, featuring conversations on the big stories, news and trends affecting your everyday financial life.   "Bank of America" and “BofA Securities” are the marketing names for the global banking businesses and global markets businesses (which includes BofA Global Research) of Bank of America Corporation. Lending, derivatives, and other commercial banking activities are performed globally by banking affiliates of Bank of America Corporation, including Bank of America, N.A., Member FDIC. Securities, trading, research, strategic advisory, and other investment banking and markets activities are performed globally by affiliates of Bank of America Corporation, including, in the United States, BofA Securities, Inc. a registered broker-dealer and Member of FINRA and SIPC, and, in other jurisdictions, by locally registered entities. ©2024 Bank of America Corporation. All rights reserved.

Here's what you need to know for today in the business of podcasting: YouTube leads in podcast discovery, global ad spend tops 1 trillion, and the DOJ takes on tech again.Find links to every article mentioned and the full write-up here on Sounds Profitable.

Here's what you need to know for today in the business of podcasting: YouTube leads in podcast discovery, global ad spend tops 1 trillion, and the DOJ takes on tech again.Find links to every article mentioned and the full write-up here on Sounds Profitable.

The founder and CEO of The Trade Desk, Jeff Green, talks about the evolution of the premium internet, his obsession with the ad tech supply chain and why the connected TV (CTV) ecosystem is ready for an upgrade.Green explains why The Trade Desk is launching Ventura, a streaming TV operating system, named after the California beach town, to improve the CTV ecosystem for publishers, advertisers and consumers.__________The Current is owned and operated by The Trade Desk Inc.

"Automation is 100% the job to be done." - Alvaro Villa, FatTailSummaryIn this episode of OOH Insider, Tim Rowe and Alvaro Villa discuss the challenges facing DOOH advertising, focusing on the importance of maintaining premium inventory standards, the role of automation in direct sales, and the challenges faced by publishers navigating the Programmatic DOOH explosion. Join us as we explore how FatTail helps publishers like WebMD and The Financial Times with end-to-end ad ops, automating direct sales strategies, and enabling programmatic growth that maintains premium standards for brands, publishers, and partners. The conversation specifically highlights and emphasizes the need for direct relationships and an understanding of how and why media is transacted.TakeawaysWhy direct advertising still generates the majority of revenue for publishers.What role does Automation serve in reducing friction and selling more?How Creativity in direct sales is a superpower and competitive advantage.The importance of integrations with existing systems to unlocking value.Embracing what makes your inventory unique and how to sell it without slowing down.Chapters01:24 Understanding Premium Content and Direct Advertising03:17 The Role of Automation in Advertising04:38 Defining 'Programmatic'07:47 Balancing Direct Sales and Programmatic Efficiency09:09 Addressing Common Publisher Pain Points11:38 Case Study: GSTV and FatTail Collaboration15:10 Integration Challenges in DOOH19:07 Strategizing Sales Approaches for DOOH PublishersLearn more about FatTail here: https://www.fattail.com/Connect with Al here: https://www.linkedin.com/in/alvaro-villa-4034627/Join OOH Insider and Placer.ai at The Premier Leadership Conference for those Building the Future with Location Analytics, December 10th, 2024 at Pier Sixty. Use discount code OOHInsider70 to save 70% at registration. Learn more here.

This week on Sg2 Perspectives, host Tori Richie is joined by Associate Principal Ryan Hallenbeck and Principal Melissa Threlkeld about the consulting approach to programmatic expansion. Ryan and Melissa discuss strategies for health care organizations to consider when planning for expansion. Key topics include setting goals, focusing efforts, filling gaps, defining centers of gravity, enhancing care coordination, improving access, and staying relevant.   We are always excited to get ideas and feedback from our listeners. You can reach us at sg2perspectives@sg2.com, or visit the Sg2 company page on LinkedIn.  

AdTechGod interviews Catherine Perloff from Adweek, exploring her journey into advertising journalism, the intersection of advertising and culture, and the constant change in ad tech. They discuss trends such as consolidation, transparency, and the challenges of fraud in advertising, as well as the impact of walled gardens and regulation on the industry. The conversation also touches on the influence of AI on journalism and content creation, emphasizing the importance of quality in an increasingly crowded digital space.TakeawaysCatherine transitioned from financial journalism to advertising to explore the intersection of business and culture.The advertising industry is evolving with a greater emphasis on creativity and technology.Consolidation in ad tech is leading to fewer players and more focus on transparency.Fraud and invalid traffic remain significant challenges in the advertising space.Walled gardens are dominating ad spend, raising concerns for open web publications.Regulation is slowly impacting how tech companies operate in the advertising space.The lack of visibility in programmatic advertising is a persistent issue.AI is changing the content landscape, but quality journalism remains essential.Mediocre content generated by AI can dilute the quality of information available online.The future of advertising will depend on balancing innovation with maintaining quality.Chapters00:00 Introduction to Ad Tech and Journalism02:07 Catherine's Journey into Advertising Journalism04:01 The Intersection of Advertising, Culture, and Technology07:51 Trends in Ad Tech: Consolidation and Transparency12:02 Challenges of Fraud and Quality in Advertising16:12 The Role of Walled Gardens in Advertising20:04 Regulation and Its Impact on the Industry23:51 The Influence of AI on Journalism and Content CreationMentioned in this episode:Sweet Suites

Associate Vice President of North America at Lemma, Angelina Marmorato, delves into the dominance of programmatic buying in CTV. Discover how Lemma, a leading omnichannel SSP, revolutionizes supply curation for emerging formats with a privacy-first approach and dynamic creative. Explore how Lemma empowers agencies and brands with unprecedented control over inventory lifecycle and audience targeting, ensuring relevance and precision in advertising. Show NotesConnect With: Angelina Marmorato: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Eli Schwartz is a leading SEO consultant and the author of Product-Led SEO. He has worked with industry giants like Zapier, Tinder, Coinbase, Quora, LinkedIn, and WordPress to build and execute global SEO strategies that significantly enhanced their organic visibility at scale. In our conversation, Eli shares:• How AI and LLMs are reshaping the SEO landscape• Why you should be focused on mid-funnel SEO strategies• How to determine if SEO is the right approach for your business• Why SEO should be treated as a product rather than just a marketing tactic• SEO myths• The future of search in light of recent legal challenges faced by Google• Much more—Brought to you by:• Pendo—The only all-in-one product experience platform for any type of application• Brave Search—A smarter way to search• OneSchema—Import CSV data 10x faster—Find the transcript and show notes at: https://www.lennysnewsletter.com/p/rethinking-seo-in-the-age-of-ai-eli-schwartz—Where to find Eli Schwartz:• X: https://x.com/5le• LinkedIn: https://www.linkedin.com/in/schwartze/• Website: https://www.elischwartz.co/• Newsletter: https://www.productledseo.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Eli's background(02:10) The impact of AI on SEO strategies(11:34) Understanding search intent(15:30) Real-world impact and structured vs. unstructured data(20:19) Top-of-funnel vs mid-funnel SEO strategies(22:57) Case studies(31:29) Steps for getting started with SEO(35:20) Examples of when not to focus on SEO(39:17) Evaluating SEO investment(44:00) Understanding the tradeoffs in marketing channels(46:23) SEO conversion metrics and expectations(52:09) Understanding the time horizon of SEO(59:37) The role of AI in content creation(01:05:26) AI overviews (01:07:40) Brand building and SEO(01:09:51) Programmatic vs. editorial SEO strategies(01:16:06) Insights from the Google antitrust verdict(01:20:36) Google's dominance in search(01:23:52) The future of SEO and user choice(01:26:28) SEO myths debunked(01:36:58) Forecasting SEO success(01:44:18) The need for SEO expertise(01:46:26) Lightning round and closing thoughts—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe