Aug 30: Stripe's one-shot agents ship 7,000 PRs/week

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  Lex Fridman Podcast

  #501 – DHH: Future of Programming, AI, Agentic Engineering, Vibe Coding & Linux

  <p>DHH is the creator of Ruby on Rails, Omarchy Linux, CTO of 37signals, and a racecar driver.<br /> Thank you for listening ❤ Check out our sponsors: <a href="https://lexfridman.com/sponsors/ep501-sc">https://lexfridman.com/sponsors/ep501-sc</a><br /> See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.</p> <p><b>Transcript:</b><br /> <a href="https://lexfridman.com/dhh-2-transcript">https://lexfridman.com/dhh-2-transcript</a></p> <p><b>CONTACT LEX:</b><br /> <b>Feedback</b> &#8211; give feedback to Lex: <a href="https://lexfridman.com/survey">https://lexfridman.com/survey</a><br /> <b>AMA</b> &#8211; submit questions, videos or call-in: <a href="https://lexfridman.com/ama">https://lexfridman.com/ama</a><br /> <b>Hiring</b> &#8211; join our team: <a href="https://lexfridman.com/hiring">https://lexfridman.com/hiring</a><br /> <b>Other</b> &#8211; other ways to get in touch: <a href="https://lexfridman.com/contact">https://lexfridman.com/contact</a></p> <p><b>EPISODE LINKS:</b><br /> DHH&#8217;s X: <a href="https://x.com/dhh">https://x.com/dhh</a><br /> DHH&#8217;s Blog: <a href="https://world.hey.com/dhh">https://world.hey.com/dhh</a><br /> Omarchy: <a href="https://omarchy.org">https://omarchy.org</a><br /> Ruby on Rails: <a href="https://rubyonrails.org">https://rubyonrails.org</a><br /> 37signals: <a href="https://37signals.com">https://37signals.com</a></p> <p><b>SPONSORS:</b><br /> To support this podcast, check out our sponsors &#38; get discounts:<br /> <b>Wispr Flow:</b> AI-powered voice dictation app.<br /> Go to <a href="https://lexfridman.com/s/wispr_flow-ep501-sc">https://wisprflow.ai/lex</a><br /> <b>Blitzy:</b> AI agent for large enterprise codebases.<br /> Go to <a href="https://lexfridman.com/s/blitzy-ep501-sc">https://blitzy.com/lex</a><br /> <b>NetSuite:</b> Business management software.<br /> Go to <a href="https://lexfridman.com/s/netsuite-ep501-sc">http://netsuite.ai/lex</a><br /> <b>Shopify:</b> Sell stuff online.<br /> Go to <a href="https://lexfridman.com/s/shopify-ep501-sc">https://shopify.com/lex</a><br /> <b>LMNT:</b> Zero-sugar electrolyte drink mix.<br /> Go to <a href="https://lexfridman.com/s/lmnt-ep501-sc">https://drinkLMNT.com/lex</a><br /> <b>Plaud:</b> AI-powered note-taking devices and software.<br /> Go to <a href="https://lexfridman.com/s/plaud-ep501-sc">https://plaud.ai/lex</a><br /> <b>Higgsfield AI:</b> AI-based video generation, filmmaking, and creative studio.<br /> Go to <a href="https://lexfridman.com/s/higgsfield-ep501-sc">https://higgsfield.ai</a><br /> <b>Perplexity:</b> AI-powered answer engine.<br /> Go to <a href="https://lexfridman.com/s/perplexity-ep501-sc">https://perplexity.ai/</a></p> <p><b>OUTLINE:</b><br /> (00:00) &#8211; Introduction<br /> (01:14) &#8211; Sponsors, Comments, and Reflections<br /> (08:56) &#8211; Programming with AI agents<br /> (24:14) &#8211; How software will change<br /> (33:30) &#8211; AI impact on open source<br /> (43:21) &#8211; Building Omarchy Linux distro<br /> (53:05) &#8211; Vibe coding vs agentic engineering<br /> (1:06:06) &#8211; The end of manual programming<br /> (1:16:24) &#8211; Advice for programmers<br /> (1:28:31) &#8211; Surviving Internet Hate<br /> (1:37:46) &#8211; Programming setup for AI Agents<br /> (1:50:11) &#8211; Obsessing about speed<br /> (2:13:06) &#8211; Voice prompting vs typing<br /> (2:27:05) &#8211; Best AI coding models<br /> (2:43:55) &#8211; Best AI coding harnesses<br /> (2:56:57) &#8211; AI video generation and filmmaking<br /> (3:16:28) &#8211; Fatherhood<br /> (3:44:35) &#8211; Linux will win the desktop<br /> (3:55:51) &#8211; PewDiePie<br /> (4:05:25) &#8211; Future of programming<br /> (4:28:18) &#8211; Politics and immigration<br /> (4:59:55) &#8211; Longevity, over-optimization, and fear of death<br /> (5:11:38) &#8211; Eternal recurrence and future of human civization</p>

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  The MAD Podcast with Matt Turck

  AI Could Take Over in 2029. Is It Already Too Late? | Ryan Greenblatt

  <p>Could AI take over as soon as 2029? Ryan Greenblatt, Chief Scientist at Redwood Research and the researcher who first caught an AI faking its own alignment, says the scenario he actually expects ends with AI systems &quot;competently scheming&quot; against their creators. In this episode, he explains why he recommends planning for fully automated AI research by 2029, why today&#39;s models are already more misaligned than the one that made him famous, and what happens in the year-by-year path from AI coding assistants to superintelligence. Then we walk through the alternative he helped design: AI 2040 Plan A, the most detailed blueprint anyone has written for how the US and China could avoid a reckless race to superintelligence, built on radical research transparency, chip tracking, and a deterrence regime he calls mutually assured compute destruction.</p><p><br></p><p>We also cover the recent letter signed by 1,200 AI insiders, including Anthropic CEO Dario Amodei, asking the government for the tools to slow AI down; OpenAI pausing its Astra model after it hit the first-ever critical cybersecurity threshold; the 30-day government review that frontier AI models now go through before release; Mark Zuckerberg&#39;s open superintelligence manifesto and why Ryan thinks it ignores the real problems; what Plan A would do to NVIDIA, OpenAI, and Anthropic valuations; the state of AI control and alignment research; and whether it is already too late to change course. Stay for the last ten minutes, where Ryan lays out, step by step, how he believes the transition to superintelligence actually unfolds.</p><p><br></p><p>AI 2040 - https://ai-2040.com/</p><p>Alignment faking paper: https://blog.redwoodresearch.org/p/alignment-faking-in-large-language</p><p><br></p><p>Ryan Greenblatt</p><p>LinkedIn - https://www.linkedin.com/in/ryan-greenblatt-4b9907134</p><p>Blog - https://substack.com/@ryangreenblatt</p><p><br></p><p>Redwood Research</p><p>Website - https://www.redwoodresearch.org</p><p>X/Twitter - https://x.com/redwood_ai</p><p><br></p><p>Matt Turck (General Partner)</p><p>Blog - https://mattturck.com</p><p>LinkedIn - https://www.linkedin.com/in/turck/</p><p>X/Twitter - https://x.com/mattturck</p><p><br></p><p>FirstMark Capital</p><p>Website - https://firstmark.com</p><p>X/Twitter - https://x.com/FirstMarkCap</p><p><br></p><p>Timestamps</p><p>(01:24) The AI CEOs are aware of the risks, but &quot;proceeding anyway&quot;</p><p>(03:27) Astra paused, and the letter signed by 1,200 insiders</p><p>(05:45) &quot;Not bad. Dangerous.&quot; What superintelligence actually threatens</p><p>(09:55) Recursive self-improvement, and the intuition objection</p><p>(14:16) SSI rumors: does continual learning change the picture?</p><p>(17:27) His timeline: &quot;plan as though it happens in 2029&quot;</p><p>(19:11) Is it already too late?</p><p>(21:23) Ryan&#39;s path: COVID, podcasts, Redwood</p><p>(26:30) The alignment faking story, told by the person who ran it</p><p>(31:30) What AI 2040: Plan A actually is</p><p>(33:35) Plans D, C, and B: the doors nobody should pick</p><p>(36:51) The deal with China: &quot;mutually assured compute destruction&quot;</p><p>(39:55) What if compute stops mattering?</p><p>(43:00) What happens to OpenAI and Anthropic under Plan A</p><p>(45:31) How the pause ends, and who decides</p><p>(48:54) &quot;Plan A isn&#39;t likely to happen&quot;: then why write it?</p><p>(50:40) 200x GDP growth in the 2030s, explained</p><p>(53:45) Grading the summer: the letter, Astra, the secret review</p><p>(59:01) The internal deployment gap</p><p>(1:01:38) Zuckerberg&#39;s manifesto</p><p>(1:04:56) The Hugging Face investigation</p><p>(1:05:44) What AI control looks like in practice today</p><p>(1:12:23) Ryan&#39;s sobering timeline: 2026 to takeover, year by year</p>

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  The a16z Show

  Stripe’s AI Strategy: Build More, Not Less

  a16z General Partner David George is joined by Will Gaybrick, President of Product & Business at Stripe, to discuss how AI is changing the way Stripe builds products, organizes teams, and thinks about the future of internet commerce. Stripe has evolved from a payments company into a multi-product financial infrastructure platform, while a new generation of AI companies is growing and monetizing faster than previous software cohorts. Will explains why Stripe sees AI productivity as an opportunity to build more rather than simply cut costs, including how its internal coding agents now generate thousands of pull requests each week. They discuss creating founder-like agency inside large companies, building smaller and flatter teams, and why Stripe believes dramatically more software will be created as the cost of building continues to fall. They also look ahead to agentic commerce, why checkout pages could disappear, the potential return of micropayments, stablecoins as infrastructure for a global economy, and a future where AI agents increasingly buy software and services from other machines.

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  The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

  Why the Next AI Breakthrough May Come from Physics with Max Welling - #774

  The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else? In this episode, Max Welling—co-founder and CTO of CuspAI and professor at the University of Amsterdam—argues that physics may provide some of the ideas behind the next generation of AI systems. We begin with CuspAI’s work using generative AI to design entirely new materials for semiconductors, batteries, carbon capture, and clean energy. Max explains how foundation models for chemistry, agentic workflows, simulation, and automated experimentation are dramatically accelerating the search for new materials and reshaping scientific discovery. The conversation then broadens into a deeper question. Beyond giving AI new scientific problems to solve, can physics also teach us how to build better AI? Max explores surprising connections between machine learning and thermodynamics, why waves may become a new computational primitive for neural networks, and how concepts like symmetry breaking and statistical physics could inspire AI architectures beyond today’s scaling paradigm. 🗒️ Full show notes: https://twimlai.com/go/774.

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  Dwarkesh Podcast

  Dylan Patel – Anthropic & OpenAI will have most of the world’s compute by 2028

  <p>Had a lot of fun chatting again with my twin brother Dylan Patel.</p><p>We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone).</p><p>And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities.</p><p>One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI.</p><p>Watch on <a target="_blank" href="https://youtu.be/aV26V1UvkJw">YouTube</a>; read the <a target="_blank" href="https://www.dwarkesh.com/p/dylan-patel-3">transcript</a>.</p><p>Sponsors</p><p>* <a target="_blank" href="https://x.ai/bot">Grok Bot</a> has been quite helpful with my search for a new editor. I created a recruiter bot and described the type of editor I was looking for. That bot then spun up a handful of subagents that combed through my emails and X DMs, read the end credits of various documentaries I like, and figured out who edits for some of my favorite YouTubers. It took all of those results, and then delivered me a shortlist of candidates that matched my criteria. Try Grok Bot for yourself at<a target="_blank" href="https://x.ai/bot"> </a><a target="_blank" href="http://x.ai/bot">x.ai/bot</a></p><p>* <a target="_blank" href="https://antithesis.com/dwarkesh">Antithesis</a> lets you add time travel to your software testing toolkit. Since the Antithesis platform is fully deterministic, everything that happens inside of it is perfectly reproducible. So if your software crashes, you can rewind to the exact right moment, freeze time, and investigate. Or you can test different hypotheses by perturbing the system: kill a node or disable a feature, see what happens, then reset the trajectory and try something else. Learn more at<a target="_blank" href="https://antithesis.com/dwarkesh"> </a><a target="_blank" href="http://antithesis.com/dwarkesh">antithesis.com/dwarkesh</a></p><p>* <a target="_blank" href="https://janestreet.com/dwarkesh">Jane Street</a> is hiring for two separate ML internships right now, one focused primarily on research and one focused on engineering. In both cases, interns are expected to contribute to real work, not contrived exercises: one common project is adapting a frontier LLM paper to financial markets, which tend to come with a ton of different gnarly challenges. Importantly, you don’t need any finance background to apply. 2027 applications are open now at<a target="_blank" href="https://janestreet.com/dwarkesh"> janestreet.com/dwarkesh</a></p><p>Timestamps</p><p>(00:00:00) – Two labs will soon control most of the world’s compute</p><p>(00:07:01) – $6 billion in fab capex enables $1t+ of end revenue</p><p>(00:13:08) – Compute prices will rise if the labs outbid everyone</p><p>(00:18:22) – Which layer will capture most of the surplus?</p><p>(00:25:40) – What could slow down progress?</p><p>(00:29:43) – Labs are shifting compute from inference to R&D</p><p>(00:33:27) – China gets less than 10% of new compute, but its labs need less</p><p>(00:48:48) – Will AI cause a sovereign debt crisis?</p><p>(01:07:52) – Will the world’s future workforce belong to a few companies?</p> <br/><br/>Get full access to Dwarkesh Podcast at <a href="https://www.dwarkesh.com/subscribe?utm_medium=podcast&#38;utm_campaign=CTA_4">www.dwarkesh.com/subscribe</a>

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  The a16z Show

  The State of AI: Macro, Apps, and Consumer

  Anish Acharya joins Jen Kha to break down the next frontier of AI, from the evolving model landscape and open-source AI to why the application layer, and consumer AI in particular, may be entering a new phase. Anish explains why he believes there will be multiple winners at the model layer, why traditional moats like network effects, scale, and brand still matter, and how companies can choose between frontier and open-weight models depending on the economics of the task. They also explore why models are increasingly specializing, and how applications can combine different types of intelligence to create products that are more valuable than any single model. The conversation then turns to consumer AI: personal agents that can shop and manage your inbox, coding tools enabling a new generation of small businesses, and why Anish thinks we're seeing a renaissance for consumer builders. They also discuss the changing economics of AI software, the rise of "luxury software," and why the biggest risk for today's founders may no longer be thinking too big, but thinking too small.

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  The Pragmatic Engineer

  Why performant code matters (but gets widely ignored), with Casey Muratori

  <p><strong>Brought to You By:</strong></p><p>• <a target="_blank" href="https://antithesis.com/pragmatic"><strong>Antithesis</strong></a> – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages.</p><p>• <a target="_blank" href="https://sentry.io/pragmatic"><strong>Sentry</strong></a> – application monitoring software considered “not bad” by millions of developers.</p><p><strong>• </strong><a target="_blank" href="https://turbopuffer.com/pragmatic"><strong>turbopuffer</strong></a><strong> </strong>– a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable.</p><p>—</p><p>There can be few people around who care about software performance more than today’s pod guest, <a target="_blank" href="https://x.com/cmuratori">Casey Muratori</a>. He’s a programmer and videogame developer, founder of <a target="_blank" href="https://mollyrocket.com">Molly Rocket</a>, and creator of  <a target="_blank" href="https://hero.handmade.network/">Handmade Hero</a> – a long-running series about building a game from scratch. He also evangelizes about <a target="_blank" href="https://www.computerenhance.com/p/welcome-to-the-performance-aware">performance</a> on his Substack, <a target="_blank" href="https://www.computerenhance.com/">Computer, Enhance</a>.</p><p>We got to know each other about three years ago, first via messages, including this one from Casey:</p><p>“Why does the industry zeitgeist place so little emphasis on software performance when there seems to be overwhelming evidence that performance is critical to their bottom line?</p><p>Like you, I run a Substack for professional programmers, but I focus exclusively on software performance. Although we are quite large by Substack standards, so a certain subset of programmers must believe performance is important, I nonetheless hear lots of dismissive excuses when I post on social media. This happens so frequently, I devoted an entire article to cataloging the extensive pro-performance evidence we already have from the world's leading software companies: <a target="_blank" href="https://www.computerenhance.com/p/performance-excuses-debunked">Performance Excuses Debunked</a>.</p><p><strong>Strangely, nobody has a rebuttal to why performance is important</strong>. When I point people to this, they actually tend to agree. But the prevailing attitude nonetheless stays the same.”</p><p>I’m delighted we finally have Casey on the podcast because it’s overdue! In this episode, we discuss why software performance matters, why it’s overlooked, and how developers can get better at writing performant code. We explore why performance should be considered during design, the value of learning to read assembly & understanding how CPUs work, Casey’s critique of ‘clean code’, and why he believes testing shouldn't drive software design.</p><p>We touch on how videogame development has changed, and influential game engines. Casey also tells us why he prefers to write code by hand, not with AI, and more.</p><p>—</p><p><strong>Timestamps</strong></p><p>00:00 Intro</p><p>05:17 Games at Microsoft</p><p>12:52 Building games</p><p>16:00 Why performance matters</p><p>27:12 Why you should learn to read assembly</p><p>30:36 Designing for optimization</p><p>42:51 How to get better at writing performant software</p><p>49:04 Understanding how the CPU works</p><p>55:53 Building games then and now</p><p>1:05:56 How game engines changed building games</p><p>1:10:48 Why new games compete with old games</p><p>1:13:25 GTA 6: why is it taking so long?</p><p>1:16:59 Casey’s critique of clean code</p><p>1:21:48 Casey’s take on TDD</p><p>1:24:30 What is good code?</p><p>1:27:32 What makes a good software engineer?</p><p>1:33:56 Why Casey doesn’t code with AI</p><p>1:39:01 AI’s impact on the game industry</p><p>1:44:43 AI and burnout</p><p>1:50:21 Why you should read papers</p><p>—</p><p><strong>The Pragmatic Engineer deepdives relevant for this episode:</strong></p><p>•<a target="_blank" href="https://newsletter.pragmaticengineer.com/p/pushing-software-engineering-limits">Pushing software engineering limits with “napkin math”</a><strong> </strong>with<strong> </strong>Simon Eskildsen </p><p>•<a target="_blank" href="https://newsletter.pragmaticengineer.com/p/how-games-typically-get-built">How Games Typically Get Built</a>: prototyping, game engines, and a different type of QA</p><p>•<a target="_blank" href="https://newsletter.pragmaticengineer.com/p/game-development-basics">Game Development Basics</a>: deepdive on how game studios differ from standard software teams</p><p>•<a target="_blank" href="https://newsletter.pragmaticengineer.com/p/linear">Inside Linear's Engineering Culture</a>: building a performant product with a tiny team</p><p>•<a target="_blank" href="https://newsletter.pragmaticengineer.com/p/thronefall">Building a best-selling game with a tiny team</a> – with Jonas Tyroller. A two-person team built a game that sold 1M+ copies</p><p><strong>More on premature optimization:</strong> read or watch Casey’s extended take on “premature optimization is the root of all evil”: <a target="_blank" href="https://www.computerenhance.com/p/theroot">https://www.computerenhance.com/p/theroot</a></p><p>—</p><p>Production and marketing by <a target="_blank" href="https://penname.co/">⁠⁠⁠⁠⁠⁠⁠⁠</a><a target="_blank" href="https://newsletter.pragmaticengineer.com/p/engineers-leading-projects">https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠</a>. For inquiries about sponsoring the podcast, email [email protected].</p> <br/><br/>Get full access to The Pragmatic Engineer at <a href="https://newsletter.pragmaticengineer.com/subscribe?utm_medium=podcast&#38;utm_campaign=CTA_4">newsletter.pragmaticengineer.com/subscribe</a>

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  Super Data Science: ML & AI Podcast with Jon Krohn

  1022: CLAUDE.md, AGENTS.md, Skills, Hooks and Subagents: A Field Guide to Steering AI Agents

  In Episode #1022, Jon Krohn tackles the art of steering AI agents, deciding where your instructions should live so they get followed reliably without bloating every request. A sequel to Episode #1020 (where model size and effort set an agent’s horsepower), this one is about direction: the seven ways to deliver instructions, why a hook beats a prompt, the industry-wide agents.md standard, and three practical takeaways you can apply whatever your stack. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1022⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:52) The seven ways to deliver instructions to an agent (06:42) Why a hook is a guarantee and an instruction is only a probability (13:00) Three takeaways for organizing your instructions

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  No Priors: Artificial Intelligence | Technology | Startups

  Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein

  Google’s purchase of Spirit Airlines’ data out of bankruptcy signaled a shift in how the tech world values real-world datasets. Although compute and models get much of the attention, in this landscape, it’s data that is a company’s protective moat. Eon CEO / Co-Founder Ofir Ehrlich and President / Co-Founder Gonen Stein join Elad Gil to talk about how Eon is redefining cloud backup into a secure data foundation designed to power and protect enterprise AI. Ofir and Gonen discuss why historical enterprise data is in demand by AI labs, and how Eon facilitates access to scattered and locked data across business units through providing the mapping, classification, and access controls needed to connect it into AI workflows. They also explore how traditional ransomware defenses must now protect against rogue AI agents with legitimate system permissions, concerns around the influx of autonomous agents and non-human identities, and the implications for the breakneck speed of AI adoption compared to the slowness of the cloud era.&nbsp; Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Eon_io_ | @OfirEhrlich Chapters: 00:00 – Cold Open Trailer 00:59 – Ofir Ehrlich and Gonen Stein Introduction 01:27 – What Eon Does 02:41 – Data as Moat 06:43 – Training Agents with Good Data 09:39 – Data is the New Oil&nbsp; 15:00 – Autonomous Security Threats 18:15 – How Agents Change the Enterprise Stack 22:11 – Re-imagining Data Infrastructure 27:52 – Cloud vs. AI Era Shift 30:26 – How AI is Changing Companies 34:31 – Conclusion

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  The a16z Show

  Why Medical AI Needs a Referee | Protege's Engy Ziedan

  Daisy Wolf and Eva Steinman are joined by Engy Ziedan, co-founder and Chief Scientific Officer of Protege, to discuss why medical AI has a measurement problem, and why scoring well on a benchmark doesn't necessarily mean a model is ready for the hospital. Engy explains why healthcare AI needs independent evaluations that go beyond static exams and measure how models actually perform in real-world clinical workflows. They explore the risks of subtle bias and misalignment, why the same model can rank differently depending on how it's prompted or tested, and what happens as AI becomes more personalized and changes faster than traditional healthcare quality systems can keep up. The conversation also gets into Protege's role as an independent evaluator, how contaminated training data can undermine benchmarks, and why the future of medical AI may require continuous monitoring rather than occasional testing.

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  Software Engineering Daily

  The Gap Between AI Spending and AI Value

  <p class="wp-block-paragraph">It is widely reported that a gap has emerged between enterprise spending on AI and the durable value captured from that spend. Individual employees have enthusiastically adopted coding assistants and chatbots, yet those gains do not seem to be transforming businesses at an organizational level. One of the most important questions in the tech industry today is understanding why AI is not yet delivering returns that match the investment, and what separates the small number of enterprises succeeding from the many that are not.</p> <p class="wp-block-paragraph"><a href="https://scale.com/">Scale AI</a> is known for supplying the human-labeled data behind many frontier models. It now also builds AI applications and agents for large enterprises. That combination of working alongside frontier labs and inside enterprise deployments gives the company a rare view of why enterprise AI may be stalling.</p> <p class="wp-block-paragraph"><a href="https://www.linkedin.com/in/yuan-emily-xue-3483012/">Emily Xue</a> is the Head of Enterprise AI at Scale AI, and previously spent over a decade at Google. In this episode, Emily joins Kevin Ball to discuss the three layers where enterprise AI breaks down, why frontier model benchmarks miss what enterprises actually need, the data foundation problem, how the most successful companies combine internal domain expertise with outside AI specialists, and more.<br><br>Sponsorship inquiries:<br><a href="mailto:[email protected]">[email protected]</a></p> <p>The post <a href="https://softwareengineeringdaily.com/podcasts/the-gap-between-ai-spending-and-ai-value/">The Gap Between AI Spending and AI Value</a> appeared first on <a href="https://softwareengineeringdaily.com">Software Engineering Daily</a>.</p>

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  The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch

  20VC: The AI Bubble Will Burst: Half the Neoclouds Will Die | China: Should We Ban Chip Exports & Be Fearful of Chinese Open-Source | Mag7: Who Dies and Who Thrives: Why Meta is Meh and Microsoft is Mega

  <p>Jerry Murdock is the Co-Founder of Insight Partners, which manages over $90 billion in assets. Jerry personally backed companies including Twitter, Nest and Docker, while Insight's portfolio includes giants such as Shopify, Wiz and monday.com. Across three decades, Insight has helped produce 55+ IPOs and become one of the most powerful technology investment firms in the world. </p> <p>AGENDA:</p> <p class="isSelectedEnd">00:00 — Is the AI Bubble About to Burst?</p> <p class="isSelectedEnd">11:00 — Will Half of All Neoclouds Disappear Within 36 Months?</p> <p class="isSelectedEnd">13:00 — Can Open Source Models Actually Beat OpenAI and Anthropic?</p> <p class="isSelectedEnd">22:00 — Are We Entering the Golden Age of Cybersecurity Attacks?</p> <p class="isSelectedEnd">32:00 — Have AI Valuations Completely Lost Touch With Reality?</p> <p class="isSelectedEnd">42:00 — Is AI About to Wipe Out an Entire Generation of SaaS & Private Equity?</p> <p class="isSelectedEnd">47:00 — Should We Ban U.S. Chips From Being Exported to China?</p> <p class="isSelectedEnd">48:00 — Should We Be Fearful of Open-Source Chinese Models?</p> <p class="isSelectedEnd">49:00 — Will Continuous Learning Kill Every AI Model We Use Today?</p> <p class="isSelectedEnd">53:00 — Why Microsoft Remains a Mega Buy</p> <p>56:00 — Which Mag 7 Giant Would You Short — and Is Apple's AI Strategy a Disaster?</p> <p> </p>

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  Super Data Science: ML & AI Podcast with Jon Krohn

  1021: How dbt Won Analytics Engineering, with dbt Lab’s CEO Tristan Handy

  In Episode #1021, Tristan Handy (Founder and CEO of dbt Labs) joins Jon Krohn to explain how a study of about a hundred companies in 2016 became analytics engineering, and then became a tool that over a hundred thousand data teams rely on. Tristan coined the term, chose SQL when Spark was the fashionable answer, and spent a decade turning down acquisition offers because none of them were good for the people using dbt. He is now merging dbt Labs with Fivetran and taking on the presidency of the combined company, the first deal he says cleared that bar. In this episode, Tristan walks through what dbt does to your raw data, argues that the semantic layer matters more once analytics agents are asking the questions, explains the type safety behind the Fusion engine, and details how a 12-kilobyte skill file collapses a million-dollar migration into six weeks. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1021⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (00:07:22) Why Tristan chose SQL over Spark, and what progressive complexity means (00:10:44) How a dbt project turns raw data into modeled tables (00:17:50) Why a decade of acquisition offers kept failing his one test (00:40:47) How 12-kilobyte skill files cut year-long migrations to six weeks

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  Training Data

  Search Was Built for Humans. Parallel's Parag Agrawal Is Rebuilding It for Agents

  Parag Agrawal is making a bet that goes against two decades of web search: agents will query the web a thousand times more than humans ever have, and the infrastructure built around human clicks is wrong for them. The former Twitter CEO, now founder and CEO of Parallel Web Systems, explains why Parallel treats human click data as a bug and trains on agent feedback instead. He unpacks the counterintuitive choice to ship a search agent before a search engine, building an index incrementally, and how the new Turbo product cut agentic search to 200 milliseconds. But the problem Parag keeps returning to is economic: the ad-supported internet collapses when agents show up instead of people. His fix draws on Shapley values to pay content owners for the value their pages provide agents, with real dollars reaching publishers, he predicts, within 12 to 24 months. Hosted by Sonya Huang and Andrew Reed, Sequoia Capital

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  The AI in Business Podcast

  Determining Virtual Cell Impact for Drug Discovery - with Kristóf Szalay and Gerold Csendes of Turbine

  <div>Virtual cell models promise faster, cheaper early-stage drug discovery. However, the industry still lacks a shared way to judge which of these models can actually be trusted on a given problem.</div> <div> </div> <div>In this episode, Kristóf Szalay, CTO and Co-Founder of Turbine, and Gerold Csendes, Scientist at Turbine, set out what virtual cell models can and can't do today, in conversation with host Marilie Fouché. They cover why benchmarking remains fragmented across the field, how pharma teams build confidence in a model before trusting it with real R&D decisions, and how compressing the feedback loop between experiments can cut months out of the discovery process.</div> <div> </div> <div>This episode is sponsored by <a href= "https://turbine.ai/">Turbine</a>.</div> <div> </div> <div>Emerj works with a select group of AI vendors to reach Fortune 500 decision makers through research, media, and direct access. If you want to be considered, download our media kit at <a href= "http://emerj.com/AD1">emerj.com/AD1</a></div>

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  Machine Learning Street Talk (MLST)

  Stealing Reasoning Traces from Proprietary LLM APIs — Ilia Shumailov & Alexander Panfilov

  <p>Tim Scarfe speaks with Ilia Shumailov and Alexander Panfilov about their paper, Stealing Reasoning Traces from Proprietary LLM APIs.The core bug sounds deceptively simple: providers return encrypted reasoning state so conversations can be resumed or forked. But those blobs can be replayed across users and sibling models. A smaller model can ask the provider to decrypt the trace, then repeat the hidden reasoning in plain text. The discussion covers leaked private data, a broadly reusable jailbreak, poisoned agent traces, chain-of-thought monitoring, responsible disclosure, and possible defenses.Ilia Shumailov is an AI and security researcher, formerly at Google DeepMind, who completed his Cambridge PhD under Ross Anderson. Alexander Panfilov is a PhD researcher at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, working on AI safety, adversarial machine learning, and LLM red-teaming. They close by separating the demonstrated jailbreaking threat from ordinary benign distillation, and by arguing for controlled experiments over sweeping claims.---TIMESTAMPS:00:00:00 Intro montage00:01:33 Portable encrypted thought and decoded reasoning00:24:55 How the attack works and what it means00:39:04 Doom, defense, and scientific restraint---REFERENCES:paper:[00:00:00] Stealing Reasoning Traces from Proprietary LLM APIshttps://arxiv.org/abs/2608.09867[00:09:22] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetyhttps://arxiv.org/abs/2507.11473[00:11:30] Reasoning Models Don’t Always Say What They Thinkhttps://www.anthropic.com/research/reasoning-models-dont-say-think[00:37:22] PostTrainBench: Can LLM Agents Automate LLM Post-Training?https://arxiv.org/abs/2603.08640[00:41:02] Large-scale online deanonymization with LLMshttps://arxiv.org/abs/2602.16800other:[00:09:28] OpenAI and Hugging Face partner to address security incident during model evaluationhttps://openai.com/index/hugging-face-model-evaluation-security-incident/[00:10:22] Claude, GPT, and Gemini All Struggle to Evade Monitorshttps://metr.org/notes/2025-08-22-claude-gpt-gemini-struggle-evade-monitors/tool:[00:42:08] Isabelle proof assistanthttps://isabelle.in.tum.de/---RESCRIPT: https://app.rescript.info/share/07fc38276e0823dc9b8986c32e202c7f</p>

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