Podcast appearances and mentions of pat grady

Former Airbus CTO Paul Eremenko shares his vision for bringing AI to physical engineering, starting with Archie—an AI agent that works alongside human engineers. P-1 AI is tackling the challenge of generating synthetic training data to teach AI systems about complex physical systems, from data center cooling to aircraft design and beyond. Eremenko explains how Archie breaks down engineering tasks into primitive operations and uses a federated approach combining multiple AI models. The goal is to progress from entry-level engineering capabilities to eventually achieving engineering AGI that can design things humans cannot. Hosted by Sonya Huang and Pat Grady, Sequoia Capital

CEO Amit Bendov shares how Gong evolved from a meeting transcription tool to an AI-powered revenue platform that's increasing sales capacity by up to 60%. He explains why task-specific AI agents are the key to enterprise adoption, and why human accountability will remain crucial even as AI takes over routine sales tasks. Amit also reveals how Gong survived recent market headwinds by expanding their product suite while maintaining their customer-first approach. Hosted by Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode:  “New paradigm of AI architectures”: Yann LeCun's talk at Davos where he talks about the world beyond transformers and LLMs in the next 3-5 years. The Beginning of Infinity: Book by David Deutsch that Amit says is “mind-changing.”

Workday CEO and Sequoia partner Carl Eschenbach explains how the company is evolving its platform to handle both human and AI workers. He shares Workday's three-pronged approach to monetizing AI through seat-based pricing, role-based agents and consumption-based API access. Eschenbach discusses why domain-specific agents with curated data will be more valuable than general-purpose models in the enterprise, and how Workday is helping enterprises navigate the transition to an AI-powered workplace while maintaining human connection. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Former Outreach CEO Manny Medina discusses his new company Paid, which provides billing, pricing and margin management tools for AI companies. He explains why traditional SaaS pricing models don't work for AI businesses, and breaks down emerging approaches like outcome-based and agent-based pricing. Manny shares why he believes focused AI applications targeting specific workflows will win over broad platforms, while emphasizing that AI companies need better tools to understand their unit economics and capture more value. Hosted by Pat Grady and Lauren Reeder, Sequoia Capital Mentioned in this episode: CPQ: Configure, Price, Quote Invent and Wander: Book by Jeff Bezos and Walter Isaacson Foundations of Statistical Natural Language Processing: 1999 book by Chris Manning and Hinrich Schütze that Manny cites as a piece of AI content every AI founder should read. (still in print, companion site here) The fox and the hedgehog:  Quandri  XBOW HappyRobot  Owl Crosby

Patrick Hsu, co-founder of Arc Institute, discusses the opportunities for AI in biology beyond just drug development, and how Evo 2, their new biology foundation model, is enabling a broad ecosystem of applications. Evo 2 was trained on a vast dataset of genomic data to learn evolutionary patterns that would have taken years to find; as a result, the model can be used for applications from identifying mutations that cause disease to designing new molecular and even genome scale biological systems. Hosted by Josephine Chen and Pat Grady, Sequoia Capital Mentioned in this episode: Sequence modeling and design from molecular to genome scale with Evo: Public pre-print of original Evo paper Genome modeling and design across all domains of life with Evo 2: Public pre-print of Evo 2 paper ClinVar: NIH database of the genes that are known to cause disease, and mutations in those genes causally associated with disease state Sequence Read Archive: Massive NIH database of gene sequencing data  Machines of Loving Grace: Daria Amodei essay that Patrick cites on how AI could transform the world for the better Arc Virtual Cell Atlas: Arc's first step toward assembling, curating and generating large-scale cellular data from AI-driven biological discovery (among many other tools) Protein Data Bank (PDB): a global archive of 3D structural information of biomolecules used by DeepMind to train AlphaFold OpenAI Deep Research: The one AI app Patrick uses daily

Christopher O'Donnell believes the fundamental problems with CRM—incomplete data, complex workflows, siloed work products and the fear of leads falling through the cracks—can finally be solved through AI. Founder of Day.ai and former Chief Product Officer of HubSpot, Christopher explains how his team is building a system that automatically captures the full context of customer relationships while giving users transparency and control. He shares lessons from building HubSpot's CRM and why he's taking a deliberate approach to product development despite the pressure to scale quickly in the AI era. Hosted by Pat Grady, Sequoia Capital  Mentioned in this episode: The Innovator's Dilemma: Classic book by Clay Christensen (referenced regarding HubSpot's second S-curve strategy) Hubspot CRM: The only product to successfully challenge Salesforce's dominance in the CRM category From Super Mario Brothers to Elden Ring: Analogy to what an AI-powered CRM experience can be through comparison of video games launched in 1985 vs 2022 Punk'd: Hidden camera–practical joke reality television series that premiered on MTV in 2003, created by Ashton Kutcher and Jason Goldberg Slow is smooth and smooth is fast: SEALs-derived concept mentioned regarding product development) Aga stove (highlighted as extraordinary product design example)

Harvey CEO Winston Weinberg explains why success in legal AI requires more than just model capabilities—it demands deep process expertise that doesn't exist online. He shares how Harvey balances rapid product development with earning trust from law firms through hyper-personalized demos and deep industry expertise. The discussion covers Harvey's approach to product development—expanding specialized capabilities then collapsing them into unified workflows—and why focusing on complex work like international mergers creates the most defensible position in legal AI. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

OpenEvidence is transforming how doctors access medical knowledge at the point of care, from the biggest medical establishments to small practices serving rural communities. Founder Daniel Nadler explains his team's insight that training smaller, specialized AI models on peer-reviewed literature outperforms large general models for medical applications. He discusses how making the platform freely available to all physicians led to widespread organic adoption and strategic partnerships with publishers like the New England Journal of Medicine. In an industry where organizations move glacially, 10-20% of all U.S. doctors began using OpenEvidence overnight to find information buried deep in the long tail of new medical studies, to validate edge cases and improve diagnoses. Nadler emphasizes the importance of accuracy and transparency in AI healthcare applications. Hosted by: Pat Grady, Sequoia Capital  Mentioned in this episode:  Do We Still Need Clinical Language Models?: Paper from OpenEvidence founders showing that small, specialized models outperformed large models for healthcare diagnostics Chinchilla paper: Seminal 2022 paper about scaling laws in large language models Understand: Ted Chiang sci-fi novella published in 1991

Palo Alto Networks's CEO Nikesh Arora dispels DeepSeek hype by detailing all of the guardrails enterprises need to have in place to give AI agents “arms and legs.” No matter the model, deploying applications for precision-use cases means superimposing better controls. Arora emphasizes that the real challenge isn't just blocking threats but matching the accelerated pace of AI-powered attacks, requiring a fundamental shift from prevention-focused to real-time detection and response systems. CISOs are risk managers, but legacy companies competing with more risk-tolerant startups need to move quickly and embrace change.  Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Cortex XSIAM: Security operations and incident remediation platform from Palo Alto Networks

MongoDB product leader Sahir Azam explains how vector databases have evolved from semantic search to become the essential memory and state layer for AI applications. He describes his view of how AI is transforming software development generally, and how combining vectors, graphs and traditional data structures enables high-quality retrieval needed for mission-critical enterprise AI use cases. Drawing from MongoDB's successful cloud transformation, Azam shares his vision for democratizing AI development by making sophisticated capabilities accessible to mainstream developers through integrated tools and abstractions. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Introducing ambient agents: Blog post by Langchain on a new UX pattern where AI agents can listen to an event stream and act on it  Google Gemini Deep Research: Sahir enjoys its amazing product experience Perplexity: AI search app that Sahir admires for its product craft Snipd: AI powered podcast app Sahir likes

Founded in early 2023 after spending years at Stripe and OpenAI, Gabriel Hubert and Stanislas Polu started Dust with the view that one model will not rule them all, and that multi-model integration will be key to getting the most value out of AI assistants. In this episode we'll hear why they believe the proprietary data you have in silos will be key to unlocking the full power of AI, get their perspective on the evolving model landscape, and how AI can augment rather than replace human capabilities. Hosted by: Konstantine Buhler and Pat Grady, Sequoia Capital 00:00 - Introduction 02:16 - One model will not rule them all 07:15 - Reasoning breakthroughs 11:15 - Trends in AI models 13:32 - The future of the open source ecosystem 16:16 - Model quality and performance 21:44 - “No GPUs before PMF” 27:24 - Dust in action 37:40 - How do you find “the makers” 42:36 - The beliefs Dust lives by 50:03 - Keeping the human in the loop 52:33 - Second time founders 56:15 - Lightning round

Years before co-founding Glean, Arvind was an early Google employee who helped design the search algorithm. Today, Glean is building search and work assistants inside the enterprise, which is arguably an even harder problem. One of the reasons enterprise search is so difficult is that each individual at the company has different permissions and access to different documents and information, meaning that every search needs to be fully personalized. Solving this difficult ingestion and ranking problem also unlocks a key problem for AI: feeding the right context into LLMs to make them useful for your enterprise context. Arvind and his team are harnessing generative AI to synthesize, make connections, and turbo-change knowledge work. Hear Arvind's vision for what kind of work we'll do when work AI assistants reach their potential.  Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  00:00 - Introduction 08:35 - Search rankings  11:30 - Retrieval-Augmented Generation 15:52 - Where enterprise search meets RAG 19:13 - How is Glean changing work?  26:08 - Agentic reasoning  31:18 - Act 2: application platform  33:36 - Developers building on Glean  35:54 - 5 years into the future  38:48 - Advice for founders

In recent years there's been an influx of theoretical physicists into the leading AI labs. Do they have unique capabilities suited to studying large models or is it just herd behavior? To find out, we talked to our former AI Fellow (and now OpenAI researcher) Dan Roberts. Roberts, co-author of The Principles of Deep Learning Theory, is at the forefront of research that applies the tools of theoretical physics to another type of large complex system, deep neural networks. Dan believes that DLLs, and eventually LLMs, are interpretable in the same way a large collection of atoms is—at the system level. He also thinks that emphasis on scaling laws will balance with new ideas and architectures over time as scaling asymptotes economically. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks, by Daniel A. Roberts, Sho Yaida, Boris Hanin Black Holes and the Intelligence Explosion: Extreme scenarios of AI focus on what is logically possible rather than what is physically possible. What does physics have to say about AI risk? Yang-Mills & The Mass Gap: An unsolved Millennium Prize problem AI Math Olympiad: Dan is on the prize committee

NotebookLM from Google Labs has become the breakout viral AI product of the year. The feature that catapulted it to viral fame is Audio Overview, which generates eerily realistic two-host podcast audio from any input you upload—written doc, audio or video file, or even a PDF. But to describe NotebookLM as a “podcast generator” is to vastly undersell it. The real magic of the product is in offering multi-modal dimensions to explore your own content in new ways—with context that's surprisingly additive. 200-page training manuals become synthesized into digestible chapters, turned into a 10-minute podcast—or both—and shared with the sales team, just to cite one example. Raiza Martin and Jason Speilman join us to discuss how the magic happens, and what's next for source-grounded AI. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

All of us as consumers have felt the magic of ChatGPT—but also the occasional errors and hallucinations that make off-the-shelf language models problematic for business use cases with no tolerance for errors. Case in point: A model deployed to help create a summary for this episode stated that Sridhar Ramaswamy previously led PyTorch at Meta. He did not. He spent years running Google's ads business and now serves as CEO of Snowflake, which he describes as the data cloud for the AI era. Ramaswamy discusses how smart systems design helped Snowflake create reliable "talk-to-your-data" applications with over 90% accuracy, compared to around 45% for out-of-the-box solutions using off the shelf LLMs. He describes Snowflake's commitment to making reliable AI simple for their customers, turning complex software engineering projects into straightforward tasks.  Finally, he stresses that even as frontier models progress, there is significant value to be unlocked from current models by applying them more effectively across various domains. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode:  Cortex Analyst: Snowflake's talk-to-your-data API Document AI: Snowflake feature that extracts in structured information from documents

Combining LLMs with AlphaGo-style deep reinforcement learning has been a holy grail for many leading AI labs, and with o1 (aka Strawberry) we are seeing the most general merging of the two modes to date. o1 is admittedly better at math than essay writing, but it has already achieved SOTA on a number of math, coding and reasoning benchmarks. Deep RL legend and now OpenAI researcher Noam Brown and teammates Ilge Akkaya and Hunter Lightman discuss the ah-ha moments on the way to the release of o1, how it uses chains of thought and backtracking to think through problems, the discovery of strong test-time compute scaling laws and what to expect as the model gets better.  Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Learning to Reason with LLMs: Technical report accompanying the launch of OpenAI o1. Generator verifier gap: Concept Noam explains in terms of what kinds of problems benefit from more inference-time compute. Agent57: Outperforming the human Atari benchmark, 2020 paper where DeepMind demonstrated “the first deep reinforcement learning agent to obtain a score that is above the human baseline on all 57 Atari 2600 games.” Move 37: Pivotal move in AlphaGo's second game against Lee Sedol where it made a move so surprising that Sedol thought it must be a mistake, and only later discovered he had lost the game to a superhuman move. IOI competition: OpenAI entered o1 into the International Olympiad in Informatics and received a Silver Medal. System 1, System 2: The thesis if Danial Khaneman's pivotal book of behavioral economics, Thinking, Fast and Slow, that positied two distinct modes of thought, with System 1 being fast and instinctive and System 2 being slow and rational. AlphaZero: The predecessor to AlphaGo which learned a variety of games completely from scratch through self-play. Interestingly, self-play doesn't seem to have a role in o1. Solving Rubik's Cube with a robot hand: Early OpenAI robotics paper that Ilge Akkaya worked on. The Last Question: Science fiction story by Isaac Asimov with interesting parallels to scaling inference-time compute. Strawberry: Why? O1-mini: A smaller, more efficient version of 1 for applications that require reasoning without broad world knowledge. 00:00 - Introduction 01:33 - Conviction in o1 04:24 - How o1 works 05:04 - What is reasoning? 07:02 - Lessons from gameplay 09:14 - Generation vs verification 10:31 - What is surprising about o1 so far 11:37 - The trough of disillusionment 14:03 - Applying deep RL 14:45 - o1's AlphaGo moment? 17:38 - A-ha moments 21:10 - Why is o1 good at STEM? 24:10 - Capabilities vs usefulness 25:29 - Defining AGI 26:13 - The importance of reasoning 28:39 - Chain of thought 30:41 - Implication of inference-time scaling laws 35:10 - Bottlenecks to scaling test-time compute 38:46 - Biggest misunderstanding about o1? 41:13 - o1-mini 42:15 - How should founders think about o1?

Adding code to LLM training data is a known method of improving a model's reasoning skills. But wouldn't math, the basis of all reasoning, be even better? Up until recently, there just wasn't enough usable data that describes mathematics to make this feasible. A few years ago, Vlad Tenev (also founder of Robinhood) and Tudor Achim noticed the rise of the community around an esoteric programming language called Lean that was gaining traction among mathematicians. The combination of that and the past decade's rise of autoregressive models capable of fast, flexible learning made them think the time was now and they founded Harmonic. Their mission is both lofty—mathematical superintelligence—and imminently practical, verifying all safety-critical software. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: IMO and the Millennium Prize: Two significant global competitions Harmonic hopes to win (soon) Riemann hypothesis: One of the most difficult unsolved math conjectures (and a Millenium Prize problem) most recently in the sights of MIT mathematician Larry Guth Terry Tao: perhaps the greatest living mathematician and Vlad's professor at UCLA Lean: an open source functional language for code verification launched by Leonardo de Moura when at Microsoft Research in 2013 that powers the Lean Theorem Prover mathlib: the largest math textbook in the world, all written in Lean Metaculus: online prediction platform that tracks and scores thousands of forecasters Minecraft Beaten in 20 Seconds: The video Vlad references as an analogy to AI math Navier-Stokes equations: another important Millenium Prize math problem. Vlad considers this more tractable that Riemann John von Neumann: Hungarian mathematician and polymath that made foundational contributions to computing, the Manhattan Project and game theory Gottfried Wilhelm Leibniz: co-inventor of calculus and (remarkably) creator of the “universal characteristic,” a system for reasoning through a language of symbols and calculations—anticipating Lean and Harmonic by 350 years! 00:00 - Introduction 01:42 - Math is reasoning 06:16 - Studying with the world's greatest living mathematician 10:18 - What does the math community think of AI math? 15:11 - Recursive self-improvement 18:31 - What is Lean? 21:05 - Why now? 22:46 - Synthetic data is the fuel for the model 27:29 - How fast will your model get better? 29:45 - Exploring the frontiers of human knowledge 34:11 - Lightning round

Customer service is hands down the first killer app of generative AI for businesses. The reasons are simple: the costs of existing solutions are so high, the satisfaction so low and the margin for ROI so wide. But trusting your interactions with customers to hallucination-prone LLMs can be daunting. Enter Sierra. Co-founder Clay Bavor walks us through the sophisticated engineering challenges his team solved along the way to delivering AI agents for all aspects of the customer experience that are delightful, safe and reliable—and being deployed widely by Sierra's customers. The Company's AgentOS enables businesses to create branded AI agents to interact with customers, follow nuanced policies and even handle customer retention and upsell. Clay describes how companies can capture their brand voice, values and internal processes to create AI agents that truly represent the business. Hosted by: Ravi Gupta and Pat Grady, Sequoia Capital Mentioned in this episode: Bret Taylor: co-founder of Sierra Towards a Human-like Open-Domain Chatbot: 2020 Google paper that introduced Meena, a predecessor of ChatGPT (followed by LaMDA in 2021) PaLM: Scaling Language Modeling with Pathways: 2022 Google paper about their unreleased 540B parameter transformer model (GPT-3, at the time, had 175B)  Avocado chair: Images generated by OpenAI's DALL·E model in 2022 Large Language Models Understand and Can be Enhanced by Emotional Stimuli: 2023 Microsoft paper on how models like GPT-4 can be manipulated into providing better results

After AlphaGo beat Lee Sedol, a young mechanical engineer at Google thought of another game reinforcement learning could win: energy optimization at data centers. Jim Gao convinced his bosses at the Google data center team to let him work with the DeepMind team to try. The initial pilot resulted in a 40% energy savings and led he and his co-founders to start Phaidra to turn this technology into a product. Jim discusses the challenges of AI readiness in industrial settings and how we have to build on top of the control systems of the 70s and 80s to achieve the promise of the Fourth Industrial Revolution. He believes this new world of self-learning systems and self-improving infrastructure is a key factor in addressing global climate change. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Mustafa Suleyman: Co-founder of DeepMind and Inflection AI and currently CEO of Microsoft AI, known to his friends as “Moose” Joe Kava: Google VP of data centers who Jim sent his initial email to pitching the idea that would eventually become Phaidra Constrained optimization: the class of problem that reinforcement learning can be applied to in real world systems  Vedavyas Panneershelvam: co-founder and CTO of Phaidra; one of the original engineers on the AlphaGo project Katie Hoffman: co-founder, President and COO of Phaidra  Demis Hassabis: CEO of DeepMind

In the first wave of the generative AI revolution, startups and enterprises built on top of the best closed-source models available, mostly from OpenAI. The AI customer journey moves from training to inference, and as these first products find PMF, many are hitting a wall on latency and cost. Fireworks Founder and CEO Lin Qiao led the PyTorch team at Meta that rebuilt the whole stack to meet the complex needs of the world's largest B2C company. Meta moved PyTorch to its own non-profit foundation in 2022 and Lin started Fireworks with the mission to compress the timeframe of training and inference and democratize access to GenAI beyond the hyperscalers to let a diversity of AI applications thrive. Lin predicts when open and closed source models will converge and reveals her goal to build simple API access to the totality of knowledge. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Pytorch: the leading framework for building deep learning models, originated at Meta and now part of the Linux Foundation umbrella Caffe2 and ONNX: ML frameworks Meta used that PyTorch eventually replaced Conservation of complexity: the idea that that every computer application has inherent complexity that cannot be reduced but merely moved between the backend and frontend, originated by Xerox PARC researcher Larry Tesler  Mixture of Experts: a class of transformer models that route requests between different subsets of a model based on use case Fathom: a product the Fireworks team uses for video conference summarization  LMSYS Chatbot Arena: crowdsourced open platform for LLM evals hosted on Hugging Face  00:00 - Introduction 02:01 - What is Fireworks? 02:48 - Leading Pytorch 05:01 - What do researchers like about PyTorch? 07:50 - How Fireworks compares to open source 10:38 - Simplicity scales 12:51 - From training to inference 17:46 - Will open and closed source converge? 22:18 - Can you match OpenAI on the Fireworks stack? 26:53 - What is your vision for the Fireworks platform? 31:17 - Competition for Nvidia? 32:47 - Are returns to scale starting to slow down? 34:28 - Competition 36:32 - Lightning round

In February, Sebastian Siemiatkowski boldly announced that Klarna's new OpenAI-powered assistant handled two thirds of the Swedish fintech's customer service chats in its first month. Not only were customer satisfaction metrics better, but by replacing 700 full-time contractors the bottom line impact is projected to be $40M. Since then, every company we talk to wants to know, “How do we get the Klarna customer support thing?” Co-founder and CEO Sebastian Siemiatkowski tells us how the Klarna team shipped this new product in record time—and how embracing AI internally with an experimental mindset is transforming the company. He discusses how AI development is proliferating inside the company, from customer support to marketing to internal knowledge to customer-facing experiences.  Sebastian also reflects on the impacts of AI on employment, society, and the arts while encouraging lawmakers to be open minded about the benefits. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: DeepL: Language translation app that Sebastian says makes 10,000 translators in Brussels redundant The Klarna brand: The offbeat optimism that the company is now augmenting with AI Neo4j: The graph database management system that Klarna is using to build Kiki, their internal knowledge base 00:00 Introduction 01:57 Klarna's business 03:00 Pitching OpenAI 08:51 How we built this 10:46 Will Klara ever completely replace its CS team with AI? 14:22 The benefits 17:25 If you had a policy magic wand… 21:12 What jobs will be most affected by AI? 23:58 How about marketing? 27:55 How creative are LLMs? 30:11 Klarna's knowledge graph, Kiki 33:10 Reducing the number of enterprise systems 35:24 Build vs buy? 39:59 What's next for Klarna with AI? 48:48 Lightning round

Crucible Moments will be back shortly with season 2. You'll hear from the founders of YouTube, DoorDash, Reddit, and more. In the meantime, we'd love to introduce you to a new original podcast, Training Data, where Sequoia partners learn from builders, researchers and founders who are defining the technology wave of the future: AI. The following conversation with Harrison Chase of LangChain is all about the future of AI agents—why they're suddenly seeing a step change in performance, and why they're key to the promise of AI.  Follow Training Data wherever you listen to podcasts, and keep an eye out for Season 2 of Crucible Moments, coming soon. LangChain's Harrison Chase on Building the Orchestration Layer for AI Agents Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned:  ReAct: Synergizing Reasoning and Acting in Language Models, the first cognitive architecture for agents SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent from researchers at Princeton Devin, autonomous software engineering from Cognition V0: Generative UI agent from Vercel GPT Researcher, a research agent  Language Model Cascades: 2022 paper by Google Brain and now OpenAI researcher David Dohan that was influential for Harrison in developing LangChain Transcript: https://www.sequoiacap.com/podcast/training-data-harrison-chase/

The current LLM era is the result of scaling the size of models in successive waves (and the compute to train them). It is also the result of better-than-Moore's-Law price vs performance ratios in each new generation of Nvidia GPUs. The largest platform companies are continuing to invest in scaling as the prime driver of AI innovation. Are they right, or will marginal returns level off soon, leaving hyperscalers with too much hardware and too few customer use cases? To find out, we talk to Microsoft CTO Kevin Scott who has led their AI strategy for the past seven years. Scott describes himself as a “short-term pessimist, long-term optimist” and he sees the scaling trend as durable for the industry and critical for the establishment of Microsoft's AI platform. Scott believes there will be a shift across the compute ecosystem from training to inference as the frontier models continue to improve, serving wider and more reliable use cases. He also discusses the coming business models for training data, and even what ad units might look like for autonomous agents. Hosted by: Pat Grady and Bill Coughran, Sequoia Capital Mentioned: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, the 2018 Google paper that convinced Kevin that Microsoft wasn't moving fast enough on AI.  Dennard scaling: The scaling law that describes the proportional relationship between transistor size and power use; has not held since 2012 and is often confused with Moore's Law. Textbooks Are All You Need: Microsoft paper that introduces a new large language model for code, phi-1, that achieves smaller size by using higher quality “textbook” data. GPQA and MMLU: Benchmarks for reasoning Copilot: Microsoft product line of GPT consumer assistants from general productivity to design, vacation planning, cooking and fitness. Devin: Autonomous AI code agent from Cognition Labs that Microsoft recently announced a partnership with. Ray Solomonoff: Participant in the 1956 Dartmouth Summer Research Project on Artificial Intelligence that named the field; Kevin admires his prescience about the importance of probabilistic methods decades before anyone else. 00:00 - Introduction 01:20 - Kevin's backstory 06:56 - The role of PhDs in AI engineering 09:56 - Microsoft's AI strategy 12:40 - Highlights and lowlights 16:28 - Accelerating investments 18:38 - The OpenAI partnership 22:46 - Soon inference will dwarf training 27:56 - Will the demand/supply balance change? 30:51 - Business models for data 36:54 - The value function 39:58 - Copilots 44:47 - The 98/2 rule 49:34 - Solving zero-sum games 57:13 - Lightning round

Pat Grady is one of the most successful growth investors of the last decade. As the Head of Sequoia's growth investing practice, Pat has invested in companies with a combined market cap exceeding $250BN. Among Pat's immense portfolio is Hubspot, Snowflake, ServiceNow, Okta, Amplitude, Zoom and Qualtrics. Pat is also one of the best acquirers of talent in venture hiring Andrew Reed, Matt Huang, Julien Bek. In Today's Episode with Pat Grady We Discuss: 1. The Sequoia Investment Process: What is the Sequoia investment process today? How has it changed over time? What could be improved about the process? Where is it weak? What is the biggest strength of the process? How do Sequoia remove politics from the investment decision-making process? Are the best deals "contrarian"? What does Pat mean when he says you do not "get extra points for being contrarian and right"? 2. What Sequoia Look for When Investing: What is Pat's framework for assessing founders? How does it differ when investing early vs late? Team, traction, TAM, how does Pat rank the three when investing? What have been Pat's biggest lessons on market sizing? Does Pat take market timing risk? How much weight does Pat place on "traction" when investing? How sustainable is PMF? 3. The Three Core Pillars of Venture: Sourcing: What does Pat rank Sequoia for sourcing? Who is the best at sourcing in the firm? Selecting: How does Pat rank Sequoia at picking? How has it changed over time? What could Sequoia do to improve their picking ability? Servicing: What does Pat give Sequoia for their "value add"? To what extent does Pat truly believe that venture investors do add value? 4. Pat Grady: AMA: Pat has hired some of the best in the next generation of venture investors; what are his biggest lessons in what he looks for when hiring investing talent? What is his single biggest takeaway from working with Alfred Lin, Roelof Botha and Doug Leone? What are his biggest takeaways from working with Hubspot, Snowflake and ServiceNow?  

As impressive as LLMs are, the growing consensus is that language, scale and compute won't get us to AGI. Although many AI benchmarks have quickly achieved human-level performance, there is one eval that has barely budged since it was created in 2019. Google researcher François Chollet wrote a paper that year defining intelligence as skill-acquisition efficiency—the ability to learn new skills as humans do, from a small number of examples. To make it testable he proposed a new benchmark, the Abstraction and Reasoning Corpus (ARC), designed to be easy for humans, but hard for AI. Notably, it doesn't rely on language. Zapier co-founder Mike Knoop read Chollet's paper as the LLM wave was rising. He worked quickly to integrate generative AI into Zapier's product, but kept coming back to the lack of progress on the ARC benchmark. In June, Knoop and Chollet launched the ARC Prize, a public competition offering more than $1M to beat and open-source a solution to the ARC-AGI eval. In this episode Mike talks about the new ideas required to solve ARC, shares updates from the first two weeks of the competition, and shares why he's excited for AGI systems that can innovate alongside humans. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models: The 2019 paper that first caught Mike's attention about the capabilities of LLMs On the Measure of Intelligence: 2019 paper by Google researcher François Chollet that introduced the ARC benchmark, which remains unbeaten ARC Prize 2024: The $1M+ competition Mike and François have launched to drive interest in solving the ARC-AGI eval Sequence to Sequence Learning with Neural Networks: Ilya Sutskever paper from 2014 that influenced the direction of machine translation with deep neural networks. Etched: Luke Miles on LessWrong wrote about the first ASIC chip that accelerates transformers on silicon Kaggle: The leading data science competition platform and online community, acquired by Google in 2017 Lab42: Swiss AU lab that hosted ARCathon precursor to ARC Prize Jack Cole: Researcher on team that was #1 on the leaderboard for ARCathon Ryan Greenblatt: Researcher with current high score (50%) on ARC public leaderboard (00:00) Introduction (01:51) AI at Zapier (08:31) What is ARC AGI? (13:25) What does it mean to efficiently acquire a new skill? (19:03) What approaches will succeed? (21:11) A little bit of a different shape (25:59) The role of code generation and program synthesis (29:11) What types of people are working on this? (31:45) Trying to prove you wrong (34:50) Where are the big labs? (38:21) The world post-AGI (42:51) When will we cross 85% on ARC AGI? (46:12) Will LLMs be part of the solution? (50:13) Lightning round

Archimedes said that with a large enough lever, you can move the world. For decades, software engineering has been that lever. And now, AI is compounding that lever. How will we use AI to apply 100 or 1000x leverage to the greatest lever to move the world? Matan Grinberg and Eno Reyes, co-founders of Factory, have chosen to do things differently than many of their peers in this white-hot space. They sell a fleet of “Droids,” purpose-built dev agents which accomplish different tasks in the software development lifecycle (like code review, testing, pull requests or writing code). Rather than training their own foundation model, their approach is to build something useful for engineering orgs today on top of the rapidly improving models, aligning with the developer and evolving with them.  Matan and Eno are optimistic about the effects of autonomy in software development and on building a company in the application layer. Their advice to founders, “The only way you can win is by executing faster and being more obsessed.” Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned:  Juan Maldacena, Institute for Advanced Study, string theorist that Matan cold called as an undergrad SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, an evaluation framework for GitHub issues Monte Carlo tree search, a 2006 algorithm for solving decision making in games (and used in AlphaGo) Language agent tree search, a framework for LLM planning, acting and reasoning The Bitter Lesson, Rich Sutton's essay on scaling in search and learning  Code churn, time to merge, cycle time, metrics Factory thinks are important to eng orgs Transcript: https://www.sequoiacap.com/podcast/training-data-factory/ 00:00 Introduction 01:36 Personal backgrounds 10:54 The compound lever 12:41 What is Factory?  16:29 Cognitive architectures  21:13 800 engineers at OpenAI are working on my margins  24:00 Jeff Dean doesn't understand your code base 25:40 Individual dev productivity vs system-wide optimization  30:04 Results: Factory in action  32:54 Learnings along the way  35:36 Fully autonomous Jeff Deans 37:56 Beacons of the upcoming age 40:04 How far are we?  43:02 Competition  45:32 Lightning round 49:34 Bonus round: Factory's SWE-bench results

My guest today is Pat Grady, a longtime growth investor at Sequoia and one of the firms senior leaders. Pat has been a part of a long list of legendary investments, ranging from Snowflake, Zoom, ServiceNow, Qualtrics, Okta, Hubspot, Notion, and OpenAI, among many others. There aren't many investors who reference as well at Pat, both inside and outside of his firm. We talk about investing, building an investing firm, and building enduring companies. Please enjoy this great conversation with Pat Grady. Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:05:48) Doug Leone's Leadership and Changes (00:06:54) Creating Internal Pressure and Structure (00:10:46) Sequoia's Team Values and Family Influence (00:13:40) Assessing Founders and Investments (00:20:28) Winning Competitive Investments (00:24:45) Pat's Early Career at Sequoia (00:29:38) Memo Writing and Investment Criteria (00:35:20) Evaluating Companies Through Three Business Criteria (00:40:15) Building Sustainable Competitive Advantage (00:47:48) Turning Bad Numbers into Good Investments (00:51:20) The AI Frontier: Market and People (01:01:13) Harvey: The AI Legal Assistant (01:05:33) Sequoia's Platform Strategy (01:17:16) The Importance of Teamwork and Performance (01:26:07) Legendary Potential: Relentless Application of Force (01:28:37) The Kindest Thing Anyone Has Ever Done for Pat

Last year, AutoGPT and Baby AGI captured our imaginations—agents quickly became the buzzword of the day…and then things went quiet. AutoGPT and Baby AGI may have marked a peak in the hype cycle, but this year has seen a wave of agentic breakouts on the product side, from Klarna's customer support AI to Cognition's Devin, etc. Harrison Chase of LangChain is focused on enabling the orchestration layer for agents. In this conversation, he explains what's changed that's allowing agents to improve performance and find traction.  Harrison shares what he's optimistic about, where he sees promise for agents vs. what he thinks will be trained into models themselves, and discusses novel kinds of UX that he imagines might transform how we experience agents in the future.      Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned:  ReAct: Synergizing Reasoning and Acting in Language Models, the first cognitive architecture for agents SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, small-model open-source software engineering agent from researchers at Princeton Devin, autonomous software engineering from Cognition V0: Generative UI agent from Vercel GPT Researcher, a research agent  Language Model Cascades: 2022 paper by Google Brain and now OpenAI researcher David Dohan that was influential for Harrison in developing LangChain Transcript: https://www.sequoiacap.com/podcast/training-data-harrison-chase/ 00:00 Introduction 01:21 What are agents?  05:00 What is LangChain's role in the agent ecosystem? 11:13 What is a cognitive architecture?  13:20 Is bespoke and hard coded the way the world is going, or a stop gap? 18:48 Focus on what makes your beer taste better 20:37 So what?  22:20 Where are agents getting traction? 25:35 Reflection, chain of thought, other techniques? 30:42 UX can influence the effectiveness of the architecture 35:30 What's out of scope? 38:04 Fine tuning vs prompting? 42:17 Existing observability tools for LLMs vs needing a new architecture/approach 45:38 Lightning round

Join us as we train our neural nets on the theme of the century: AI. Sequoia Capital partners Sonya Huang and Pat Grady host conversations with leading AI builders and researchers to ask critical questions and develop a deeper understanding of the evolving technologies and their implications for technology, business and society. The content of this podcast does not constitute investment advice, an offer to provide investment advisory services, or an offer to sell or solicitation of an offer to buy an interest in any investment fund.

Pat Grady is the Founder of Amazing Ads, an international advertising agency focusing on PPC management and optimization on Amazon, Walmart, Target, Google Ads, YouTube, and other mainline advertising platforms. He also founded print-on-demand companies RhinoFish Media, Just So Posh, and Regalo Marketing, which are involved in wholesale, retail, and print preview and visualization technology. Pat has a reputation of driving results with analytical skills focused on optimization problem solving, tracking, attribution, and tool-building. Sal Conca is the CEO of Windfall Media, a boutique marketing agency with expertise in content marketing and video production developed for performance-based advertising. He is an inbound marketing specialist who helps businesses build, grow, and solidify their potential by creating content and marketing campaigns that speak to their customers and core audiences at every step of their customer journey. Sal started his career as an affiliate relationship specialist and expanded his skill set to plan and manage integrated marketing campaigns, including email marketing, content marketing, and social media, as well as mobile and display advertising. He is also the Co-founder of Tough Monkey Entertainment and Big Lou's Onion Sauce. In this episode… In today's digital age, marketing is more critical than ever for the success of any ecommerce business. However, executing effective marketing strategies without the proper expertise can be challenging — and that's where a professional marketing agency comes in. Marketing experts Pat Grady and Sal Conca say that by partnering with marketing agencies, you gain a wealth of knowledge and experience to help you develop dynamic strategies that propel your brand forward. Whether it's video marketing, content marketing, targeted marketing, or PPC management and optimization, these professionals can help you navigate the complex world of digital marketing. They share their journey running an internet marketing agency to help ecommerce brands create a comprehensive marketing plan calculated for maximum impact and ROI. In this episode of the Quiet Light Podcast, Pat Yates sits down with Pat Grady, Founder of Amazing Ads, and Sal Conca, CEO of Windfall Media, to discuss how ecommerce brands can thrive through marketing. They talk about the philosophy behind the start of Amazing Ads, video and targeted advertising, using data and analytics for effective advertising, and Amazing Ads' business model.

Founders Brian Halligan and Dharmesh Shah reveal how they took a blog started as a hobby and turned the ideas behind it into a $20+ billion success. In 2006, HubSpot upended traditional approaches to marketing by taking advantage of the the nascent internet in a new way: By capitalizing on seach engines and social media, they offered a way to pull customers in rather than pushing ads and mailers out. They coined the new category “inbound marketing.” They continued to defy conventional wisdom, deciding to serve small businesses over big enterprises, and taking on a Goliath in a new category. As the founders explain, zigging where others zag is the key to their success.   Host: Roelof Botha, Sequoia Capital  Featuring: Brian Halligan, Dharmesh Shah, Pat Grady, Dannie Herzberg Transcript: https://www.sequoiacap.com/podcast/crucible-moments-hubspot/ Learn more about your ad choices. Visit podcastchoices.com/adchoices

In the latest episode of the NVIDIA AI Podcast, host Noah Kravitz is joined by Pat Grady and Sonya Huang, partners at Sequoia Capital, to discuss their recent essay, “Generative AI: A Creative New World.” The authors delve into the potential of generative AI to enable new forms of creativity and expression, as well as the challenges and ethical considerations of this technology. They also offer insights into the future of generative AI. Grady and Huang emphasize the potential of generative AI to revolutionize industries such as art, design and media by allowing for the creation of unique, personalized content on a scale that would be impossible for humans to achieve alone. They also address the importance of considering the ethical implications of the technology, including the potential for biased or harmful outputs and the need for responsible use and regulation. Listen to the full episode to hear more about the possibilities of generative AI and the considerations to be made as this technology moves forward.

Stocks struggling for direction following the stronger than expected June jobs report. Moody's Analytics Mark Zandi says the strong jobs growth signals a good chance of avoiding an economic downturn, but Fmr. IMF Chief Economist Ken Rogoff believes the odds of a significant recession remain high. Fmr. Fed Vice Chair Roger Ferguson says the jobs report and jump in wage growth ensures the Federal Reserve will raise interest rates by 75 basis points at its next meeting. Sequoia Capital's Pat Grady explains how the current market volatility is impacting venture capital deals and why he is so bullish on cryptocurrencies despite their recent collapse. And Telsey Advisory's Dana Telsey Levi's will remain a strong performer following its better than expected earnings. She reveals the other retail names she likes in the face of rising consumer spending concerns.

Get ready for some high-level perspective and conversation on how you can move your business forward! This week, hosts Kipp Bodnar and Kieran Flanagan go on a Twitter deep dive on fascinating things that will tilt everyone's perspective on growth. Keep your ears peeled for talks on how simplicity is underrated Web 3 and how it's going to put the customer acquisition game on its head (hint: incentives!) how this founder showcases the value proposition of her company's tools with one tweet They also discuss how LinkedIn joins the Podcast Network game, YouTube can win podcasts, and what podcast discovery engines are missing. Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934 If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. As a way to celebrate the launch, we're giving away over $9k in tech prizes! Including a Playstation 5, Airpods Max, Eight Sleep Mattress, and so much more! Click here https://upvir.al/129763/MATG1?utm_source=MATG-Show-Notes&utm_medium=owned&utm_campaign=MATG-External to sign up. The giveaway ends on April 13th. Links: Kipp Bodnar, https://www.hubspot.com/company/management/kipp-bodnar Kieran Flanagan, https://blog.hubspot.com/marketing/author/kieran-flanagan Twitter deep-dive links: Pat Grady's thread on 15 Lessons from 15 Years at Sequoia Capital: https://twitter.com/gradypb/status/1500853901901197314 Chris Cantino's tweet on Web 2 and Web 3 acquisition differences: https://twitter.com/chriscantino/status/1496546019244490757 Yuliya Bel's tweet on Who people are engaging with the most on Twitter: https://twitter.com/ybelyayeva/status/1493265844352294917 ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network

Luciana Lixandru is a Partner @ Sequoia, one of the world's most renowned and successful venture firms with Sequoia-backed companies accounting for more than 20% of NASDAQ's total value. As for Luciana, at Sequoia she has led investments in the likes of PennyLane, Xentral, Veed and Ledgy to name a few. Prior to joining Sequoia in 2020, Luciana was a Partner @ Accel where she made investments in Hopin, Miro, UiPath, Tessian and Deliveroo. As a result of such investing success, Luciana was #2 on the Midas List in 2021. In Today's Episode with Luciana Lixandru You Will Learn: 1.) Origins: How did Luciana make her way from a small town in Romania to being Partner @ Sequoia? What were the 1-2 crucible moments in her life that changed the course of her life? 2.) Luciana: The Investor How has Luciana's investing style changed and developed over the years? How does Luciana reflect on her own relationship to price? What misses caused these changes? Hopin, Miro, Deliveroo, UiPath, how did having such winners so early impact Luciana's investing mindset? What would Luciana say is her biggest insecurity today? What drives this? 3.) Sequoia: The Team What are Luciana's biggest takeaways from working with Doug Leone, Alfren Lin, Roelof Botha and Pat Grady? What does the decision-making process look like for new deals within Sequoia? How does the Sequoia partnership create an environment of safety where everyone can discuss and debate freely? How does Luciana approach training and mentorship? What works and what does not? 4.) Sequoia in Europe + Sequoia's Arc: What is Arc? Why was now the right time for Sequoia to do it? What is the structure for the program? How many startups are part of it? Who is able to apply? How much capital do the startups receive? What else do they receive in mentoring etc? In 5 years time, what would success look like for Luciana with Arc? Item's Mentioned In Today's Episode with Luciana Lixandru Luciana's Favourite Book: The Spy and the Traitor: The Greatest Espionage Story of the Cold War

Don't miss out on the next #womenintech podcast episode, get notified by signing up here http://womenintechshow.com.Be featured in the Women in Tech Community by creating your profile here http://womenintechvip.com/“Meagan Loyst of Lerer Hippeau and Gen Z VCs” #womenintech Show is a WeAreTech.fm production.To support the Women in Tech podcast go to https://www.patreon.com/womenintechTo be featured on the podcast go to http://womenintechshow.com/featureHost, Espree Devorahttps://twitter.com/espreedevorahttps://www.linkedin.com/in/espreeGuest,Meagan Loysthttps://twitter.com/meaganloysthttps://www.linkedin.com/in/meaganloyst/Be featured in the Women in Tech Community by creating your profile here http://womenintechvip.com/Listener Spotlight,Jo Petersonhttps://www.linkedin.com/in/jopeterson1/In LA? Here's some awesome resources for you to become immersed in the LA Tech scene -For a calendar of all LA Startup events go to, http://WeAreLATech.comGet Podcast Listeners, http://getpodcastlisteners.com/Resources Mentioned:Lerer Hippeau, https://www.lererhippeau.comGen Z VCs, https://www.genzvcs.comIntros, https://www.intros.aiThe Twenty Minute VC, https://www.thetwentyminutevc.comSmart Women Securities, smart women securitiesGirls Who Invest, https://www.girlswhoinvest.orgBlogHer, https://www.blogher.com/events/blogher-creators-summit/Sequoia Capital, https://www.sequoiacap.comGoody, https://www.ongoody.comTwilight, https://www.barnesandnoble.com/b/series/twilight-saga-series/_/N-2k2tPeople Mentioned:Warren Buffett, https://www.forbes.com/profile/warren-buffett/?sh=4cd71d646398Andrea Hippeau, https://www.linkedin.com/in/andrea-hippeau-64658227/Pat Grady, https://www.linkedin.com/in/gradypb/Harry Stebbings, https://twitter.com/HarryStebbingsCredits:Produced and Hosted by Espree Devora, http://espreedevora.comStory Produced, Edited and Mastered by Cory Jennings, https://www.coryjennings.com/Production and Voiceover by Adam Carroll, http://www.ariacreative.ca/Team support by Janice GeronimoMusic by Jay Huffman, https://soundcloud.com/jayhuffmanShort Title: Meagan Loyst 

This week on the Digital Velocity Podcast, Pat Grady, founder of Amazing Ads, joins Tim and Erik to discuss the value of marketplaces and the importance of being ready and willing to adapt to changes in the marketplaces. https://www.digitalvelocitypodcast.com/episodes/08-adapting-to-changes-in-the-marketplaces-pat-grady

In this interview, Pat discusses some of the key trends regarding observability, the benefits of taking a best of breed approach to it, and why observability is part of the solution to data privacy, not the problem. We discuss some first steps those low on the maturity curve relative to observability can take and why it has been a key to the digital acceleration that has happened during the pandemic. Lastly, we discuss how COVID has dramatically accelerated Sequoia's business, some of the indelible marks of COVID that will become permanent, among a variety of other topics. Pat has a significant track record of success as his investment portfolio consists of companies like Zoom, Okta, ServiceNow, and Snowflake.

In this interview, Pat discusses some of the key trends regarding observability, the benefits of taking a best of breed approach to it, and why observability is part of the solution to data privacy, not the problem. We discuss some first steps those low on the maturity curve relative to observability can take and why it has been a key to the digital acceleration that has happened during the pandemic. Lastly, we discuss how COVID has dramatically accelerated Sequoia's business, some of the indelible marks of COVID that will become permanent, among a variety of other topics. Pat has a significant track record of success as his investment portfolio consists of companies like Zoom, Okta, ServiceNow, and Snowflake.

Pat Grady is a Partner @ Sequoia, one of the world's leading and most renowned venture firms with a portfolio including WhatsApp, Zoom, Stripe, Airbnb, Github and many more incredible companies. As for Pat, at Sequoia he co-leads the firms growth investment team and has been involved with some of the true greats, Hubspot, Zoom, Okta, Qualtrics, the list goes on. Prior to Sequoia Pat spent three years with Summit Partners. In Today’s Episode You Will Learn: 1.) How Pat made his way from Summit Partners to co-leading Sequoia's growth investment team? Was it intimidating for Pat entering a partnership with Jim Goetz, Don Valentine, Roelof Botha? How did he manage those nerves? 2.) So many different funds and activities, so what is Sequoia focused on today? Where does Sequoia think about their ideal insertion point today? How do they see the deployment of their blended capital across rounds? Does Pat believe in ownership on first check or building ownership over time? How does Pat think about the extended window of privatisation with IPOs being continuously delayed? 3.) Does Pat believe that VC really is a team sport today? Does Pat agree with Josh Kopelman's statement, "I would rather be a better picker of partners than investments"? What are the core requirements, skills and traits that Sequoia looks for when adding to their partnership? 4.) What is the investment decision-making process at Sequoia? How do they feel about unanimity vs conviction based investment decisions? What are the pros and cons of each? What does Pat believe is the most non-obvious investment decision that Sequoia have made? Sequoia run an incredibly rigorous process when investing, how does Pat balance between that level of rigour with the speed to win the deal? 5.) What advice would Pat give to someone that has just gained their first institutional board? What does Pat know now that he wishes he had known when he started in VC? How does Pat think about time allocation across the portfolio? Concentrate on winners or the strugglers are where your reputation is built? Leading Sequoia Growth and with a beautiful family, how does Pat approach work/life balance? Items Mentioned In Today’s Show: Pat’s Fave Book: God Friended Me Pat’s Most Recent Investment: Embark: Revolutionizing Commercial Transport  As always you can follow Harry, The Twenty Minute VC and Pat on Twitter here! Likewise, you can follow Harry on Instagram here for mojito madness and all things 20VC.

In this follow-up to Episode 20 with David Cancel (DC) the founder and CEO of Drift,  Christopher talks with Dave Gerhardt (DG), the head of marketing for Drift and fellow podcaster. They are co-authors a of brand new, number one best-selling book called Conversational Marketing: How The World's Fastest Growing Companies use Chatbots to Generate Leads. In this episode, we unpack how Drift is designing a new category and we go deep into the strategies and tactics Drift is using and how they executed this most legendary lightning strike centered around the new book. If you're into marketing, there's a ton of gold in this episode.  You'll get insight into how to get a massive amount of attention for your category, brand and point of view. How to execute an approach called the Multiplier Effect so that each component of your marketing mix multiplies the value of the other components. How to make your company and your category undeniable.  And, how to do my favorite kind of marketing which causes your competition to have emergency board meetings. "Whether the (category) name is sexy, good or not, you have to name it." - Dave Gerhardt  Almost two years ago, Dave was introduced to Christopher through his book after Sequoia invested in Drift, Pat Grady sent him five copies of his book Play Bigger and said he needed to read it. Dave was blown away that how much was in the book was what they were doing without really talking about it. Play Bigger clarified what they were working on. Hurdles to Overcome The team at Drift knew they were building a category but didn't' really know about category design. During their efforts to write the book and get a publisher, Dave and David discovered that thing, that category that they were building meant nothing until they named it. "In order for us to win, we need to elevate the category of Conversational Marketing." - Dave Gerhardt They had a little traction but not enough. They needed a following, big investors, and the social proof. Fast forward to early 2018. They spent the year writing the book and published on January 30, 2019. After a month, it's a top 20 business book in the US and in the top 1.5% of all books being sold on Amazon.  Why Competition is Good In order for the book to succeed, Dave wants competitors and people in the conversational marketing space. They don't want people to just think of Drift when they think of conversational marketing.  Dave compares it to a part of Play Bigger. Apple didn't invent the tablet but they created a category that elevated it. That's Drift's goal; to elevate the category of conversational marketing. To hear more about Dave's legendary marketing strike, download and listen to the episode. Dave Gerhardt Bio: Dave Gerhardt is a B2B marketing leader, brand builder, and copywriter.  As VP of Marketing of Drift, he's helped grow Drift from $0 to over eight-figures in revenue in just two years, and his work has been featured in 100+ news sites and publications, including Forbes, Fortune, Inc., Entrepreneur, TechCrunch, and Harvard Business Review. He also co-hosts the popular Seeking Wisdom podcast with Drift CEO David Cancel, and he's the co-author of the definitive book on Conversational Marketing. Links: Linkedin Drift.com Podcast: Seeking Wisdom iTunes Seeking Wisdom We hope you enjoyed Dave Gerhardt on this episode of Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, Twitter, Instagram and subscribe on iTunes!

On this week's episode of  Your Turn radio show, Walton Francis answers your questions. He's the guy who wrote the book on how to pick the best federal health plan: Checkbook's Guide To Health Plans For Federal Employees. Next Mr. Pat Grady, TRICARE Health Plan chief, tells us about changes that active military and veterans should be aware of this Open Season.

Sarah Tavel is a General Partner at Benchmark, one of the world’s leading VC funds with a portfolio including the likes of Twitter, Uber, Snapchat, eBay, WeWork, Yelp and many more revolutionary companies of the last decade. As for Sarah, Sarah has led Benchmark's investments in and currently sits on the boards of Chainalysis and Hipcamp. Prior to Benchmark, Sarah was a Partner at Greylock Partners, where she led Greylock's investment in Sonder and another (unannounced) company. Before Greylock, Sarah was one of the first 35 employees at Pinterest where she led the company's international expansion and aided in the closing of the Series C financing. Sarah was also the product lead for search, recommendations, machine vision, and pin quality and led three acquisitions as she helped the company scale through a period of hyper-growth. In Today’s Episode You Will Learn: 1.) How Sarah made her first foray into the world of venture with Bessemer over 10 years ago? How that led to Pinterest and how she came to be a GP at Benchmark today? 2.) Speaking of Sarah's operating career with Pinterest, Pat Grady said on the show "never has the rate of decay on operating experience been greater". How does Sarah think about and respond to this? How has operating made Sarah a strong investor? What are the drawbacks that this operating experience can present for investors? 3.) Moving to evaluation, Andy Rachleff, Founder @ Benchmark said on the show, "good team poor market, market wins; good market, poor team, market wins. How does Sarah think about the balance between founder vs market? Why is going after big markets so hard? What should investors look for in a market with that in mind? How does Sarah determine the right time to open up adjacent markets? 4.) There has never been a greater supply of capital in the market than today, does Sarah believe we have an excess supply today? Does Sarah agree with her Partner, Peter Fenton, "no good deal is too expensive in hindsight"? How does Sarah assess her own price sensitivity? How does it depend on the opportunity? How has it changed over time? 5.) Having 2,5000 hours on boards, how has Sarah seen herself develop and change as a board member? What have been some of the biggest learning curves? What are the commonalities in the very best board members Sarah works with? how doe the best entrepreneurs manage and use their boards effectively? 6.) Why does Sarah think that crypto today is very much like the world of adtech in the early days? How does Sarah think about the requirement for specialisation in the space? WIll this be a game for the specialised crypto funds or can generalist VC funds compete? Items Mentioned In Today’s Show: Sarah’s Fave Book: Creating the Kingdom of Ends Sarah’s Most Recent Investment: Hipcamp As always you can follow Harry, The Twenty Minute VC and Sarah on Twitter here! Likewise, you can follow Harry on Instagram here for mojito madness and all things 20VC.

Crystal Huang is a Principal @ GGV Capital, one of the world's leading venture firms partnering with entrepreneurs in the world's largest markets, the US and China. At GGV Crystal led the firm's investment in Wigo (acquired by Cinemagram) and attends board meetings at Tile and Flightcar. Crystal is also a board member @ NextGen partners, the organization representing the future General Partners within the bay area. Prior to joining GGV, Crystal worked as an analyst in Blackstone's Technology M&A Advisory Group and due to her immense promise and success already, Crystal has been named to Forbes' 30 Under 30 Venture Capital. In Today’s Episode You Will Learn: 1.) How Crystal made her way into the world of VC from Tech M&A with Blackstone? 2.) How does Crystal view the ongoing debate of operator vs non-operator experience? Does Crystal agree with Pat Grady that the rate of decay on operating experience has never been greater? What elements of operational experience, applied to VC, do stand the test of time? 3.) What does Crystal believe are the 3 key differences when comparing the US and Asian tech markets? How do deal sizes change across geographies? What does this do to the unit economics of the businesses? How does vendor engagement and sales cycles differ? 4.) In a world of Baidu, Alibaba and Tencent quickly acquiring or copying innovative ideas, is there a market for true later stage VC in Asia? Where are the market opportunities? How does incumbent power in Asia differ to incumbent power in the US? 5.) How does Crystal fundamentally see distribution models vary between the US and Asia? Has Asia enjoyed the same rise of the "self-service model" enacted by many in the US? What does this mean for internal org structures and unit economics? Items Mentioned In Today’s Show: Crystal’s Fave Book: The Code Book Crystal’s Most Recent Investment: BitSight As always you can follow Harry, The Twenty Minute VC and Crystal on Twitter here! Likewise, you can follow Harry on Snapchat here for mojito madness and all things 20VC. NatureBox Unlimited snack plans offer all you can eat snacks for one fixed price per employee. Naturebox use simple ingredients you can trust to create bold flavors you can’t find anywhere else. All NatureBox snacks are free from artificial junk and variety is endless with options from sweet or savory to vegan or gluten-free. Simply choose the plan that fits your team’s unique snacking habits and select any of NatureBox’s time-saving add-on’s. And beyond Unlimited snacks, you’ll receive perks such as free kitchen setup, no contracts, a dedicated account manager and more. Simply click here to and use the offer code VC20 to get 20% of your first Naturebox month. Leesa is the Warby Parker or TOMS shoes of the mattress industry. Leesa have done away with the terrible mattress showroom buying experience by creating a luxury premium foam mattress that is ordered completely online and ships for free to your doorstep. The 10-inch mattress comes in all sizes and is engineered with 3 unique foam layers for a universal, adaptive feel, including 2 inches of memory foam and 2 inches of a really cool latex foam called Avena, design to keep you cool. All Leesa mattresses are 100% US or UK made and for every 10 mattresses they sell, they donate one to a shelter. Go to Leesa.com to start the New Year with better nights sleep!

If you liked this episode, we bet that you’ll love our blog content. blog.drift.com/#subscribe Subscribe to never miss a post & join the 20,000+ other pros committed to getting better every day. ----- For the 100th episode of Seeking Wisdom, we thought it would be fun to pull together some hits -- roughly 10 clips, 10 minutes each, for 100 minutes of the most popular episodes of Seeking Wisdom. Here's the full rundown: 0:48-5:37 - How SW came to be from EP #86 Live at WeWork 5:39-13:43 - Highlights from #2 Learning Machine 13:46 - 22:44 - Highlights from #93 w/Pat Grady on First Principles and Advice for Startups 22:47-45:34 #19 on Carrying the Water 45:35- 1:01:43 - #90 w/ Patty McCord 1:01:44 - 1:05:07 - #51 How To Run A Meeting 1:05:09-1:10:20 - #6 On Hiring Product Managers 1:10:21 - 1:16:57 - #36 How To Work 1:16:58 - 1:30:55 - Books That Have Had The Biggest Impact 1:30:57-1:38:26 - #95 With Brad Stulberg 1:38:28 - 1:39:18 - #28 How to Come Up With Better Ideas 1:39:21 - 1:40:54 - 6 Stars Only! The only question now is: what will be on Greatest Hits, Vol. 2?

This Week’s Guest: Pat Grady Pat Grady, Co-Owner, Founder of RhinoFish Media joined me to chat on my podcast, This is Affiliate Marketing with Shawn Collins. Episode 53 I wanted to learn more about the real Pat, so I asked him a variety of questions I figured he had not been asked in previous interviews. We discussed... Atlas Shrugged and Ayn Rand Kiteboarding Serving in the U.S. Navy Spud Webb Links from this episode Pat on Facebook Pat on Instagram Pat on LinkedIn Spud Webb slam dunking Manute Bol and Spud Webb Spud Webb Tweet Thank you for listening Please leave a comment or feel free to contact me. And if you enjoyed this episode of This is Affiliate Marketing with Shawn Collins, please share it.

Carl Eschenbach is a Partner @ Sequoia Capital, one of the world's leading funds with investments in the likes of Google, Apple, Whatsapp, Paypal and Stripe just to name a few. As for Carl, prior to Sequoia, Carl was President and CEO @ VMWare where he grew the team from 200 to 20,000 people. If that was not enough Carl has also stepped in as CFO at a $5Bn company and transitioned 3 CEOs. In Today’s Episode You Will Learn: 1.) How Carl made the move from President and CEO @ 20,000 person company, VMWare to Partner @ Sequoia? 2.) Question from Alfred Lin, Partner @ Sequoia: How did Carl look to build a culture of performance at VMWare? 3.) Question from Pat Grady, Partner @ Sequoia: What were Carl's biggest learnings with regards to high performance teams? What are the common mistakes in scaling a go to market strategy? 4.) How does Carl view the internal workings at Sequoia to the external perception? Was the move to Sequoia everything he expected? What was different or challenging? 5.) What does Carl believe are the core components that make a great board member? How has Carl's time in operations affected his view of boards and their working? Items Mentioned In Today’s Show: Carl’s Fave Book: Half Time Carl’s Most Recent Investment: Zoom As always you can follow Harry, The Twenty Minute VC and Carl on Twitter here! Likewise, you can follow Harry on Snapchat here for mojito madness and all things 20VC. X.ai is AI-poweredered personal assistant for scheduling meetings bringing you Amy or Andrew. The assistant you interact with like you would to any other person and it allows you to avoid the tedious hours of email ping pong in order to schedule one meeting. Even better, there is no sign in, no password, no download, all you do is cc amy@x.ai beautiful! And you can check it out now on x.ai it really is a must! Workable is the all-in-one recruiting software for ambitious companies. From posting a job to tracking and managing candidates, Workable provides everything you need to hire better. Transparent communication, organized candidate profiles, structured interviews and a full reporting suite gives hiring teams the information they need to make the best choice. Workable is available for desktop and mobile and you can find out more on workable.com where you can try it for free.

Pat Grady is a Partner @ Sequoia Capital, one of the world's leading funds with prior investments in the likes of Apple, Google, Whatsapp, Paypal, Stripe and many more. At Sequoia, Pat has made investments in many past guests of this show and the SaaStr podcast including Zoom, Namely and Qualtrics, just to name a few. Prior to Sequoia, Pat spent 3 years with the team at Summit Partners. In Today’s Episode You Will Learn: 1.) How Pat made his way into the world of VC and one of the most famous funds in the world, Sequoia? 2.) How does Pat view the apprenticeship model in VC? How can VCs attempt to develop and convey the founder empathy when they have not been a founder? Do 'career VCs' get the same respect from founders? 3.) What does Pat believe to be the defining characteristics of the next generation of great software? What are the commonalities? 4.) Pat has said before that 'data is the new oil', what makes Pat think this? How does this affect his investment thesis going forward? Are there elements of being 'data long' that make Pat nervous? 5.) How does Pat think the data incumbency advantage can be mitigated? What can startups do to build a moat? Items Mentioned In Today’s Show: Pat’s Fave Book: The Boys In The Boat Pat's Fave Blog: CB Insights Pat’s Most Recent Investment: Namely  As always you can follow Harry, The Twenty Minute VC and Pat on Twitter here! Likewise, you can follow Harry on Snapchat here for mojito madness and all things 20VC. X.ai is AI-poweredered personal assistant for scheduling meetings bringing you Amy or Andrew. The assistant you interact with like you would to any other person and it allows you to avoid the tedious hours of email ping pong in order to schedule one meeting. Even better, there is no sign in, no password, no download, all you do is cc amy@x.ai beautiful! And you can check it out now on x.ai it really is a must! Workable is the all-in-one recruiting software for ambitious companies. From posting a job to tracking and managing candidates, Workable provides everything you need to hire better. Transparent communication, organized candidate profiles, structured interviews and a full reporting suite gives hiring teams the information they need to make the best choice. Workable is available for desktop and mobile and you can find out more on workable.com where you can try it for free.

TweetI can honestly say this is an exclusive, this guest doesn’t do interviews. Cindy and I talked with Pat Grady, the infamous Donuts from the ABestWeb glory days. Pat is one of those people in the industry that you “need to know” and if you don’t meet him at a conference such as Affiliate Summit, […]