Podcast appearances and mentions of mike knoop

In this fascinating episode, we dive deep into the race towards true AI intelligence, AGI benchmarks, test-time adaptation, and program synthesis with star AI researcher (and philosopher) Francois Chollet, creator of Keras and the ARC AGI benchmark, and Mike Knoop, co-founder of Zapier and now co-founder with Francois of both the ARC Prize and the research lab Ndea. With the launch of ARC Prize 2025 and ARC-AGI 2, they explain why existing LLMs fall short on true intelligence tests, how new models like O3 mark a step change in capabilities, and what it will really take to reach AGI.We cover everything from the technical evolution of ARC 1 to ARC 2, the shift toward test-time reasoning, and the role of program synthesis as a foundation for more general intelligence. The conversation also explores the philosophical underpinnings of intelligence, the structure of the ARC Prize, and the motivation behind launching Ndea — a ew AGI research lab that aims to build a "factory for rapid scientific advancement." Whether you're deep in the AI research trenches or just fascinated by where this is all headed, this episode offers clarity and inspiration.NdeaWebsite - https://ndea.comX/Twitter - https://x.com/ndeaARC PrizeWebsite - https://arcprize.orgX/Twitter - https://x.com/arcprizeFrançois CholletLinkedIn - https://www.linkedin.com/in/fcholletX/Twitter - https://x.com/fcholletMike KnoopX/Twitter - https://x.com/mikeknoopFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:05) Introduction to ARC Prize 2025 and ARC-AGI 2 (02:07) What is ARC and how it differs from other AI benchmarks (02:54) Why current models struggle with fluid intelligence (03:52) Shift from static LLMs to test-time adaptation (04:19) What ARC measures vs. traditional benchmarks (07:52) Limitations of brute-force scaling in LLMs (13:31) Defining intelligence: adaptation and efficiency (16:19) How O3 achieved a massive leap in ARC performance (20:35) Speculation on O3's architecture and test-time search (22:48) Program synthesis: what it is and why it matters (28:28) Combining LLMs with search and synthesis techniques (34:57) The ARC Prize structure: efficiency track, private vs. public (42:03) Open source as a requirement for progress (44:59) What's new in ARC-AGI 2 and human benchmark testing (48:14) Capabilities ARC-AGI 2 is designed to test (49:21) When will ARC-AGI 2 be saturated? AGI timelines (52:25) Founding of NDEA and why now (54:19) Vision beyond AGI: a factory for scientific advancement (56:40) What NDEA is building and why it's different from LLM labs (58:32) Hiring and remote-first culture at NDEA (59:52) Closing thoughts and the future of AI research

We are joined by Francois Chollet and Mike Knoop, to launch the new version of the ARC prize! In version 2, the challenges have been calibrated with humans such that at least 2 humans could solve each task in a reasonable task, but also adversarially selected so that frontier reasoning models can't solve them. The best LLMs today get negligible performance on this challenge. https://arcprize.org/SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT:https://www.dropbox.com/scl/fi/0v9o8xcpppdwnkntj59oi/ARCv2.pdf?rlkey=luqb6f141976vra6zdtptv5uj&dl=0TOC:1. ARC v2 Core Design & Objectives [00:00:00] 1.1 ARC v2 Launch and Benchmark Architecture [00:03:16] 1.2 Test-Time Optimization and AGI Assessment [00:06:24] 1.3 Human-AI Capability Analysis [00:13:02] 1.4 OpenAI o3 Initial Performance Results2. ARC Technical Evolution [00:17:20] 2.1 ARC-v1 to ARC-v2 Design Improvements [00:21:12] 2.2 Human Validation Methodology [00:26:05] 2.3 Task Design and Gaming Prevention [00:29:11] 2.4 Intelligence Measurement Framework3. O3 Performance & Future Challenges [00:38:50] 3.1 O3 Comprehensive Performance Analysis [00:43:40] 3.2 System Limitations and Failure Modes [00:49:30] 3.3 Program Synthesis Applications [00:53:00] 3.4 Future Development RoadmapREFS:[00:00:15] On the Measure of Intelligence, François Chollethttps://arxiv.org/abs/1911.01547[00:06:45] ARC Prize Foundation, François Chollet, Mike Knoophttps://arcprize.org/[00:12:50] OpenAI o3 model performance on ARC v1, ARC Prize Teamhttps://arcprize.org/blog/oai-o3-pub-breakthrough[00:18:30] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Jason Wei et al.https://arxiv.org/abs/2201.11903[00:21:45] ARC-v2 benchmark tasks, Mike Knoophttps://arcprize.org/blog/introducing-arc-agi-public-leaderboard[00:26:05] ARC Prize 2024: Technical Report, Francois Chollet et al.https://arxiv.org/html/2412.04604v2[00:32:45] ARC Prize 2024 Technical Report, Francois Chollet, Mike Knoop, Gregory Kamradthttps://arxiv.org/abs/2412.04604[00:48:55] The Bitter Lesson, Rich Suttonhttp://www.incompleteideas.net/IncIdeas/BitterLesson.html[00:53:30] Decoding strategies in neural text generation, Sina Zarrießhttps://www.mdpi.com/2078-2489/12/9/355/pdf

In this episode of Gradient Dissent, host Lukas Biewald sits down with Mike Knoop, Co-founder and CEO of Ndea, a cutting-edge AI research lab. Mike shares his journey from building Zapier into a major automation platform to diving into the frontiers of AI research. They discuss DeepSeek's R1, OpenAI's O-series models, and the ARC Prize, a competition aimed at advancing AI's reasoning capabilities. Mike explains how program synthesis and deep learning must merge to create true AGI, and why he believes AI reliability is the biggest hurdle for automation adoption.This conversation covers AGI timelines, research breakthroughs, and the future of intelligent systems, making it essential listening for AI enthusiasts, researchers, and entrepreneurs.Mentioned Show Notes:https://ndea.comhttps://arcprize.org/blog/r1-zero-r1-results-analysishttps://arcprize.org/blog/oai-o3-pub-breakthrough

Nathan interviews Mike Knoop, co-founder of Zapier and co-creator of the ARC Prize, about the $1 million competition for more efficient AI architectures. They discuss the ARC AGI benchmark, its implications for general intelligence, and the potential impact on AI safety. Nathan reflects on the challenges of intuitive problem-solving in AI and considers hybrid approaches to AGI development. Apply to join over 400 founders and execs in the Turpentine Network: https://hmplogxqz0y.typeform.com/to/JCkphVqj RECOMMENDED PODCAST: Patrick McKenzie (@patio11) talks to experts who understand the complicated but not unknowable systems we rely on. You might be surprised at how quickly Patrick and his guests can put you in the top 1% of understanding for stock trading, tech hiring, and more. Spotify: https://open.spotify.com/show/3Mos4VE3figVXleHDqfXOH Apple: https://podcasts.apple.com/us/podcast/complex-systems-with-patrick-mckenzie-patio11/id1753399812 SPONSORS: Oracle Cloud Infrastructure (OCI) is a single platform for your infrastructure, database, application development, and AI needs. OCI has four to eight times the bandwidth of other clouds; offers one consistent price, and nobody does data better than Oracle. If you want to do more and spend less, take a free test drive of OCI at https://oracle.com/cognitive The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/ Head to Squad to access global engineering without the headache and at a fraction of the cost: head to https://choosesquad.com/ and mention “Turpentine” to skip the waitlist. CHAPTERS: (00:00:00) About the Show (00:06:06) The ARC Benchmark (00:09:34) Other Benchmarks (00:10:58) Definition of AGI (00:14:38) The rules of the contest (00:18:16) ARC test set (Part 1) (00:18:23) Sponsors: Oracle | Brave (00:20:31) ARC test set (Part 2) (00:22:50) Stair-stepping benchmarks (00:26:17) ARC Prize (00:28:34) The rules of the ARC Prize (00:31:12) Compute costs (Part 1) (00:34:47) Sponsors: Omneky | Squad (00:36:34) Compute costs (Part 2) (00:36:40) Compute Limit (00:41:00) Public Leaderboard (00:42:58) The current AI ecosystem (00:47:23) The four steps of solving a puzzle (00:51:20) Intuition (00:54:32) Human Intelligence (00:56:06) Current Frontier Language Models (00:57:44) Program Synthesis (01:04:10) Is the model learning or memorizing? (01:09:51) Improving the ARC dataset (01:11:34) Step 3: Guessing the Rule (01:12:51) Dealing with Ambiguity (01:15:02) Exploring Solutions (01:17:02) Non-backpropagation evolutionary architecture search (01:19:49) Expectations for an AGI world (01:24:11) Reliability and out of domain generalization (01:28:35) What a person would do (01:29:51) What is the right generalization (01:35:32) The ARC AGI Challenge (01:37:01) Postscript (01:38:07) DSpi (01:39:55) Statespace models (01:43:28) Hybrid models (01:48:32) FunSearch (01:50:41) Kolmogorov-Arnold-Networks (01:54:18) Grokking (01:55:42) Outro

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

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LLM Generality is a Timeline Crux, published by Egg Syntax on June 24, 2024 on The AI Alignment Forum. Short Summary LLMs may be fundamentally incapable of fully general reasoning, and if so, short timelines are less plausible. Longer summary There is ML research suggesting that LLMs fail badly on attempts at general reasoning, such as planning problems, scheduling, and attempts to solve novel visual puzzles. This post provides a brief introduction to that research, and asks: Whether this limitation is illusory or actually exists. If it exists, whether it will be solved by scaling or is a problem fundamental to LLMs. If fundamental, whether it can be overcome by scaffolding & tooling. If this is a real and fundamental limitation that can't be fully overcome by scaffolding, we should be skeptical of arguments like Leopold Aschenbrenner's (in his recent 'Situational Awareness') that we can just 'follow straight lines on graphs' and expect AGI in the next few years. Introduction Leopold Aschenbrenner's recent 'Situational Awareness' document has gotten considerable attention in the safety & alignment community. Aschenbrenner argues that we should expect current systems to reach human-level given further scaling[1], and that it's 'strikingly plausible' that we'll see 'drop-in remote workers' capable of doing the work of an AI researcher or engineer by 2027. Others hold similar views. Francois Chollet and Mike Knoop's new $500,000 prize for beating the ARC benchmark has also gotten considerable recent attention in AIS[2]. Chollet holds a diametrically opposed view: that the current LLM approach is fundamentally incapable of general reasoning, and hence incapable of solving novel problems. We only imagine that LLMs can reason, Chollet argues, because they've seen such a vast wealth of problems that they can pattern-match against. But LLMs, even if scaled much further, will never be able to do the work of AI researchers. It would be quite valuable to have a thorough analysis of this question through the lens of AI safety and alignment. This post is not that[3], nor is it a review of the voluminous literature on this debate (from outside the AIS community). It attempts to briefly introduce the disagreement, some evidence on each side, and the impact on timelines. What is general reasoning? Part of what makes this issue contentious is that there's not a widely shared definition of 'general reasoning', and in fact various discussions of this use various terms. By 'general reasoning', I mean to capture two things. First, the ability to think carefully and precisely, step by step. Second, the ability to apply that sort of thinking in novel situations[4]. Terminology is inconsistent between authors on this subject; some call this 'system II thinking'; some 'reasoning'; some 'planning' (mainly for the first half of the definition); Chollet just talks about 'intelligence' (mainly for the second half). This issue is further complicated by the fact that humans aren't fully general reasoners without tool support either. For example, seven-dimensional tic-tac-toe is a simple and easily defined system, but incredibly difficult for humans to play mentally without extensive training and/or tool support. Generalizations that are in-distribution for humans seems like something that any system should be able to do; generalizations that are out-of-distribution for humans don't feel as though they ought to count. How general are LLMs? It's important to clarify that this is very much a matter of degree. Nearly everyone was surprised by the degree to which the last generation of state-of-the-art LLMs like GPT-3 generalized; for example, no one I know of predicted that LLMs trained on primarily English-language sources would be able to do translation between languages. Some in the field argued as...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LLM Generality is a Timeline Crux, published by eggsyntax on June 24, 2024 on LessWrong. Short Summary LLMs may be fundamentally incapable of fully general reasoning, and if so, short timelines are less plausible. Longer summary There is ML research suggesting that LLMs fail badly on attempts at general reasoning, such as planning problems, scheduling, and attempts to solve novel visual puzzles. This post provides a brief introduction to that research, and asks: Whether this limitation is illusory or actually exists. If it exists, whether it will be solved by scaling or is a problem fundamental to LLMs. If fundamental, whether it can be overcome by scaffolding & tooling. If this is a real and fundamental limitation that can't be fully overcome by scaffolding, we should be skeptical of arguments like Leopold Aschenbrenner's (in his recent 'Situational Awareness') that we can just 'follow straight lines on graphs' and expect AGI in the next few years. Introduction Leopold Aschenbrenner's recent 'Situational Awareness' document has gotten considerable attention in the safety & alignment community. Aschenbrenner argues that we should expect current systems to reach human-level given further scaling and 'unhobbling', and that it's 'strikingly plausible' that we'll see 'drop-in remote workers' capable of doing the work of an AI researcher or engineer by 2027. Others hold similar views. Francois Chollet and Mike Knoop's new $500,000 prize for beating the ARC benchmark has also gotten considerable recent attention in AIS[1]. Chollet holds a diametrically opposed view: that the current LLM approach is fundamentally incapable of general reasoning, and hence incapable of solving novel problems. We only imagine that LLMs can reason, Chollet argues, because they've seen such a vast wealth of problems that they can pattern-match against. But LLMs, even if scaled much further, will never be able to do the work of AI researchers. It would be quite valuable to have a thorough analysis of this question through the lens of AI safety and alignment. This post is not that[2], nor is it a review of the voluminous literature on this debate (from outside the AIS community). It attempts to briefly introduce the disagreement, some evidence on each side, and the impact on timelines. What is general reasoning? Part of what makes this issue contentious is that there's not a widely shared definition of 'general reasoning', and in fact various discussions of this use various terms. By 'general reasoning', I mean to capture two things. First, the ability to think carefully and precisely, step by step. Second, the ability to apply that sort of thinking in novel situations[3]. Terminology is inconsistent between authors on this subject; some call this 'system II thinking'; some 'reasoning'; some 'planning' (mainly for the first half of the definition); Chollet just talks about 'intelligence' (mainly for the second half). This issue is further complicated by the fact that humans aren't fully general reasoners without tool support either. For example, seven-dimensional tic-tac-toe is a simple and easily defined system, but incredibly difficult for humans to play mentally without extensive training and/or tool support. Generalizations that are in-distribution for humans seems like something that any system should be able to do; generalizations that are out-of-distribution for humans don't feel as though they ought to count. How general are LLMs? It's important to clarify that this is very much a matter of degree. Nearly everyone was surprised by the degree to which the last generation of state-of-the-art LLMs like GPT-3 generalized; for example, no one I know of predicted that LLMs trained on primarily English-language sources would be able to do translation between languages. Some in the field argued as...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LLM Generality is a Timeline Crux, published by eggsyntax on June 24, 2024 on LessWrong. Short Summary LLMs may be fundamentally incapable of fully general reasoning, and if so, short timelines are less plausible. Longer summary There is ML research suggesting that LLMs fail badly on attempts at general reasoning, such as planning problems, scheduling, and attempts to solve novel visual puzzles. This post provides a brief introduction to that research, and asks: Whether this limitation is illusory or actually exists. If it exists, whether it will be solved by scaling or is a problem fundamental to LLMs. If fundamental, whether it can be overcome by scaffolding & tooling. If this is a real and fundamental limitation that can't be fully overcome by scaffolding, we should be skeptical of arguments like Leopold Aschenbrenner's (in his recent 'Situational Awareness') that we can just 'follow straight lines on graphs' and expect AGI in the next few years. Introduction Leopold Aschenbrenner's recent 'Situational Awareness' document has gotten considerable attention in the safety & alignment community. Aschenbrenner argues that we should expect current systems to reach human-level given further scaling and 'unhobbling', and that it's 'strikingly plausible' that we'll see 'drop-in remote workers' capable of doing the work of an AI researcher or engineer by 2027. Others hold similar views. Francois Chollet and Mike Knoop's new $500,000 prize for beating the ARC benchmark has also gotten considerable recent attention in AIS[1]. Chollet holds a diametrically opposed view: that the current LLM approach is fundamentally incapable of general reasoning, and hence incapable of solving novel problems. We only imagine that LLMs can reason, Chollet argues, because they've seen such a vast wealth of problems that they can pattern-match against. But LLMs, even if scaled much further, will never be able to do the work of AI researchers. It would be quite valuable to have a thorough analysis of this question through the lens of AI safety and alignment. This post is not that[2], nor is it a review of the voluminous literature on this debate (from outside the AIS community). It attempts to briefly introduce the disagreement, some evidence on each side, and the impact on timelines. What is general reasoning? Part of what makes this issue contentious is that there's not a widely shared definition of 'general reasoning', and in fact various discussions of this use various terms. By 'general reasoning', I mean to capture two things. First, the ability to think carefully and precisely, step by step. Second, the ability to apply that sort of thinking in novel situations[3]. Terminology is inconsistent between authors on this subject; some call this 'system II thinking'; some 'reasoning'; some 'planning' (mainly for the first half of the definition); Chollet just talks about 'intelligence' (mainly for the second half). This issue is further complicated by the fact that humans aren't fully general reasoners without tool support either. For example, seven-dimensional tic-tac-toe is a simple and easily defined system, but incredibly difficult for humans to play mentally without extensive training and/or tool support. Generalizations that are in-distribution for humans seems like something that any system should be able to do; generalizations that are out-of-distribution for humans don't feel as though they ought to count. How general are LLMs? It's important to clarify that this is very much a matter of degree. Nearly everyone was surprised by the degree to which the last generation of state-of-the-art LLMs like GPT-3 generalized; for example, no one I know of predicted that LLMs trained on primarily English-language sources would be able to do translation between languages. Some in the field argued as...

The first step in achieving AGI is nailing down a concise definition and  Mike Knoop, the co-founder and Head of AI at Zapier, believes François Chollet got it right when he defined general intelligence as a system that can efficiently acquire new skills. This week on No Priors, Miked joins Elad to discuss ARC Prize which is a multi-million dollar non-profit public challenge that is looking for someone to beat the Abstraction and Reasoning Corpus (ARC) evaluation. In this episode, they also get into why Mike thinks LLMs will not get us to AGI, how Zapier is incorporating AI into their products and the power of agents, and why it's dangerous to regulate AGI before discovering its full potential.  Show Links: About the Abstraction and Reasoning Corpus Zapier Central Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mikeknoop Show Notes:  (0:00) Introduction (1:10) Redefining AGI (2:16) Introducing ARC Prize (3:08) Definition of AGI (5:14) LLMs and AGI (8:20) Promising techniques to developing AGI (11:0) Sentience and intelligence (13:51) Prize model vs investing (16:28) Zapier AI innovations (19:08) Economic value of agents (21:48) Open source to achieve AGI (24:20) Regulating AI and AGI

Here is my conversation with Francois Chollet and Mike Knoop on the $1 million ARC-AGI Prize they're launching today.I did a bunch of socratic grilling throughout, but Francois's arguments about why LLMs won't lead to AGI are very interesting and worth thinking through.It was really fun discussing/debating the cruxes. Enjoy!Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Timestamps(00:00:00) – The ARC benchmark(00:11:10) – Why LLMs struggle with ARC(00:19:00) – Skill vs intelligence(00:27:55) - Do we need “AGI” to automate most jobs?(00:48:28) – Future of AI progress: deep learning + program synthesis(01:00:40) – How Mike Knoop got nerd-sniped by ARC(01:08:37) – Million $ ARC Prize(01:10:33) – Resisting benchmark saturation(01:18:08) – ARC scores on frontier vs open source models(01:26:19) – Possible solutions to ARC Prize Get full access to Dwarkesh Podcast at www.dwarkeshpatel.com/subscribe

Hosts @berman66 (Vowel, Nanit) @kevingibbon (Shyp, Airhouse) @blader (Runway, Sandbox VR, Postmates, Hey)

#zapier #workflowautomation #workflow #aiautomation The rising significance of enterprise AI presents a unique hurdle: seamlessly integrating AI-based business workflows into operational systems, especially for non-programmers. On CXOTalk episode 808, we explore these issues with Mike Knoop, co-founder of Zapier and the company's AI lead. The conversation with Mike covers the rationale behind integrating AI, the technological advancements AI brings to workflow automation solutions, and its broader impact on business agility. Join the CXOTalk community: www.cxotalk.com/subscribeRead the full transcript: https://www.cxotalk.com/episode/ai-workflows-in-business-a-practical-guideKey points in the discussion include: ► The potential of AI-powered automation to empower more business users with customized workflows. But governance, accuracy, and security are key challenges to consider when implementing AI workflows.► Initial use cases include generating creative ideas, summarizing unstructured data, and making powerful business process automations easier to build for non-technical users.► Customer service and marketing are excellent starting points for AI automation.Watch this conversation to gain practical advice on using low-code, no-code tools to automate AI in the enterprise.Mike Knoop is the co-founder and Head of Zapier AI at Zapier. Mike has a B.S. in mechanical engineering from the University of Missouri, where his research topic was focused on finite element modeling and optimization.Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world's top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.

Wade Foster is the Co-founder & CEO at Zapier, a platform for building workflow automations without a developer. Zapier was started during 2011 in Columbia, Missouri, and by 2021, it was valued at $5b, having only raised $1.3m. Prior to founding Zapier, Wade had just two professional jobs, and had never managed or hired anyone. He worked as a PM on a web app used by 20k students, and as an Email Marketing Manager at Veterans United - a role that had a significant influence on Zapier's eventual success. In today's episode, we discuss: The stories and thinking behind Zapier's most unorthodox decisions How Wade thinks about product market fit How Zapier built their powerful distribution engine The fascinating story of Veterans United, and its impact on Zapier How Wade thinks about fundraising Why Wade lives by “don't hire ‘til it hurts” Key lessons on people management Referenced: Basecamp: https://basecamp.com/ Bingo Card Creator: https://www.bingocardcreator.com Bryan Helmig, Co-founder of Zapier: https://www.linkedin.com/in/bryanhelmig John Wooden quote: https://www.thewoodeneffect.com/be-quick-but-dont-hurry/ Mailchimp: https://mailchimp.com/ Mike Knoop, Co-founder of Zapier: https://www.linkedin.com/in/mikeknoop Patrick Mckenzie, creator of Bingo Card Creator: https://www.linkedin.com/in/patrickmckenzie/ PayPal: https://www.paypal.com/ Salesforce: https://www.salesforce.com/ SMBs: https://www.techtarget.com/whatis/definition/SMB-small-and-medium-sized-business-or-small-and-midsized-business Stripe: https://stripe.com/ Thinking in Bets by Annie Duke: https://www.amazon.com.au/Thinking-Bets-Annie-Duke/dp/0735216355 Tony Xu, CEO of DoorDash: https://www.linkedin.com/in/xutony/ Twilio: https://www.twilio.com/ Veterans United Home Loans: https://www.veteransunited.com/ Zapier: https://zapier.com/ Where to find Brett Berson Twitter: https://twitter.com/brettberson LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Where to find Wade Foster Twitter: https://twitter.com/wadefoster LinkedIn: https://www.linkedin.com/in/wadefoster Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter: https://twitter.com/firstround Youtube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps (05:46) The fascinating story of Veterans United (06:55) Lessons from Veterans United (08:35) The most important things Zapier got right (10:13) How Zapier built their powerful distribution engine (16:56) Why Zapier didn't move to focusing on enterprise (19:06) How Wade thinks about product market fit (24:26) The role of skill vs luck in Zapier's success (26:23) What was hard about building Zapier (30:03) Key lessons on people management (32:35) Rule of thumb: "don't hire ‘til it hurts” (36:42) Zapier's #1 hiring mistake (42:50) How to test for scrappiness in the hiring process (44:31) Do hiring playbooks transfer between companies? (50:01) The 12 year evolution of Zapier's product (53:20) How Zapier makes product decisions (55:40) How Zapier thought about competition (60:11) How to foster intellectual honesty in yourself and your org (65:35) The people who most impacted Wade's worldviews

Mike Knoop is Co-founder & President of Zapier. He joins the show to talk about Zapier's efficient tech solutions, why and how he started it, and why every business should implement it into their processes. Websites: www.Zapier.com www.mikeknoop.com

This episode is sponsored by MindStudio by YouAi. MindStudio is the best way to build an AI business. Start driving some serious revenue before everyone else. Mind Studio allows you to use conversational language to program incredibly powerful AI tools. No coding knowledge is needed to start your AI business. Sign up now- https://youai.ai/mindstudio On episode #136 of the Eye on AI podcast, Craig Smith sits down with Mike Knoop, Co Founder of Zapier, a tool that simplifies workflows by connecting various apps and services, making automation accessible to a wide range of users. In this episode, we dive deep into Mike's journey, from his early engineering days to building Zapier into a $5 Billion Dollar business. Zapier's capabilities have breached boundaries beyond just business process automation. Mike also shares heartening stories of it being used to prototype products and even kickstart businesses. Discover how they managed to carve out a niche in the mid-market and growth stages, all while sticking to their low-cost user acquisition model.We also explore the revolution of language models in automating workflows and the transformative power of turning unstructured data into structured gems. In the final segment, we navigate the competitive landscape for startups, shedding light on the unique struggles faced by AI startups and offering insights on how to make your mark amidst the giants. We also discuss the impact of AI on the market and how consumers can make sense of the plethora of AI offerings. It's a conversation packed with invaluable insights on AI, automation, and business. Don't miss this chance to learn from an industry leader!   Mike Knoop's LinkedIn: https://www.linkedin.com/in/mikeknoop Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI   00:00 Preview 00:57 Introduction 01:38 MindStudio by YouAi 03:06 Mike's Journey from Engineering to Zapier 10:35 Finding Market Fit for AI Tools 15:32 Integration of Language Models in Automation 22:48 How Does Zapier Leverage AI? 30:51 UI Roadmaps and Zapier's Evolving Market 35:01 Zapier's Challenges and Competitors 40:16 Innovation and Differentiation in Startups 44:32 Rise in AI Startups 48:59 MindStudio by YouAi

This Week in Startups is presented by: Embroker. The Embroker Startup Insurance Program helps startups secure the most important types of insurance at a lower cost and with less hassle. Save up to 20% off of traditional insurance today at Embroker.com/twist. While you're there, get an extra 10% off using offer code TWIST. Lemon.io - Hire pre-vetted remote developers, get 15% off your first 4 weeks of developer time at https://Lemon.io/twist Eight Sleep. Good sleep is the ultimate game changer. Now you can add the Pod Pro Cover to any mattress! Go to eightsleep.com/twist to check out the Pod Pro Cover and get $150 off at checkout! * Today's show: Zapier's Mike Knoop joins Jason to discuss the early days of Zapier before breaking down the evolution of app integrations and API usage (1:20). They dive into reducing friction for Zapier users, regulating AI, the limitations of present-day AI architecture, and more (43:06). * Check out Zapier: https://zapier.com/ Follow Mike: https://twitter.com/mikeknoop * Time stamps: (0:00) Mike Knoop joins Jason (1:20) Zapier's origin story (8:40) Zapier's key inflection point and its profit-sharing model (14:05) Zapier's business model (15:48) Embroker - Use code TWIST to get an extra 10% off insurance at https://Embroker.com/twist (17:03) The evolution of app integrations and API usage (23:41) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (25:00) Zapier demo + incorporating AI into your workflow (36:43) Eight Sleep - Go to https://eightsleep.com/twist to check out the Pod Cover and get $150 off at checkout! (38:14) Linkedin tightening the belt on its API (41:21) Zapier's enterprise customers (43:06) Reducing friction for Zapier users (51:41) Regulating AI (55:19) The limitations of present-day AI architecture * Read LAUNCH Fund 4 Deal Memo: https://www.launch.co/four Apply for Funding: https://www.launch.co/apply Buy ANGEL: https://www.angelthebook.com Great recent interviews: Steve Huffman, Brian Chesky, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland, PrayingForExits, Jenny Lefcourt Check out Jason's suite of newsletters: https://substack.com/@calacanis * Follow Jason: Twitter: https://twitter.com/jason Instagram: https://www.instagram.com/jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis * Follow TWiST: Substack: https://twistartups.substack.com Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin * Subscribe to the Founder University Podcast: https://www.founder.university/podcast

On this episode, Mike Knoop, Co-founder and President at Zapier, dives into how they built a $5 billion company with little to no funding. A completely remote company utilizing little to no venture funding isn't the norm for highly successful companies, but that's exactly how Zapier started.Fast forward to today, and the company is helping millions of businesses connect with 5,000+ apps to aid productivity and save up to 20 hours per week. Specifically, Mike covers:   - How Zapier got its start and self-funded its growth for so long. - Finding product-market fit and getting customers to pay early on. - How to think about adding new features and products.  - A framework for decision-making.   Learn more at https://tractionconf.io   Learn more about Zapier at https://zapier.com/   This episode is brought to you by:   In a digital world, customers demand more, especially from support. Intercom enables businesses to connect with their customers at exactly the right moment using powerful messaging and automation. This enables customer service teams to scale without additional investment while still providing efficient and personal customer experiences. Welcome to a whole new way to support your customers. Eligible startups get advanced Intercom features at a 95% discount. Visit https://Intercom.com/traction   Each year the U.S. and Canadian governments provide more than $20 billion in R&D tax credits and innovation incentives to fund businesses. But the application process is cumbersome, prone to costly audits, and receiving the money can take as long as 16 months. Boast automates this process, enabling companies to get more money faster without the paperwork and audit risk. We don't get paid until you do! Find out if you qualify today at https://Boast.AI   Launch Academy is one of the top global tech hubs for international entrepreneurs and a designated organization for Canada's Startup Visa. Since 2012, Launch has worked with more than 6,000 entrepreneurs from over 100 countries, of which 300 have grown their startups to seed and Series A stage and raised over $2 billion in funding. To learn more about Launch's programs or the Canadian Startup Visa, visit https://LaunchAcademy.ca   Content Allies helps B2B companies build revenue-generating podcasts. We recommend them to any B2B company that is looking to launch or streamline its podcast production. Learn more at https://contentallies.com #startup #growth #product #marketing #automation

It's 10:20 am on the 8th of September in 2011, Bryan Helmig sends Wade Foster a text with a business idea...

I couldn't help but geek out in this interview as I've been a @Zapier user since almost the beginning. This company has been doing "Work from Home" since the very beginning and knows a thing or two. Co-Founder @MikeKnoop joins us as we dive into WFH and how the company helps business owners, and individuals, connect cloud-based services to redefine what you can do with "automation". Learn more about your ad choices. Visit megaphone.fm/adchoices

Ramon RAy, founder of SmartHustle.com chats with Zapier founder, Mike Knoop on the power of a NO CODE mindset - www.smarthustle.com/nocode

In Silicon Valley today, it’s become a phenomenon to talk about raising less venture capital and going remote to offset capital cost and get better access to talent. This wasn’t always the dominant perspective; in fact it was often looked upon as an inhibitor to building a great company. Zapier - founded roughly a decade ago - has turned those two principles, amongst others on their head. Today the business has (still!) raised less than $1.5M, just recently crossed $50M in ARR and has been fully remote since Day 1. This episode was a ton of fun - I talked to Mike about how him, Wade and Bryan founded the company, their original vision for the business and how it has transformed a decade later, and how they have successfully led a remote company. Mike’s authenticity is audible in his voice - it was great to hear his very candid and humble perspective on building a once in a generation company.

Wade Foster shares super-simple mindsets, tools and tricks to automate repetitive work  tasks and liberate extra time.   You'll Learn: Just how much time you can save through automation Where automation works, and where it doesn’t The latest low-cost software tools to optimize your workflow   About Wade: Wade Foster is the co-founder and CEO of San-Francisco-based Zapier, a company offering a service that makes it easy to move data among web apps to automate tedious tasks. He, along with co-founder Mike Knoop, was featured on Forbes’ 30 under 30: for Enterprise Tech. Wade’s company: Zapier Wade’s Twitter: @wadefoster Wade’s email: wade@zapier.com   Resources mentioned in the show: Tool: Todoist Tool: Omnifocus Tool: Workona Tool: Calendly Tool: Slack Tool: monday.com Tool: Asana Tool: Trello Tool: Jira Tool: Google Sheets Tool: Airtable Tool: Coda Tool: Typeform Tool: Wufoo Tool: Superhuman Tool: Twilio Website: Upwork.com Book: “The Elements of Eloquence” by Mark Forsyth Previous episode: Episode 456: Finding Enrichment Through Side Hustles with Nick Loper   View transcript, show notes, and links at http://AwesomeAtYourJob.com/ep466

Mike Knoop is cofounder and Chief Product Officer at Zapier, which was in the YC Summer 2012 batch. Zapier moves information between your web apps automatically.Kevin Hale is a Visiting Partner at YC. Before YC Kevin was the cofounder of Wufoo, which was funded by YC in 2006 and acquired by SurveyMonkey in 2011.You can find Mike on Twitter at @mikeknoop and Kevin at @ilikevests.The YC podcast is hosted by Craig Cannon.***Topics00:43 - Kevin's intro01:03 - Mike's intro2:03 - How Mike and Kevin met4:03 - Market sizing for consumer software5:13 - Zapier's growth strategy today vs 20126:28 - Jumpstarting a platform like Zapier9:03 - Building an app directory before building a product11:03 - Applying to YC twice13:23 - Zapier after Demo Day14:48 - Zapier's first remote hire16:48 - Remote companies not being perceived as legitimate18:48 - Noticing remote was working then committing21:28 - Qualities to look for when hiring remote employees24:28 - Nina Mehta asks - What’s the best way to share work and knowledge across designers working on different parts of product without distracting from focused working time?25:58 - Remote mistakes in the early days27:33 - When to change modes of communication to allow for deep work29:28 - When to ask for someone's full attention31:33 - Product and design practices at Zapier34:38 - OKRs for teams vs individuals39:48 - Tools for remote teams43:48 - No internal email at Zapier46:53 - Keeping morale high in a remote team49:28 - What happens at a Zapier retreat51:43 - Remote design critiques56:43 - Serendipity and over optimizing for it58:33 - Setting up a remote company for success

Bryan Helmig, Wade Foster, and Mike Knoop started Zapier in 2011 as a side hustle. They ultimately applied to Y Combinator, twice. And this year they hit $35 Million dollars in annual revenue. I talked with Bryan Helmig (CTO) through the backstory of starting this company, being 100% distributed, the flexibility as well as the constraints of being remote-only, how they reached product market fit, growth, scaling their teams, and how they bring everyone together for company wide retreats.

Bryan Helmig, Wade Foster, and Mike Knoop started Zapier in 2011 as a side hustle. They ultimately applied to Y Combinator, twice. And this year they hit $35 Million dollars in annual revenue. I talked with Bryan Helmig (CTO) through the backstory of starting this company, being 100% distributed, the flexibility as well as the constraints of being remote-only, how they reached product market fit, growth, scaling their teams, and how they bring everyone together for company wide retreats.

In 2012, Zapier's ranks totaled three. Three co-founders who found success working nights and weekends on an idea to help people automate the boring and tedious parts of their jobs. Today, Zapier is 170+ strong, with a workforce that spans 15+ countries and 13 different timezones. After hiring their first remote worker years ago, they haven't looked back. So how do you foster culture, communication and collaboration when there are no set core hours, no daily in-person cues, no physical compound? Mike Knoop, Co-founder and CPO at Zapier, joins us to talk about the top two remote challenges, four pillars of communication and how everything at Zapier—from hiring to onboarding, communication channels, retreats and more—is intentional by design.

Mike Knoop, co-founder of Zapier, talks about the deep thinking they've done on building an organization that works entirely outside the home office. See acast.com/privacy for privacy and opt-out information.

In this Topical Zoom episode, I speak with 4 Experts about their career success secrets. I speak with Mona Patel, John Vars, Mike Knoop, Kevin Steigerwald who share their successes. Success hacking is a topic everyone is interested in. I speak to four successful people to understand how they think about their success. Here are […]The post DYT 122: 4 Career Success Secrets You Must Know appeared first on .

In this Topical Zoom episode, I speak with Mike Knoop, a co-founder and CPO at Zapier to talk about his interest in doing side-projects and he also talks about hiring product managers in a remote-first company. Who is Mike Knoop? Mike is a co-founder and CPO at Zapier, a 100% remote company that aims to […]The post DYT 106 : Product Managers Are Hole-Fillers | Mike Knoop appeared first on .

In this Topical Zoom episode, I speak with Mike Knoop, a co-founder and CPO at Zapier to understand the learnings from his experience scaling idea and building Zapier. He talks about his success, the birth of an idea, the role of Y Combinator, how he eats his own dog food and more. Who is Mike […]The post DYT 105 : Scaling Idea, Building Zapier | Mike Knoop appeared first on .

With the number of nifty SaaS apps used in your business growing by the day, it becomes vitally important to be able to get those apps to hand off data and work together. Using tools like Zapier you can build marketing automation workflows from simple to as complex as you need. The post TAM 076: Automating With Zapier – Mike Knoop appeared first on The Active Marketer.

Mike Knoop, Co-founder, CPO at Zapier on hiring first PM, building a growth team etc