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We return from a break to discuss the effects of an avalanche of app making due to Claude and Codex, including “Camp,” an experiment by Nabeel in native multiplayer AI-assisted group work beyond shareable outputs. We cover: In order to be a founder leading AI transformation do you need to lead by example? Conductor’s viral “prompt feature requests” workflow, reality of one-shot apps versus iterative prompting, how teams may use less open-source, Gemini's comparative strengths, and what does it mean when the engineering pod optimal size has moved from six to two. We end by discussing Granola’s MCP and why data moats are fragile, favoring best interfaces and customer-centric access. 00:00 Divergent Paths: Two types of Engineering post Claude 4.500:00 Introduction: The New Reality of Coding00:21 Building Camp: Multiplayer Knowledge Work03:12 Open Source in the Age of Models08:56 The Recommendation Problem: From Average to Expert15:29 Why Gemini Works for Personalization18:32 Submit a Prompt: Conductor's Product Innovation21:50 Two types of Engineering: Automatic vs Iterative32:08 Rethinking Team Structure: From Six to Two35:01 Can you AI transform without living it yourself?40:12 Data Moats and the MCP Shift44:05 Making Context Ubiquitous (00:00) - The two types of engineering post Claude 4.5 (00:00) - Introduction: The New Reality of Coding (00:21) - Building Camp: Multiplayer Knowledge Work (03:13) - Open Source in the Age of Models (08:57) - The Recommendation Problem: From Average to Expert (15:30) - Why Gemini Works for Personalization (18:33) - Submit a Prompt: Conductor's Product Innovation (21:51) - Two types of Engineering: Automatic vs Iterative (32:09) - Rethinking Team Structure: From Six to Two (35:02) - Can you AI transform without living it yourself? (40:13) - Data Moats and the MCP Shift (44:06) - Making Context Ubiquitous

Why are dentists so into vibe coding? Where's the Steve Jobs of this era? In this episode of Hallway Chat, Fraser and Nabeel  discuss the viral AI assistant Poke, within the broader context of the rise of niche AI applications. They also talk about the importance of specialization in AI applications, AI’s role in advancing scientific discovery and societal progress, and the future of AI in creative fields like music and art. Plus, a compelling use case for AI-enabled accessibility. (00:00) - Cold Open (02:28) - Exploring Poke, a New Email Assistant (06:41) - The Role of Personality in AI Products (09:33) - Raising the Floor on AI Email Assistants (11:00) - The Future of AI Assistants (14:55) - Customer-Centric Product Development (28:00) - The Evolution of Coding AI (30:22) - Specialized AI Applications (31:59) - The Impact of Open World Agents (36:53) - Raising the Bar in AI Innovation (45:51) - Teaching AI Ethics (49:26) - The Future of AI in Creative Industries (52:29) - AI and Accessibility

In this episode of Hallway Chat, Nabeel and Fraser unpack the emerging power of tool use, reasoning traces, and code execution in modern models. They reflect on how startups can stay ahead in a market that feels like it changes every 90 days, and what product choices separate the winners from the also-rans. The conversation spans Conductor’s clever planning/execution design, Notion’s everywhere-at-once API strategy, and the subtle ways models persuade us—sometimes too effectively. Along the way, they explore how founders should handle VCs circling during competitive raises, why misinformation concerns may be shifting, and why Fraser still turns to Gemini for certain jobs despite its quirks. It’s a candid look at the red-ocean dynamics of AI, the importance of owning your narrative, and how the future of work may mean managing a team of agents as naturally as we once managed people. (00:00) - Cold Open: Not Owning the UI (00:28) - Welcome Back + Era Check (01:26) - Tool Use Crosses the Rubicon (02:24) - Code + Browse + Reasoning = The New Triumverate (04:47) - Steerability & Plan Mode UX (05:46) - Why Code-Writing Hasn't Infiltrated Apps (Yet) (07:55) - Be Water: Notion's Everywhere Strategy (08:55) - Managing Many Agents Is... Tiring (15:41) - Model Divergence & The Need for a Picker (20:19) - Red-Ocean Reality & Controlling the Narrative

Nabeel and Fraser sit down with Andrew Mason, founder of Descript, for a deep dive into evolution "open world agents" in AI products. The trio uses the recent Airtable re-founding as a lens to look at open world agent architecture, what makes a great AI-enhanced user experience, and how Descript is embracing agent-first workflows as well. From the positioning of chat windows to the philosophy of “rewiring” users for AI, this conversation explores the strategic decisions shaping the next generation of software tools—and what it really takes to build a product users trust, understand, and love. (00:00) - Open World AI w/ Andrew Mason (00:47) - Introduction and Guest (01:01) - Discussing Airtable's AI Implementation (01:49) - "Cursor for X" (03:35) - The Challenge of Surfacing AI Work (05:08) - Review States and AI Adoption (07:05) - Airtable's Refounding Moment (08:13) - Open World Agents vs Agents on Rails (10:56) - The Power of Chat as Primary Interface (37:55) - Feature Discovery Through AI (45:25) - The Future of Product Design with AI

Craft in the age of AI. Nabeel and Fraser start with a dive into what makes the Boundary backpack so great. They explore how AI is changing the way we do customer research, the importance of obsessive attention to detail, and why great founders still need great taste. Along the way, they debate what it takes to recreate the creative magic of “1930s Paris” for modern product builders.  (00:00) - Opening (00:27) - Backpacks and Craft (05:36) - AI's potential role in product refinement and customer research (08:42) - The Down Power Wash Dish Spray? (10:08) - Consumer packaged goods and the importance of user research (15:38) - Creating environments that foster creativity and innovation (35:21) - The importance of solving fundamental problems in startups (37:47) - Microsoft Teams as a signal about founders

In this episode of Hallway Chat, Nabeel and Fraser explore the future of AI through the lenses of memory, context, and product design. They debate whether users will maintain separate AIs for work and home, why visually verifiable output is critical to product success, and how startups should behave in a world where the best models become commodities. From the case for uneconomical AI to the trade-off between 500 average agents and one brilliant one, they unpack what it takes to build great AI products—and when, if ever, VCs should preempt funding rounds. (00:00) - Chapter 1 (01:54) - Will AI Democratize Like the iPhone? (03:03) - Should You Burn Tokens to Win? (06:25) - The Case for Going Big with Compute (09:09) - 500 Agents or One Genius? (18:19) - The Importance of Proof of Work (19:49) - Building the Right UI for Agents (22:23) - Visualizing AI Output: A Design Challenge (33:37) - Memory Wars: Work AI vs. Personal AI (35:01) - Consumer Benefit > Privacy? (37:36) - Whats at stake, your memory (45:28) - Model Switching as a Feature, Not a Bug (53:53) - VC Talk: When to Preempt a Round

Nabeel and Fraser debrief on GDC and the opportunities for innovation beyond the conservative approaches in an industry going through a funk. They touch on the experiences at the recent GDC, including the growth of AI in gaming and the recurring challenges faced by developers. The discussion also covers the competitive landscape of AI startups, the importance of stacking wins, and the nuanced differences between building games and software products. Notably, they highlight the intriguing possibility of a vibrant game creation ecosystem driven by new model capabilities and the compelling idea of making game creation itself a multiplayer experience. (00:00) - Introduction: Bridging the Gap in the Gaming Industry (00:35) - GDC Week Highlights and Reflections (01:41) - Challenges and Innovations in AI and Gaming (03:50) - The Broken Ecosystem of Game Distribution (07:45) - The Future of Game Development and Creativity (19:59) - The Future of Multiplayer Coding (21:09) - The Magic of New Capabilities (22:07) - From Capabilities to Durable Products (23:39) - Stacking Wins for Long-Term Success (33:54) - Innovations in the Gaming Industry

We have evals for AI models, what about AI products? Today, Nabeel and Fraser talk about building computer games through vibe coding, and just when we might see a breakout success of an AI-built game. Through this discussion, they also explore what building a platform that curates weird AI experiments might look like, particularly one that builders could use for discovery and inspiration. (00:00) - Ep. 33 - Do we need a leaderboard for AI products (01:32) - Exploring Vibe Coding and Community Building (03:41) - AI Gaming and Future Prospects (07:36) - separate cross talk (09:28) - Creating an AI Meta (11:31) - Exploring AI Apps and Platforms (15:10) - Building a Curated AI Product Platform

Today, Nabeel and Fraser tackle the questions they've been asking internally. What level of improvement would it take to disrupt an incumbent product? They also explore what advantages second movers have in product markets, the emerging importance of reasoning models and computer use products, and what makes certain legacy markets ripe for AI reinvention. (00:00) - Intro (01:28) - Can 10% better, year after year, win? (10:43) - The questions we are asking (11:11) - Second Mover Advantage (26:55) - Exploring Deep Research and Competitors (29:11) - The Future of Computer Use (35:56) - AI as Muse: Enhancing Human Creativity

Nabeel and Fraser sit down for a round-table discussion with Chris Pedregal, founder and CEO of Granola, an AI note taking app, to discuss Granola’s evolution and product philosophy. Their conversation touches on the challenges of developing user-friendly AI products, and how to balance that with product vision. They also talk about what it’s like being a second-time founder, as well as if and when AI wearables will gain widespread adoption.  (00:00) - Christopher Pedregal - Granola & the Art of Invisible AI (01:05) - Second time founder syndrome (01:42) - The path to Granola (04:13) - Starting by "playing" (11:16) - The friend test (12:25) - Product philosophy behind Granola (16:25) - AI as Augmentor (22:04) - Balancing beginner and expert users (34:21) - Granola's Initial Launch and Challenges (34:52) - Balancing Product Development and Market Fit (35:58) - Where Granola is headed from here (40:00) - Vertical AI vs. General Assistants, who wins where? (42:44) - The Wisdom of Experts Era (50:54) - AI passing judgement (53:51) - Wearables role in AI (01:00:25) - Social Dynamics and Privacy Concerns (01:02:32) - The Need for New Norms and Labels (01:03:01) - What else could be Granola-ized

What lessons will we take from 2024's AI products into next year? How better to reflect on advancements in the field of AI over this past year than to ask AI itself? Today, Fraser and Nabeel use AI models like ChatGPT, Claude, and o1 to first look back and see if products and innovations of 2023 lived up to their hype in 2024. And to look at the show’s year in review, Nabeel puts these AI models to the test by feeding them episode transcripts and seeing which model produces a usable summary. Together, they also dive into the effectiveness of Google Gemini’s Deep Research tool, the rise of agentic computing in 2024, and whether products ever fundamentally change or just get constantly re-invented.  (00:00) - Introduction (00:48) - Reflecting on a Year of AI Releases (00:23) - The Rise of Agentic AI (00:38) - Google Gemini’s Deep Research (00:47) - Using AI Models to Review 2024 at Hallway Chat (00:56) - The Role of Taste in Making Great AI Products (00:06) - The Rollout of o1 (00:06) - WebSim - Self Expression Through Software (00:36) - Same Products, Different Iterations (00:39) - A Second Look at Apple Intelligence (00:10) - Living Agentically with AI (00:18) - Final Thoughts

Everywhere you turn, companies have shoved AI features into their products that no one is using. Today, Fraser and Nabeel ask each other—are there any AI features that you’re actually using in older products? Plus: How kids will be shaped using AI products daily. What would a fully unstructured Slack look like. AI email habits. Do we think incumbents will catch up with AI integrations? PREM framework for startup prioritization. Is this startups vs incumbents, or just startups vs the big llms? (00:00) - Intro (00:52) - Welcome to Hallway Chat (01:05) - What have incumbents really done with AI? (08:59) - Trends in AI integrations that work (20:21) - Reflecting, it's still early (23:42) - Creating AI that manages up (28:47) - PREM: A framework for prioritization (34:32) - Forget incumbents, how do startups fight fend off the LLMs? (37:24) - What are the new horizontals?

Open format Q&A this week. Fraser and Nabeel explore AI data privacy, the ethics of copying features, and maintaining innovation. They discuss enterprise data challenges, the importance of a strong product identity, and strategies for early-stage startups during fundraising season. (00:00) - Open (00:53) - Q&A Session Kickoff (01:14) - The data you gather is your roadmap (14:03) - The gravity of slowing down in your startup (21:20) - Your quarterly goal: Something Fundamentally Changes (21:57) - Low Risk, Low Outcomes (24:31) - Large Organizations and Mediocrity (30:58) - When to steal a feature?

Join Fraser, Nabeel, and special guest Anil from Meter as they dive into the innovation behind Meter Command. Meter's new interface has been called the "future of software" and a far ranging interview talks about the problem of designing product around personas, how command interfaces bridge the gap between CLI and dashboards, the importance of owning your tech stack, how your data is your product roadmap, and the orientation to long term thinking while still staying on the cutting edge. Links* See a demo of Meter Command* There's a lot of fine work from Bret Victor on visualization and interfaces, but here's one to get you started* Geoffrey Litt's Malleable SW in the age of LLMs* Hunter Walk's post on OKRs post his stint at Google (00:00) - Intro (00:19) - Meter Command and the future of software (03:36) - What we forget about software making (06:39) - Models make you reassess your own product (07:27) - Why aren't there more AI explorations in software? (09:38) - The "efficiency era" of startups (13:21) - Software should be soft (17:13) - The problem of "persona building" software (21:29) - Enterprise software is reporting (22:37) - Your data is your product roadmap (25:13) - Emersing in the trends of AI as a founder (27:27) - From janky prototype to custom model building (29:55) - "No AI" - Build for the problem, not the technology (36:50) - Full stack first, APIs over time (40:14) - If you want to be early you have to be full stack (48:28) - Long term orientation (50:56) - What one thing would you work on now (55:41) - End

How would you design an AI product differently for a hobbyist vs prosumer vs professional? Using MidJourney as a case study in the passion economy, they delve into how AI tools can help build communities and support semi-professional creators. They explore how these tools can democratize creativity, the role of art vs craft, the role of grit in skill building, cheating vs copiloting, the missing middle of the creative markets, and more. They also touch on the broader implications of AI in different creative fields from music, writing and podcasts. It's an exploration of how AI can help users maintain creative flow and the underserved opportunity for AI to help unleash the hobbyist.  Nabeel also spoke on Midjourney's unique growth loops on Unsolicited FeedbackLe Jin has great writing on the passion economy and wrote about the creative middle classLinus has an excellent article on Epistemic calibration that touches on taste, that we didn't get in the episode but worth seeing. Simply Drawing - ai feedback meets artRobert Morris' the Power Broker, which touches briefly on the rise of the professionalized classa little more on why bees wiggle outside of near-term evolutionary needs00:00 Intro 00:52 AI in Creative Endeavors 01:58 Market Expansion 03:01 The underexplored customer in AI 08:23 Finding other AI hobbiest opportunities 11:31 If they AI made it, are we building skill? 13:36 Difference between art and craft 14:40 Homework Helpers vs Writing Copilots 16:07 Hobbiests are not quite the creator economy 17:47 Product for hobbists 21:03 The missing middle of creative markets 26:42 AI's potential in the education of craft (00:00) - Intro (00:52) - AI in Creative Endeavors (02:00) - Market Expansion (03:02) - The underexplored customer in AI (08:25) - Finding other AI hobbiest opportunities (11:32) - If they AI made it, are we building skill? (13:38) - Difference between art and craft (14:42) - Homework Helpers vs Writing Copilots (16:09) - Hobbiests are not quite the creator economy (17:49) - Product for hobbists (21:04) - The missing middle of creative markets (26:44) - AI's potential in the education of craft