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

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

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

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

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 Feedback
• Le Jin has great writing on the passion economy and wrote about the creative middle class
• Linus 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 art
• Robert Morris' the Power Broker, which touches briefly on the rise of the professionalized class
• a little more on why bees wiggle outside of near-term evolutionary needs

00:00 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

Fraser and Nabeel start by retcon'ing their earlier "maybe we should stop talking about models" and reflecting on the importance of adapting product strategy based how much disruption is happening in your market. The episode highlights the release of Claude Artifacts by Anthropic, and comparing it to other AI product innovations. They also nerd out on the Xbloom coffee machine and a robotic mop. Lastly, Fraser asks about how to navigate when a founder gets introduced to the wrong partner at a VC firm.

• (00:00) - Product development early on the S curve, exploring Anthropic Artifacts

Fraser and Nabeel dive into the evolving landscape of AI note-taking apps, focusing on a new product, Granola. They discuss Granola's unique approach of "enhancing your thought" instead of "thinking for you." They also explore: applying this idea to other categories, the influence of LLMs on UX, and the need for more experimental user workflows to unlock AI. They also discuss the challenges and dynamics of startup funding, the significance of capital in achieving market success, and how dynamics of AI team building are changing. 

• (00:00) - Introduction

Is it still worth it to discuss models when discussing startups? Nabeel and Fraser discuss how that may be the wrong question to ask in the current landscape, and why customer-centric questions and user experience should be the basis of product experience. Later, they deliberate who might come out on top in the “horse race” for AI product dominance, and whether it will come from a large, established company, or if the frontier of capabilities belongs to small innovators.

• (00:00) - Decoding the Future: Puzzles vs. Mysteries in Tech

What is the extent of autonomous coding engineer Devin’s ability to generate real, functional applications with little to no help? Nabeel and Fraser dive into the buzz about Cognition’s ‘Devin’, what makes it different, and the transformative potential of AI in software engineering, particularly focusing on autonomous coding software. Later, they get into the innovator's dilemma and the lessons the .com era can lend to this new time in AI.

(00:00) Intro

(01:23) The buzz about Cognition’s Devin

(07:44) What makes Devin different?

(12:19) Tolerance for time

(16:07) The interface of the future

(22:19) Innovating around the incumbent’s advantage

(25:30) Cutting edge products mean new user bases

(29:52) Netscape was the Open AI of the Mobile Revolution

(33:42) Optimism as the engine of capitalism

(37:56) The model is not the product

This week is a discussion of the points of differentiation vs commodification in various AI models. Including how these points might change over time, and might change between language, image, and video models. Then Fraser pivots to orienting around jobs to be done in AI, and how the various models have a huge gap between being capable of doing something, and doing it well. 

We then talk about Claude 3, the nature of benchmarking, and the rapid dropping LLM prices. Lastly, we cover a startup subject, debating the merits of SAFE (Simple Agreement for Future Equity) vs. priced equity rounds in early-stage funding. 

Links:

1. Claude 3 from Anthropic
2. LMSys Chatbot Leaderboard for which models are sticking out right now
3. For areas where models don't differentiate, we are back to the 7 powers framework. Hamilton Helmer's website with overview of the 7 Powers framework: https://7powers.com/, Sachin Rekhi has a good detailed primer on the 7 Powers as well.
4. Carta guide on priced rounds vs SAFEs, which we don't really agree with all the pros and cons, but is a decent overview

• (00:00) - Opening