Market Analysis: AI Tools for Productivity
Productivity 📈📈📈
Hello again! Welcome back to another edition of Product, a section where I post about my journey to product management. This edition covers my thought process while analyzing the market of AI tools for productivity.
The Problem Statement
As part of NextLeap’s (last!) Learn in Public challenge, the cohort was tasked with analyzing one of the following markets and creating a case study:
Credit on UPI
AI Tools for Productivity
Indian Space Economy
The Creator Economy
Mental Health & Wellness
Initial Thoughts
The first thought in my mind, obviously, was to decide which of these markets to focus on. After a brief while of brainstorming and imagining the directions each topic would take, I decided to play by my strengths.
As someone who used to write about (productivity) apps for a living and has an ongoing newsletter on productivity and tech, I convinced myself that I wouldn’t go wrong picking the AI tools for productivity market.
Segments
I broadly classified the AI tools for productivity market as follows:
Chatbots
Customer support
Content creation and editing
Project management
Legal services
Coding assistants
Of these segments, I focused on the content creation and editing segment and continued my research.
The Research
I typically use AI tools at the end of my research to validate it. This way, I have enough context to understand if the LLM is giving me factual and relevant information. This time around, I introduced AI towards the middle of my research and let it be my research partner for the rest of the process.
The most valuable resources I found during the research were the AI Ascent 2024 keynote from Sequoia Capital (linked below) and Figma’s Design Trend Report. It was also fascinating to explore the products of the top AI Assistant startups funded by Y Combinator.
It’s not all sunshine and rainbows though. Goldman Sachs1 and The Economist have offered a counter view calling out the relatively low return on investment and the significant infrastructure cost of AI data centers so far. That’s also highlighted in Sequoia’s AI’s $600B Question piece, where they argue the gap between AI infrastructure investment and actual revenue growth in the AI ecosystem has widened significantly.
This is not the first time AI has been the hot thing either. It has been happening since the 1970’s, leading to a phenomenon known as the AI winter.
Case Study | Closing Thoughts
Here’s the case study:
From this exercise, I realized we have a while to reach peak AI. The key is to solve user problems with AI, instead of leveraging AI for the sake of it and slapping an AI label over a feature that could work just fine without it.
It also gave me a sense of bhai kya kar raha hai tu for sitting on the sidelines. Will I end up taking a leap to start/join an AI startup or work on an AI-enabled product? Stay tuned to find out!
Until next time,
Subin
Both ChatGPT and Claude fell flat when I asked to summarize this 31-page report. Productivity 📉📉📉