I’m Tanay Jaipuria, a partner at Wing and this is a weekly newsletter about the business of the technology industry. To receive Tanay’s Newsletter in your inbox, subscribe here for free:
Hi friends,
ChatGPT released a really interesting paper last week that went deep into how it’s being used by its over 700 million monthly active users over the last year.
About 73% of its usage is now for non-work tasks, with 23% for works tasks. But perhaps most interesting was the graphic below which broke down how people are using ChatGPT for different use cases and different “jobs to be done”. It clearly shows that ChatGPT has become an all-in-one assistant across personal and work tasks and multiple jobs to be done.
Some of these jobs to be done required others websites or tools to serve the same need, and some of these jobs to be done are things where there wasn’t a good parallel prior to the advancements in LLMs. Let’s discuss further.
“There are only two ways to make money in business. You can bundle, or you can unbundle.” — Jim Barksdale.
Today, it’s clear that ChatGPT is basically used as an all-in-one tool that bundles and absorbs many jobs that used to live in separate products.
Some of the top use cases of ChatGPT per the chart are:
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Practical Guidance (28.3%) which includes brainstorming ideas, getting how to type help on aspects of life, fitness, self care as well as Tutoring or teaching. These are things which people relied on websites such as YouTube, Reddit, etc before for the former, and YouTube, KhanAcademy and Chegg for the latter, but were not personalized or adaptable to any question.
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Writing and editing (28.1%) including writing drafts of posts, emails, etc, editing and summarizing existing text. This is something which may have been done by things like spellcheck, Grammarly, and parts of these (first drafts from scratch) really weren’t possible or accessible before.
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Seeking Information (21.3%) which is very clearly a form of search and information retrieval which people relied on Google, specific vertical blogs and forums for.
Below is a version of the chart showing what various tools and products ChatGPT can “substitute” across the various use cases by fulfilling the job to be done itself.
Bundling wins here for simple reasons. One interface replaces a pile of tabs/apps, which cuts switching costs and makes the next step instant. A single memory carries context across jobs, so the draft you wrote informs the summary you ask for, which informs the email you send. Coverage matters too. ChatGPT is good enough across many jobs in the same session, which is how real work and life actually happen and so if you aren’t a frequent user of a use case, you never need to bother finding the best product for it. And then ofcourse, speed improves because the model does the next action (giving you the answer) rather than handing you links.
But it is not just that ChatGPT can perform many jobs other tools could do. It enables jobs that are bigger and more fluid because of how LLMs work. The paper frames usage as Asking, Doing, and Expressing. Asking is about getting information or advice. Doing is asking the model to produce an output you can plug into a process. Expressing is sharing views or feelings.
Across the sample, about 49% of messages are Asking, 40% are Doing, and 11% are Expressing. For work messages, about 56% are Doing, and most of those are writing tasks. By late June 2025 the split was 51.6% Asking, 34.6% Doing, and 13.8% Expressing. The chart below shows the split across these.
Split of type of request by area
Why does this matter? Doing is the net-new capability at consumer scale. The assistant does not only answer questions (which in and itself is better than the past form of getting answers manually). It produces artifacts and chains steps with context that carries across turns. You ask for a plan, then ask for the spreadsheet version, then ask for a manager-ready summary, all in one thread. You describe constraints and get a finished draft that fits your situation rather than a generic page of links.
This is what lifts the ceiling on old jobs to be done like tutoring, cooking, or data pokes, and it also creates new habits that did not exist for most people before.
Bundles create demand by collapsing steps and reducing friction. Specialists then peel off power users who do one job all the time and need depth, speed, and tight integrations. That is when unbundling shows up. You can already see the signs of it.
For those that code a lot, specialized products such as Cursor and Windsurf that leverage the capabilities underlying ChatGPT (i.e., the models) are better. For those that create a lot of images, using Midjourney or Krea may be better. For those that are writing for work, Notion that has the context of your related knowledgebase may be better.
If you do one of these tasks heavily in your job, a focused tool will often beat the general assistant that is ChatGPT. That idea of the unbundling that is to follow and where we are likely to see it next is what I will cover next week.
