OpenAI has been quietly developing collaboration features for ChatGPT that would let multiple users work together on documents and chat about projects, a direct assault on Microsoft’s core productivity business. The designs have been in development for nearly a year, with OpenAI’s Canvas feature serving as a first step toward full document collaboration tools.
This would be a change towards something like simultaneous collaboration with colleagues on a shared document in Microsoft Office. At present, a ChatGPT Team account allows more than one person in the Team account to work in a project space and to take part in the chats within that project, but only one person at a time, as I understand it.
A few hours spent with ChatGPT-o3 resulting in good first draft of a framework for thinking about our labs. It covers
types of lab
the roles of people involved with the labs
the core technical configuration of a lab
assets needed to launch, operate, and archive a lab
a naming convention for these assets
No doubt the framework will need to be tweaked and added to as our ideas mature.
The chat with o3 was a valuable mind-clearing exercise for me, and I was impressed by how much more “intellectual” it is compared to the 4o model. Like many intellectuals, it also displayed a lack of common sense on occasions, especially when I asked for simple formatting corrections to the canvas we were editing together. The 4o model is much more agile in that respect.
During the chat, when the flow with ChatGPT didn’t feel right, I hopped back and forth to consult with Perplexity and NotebookLM. Their outputs provided interestingly and usefully different perspectives that helped to clear the logjam.
A decision arising from my joint AI consultation process was the choice of Google Workspace for the office productivity suite within our labs. This will allow for much better collaboration when using office tools with personal licences than would be the case with Microsoft Office 365. Given the ad hoc nature of labs and the cost constaints we have, this is an important consideration.
Posting in his SubStack Adjacent Possible, Steven Johnson discusses how “language models are opening new avenues for inquiry in historical research and writing“. He suggests they can act as collaborative tools, rather than replacements for the writer’s engagement with primary sources.
Johnson argues that NotebookLM is designed to facilitate rather than replace the reading of original sources. I t does so by making the entire source readable within the app, and by provinding inline citations linked directly to the original material.
He identifies some interesting use cases.
The AI can be a tool for collaborative brainstorming by allowing users to explore different hypotheses and see patterns within personally curated sources.
NotebookLM can be used for targeted information retrieval.
It can help “fill in blank spots” or remind users of forgotten details from their readings.
The tool is valuable for fact-checking against uploaded source material.
For specific information, like in a car manual, it can provide direct answers to questions through a conversational Q&A format.
It can enhance serendipitous discovery by suggesting surprising, less obvious connections amongst the sources.
It can create mind maps from the sources, in effect indexing them on the fly.
Finally he speculates on a future where e-books could come with a NotebookLM-like interface. This would bundle together the main work with all the original sources used by the author, enabling “timelines, mind maps, and explanations of key themes, anything you can think to ask”.
Our latest Lab Note records a quick experiment where I asked two ChatGPT models to draft the outline for an “acclimatisation” session – the starter lab we plan to run with newcomers to AI.
Highlights:
Model face‑off: I ran the same prompt in parallel on model o3 and model 4o. The reasoning‑focused o3 delivered a tight nine‑part outline. 4o wandered off‑piste.
Time cost: The branch test took three minutes and gave us a clear winner.
Transparency: The Lab Note carries an AI‑heavy label because most of the prose came straight from o3. I trimmed, corrected one hallucination, and signed off.
If you are curious about our process or want to see how structured prompting keeps the bot on track, read the full note here: First Acclimatisation Session Lab Note →
Use ChatGPT as a thinking partner to create a first draft of an acclimatisation lab setup for Anapoly AI Labs and to compare output quality between two models (o3 and 4o).
Author and date
Alec Fearon, 24 June 2025
Participants
Alec Fearon – experiment lead
ChatGPT model o3 – reasoning model
ChatGPT model 4o – comparison model
Lab configuration and setup
Interaction took place entirely in the ChatGPT Document Workbench. The first prompt was duplicated into two branches, each tied to a specific model. All files needed for reference (conceptual framework, lab note structure) were pre‑uploaded in the project space.
Preamble
Alec wanted a concise, critical first draft to stimulate team discussion. The exercise also served as a live test of whether o3’s “reasoning” advantage produced materially better drafts than the newer 4o model.
… what I can say is that a theme throughout this self-analysis is this: I find ChatGPT to be a really useful tool when I already have some idea of what I want to do and when I’m actually engaged with the issue. I find it much less reliable or useful for completely automating parts of the process. There’s no substitute for reading the paper.
It hit me that generative AI is the first kind of technology that can tell you how to use itself. You ask it what to do, and it explains the tool, the technique, the reasoning—it teaches you. And that flipped something for me. It stopped being a support tool and became more like a co-founder.
A quote from BRXND Dispatch, a SubStack newsletter by Noah Brier, which featured Craig Hepburn, former Chief Digital Officer at Art Basel.
To identify and configure an AI toolkit for Anapoly AI Labs that credibly models the use of general-purpose AI tools in a small consultancy setting.
Author and date
Alec Fearon, 24 June 2025
Participants
Alec Fearon, with Ray Holland and Dennis Silverwood in email consultation ChatGPT-4o
Lab configuration and setup
This setup models a real-world micro-consultancy with three collaborators. It assumes limited budget, modest technical support, and a practical orientation. We aim to reflect the toolkit choices we might recommend to participants in Anapoly AI Labs sessions.
Preamble
If Anapoly AI Labs is to be a credible venture, we believe it must model the behaviour it explores. That means our own internal work should demonstrate how small teams or sole traders might use AI tools in everyday tasks – writing, research, analysis, and communication – not just talk about it. This lab note outlines our proposed working configuration.
Procedure
We identified common functions we ourselves perform (and expect others will want to model), for example:
Writing, summarising, and critiquing text
Researching topics and checking facts
Extracting and organising information from documents
Sharing and collaborating on files
Managing project knowledge
We then selected tools that:
Are available off the shelf
Require no specialist training
Are affordable on a small-business budget
Can be configured and used transparently
Findings
Core Tools Selected
Function
Tool
Licence
Notes
Writing & prompting
ChatGPT Team
£25–30/m/user
Main workspace for drafting, reasoning, editing
Search & fact-checking
Perplexity Pro
$20/m/user
Fast, source-aware, good for validating facts
Document interrogation
NotebookLM
Free (for now)
Project libraries, good with PDFs and notes
Office apps
MS 365 or Google
£5–15/m/user
Matches common small business setups
Visual inputs
ChatGPT Vision
Included with ChatGPT
Used for images, scans, and screenshots
Discussion of findings
This configuration balances affordability, realism, and capability. We expect participants in Anapoly AI Labs to have similar access to these tools, or to be able to get it. By using these tools ourselves in Anapoly’s day-to-day running, we:
Gain first hand experience to share
Create reusable examples from real work
Expose gaps, workarounds, and lessons worth documenting
We considered whether personal licences could be shared during lab sessions. Technically, they can’t: individual ChatGPT and Perplexity licences are for single-user use. While enforcement is unlikely, we’ve chosen to adopt the position that participants should bring their own their own AI tools – free or paid – to lab sessions as part of the learning experience. This avoids ambiguity about licencing and sets the ethical standard we want to maintain.
Conclusions
This toolkit would enable us to model our own small-business operations, treating Anapoly itself as one of the lab setups. That would reinforce our stance: we don’t claim to be AI experts; we’re practitioners asking the questions small businesses wish they had time to ask, and showing what happens when you do.
Recommendations
Configure project workspaces in ChatGPT Team to reflect different lab contexts
Maintain prompt libraries and reasoning trails
Make costs, configurations, and limitations explicit in diary and lab notes
Evaluate whether to add AI-enhanced spreadsheet or knowledge tools (e.g. Notion, Obsidian) in future iterations
Tags
ai tools, toolkit, configuration, modelling, small business, chatgpt, perplexity, notebooklm, office software, credibility
Glossary
ChatGPT Team – OpenAI’s paid workspace version of ChatGPT, allowing collaboration, custom GPTs, and project folders. NotebookLM – A Google tool for working with uploaded documents using AI, currently free. Perplexity Pro – A subscription AI assistant known for showing sources. Vision input – The ability to upload images (photos, scans) and have the AI interpret them.
As Anapoly AI Labs begins to take clearer shape, we’ve stepped back to ask: what exactly is a lab, and how should we think about the different types we’re running or planning?
We now have an answer in the form of framework that describes what labs are for, how they vary, and what kind of value they generate. This will help us give lab participants a sense of where they are and what comes next in the process of understanding how to make use of general-purpose AI tools. The framework will help us design better labs. It will evolve in line with our thinking about all these things.
The framework defines four key functions a lab can serve:
Acclimatisation – helping people get comfortable with AI tools
Experimentation – trying out tasks to see what works and what doesn’t
Proof of concept – asking whether AI could handle a specific challenge
Iteration – going back to improve on an earlier result
It also distinguishes between domains (like consultancy or authorship) and contexts (like “a solo consultant writing a project bid”). Labs are set up to reflect domain + context.
The framework defines a simple set of participant roles – observer, explorer, and facilitator – and outlines the kinds of outcomes we’re hoping for: confidence, insight, and learning.
The full conceptual framework is here, and we’ll continue to refine it as our practice develops.
This diary post is part of our public notebook. It helps document not just what we’ve tried, but how we’re thinking and rethinking as we go.
Transparency Label Justification: This post was developed in dialogue with ChatGPT. Alec directed the content, structure, and tone, while ChatGPT contributed drafts, edits, and structural suggestions. All decisions about framing and language were reviewed and approved by Alec. This collaborative process fits the “AI-assisted” classification under the Anapoly Online transparency framework.