transparency label: Human-only
… 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.
Source: Sean Trott in his newsletter How I use (and don’t use) ChatGPT on 24 June 2025
transparency label: Human only
| ChatGPT-4o | fast, for brainstorming, quick questions, general chat |
| o3 | powerful, for serious work (analysis, writing, research, coding) |
| o3-pro | ultra-powerful, for the hardest problems |
Source: One Useful Thing, Substack newsletter by Ethan Mollick, 23 June 2025
transparency label: Human only
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.
Alec Fearon, 24 June 2025
Alec Fearon, with Ray Holland and Dennis Silverwood in email consultation
ChatGPT-4o
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.
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.
We identified common functions we ourselves perform (and expect others will want to model), for example:
We then selected tools that:
| 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 |
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:
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.
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.
ai tools, toolkit, configuration, modelling, small business, chatgpt, perplexity, notebooklm, office software, credibility
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.
Transparency label: AI-assisted
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:
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.
transparency label: AI-assisted
Purpose of Experiment
To test whether a general-purpose AI (NotebookLM) can generate a credible, insightful articulation of Anapoly AI Labs’ value proposition when prompted appropriately.
Author and Date
Alec Fearon, 22 June 2025
Participants
Alec Fearon, Ray Holland, NotebookLM (AI), Perplexity (AI)
Lab Configuration and Setup
This experiment was run using NotebookLM, in a notebook containing background files on Anapoly AI Labs. To explore how we might play to the strengths of different AIs, a follow-up was performed using Perplexity with the NotebookLM output as input.
Preamble
Procedure
NotebookM was given this prompt:
“What is Anapoly AI Labs’ core value proposition and how does it differentiate itself?”
NotebookLM’s response was then given to Perplexity, with this prompt:
“How does Anapoly’s hands-on approach help me understand AI better in my work?”
Findings
NotebookLM responded with a detailed analysis of Anapoly’s strategic positioning:
Anapoly AI Labs’ core value proposition lies in bridging the gap between AI curiosity and practical application for non-technical professionals and small businesses. They address the common issue where individuals are interested in AI but are unsure how to use it effectively in their daily work. Their purpose is to help these professionals explore how general-purpose AI can assist with everyday tasks, such as improving productivity, creating better quality output, or saving money.
Anapoly AI Labs differentiates itself through a distinctive “modeling exploration” approach and its stance on credibility:
- Hands-on, Practical Exploration over Theory or Expertise: Unlike traditional courses, product demos, or sales pitches, Anapoly AI Labs focuses on hands-on exploration of general-purpose AI tools in simulated real-world contexts. The emphasis is on demonstrating “what AI tools can and can’t do when pointed at everyday work,” rather than teaching complex AI theory or coding. This involves setting up specific “labs” for different market segments, such as a Small Consultancy Office or Independent Author, to apply AI to real problems.
- Credibility Through Transparency and Shared Journey: A central aspect of their differentiation is their approach to credibility. They explicitly state they “don’t claim to be AI experts”. Instead, their credibility is built upon a “shared journey of discovery and documented practical insights”. As practitioners “in the early stages of building with the tools”, they aim to show that they are serious, curious, and hands-on. The AI itself helped refine this stance, suggesting that their value comes from “modeling exploration” and “asking the questions small businesses wish they had time to ask, and showing what happens when you do”. This is presented as more credible than a static CV or pre-existing expert status.
- Public Documentation and Transparency: Anapoly AI Labs maintains an online presence called “Anapoly Online,” which functions as a “public notebook”. This platform is used to “document real tests, learning, missteps, and insights,” sharing what works and what doesn’t. It is designed to be “lean, understated, and quietly credible”, building a “body of work others can trust” bit by bit through openness and demonstrated effort. They also have a transparent system for disclosing AI involvement in all published material, using classifications like “Human-led,” “AI-assisted,” or “AI-heavy”.
- Focus on the “Capability-Absorption Gap”: The labs directly address the “capability-absorption gap,” which refers to why businesses struggle to adopt AI despite its capabilities. By providing a low-barrier-to-entry, informal, and social environment for peer learning, they aim to make AI adoption more accessible and less confusing for non-technical users.
Perplexity then provided the following simplified summary:
“Anapoly’s hands-on approach helps you understand AI better in your work by letting you actually try out AI tools on real tasks that matter to you, instead of just reading about them or watching demos. This practical experience shows you what AI can and can’t do in your daily work, making it easier to see how it fits into your routine and where it can save you time or improve your results.
You get to experiment in a safe, informal setting, learn from real examples, and see both successes and mistakes. This helps you build real confidence with AI, instead of just theory, and makes it much easier to spot where AI could help you most in your job.”
Discussion of findings
The NotebookLM output demonstrated that a general-purpose AI can synthesise a coherent and plausible value proposition from structured background material. Perplexity, by contrast, generated a shorter and more audience-friendly version in response to a focused, user-oriented question.
Together, these outputs show complementary strengths: NotebookLM delivers a comprehensive internal formulation; Perplexity extracts an accessible, benefit-led summary. Used in tandem, these tools help refine messaging for different audiences: internal strategists vs. prospective participants.
Of particular interest is NotebookLM’s identification of the “capability-absorption gap”, a concise and useful term for a key problem that Anapoly AI Labs addresses. While the founders had recognised this issue in practical terms, the AI’s phrasing sharpens it into a strategic talking point. Framing Anapoly’s purpose in terms of reducing this gap may prove valuable in both internal planning and external communication.
This experiment also highlights the value of re-prompting and testing across different AI models to triangulate clarity and tone.
Recommendations
Tags
value, lab-setup, worked, use-cases, prompting, ai-only, positioning, credibility, communication, capability-absorption-gap
Glossary
Transparency Label Justification. The experimental outputs (NotebookLM and Perplexity responses) were AI-generated and included unedited. However, the lab note itself – its framing, interpretation, and derived recommendations – was co-written by a human and ChatGPT in structured dialogue.
ChatGPT’s role included: drafting the findings and recommendations, articulating the reasoning behind terms like “capability-absorption gap”, refining the explanatory framing, tags, and glossary.
The EU AI Act establishes a risk-based classification system for AI systems. Compliance requirements depend on the risk the system poses to users. Risk levels are set at unacceptable and high. General-Purpose AI systems like ChatGPT are not classified as high-risk but are subject to specific transparency requirements and must comply with EU copyright law.
Also, the Act “aims to support AI innovation and start-ups in Europe, allowing companies to develop and test general-purpose AI models before public release. That is why it requires that national authorities provide companies with a testing environment for AI that simulates conditions close to the real world. This will help small and medium-sized enterprises (SMEs) compete in the growing EU artificial intelligence market.“
Unlike the EU, the UK has no general-purpose AI sandbox. The UK takes a sector-led approach to AI oversight, relying on existing regulators to operate their own sandbox initiatives under the government’s pro-innovation framework. Each sandbox is designed around the compliance needs and risk profiles of its domain. Existing sandboxes are focused on sector-specific or compliance-heavy contexts, for example:
These UK sandboxes are geared toward testing purpose-built AI tools or integrations into regulated industry IT systems. There is no current sandbox designed for SMEs exploring general-purpose AI like ChatGPT in everyday, low-risk tasks. While tools like ChatGPT could be included in sandbox trials (e.g. via the ICO for data privacy concerns or the FCA for financial services), these environments are not designed for routine or everyday use. They are structured for defined compliance challenges, not routine experimentation, and they require a defined project with specific compliance aims.
A search by ChatGPT found no evidence of any current provision for open-ended, exploratory use of general-purpose AI by SMEs in unregulated or lightly regulated workflows. Anapoly AI Labs can occupy that space, modelling practical, credible AI use outside formal regulation.
In that context, Anapoly’s modelling approach could include a cycle of experimentation followed by proof of concept (POC). A proof of concept is a small-scale test to check whether an idea works before investing in full implementation.
Transparency Label Justification. This piece was authored by Alec Fearon. ChatGPT was used to assist with regulatory research (e.g. the EU AI Act and UK sandbox landscape), structure the argument, and refine language. The ideas, framing, and conclusions are Alec’s. AI contributed supporting detail and wording options, but did not generate or structure the content independently.
I subscribe to quite a lot of newsletters, covering topics that interest me. But it’s difficult to find the time needed to filter out which newsletters merit a closer look. I remembered reading that Azeem Azhar solves this problem by telling an AI what kinds of things he is looking for, giving it a week’s worth of newsletters, and asking it to give him a concise summary of what might interest him. So I followed his example. I gave NotebookLM background information about Anapoly AI Labs, and asked it for a detailed prompt that would flag anything in my newsletters that might suit a diary post.
When I used that prompt, one of the points it picked up was that our transparency framework ties in well to the EU AI Act, which has a section on transparency requirements That prompted me to look into the UK’s approach. I learned that, instead of regulations, we have regulatory principles for the guidance of existing regulatory bodies such as the Information Commissioner’s Office or Ofcom. The principles cover:
It occurred to me that some of the experimentation in our labs could focus on these aspects, individual labs being set up to specialise on one aspect, for example. Equally, we might explore how the regulatory princples could form the basis for a quality assurance framework applicable to business outputs created with AI involvement. This will become an important consideration for small businesses and consultancies.
A final thought: any enterprise doing business in the EU must comply with the EU AI Act. That alone could justify a focused lab setup. We might simulate how a small consultancy could meet the Act’s transparency and accountability requirements when using general-purpose AI tools, modelling practical compliance, not just reading the rules. This, too, might merit some experimentation by Anapoly AI Labs.
Transparency Label Justification. This post was drafted by Alec Fearon. Newsletter filtering was supported by NotebookLM as a separate exercise. ChatGPT was used to revise wording and clarify structure. All reflections and framing are human-authored.
transparency label: AI-assisted
Most of what we publish here is written with the help of AI. That’s part of the point. Anapoly AI Labs is about trying these tools out on real work and seeing what they’re good for.
To keep things transparent, we label every post to show how much AI was involved. The label links to our transparency framework. This doesn’t try to assign percentages. Instead, we use a straightforward five-level scale:
We sometimes add the following:
Transparency Label Justification. This post was developed collaboratively. The human author defined the purpose, structure, and tone; the AI assisted with drafting, tightening prose, applying style conventions, and rewording for clarity. The final version reflects a human-led editorial process with substantial AI input at multiple stages.
In the last few weeks, we’ve been thinking more clearly about how to describe what Anapoly AI Labs is for and how we intend to run it.
The idea, from the start, was not to teach AI or to promote it, but to work out what it’s actually good for. Not in theory, but in the real day-to-day work of people like us. We’d do this by setting up simulated work environments (labs) and running real tasks with current AI tools to see what helps, what doesn’t, and what’s worth doing differently.
To sharpen that thinking, I used ChatGPT as a sounding board. I gave it access to our core documents, including the notes from a deep dive discussion in NotebookLM about what we’re trying to build. Then I asked it to act as a thinking partner and help me write a strategy.
The result is a good working draft that sets out our purpose, stance, methods, and measures of success. It’s a helpful starting point for discussion. One thing we’ve been clear about from the start: we don’t claim to be AI experts. We’re practitioners, working things out in public. That’s our stance, and the strategy captures it well.
We’ve published the draft as a PDF. It explains how Anapoly AI Labs will work: how the labs are set up, what kind of people they’re for, how we plan to run sessions, and what success would look like. We now want to shift focus from shaping the idea to working out how to make it happen.
Download the full document if you’d like to see more. We’d welcome thoughts, questions, or constructive criticism – we’re still working it out.