Directional Signals, Needle Facts, and Option Space: A Framework for Thinking About Markets with AI

Most people approach AI and the stock market in the same way they approach financial news: “What should I buy?” The problem is that both analysts and AI tend to surface the same obvious trends; the headlines everyone is already talking about. When AI answers that question, it usually produces directional signals: the visible momentum of the market. In the AI sector right now, that might mean companies like NVIDIA or Cisco — the names dominating media narratives and investor attention. But markets are shaped by shared stories, and when everyone follows the same narrative, those signals quickly become crowded and self-reinforcing.

A more useful way to work with AI is to treat it as a structured thinking partner rather than a prediction engine. In this video, I demonstrate a four-step framework for exploring investments with AI: starting with directional signals (the obvious trends), moving to needle facts (the overlooked suppliers and dependencies beneath the headlines), then exploring option space through scenario analysis, and finally testing your assumptions. The goal is not to outsource judgement to the model, but to use AI to surface hidden structures, challenge your thinking, and help you understand an industry more deeply before making any decision.

Video of AI and the Stock Market – Direction Signals, Needle Facts and More

Transcript of Beyond the Headlines: Using AI to Explore Investment Structures

DIRECTIONAL SIGNALS FOR STOCK MARKET (00:00:00.20) My name is Laurel Papworth, and I thought I’d go through with you how AI can help and will help in the future with the stock market and investment. I’m going to stick in my stream, which is about AI, and you can apply it, the structured prompt and the scaffolding, to any sector that you’re interested in. And it’s not about investment. This is about how to work with AI as a thinking partner. So a lot of people would ask AI, “Oh, there’s a lot of talk about AI at the moment. What stock should I invest in?” And AI will do what every, I guess, financial paper will do, financial media or any analyst article, anything like that. It will go with the headlines, and it will use what’s called directional signals for trends. What are the trends in investing in AI? And it’s going to come back and say, I don’t know, “NVIDIA. Very popular”. And the thing with the market is that it’s a shared narrative. So as the narrative moves towards NVIDIA, it becomes self-fulfilling. And then once the tide turns and it moves away, same thing. So a directional signal is a trend. Tell me what’s obvious. Tell me what the media is talking about. Tell me what the analysts are talking about that thing. If you don’t frame it within an analysis… If you don’t frame it within analysts and media commentary, then it’s difficult to move on to the next part. Which we’re going to do now.

NEEDLE FACTS FOR STOCK MARKET (00:01:48.12) So the second question that I would ask once it had come up with NVIDIA and maybe Cisco for their quantum computing networking stuff is called needle facts. And if directional signals is, tell me what the bleeding obvious is, the needle facts would be, tell me the stuff I’m not thinking about. Tell me the stuff that isn’t clearly stated but can be inferred. Tell me the stuff that doesn’t really appear except as a footnote in an addendum, like what am I missing? What’s being missed here? And this is where you find the little gems. So in the terms of AI, you might ask for something that’s second or third level. What specific non-visible structural dependencies are there that I could invest in? For example, which glass, ceramics, materials, specialty materials, provide the fabrication material? So it’s not the rare earth minerals, it’s the next level on. It’s the little that goes into a chip, that goes onto a card, that goes into a machine, that goes into a data centre, that becomes AI later on. There’s a company in Japan that makes a glad wrap, a saran wrap, and all of NVIDIA chips use it. So they’re a supplier, a vendor to NVIDIA. So there’s a flow on effect. If NVIDIA is doing well, these guys will do well as well. And in fact, I think they’re fully sold out for the next two years. And they have a waitlist. So does the company that makes the cleaning fluid for the chips. So does another company, Toto, that makes bidets, and they also make something else to do with fabrications. So once you’ve got directional signals, which is the framing of the main shift towards NVIDIA or Cisco or something like that, and then you have needle facts, which are there’s this substrata of an ecosystem of smaller companies that are doing extremely well.

OPTIONS SPACE (00:04:04.22) The next question is, what is the options space for the next three to five years? So we build our story, our narrative, the human narrative on prior information. And so does AI. AI uses tokens and data sets and stuff like that to build its story out. So the options base, which is the third one, is going to be looking at a tree of thoughts style prompt as opposed to chain of thoughts. You’re used to chain of thoughts. That’s tell me step by step how to get from here to here, step by step by step. Tree of thoughts are, give me the options. It’s an “if this, then that” type of scenario where you’re running along the branches saying, what if NVIDIA fails and it’s a bubble? What if utilities become overly regulated? What if this happens? What if that happens? And the AI is brain is big enough to hold all that stuff if you articulate the scaffolding of the prompt correctly. So what are the realistic scenarios that could unfold in the next three to five years? What if FAB construction slows down? What if geopolitical changes around regulations and tariffs and things like that impact these Japanese and Taiwanese companies that are making the bits and bobs that go into American, European, and Australian data centres, and what if there is a continued AI CapEx search or there isn’t. So it knows to start to play with the weighting, and you’re effectively using options-based to create tension because AI doesn’t hold tension. Humans do. So we’re creating a liminal space for the AI to hold tension and then to think through, “Oh, what if this happens?”

TESTING HYPOTHESIS IN STOCK MARKET AI (00:05:55.14) And then the fourth one is testing. I wish the people that pitch stuff to me would put this through, their pitches through the testing mechanism. But what if I’m wrong? What if I’ve got a basic assumption wrong? What if I’m missing something critical that should be in here? What If things change dramatically, what would invalidate my hypothesis that the substrata glass and ceramic companies that support AI will have a boom? What if the AI cycle cools? I’m not saying we head into another winter or bust, but what if for one reason or another, everybody starts to back off, gets a bit of cold feet? It happens. It tends not to happen TOO much in emergent tech. A lot of people talk about it. By the time there is the backing off. It’s so truly ingrained(normalised) that the market has stabilised. But testing your hypothesis and telling the to not support you, but to give you critiques and criticism, be gentle with me, though, is really super useful.

OUTPUT FORMAT (00:07:10.00) I would tell the model to keep each of those four sections separate. The directional signal, which is the headline stuff, the needle facts, which are the little known gems, the option space, which is the “if this, then that” tree of thoughts scenario building, And the fourth one, which is testing. I would say definitely keep them separate. We really want to go from trend noise to structural substrate, to scenario building, to testing our hypotheses. So that we’re not just saying, “I got a bit of pocket money, what should I invest in?” And we’re moving towards a much more risk averse foundational strategy.

AI MENTOR for INVESTING IN STOCK (00:07:59.04) On one last note I just want to remind people that Warren Buffett said he wouldn’t invest in any industry that he didn’t understand fully. I think he said that and airlines, but that if he didn’t understand the industry, he wouldn’t invest. And I understand that. I probably even agree with him. But if you use AI as a thinking partner rather than as a stock prediction oracle, or the Delphic oracle of stock markets, it will help you think through and understand better what you are investing in and what the risk assessments are, what the salience is of the headlines, all of that kind of stuff. Anyway, I hope you found it useful, and I’ll see you in the next video. Thanks. Bye.

This is not investment advice but how to consider thinking about investing with AI as a mentor.

Resources for beyond the Headlines: Using AI to Explore Investment Structures

• Harvard Business Review (Michael E Porter) https://hbr.org/1996/11/what-is-strategy
• Carlota Perez https://carlotaperez.org/ and books on Technological Revolutions and Financial Capital
• McKinsey The Economic Potential of GenAI https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20economic%20potential%20of%20generative%20ai%20the%20next%20productivity%20frontier/the-economic-potential-of-generative-ai-the-next-productivity-frontier.pdf
• Probably Richard Rumelt — Good Strategy / Bad Strategy book if you can find it.
• Warren Buffet https://www.berkshirehathaway.com/letters/1989.html (his comment on Peter Pan investors “I believe” is funny and relevant)
• Congress, Semiconductors competition and policy https://www.congress.gov/crs-product/R46581
• ARXIV Decision Trees for Decision-Making under the Predict-then-Optimize Framework https://arxiv.org/abs/2003.00360
• ARXIV On the Complexity of Decision Making in Possibilistic Decision Trees https://arxiv.org/abs/1202.3718

Youtube video on OpenAI vs Anthropic for Department of Defence/War USA

My take on the AI In Military contract.

Hello, my name is Laurel Papworth, and I want to walk through the recent issue between Anthropic and the United States Department of Defence, and then OpenAI’s response, which ultimately secured the contract.

Anthropic refused to allow certain uses of its model.

Specifically, they drew a line around mass surveillance of American citizens and fully autonomous agentic warfare. In other words, you cannot simply hand decision-making over to the system and say, “Do whatever you want.” They framed this as a matter of democracy, privacy, and accountability.

It’s a position many people would find understandable. But while Anthropic articulated the problem clearly, they didn’t offer a structural solution that met the Department’s operational demands. The Department responded strongly, indicating that if Anthropic maintained those constraints, its models could be excluded not just from Defence but potentially from other government departments.

On the same day, OpenAI stepped in and signed an agreement to operate within classified environments. What’s interesting is that OpenAI’s “red lines” were not dramatically different. They also stated no mass domestic surveillance, no autonomous weapons targeting, and no high-stakes automated decision-making. The difference was architectural.

OpenAI proposed a layered safeguards approach.

First, the models would run on OpenAI’s infrastructure (essentially within Microsoft Azure OpenAI Cloud) rather than being installed privately or at the edge. That means the provider retains oversight of inputs, outputs, telemetry, and execution context. There are audit logs. There is real-time monitoring. There are dashboards. It is not an invisible black box sitting inside a private facility.

Second, there must be a clear personnel escalation hierarchy, what in corporate settings we call an escalation chart, and in military settings resembles chain of command. Accountability is explicitly human. The AI may optimise toward a goal, but it does not hold judgement, shame, blame, or legal responsibility. You cannot sue a model. Responsibility remains with named individuals inside institutional structures.

This is the part many organisations underestimate. Automation does not eliminate accountability; it relocates it.

In high-stakes environments (particularly Defence) decisions escalate. An operator reports to a supervisor, who reports upward. Legal and ethical oversight sits within that chain. AI outputs must be tied back into those same authority structures.

Third, OpenAI emphasised what is often called the safety stack: prompt filters, behavioural constraints, output sanitisation layers, anomaly monitoring. These are standard components in system cards and AI integration documentation, but here they were contractually reinforced.

Critically, the provider retained discretion. If the model is pushed into prohibited domains, OpenAI can shut the service down. There are audit requirements. There are compliance reviews. There are penalties for breach. These are not soft “please comply” clauses; they are enforceable conditions.

That has implications. Cloud control concentrates power with the provider. Contractual enforcement is not self-executing; it requires monitoring. Safety mechanisms can be bypassed if humans decide to override them. And humans themselves can become the weakest link (not necessarily through malice, but through rubber-stamping, fatigue, or poor system design).

What this case surfaces is a larger tension many organisations are approaching. There is a strong push toward full automation. But systems optimise toward defined goals. Humans hold competing tensions: speed versus compliance, cost versus reputation, short-term gain versus long-term stability. AI does not naturally hold those tensions in the way institutions must.

Delegating routine tasks (even weaponised processes) does not mean delegating responsibility.

Technical constraints, institutional controls, contractual enforcement, and operational transparency form a governance bundle. Without that bundle, optimisation can drift beyond intended boundaries.

Initially, I saw this as Anthropic good, OpenAI bad. On closer inspection, Anthropic framed the problem clearly. OpenAI translated that problem into governance architecture that institutions could operationalise. In environments like Defence, where stakes are existential, that architecture matters.

Let me repeat: you do not outsource judgement to a system. You remain vigilant:

• You surface risk earlier.
• You maintain escalation paths.
• You tie automated outputs back to legal accountability.

That’s not a military issue. That’s an AI governance issue for every serious organisation.

Resources for Military AI (Department of Defence)

• REUTERS: Anthropic cannot accede to Pentagon’s request in AI safeguards dispute, CEO says https://www.reuters.com/sustainability/society-equity/anthropic-rejects-pentagons-requests-ai-safeguards-dispute-ceo-says-2026-02-26
• ASIS (security): https://www.asisonline.org/security-management-magazine/latest-news/today-in-security/2026/february/Anthropic-Refusal
• ANTHROPIC statement: https://www.anthropic.com/news/statement-department-of-war
• OPENAI statement: https://openai.com/index/our-agreement-with-the-department-of-war/
• ORACLE relationship with DoW (AI, data and infrastructure should be here): https://www.oracle.com/au/defense-intelligence/
• SALESFORCE AI and Military Defence: https://www.salesforce.com/news/press-releases/2026/01/26/us-army-department-of-war-missionforce-announcement/
• REUTERS on layered protections and red lines defence https://www.reuters.com/business/media-telecom/openai-details-layered-protections-us-defense-department-pact-2026-02-28
• WJARR paper on anticipatory models: https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-3505.pdf

This post was lightly edited by ChatGPT for comprehension and betterer English.

AI News Australia – Microsoft 365 Copilot has Memories added (same as ChatGPT Memories).

AI NEWS: Microsoft adds Memories to Copilot

[00:00:00.00]
Hello, my name is Laurel Papworth, and this is AI news. So a quick copilot update. In October 2025,
Microsoft added custom instructions to copilot, so you could tell it how to respond and not just what
to do. Now, in December 2025, Copilot has added memory. This means it can reference previous
prompts and outputs to give more consistent answers over time. This matters because pretrained AI
models, GPTs, don’t remember anything, so they need a memory function. You can turn memory off if
you want fresh eyes. It’s under the personalization menu in settings. But for ongoing work, this will
make copilot far more useful. It now is more like a long-term colleague than an intern that started this
morning.
[00:00:51.17]
Just make sure they don’t steal the last muffin out of the breakout room. See you in the next episode,
session, whatever. See you then. Thanks.

Resources:
Microsoft Tech Blog https://techcommunity.microsoft.com/blog/microsoft365copilotblog/what%E2%80%99s-new-in-microsoft-365-copilot–november–december-2025

You will learn about the various use cases of AI, from large language models like GPT to assist you with copywriting and brainstorming, to applications like Dall-e and Microsoft 365 copilot which create imagery based on your input. Have GPT work with you to create strategies, and policies, campaigns and job descriptions, ads and blog posts as well as evaluate annual reports and spreadsheets/financials.

Upcoming Dates: this course has been run several times a month since March 15th 2023 including at Australian Institute of Management from 2023 until 2025. Please email pa@laurelpapworth.com for 28 July 2025

Executive Summary of the AI Is My Copilot Course

Boost productivity and efficiency with Microsoft 365 Copilot and AI tools like ChatGPT. This course provides a hands-on approach to integrating AI into your daily workflow, automating tasks, and making smarter business decisions. (see learning outcomes below for more information).

• Use AI and Microsoft 365 Copilot to streamline work in Word, Excel, PowerPoint, Outlook, and Teams
• Create custom AI instructions to tailor Copilot and ChatGPT to your role and business needs and projects
• Master advanced prompting techniques better inputs for better outputs (AI-generated results)
• Using Reasoning Models to write reports, strategies and indepth papers with citations.
• Train AI on your documents to extract and utilise key business insights – working with finetuned AIs.
• Generate AI-powered images and videos with DALL-E and Sora and Microsoft Designer Copilot
• Leverage private AI models for secure and controlled enterprise use
• Developing an AI strategy for the organisation. 

Who Is Laurel Papworth?

In 2023, Laurel Papworth is pleased to announce she has licensed her AI Is My CoPilot course at Australian Institute of Management (AIM) as she presents ChatGPT and other artificial intelligence tools at conference keynotes to large businesses, for the City of Sydney and other local gov entities as well as to small business as well presenting the workshop to Corporate and Government and NonProfits. AI Is My Copilot is a practical course on how to “centaur” or “copilot”, in other words, job share with AI, as a hands-on approach.

Her courses, since 2008, include artificial intelligence in algorithms and big data including F.A.I.R (Facebook AI), object recognition, behavioural AI and semantic AI such as Lexicon, Deep Face/Deep Text and Deep Mind.

In 2005 Laurel used her background in virtual currencies to submit a patent on decentralised currency for Play to Earn fitness using IoT, 3 years before Bitcoin. She taught Augmented Reality community building to Facebook, Google and Microsoft in California in 2011. Named by Forbes Magazine as Top 50 Most Influential Social and Online Community Expert, Laurel mentors companies visualising, building and managing Web3 and Metaverse solutions Artificial Intelligence solutions, merging technology, philosophy and social psychology to present a holistic Metaverse strategy.

Laurel Papworth consults with Australian Parliament House, Sony, banks, Channel Ten and non profits, she has 22,000 students studying her online courses and was Head of Community (and then Social) for virtual worlds like Sony games and Iron Will games since 2001 and taught “How to Build Massive Engaged Communities” in games and on web2 social media platforms at the University of Sydney from 2005.

More on Laurel on her podcast Alchemy of Innovation, YouTube, LinkedIn and her website laurelpapworth.com.

…and she regularly plays World of Warcraft, Roblox and other online MMORPG games with her avatar, SilkCharm.

HigherEd BIO Artificial Intelligence, Algorithms and Big Data – Australia and overseas:

Adult Education Trainer (Professional Development) in Social Media and other courses in the University of Sydney CCE from 2005 until 2017 (12 years continuous) Associate Lecturer in Bachelor of Design in the University of Sydney Faculty of Architecture in 2016 Lecturer in Creative Industries in the University of Western Sydney from 2012 Lecturer Post Graduate Communications in the University of Technology Sydney circa 2011 – 2014 Lecturer in Masters of Convergent Media course in the University of Western Sydney from 2008 -2010 Tutor in Computing in the University of Adelaide from 1989 – 1992 Lecturer AFTRS Lecturer MetroScreen SchoolGuest lecturer Universities in Singapore, Portugal, Effat/Jeddah (Saudi Arabia), community colleges and TAFE. Laurel also has a Cert IV in Training and Assessment.

PRIVATE TRAINING ON CHATGPT and AI for your Organisation: please contact me on 0432694992 or email pa@laurelpapworth.com for information on an inhouse/conference workshop or facilitated course.

PUBLIC TRAINING on CHATGPT and AI is primarily run through Australian Institute of Management (see below)

ChatGPT: AI Is My CoPilot course

My course is now licensed to Australian Institute of Management (delivered by me, Laurel Papworth).
Register your interest today (on the AIM website) for course dates.

Key Learning Outcomes Of ChatGPT and AI course:

Upon successful completion of this course you will:

• Gain an Understanding of AI Basics
  Develop a foundational knowledge of artificial intelligence, exploring different types of AI such as ChatGPT and Microsoft 365 Copilot. Learn how these tools are transforming industries including business, healthcare, and more, reshaping workflows and unlocking new possibilities.
• Explore Essential AI Tools and Technologies
  Dive into key AI technologies, such as ChatGPT for conversational AI and Microsoft 365 Copilot for productivity enhancement. Understand the mechanisms behind these tools and private large language models (LLMs). The course content is designed to be accessible for nontechnical professionals, ensuring everyone can confidently engage with these tools.
• Learn to Create Custom AI Instructions
  Discover how to design and save customised AI instructions using tools like ChatGPT and Microsoft Copilot Studio. These tools enable you to build profiles of key organisational data and create tailored, dynamic AI interactions that align with your specific business or job role.
• Master Advanced Structured Prompting Techniques
  Unlock the power of advanced prompting methods to frame questions and tasks in a way that maximises AI effectiveness. With tools like ChatGPT and Microsoft 365 Copilot, learn how to craft precise prompts that yield better, more accurate outputs by improving the quality of your inputs.
• Train the AI on Your Documents
  Learn to upload and integrate your organisation’s documents into AI systems using tools like ChatGPT and Microsoft Copilot Studio. These tools enable the AI to analyse, interrogate, and extrapolate information specific to your unique business needs, resulting in more customised and impactful solutions.
• Create AI-Generated Images and Videos
  Enhance your content creation skills by using tools like ChatGPT, DALL-E, and Microsoft Designer Copilot to produce visually engaging images and videos. These tools can transform your approach to marketing, presentations, and storytelling, making your content more impactful and professional.
• Increase Efficiency with Microsoft 365 Copilot
  Gain hands-on experience with Microsoft 365 Copilot—a powerful productivity tool that works seamlessly within Word, Excel, PowerPoint, Outlook, and Teams. Learn how to automate tasks, streamline workflows, and save time across your daily activities.
• Understand Private AI Models
• Explore the benefits of private AI models for your organisation. Learn how to maintain data privacy and control while leveraging advanced AI capabilities tailored to your specific requirements.

Typical Curriculum for 1 Day Artificial Intelligence Workshop

Understanding the technology

• We start the day by exploring essential AI tools such as ChatGPT and DALL-E. You’ll log in and learn how to use prompts, modifiers, and other AI clarifiers to get the results you want. Whether you want to create art, write a report, or generate code for a website, you’ll understand how AI learns from your inputs to refine its outputs, getting closer to your desired outcome. While this course is for non-technical people you will gain a solid foundation in the techstack required to build out an AI that can be trained on your business documents.

Structured Prompting – Better Input = Better Output!

• There are many ways to prompt using NLP (natural language processing) an LLM (large language model like ChatGPT). By understanding when and how to use the various types of prompts you’ll progress from asking a simple question to structuring sophisticated threads to get the output you want!

Exploring AI Tools for Business including Microsoft 365 Copilot

• You will receive a list of over 40 AI tools applicable to various business needs. While we won’t have time to cover all of them in class, we will prioritize a few key tools for in-depth exploration. From search to images to creating Powerpoint there are many options with integrating AI into your business.
• Custom GPTs allow various departments to create an AI that uses ChatGPT underneath but has their own documents. HR can create one, as can Customer Service as can Marketing. Let’s explore! Evaluate similar solutions for Microsoft 365 Copilot integrations.

Private LLMs and Risk and Regulation

• We will discuss privacy, plagiarism, and other ethical issues related to AI. This session includes an open forum to add and discuss additional concerns, exploring what can and cannot be controlled. Learn tips for engaging with government and regulatory bodies, and understand how to shape the future of AI collaboratively.

The Future of AI

• If relevant we explore how AI will impact jobs in your sector, tasks in your office, and the broader future of your industry. Building on our group discussion, you’ll use AI tools to generate a video, email, report, or image, applying the strategies and tactics learned during the course. This hands-on session helps you envision and prepare for the AI-driven future.

Alternative: Private one-on-one Single day in the Blue Mountains
for 1 to 3 people in your team

NOTE: if you are booking a private consulting day with me in the Blue Mountains during April 2023, please use THIS link after discussion with me. +61432684992. Limited number of days available!

Central Finance Office needs a proposal and/or quote? If you have any questions, want to run this privately for a conference or offsite, need a specialist Proposal etc, please email us with question, dates and detailed request. A GST Invoice/Receipt is issued if booked online, but sometimes Head Office needs more. Alternative call +61432684992

Not ready to join a course – watch my free videos!

• 

  Artificial Intelligence

  Stock Market, Pick and Shovel, Artificial Intelligence #Australia

  ByLaurel Papworth

  A practical framework for using AI as a thinking partner in investment analysis: moving from market trends to hidden dependencies, scenario planning, and testing assumptions.

• 

  AI News Australia | Artificial Intelligence

  AI in Defence: Why Governance, Not Automation, Won the Contract

  ByLaurel Papworth

  A recent standoff between Anthropic and the U.S. Department of Defence wasn’t really about patriotism, politics, or even procurement — it was about where judgement lives when systems start making decisions at scale. Anthropic refused certain uses of its model CLAUDE, drawing a clear ethical boundary around surveillance and autonomous targeting. OpenAI stepped in and…

• 

  AI News Australia | Artificial Intelligence

  Microsoft Copilot Memories – AI News Australia

  ByLaurel Papworth

  Microsoft Copilot gains Memories – the ability to remember.

• 

  Artificial Intelligence | Australia

  33 Critical Cost of Inaction (CoI) Risks to Strengthen Your Artificial Intelligence Strategy for Executives

  ByLaurel Papworth

  Discover 33 critical Cost of Inaction (CoI) risks you can add to your Artificial Intelligence strategy to future-proof your workforce, customers, and governance for Executives and CEOs in Australia

• 

  Artificial Intelligence

  Create, Edit and Transform documents with ChatGPT- tips – 0024

  ByLaurel Papworth

  Step by step ChatGPT to get over the blank page, to edit a final document and to transform into other documents simply and easily.

• 

  Artificial Intelligence

  Chain of Density Prompt with ChatGPT

  ByLaurel Papworth

  Chain of Density is a structured technique that transforms a lengthy transcript into a series of progressively denser summaries

About Laurel Papworth

Artificial Intelligence

Laurel has taught AI, algorithms and big data at University of Sydney, UWS, UTS, UoA, AFTRs, and Universities in Singapore, Portugal and Saudi Arabia and currently teaches AI at Australian Institute of Management.

Educator

CERT IV Training and Assessment certified trainer (Diplomas and Certificates etc) Adult Education. Taught social media marketing at Uni of Sydney for 12 years. More Info

Clients

Laurel has consulted, mentored, spoken at conferences, run workshops and project managed clients including: Senators and Ministers (over 20 of them), Australia Parliament House, Westpac bank, Prime Minister of Malaysia, Royal House of Saud, multiple universities globally, NBC Universal Studios, Channel Ten Big Brother community, Ministry of Defence Singapore and more. Client List

Personally

Laurel has 22,000 online students. Laurel Papworth personally connects to 11 million followers online and has taught around 100,000 people in the last 10 years. Named by Forbes Magazine in the Top 50 Marketing Influencers globally, named Head of Industry, Social Media (Marketing Magazine) and in the Power150 Media & Marketing bloggers (AdAge) Testimonials

Artificial Intelligence Course Outline: 

Looking for a 1 Day Foundation or 2 Day Intensive workshop on the new artificial intelligence tools made available to business and individuals? Want information tools like Midjourney, Dall-e, Stable Diffusion, ChatGPT, – when to use them and how artificial intelligence can save you time and resources? Want a comprehensive list of the prompts and modifiers to use those tools to their optimum – and get the results you want?? Plus there are a hundred new custom GPTs (apps or plugins) and AI Addons being released a week – which ones are best for your organisation or business? This course is also aimed at you if you want to detect if AI has been used in a business report or student essay – there are AI detectors available! We cover the Future of AI in various industries from content creation to marking exams to predicting behaviours. Also how will artificial intelligence underpin the Metaverse/Web3 – From ChatGPT, Dall-e from OpenAI to MidJourney and beyond. This is an intensive course. 

AI Proof Your Career Workshop

We are living through one of the biggest shifts in the world of work since the industrial revolution – and it’s happening fast. Artificial intelligence is no longer a future trend; it’s here, reshaping industries, roles, and expectations. AI Proof Your Career is a half-day workshop designed to help you respond with confidence. You’ll learn how to work with tools like ChatGPT and Microsoft Copilot, not just to keep up – but to stay relevant, competitive, and prepared for what’s next. Whether you’re navigating a career change, running your own business, or simply want to sharpen your edge, this course gives you practical, professional tools that you can use immediately.

Who This Workshop Is For?

This workshop is for anyone who wants to stay competitive in a world where AI tools like ChatGPT and Microsoft Copilot are rapidly changing how we work. You might be:

• A professional who wants to use ChatGPT and Copilot to enhance productivity, not be replaced by it
• A freelancer looking to streamline content creation, research, or client communication with AI
• A jobseeker who wants to learn how to use AI to tailor resumes, practise interviews, and explore new career options
• A small business owner curious about how structured prompting and AI tools can save time and reduce admin
• Someone who’s heard about generative AI and wants a hands-on, practical session to finally understand and use it
• And on that note: Someone simply curious and ready to do, not just read about, AI

No AI experience required. Just bring your laptop and a willingness to explore how these tools can support your career. Don’t forget your power supply. Laptops have a bad habit of running out of juice.

What You’ll Gain?

This workshop is for people who want to go beyond casual ChatGPT use and actually understand how to work with AI – logically, strategically, and creatively.

By the end of this session, you’ll:

• Understand and apply structured prompting frameworks like Chain of Thought, Role-Based Reasoning, and Tree of Thoughts
• Know how to guide AI step by step through tasks like drafting, rewriting, researching, and reflecting – so it’s not guessing, it’s thinking with you
• Practise using reasoning models that help AI produce more accurate, helpful, and contextual results in professional settings
• Learn when to use ChatGPT and when to lean on Copilot tools in Word, Outlook, and beyond
• Use AI to analyse your career path, reframe your value, and explore adjacent opportunities using exploratory prompts
• Build confidence not just in the tools—but in your ability to shape and lead the interaction

You’ll walk out with the practical skills to collaborate with AI in your daily work, and a clear understanding of how structured prompting gives you the edge in a fast-changing job market.

Learning Outcomes for AI Proof My Career Workshop?

By the end of this half-day workshop, you’ll be able to:

• Write structured prompts that produce accurate, relevant responses in ChatGPT and Copilot
• Apply AI tools to everyday tasks like writing, summarising, researching, and organising
• Practise using AI as a coach or assistant to support your current role or career planning
• Recognise when to use different prompting techniques to improve results
• Build confidence in using AI tools thoughtfully and responsibly in your work

Tickets to AI Proof My Career Workshop

If you prefer to pay by bank deposit/invoice please email pa@laurelpapworth.com
Choose from: Brisbane 13 October, Virtual (Zoom) 27 October, Sydney 10 November, Melbourne 1 December, Virtual (Zoom) 8 December.
Please note: a location and map will be sent to you in an email upon booking.

About Laurel Papworth

Laurel Papworth is one of Australia’s leading educators in artificial intelligence, with a special focus on practical, hands-on skills for business and government. She has taught AI applications for over 20 years, including 12 years at the University of Sydney and, until late 2025, offered her popular AI Is My CoPilot course through the Australian Institute of Management (AIM).

Today, Laurel specialises in teaching individuals and teams how to work effectively with tools like ChatGPT and Microsoft 365 Copilot, with a focus on structured prompting, workflow optimisation, and finetuning AI on organisational documents. Her background in decentralised economies and virtual communities—recognised by Forbes—informs her unique approach that blends social psychology with emerging tech.

She is also the host of The Alchemy of Innovation podcast and continues to lead both in-person and online workshops on AI, innovation, and future-of-work skills.

PRIVATE TRAINING:
To enquire about in-house workshops, team training, or AI sessions at your conference, please contact Laurel directly:
0432 694 992
pa@laurelpapworth.com

For public and private workshops Laurel’s Website.

Not ready to join a course? – More on Artificial Intelligence!

• 

  Artificial Intelligence

  Stock Market, Pick and Shovel, Artificial Intelligence #Australia

  ByLaurel Papworth March 9, 2026March 9, 2026

  A practical framework for using AI as a thinking partner in investment analysis: moving from market trends to hidden dependencies, scenario planning, and testing assumptions.

• 

  AI News Australia | Artificial Intelligence

  AI in Defence: Why Governance, Not Automation, Won the Contract

  ByLaurel Papworth March 4, 2026March 4, 2026

  A recent standoff between Anthropic and the U.S. Department of Defence wasn’t really about patriotism, politics, or even procurement — it was about where judgement lives when systems start making decisions at scale. Anthropic refused certain uses of its model CLAUDE, drawing a clear ethical boundary around surveillance and autonomous targeting. OpenAI stepped in and…

• 

  AI News Australia | Artificial Intelligence

  Microsoft Copilot Memories – AI News Australia

  ByLaurel Papworth December 21, 2025December 22, 2025

  Microsoft Copilot gains Memories – the ability to remember.

Further information:

Whether you are doing a private (corporate or Government) course on ChatGPT and Copilot or a public individual course (such as AI Proof Your Career) please make sure you have access to ChatGPT (free or Plus etc version) and/or Copilot (chat or 365 or studio or Pro). if these are blocked on your computer by your IT department you will struggle to do the exercises. Please contact us if in doubt.

In this article and video, I’ve listed 33 critical Cost of Inaction points you can include directly in your AI strategy. These risks will help you future-proof your organisation, create executive alignment, and ensure your leadership team fully understands why urgent action matters. Use this list to build a stronger, more compelling case for AI in your business.

1. Workforce and Talent Risks

1. Talent attrition due to manual, repetitive tasks.
2. Employer brand weakens without AI adoption.
3. Staff use AI tools unofficially, increasing compliance risks.
4. Policy vacuum or “no AI allowed” rules create shadow AI usage.
5. Morale drains from repetitive, low-value work.
6. Remaining workforce may lack top-tier talent motivation.
7. Leadership perceived as stagnant and out of touch (e.g., Kodak effect).

2. Productivity and Efficiency Risks

8. Workflow speeds remain slow; productivity flatlines.
9. Critical tasks take weeks instead of hours without AI.
10. Meetings stay inefficient without AI summarisation and action extraction.
11. Manual staff onboarding persists instead of AI-enhanced onboarding.
12. Operating margins shrink as competitors automate.
13. Lagging behind in adopting agentic workflows while competitors surge ahead.

3. Innovation and Strategic Blindspots

14. Innovation stalls without AI-driven insights and efficiencies.
15. Strategic blindspots increase when the same patterns repeat.
16. Decision-making cycles slow down without AI augmentation (e.g., Tree of Thoughts prompting).
17. Long-term growth potential declines without embedding AI.
18. ESG, digital transformation, and digital storytelling efforts falter without AI integration.

4. Customer and Market Impact

19. Customers switch to AI-enabled competitors unknowingly (better UX, intelligent tools).
20. Market share erodes due to slower innovation and service.
21. Customer satisfaction drops with outdated chatbot and support tech.
22. Brand loses relevance in digital-first markets.
23. Missed opportunities for new revenue streams, products, and services driven by AI insights.

5. Data and Risk Management Failures

24. Organisational data remains underutilised (“goldmine” left untouched).
25. Human errors (hallucinations) increase without AI double-checks.
26. Compliance risks grow without AI-assisted monitoring and governance.
27. Regulatory reporting and audit trails stay weak and human-dependent.
28. Shareholder confidence erodes due to lack of visible AI adoption.

6. Reputation and Governance Risks

29. Investor relations suffer without clear AI communication strategies.
30. Industry analysts favour AI-first companies, reducing visibility and influence.
31. Organisation becomes reactive instead of proactive toward technological change.
32. Regulatory bodies may force sudden AI adoption, creating chaos.
33. Mergers and acquisitions become harder when AI-readiness is lacking.

Transcript of Cost of Inaction for AI Strategy

Hi, my name’s Laurel Papworth, and I’m a lecturer and course facilitator in Artificial Intelligence. If
you’re writing an AI strategy, you’ll almost certainly have a return on investment section for your
strategy and a cost of inaction. So I want to go through about two dozen cost of inactions for your AI
strategy so that you have a clear list that you can prioritise and decide which ones, I guess, are
important for you.

Now, I am I’m in a hotel in Brisbane. I’m teaching here for the next few days, and so there are strange
noises, but I really wanted to get this done. So let’s do it now.

WORKFORCE and TALENT: First, talent acquisition, HR, employer branding, those things. The talent
leaves because they’re being made to manually transcribe meeting minutes, and it takes them an hour
to do it, and their mate across the road has an AI doing it in seconds, and they’re like, Why can’t I do
that? Your employer branding will weaken because staff will be like, Oh, what AI are you seeing? I’m
good with AI. And then, No, we don’t use AI here. Of course, staff are using AI without training. That’s
me, by the way, hint, hint. But also without a policy, or the policy is, “Don’t use AI”, or, “Our clients won’t
let us use AI”, or, “The government has said no to AI”, and then they’re using it anyway (hello, SIGNAL),
and that puts you in a compliance and regulation risk area. Uploading documents to fine-tune an AI is
something that’s so easy to do, and they’ll figure it out. Probably their teenage kids will show them
how to do it, but they’ll figure it out. Repetitive tasks drain morale. But I think also the people that stay
that are willing to do those repetitive tasks may not be the cream of the crop, just saying.
And as as executives, leadership appears stagnant, unwilling to change, and that will echo through
the whole organisation. I don’t know that you’d want to preside over a company that will be lumped
together with Kodak. You know what I mean?

PRODUCTIVITY and EFFICIENCY: Second one is productivity and efficiency. So the speed of which
things are done stays slow and productivity flat lines. And the reason for that is if there’s only a
certain amount of things that a human can fit in the day and something that takes them two weeks
takes two weeks, then it’s flat-lined. You can’t get more out of people other than forcing them to stay
longer and things like that. So with an AI, something that would take two weeks might take four hours.
It would take zero. But four hours isn’t bad with a human piloting, the copilot. It’s about creating a
rubric around speed, efficiency, accuracy, compliance, and a whole range of things for cost of
inaction, and the AI strategy as a whole. Meetings will just stay as they always do. If you have an AI
listening to the meeting, transcribing it, putting together to-do lists, saying what’s missing, picking up
on the risk assessments, all that stuff, meetings will change dramatically. Onboarding of staff will
stay manual. I could go through department by department and point out where things will stay the
same, but I think you can put that together pretty easily in your own strategy without me pointing out
the bleeding obvious. Your operating margins will definitely shrink as your competitors find their feet
with AI. So one of the costs of inaction is that gap. Your competitors are going to automate agentic
workflows and things like that, and you’ll still be figuring out if you’re going to let the staff use the free
version of ChatGPT. It’s not a great place to be.

INNOVATION and STRATEGY: So another one is innovation and strategy. Innovation is going to stall.
You can only get so far without AI. There’ll be strategic blind spots because the same people are
doing the same thing the same way, and nobody’s there. No little AI is there to say, Have you thought
about doing it this way? Or, I can see expediencies here. So that’s one of the great things with AI.
Decision making lags. If you’re not able to use a tree of thoughts prompt as an external consultant to
give you options and ideas and to help you prioritise things, then your decision making is going to go
around the same loop as they always do. It does for me anyway. Your long term growth potential
declines. There is no doubt AI is arriving. It’s not locking on the door. It’s just going to walk straight in.
So therefore be prepared because long long term growth is dependent on AI. You might not see it
right now, but it is. ESG or digital transformation, digital storytelling, any of those things will fall short
if you don’t have AI as a puzzle piece in there. I mean, I think that’s obvious.
It’s like saying, oh, we’re not going to consider non-fungible tokens on-chain. That’s not part of our
future. When digital identity is so much part of our future. So think about those things.

CUSTOMER and MARKET IMPACT I think the next one is the direct to the customer, the customer and
market impact. So clients may switch to AI-enabled rivals because their tools are cool. They won’t
even realise When they open up, I don’t know, into its Quickbooks versus, say, Xero, the thing at the
bottom, which is helping them, is actually a GPT integration from the API. They’ll just know, Wow, it
was able to see things and tell me things. So they’ll switch to that tool. Market share erodes, and
sometimes just vanishes completely. So if you have a look at Cheggs, the tutorial company, and then
their AI integration of CheckMate, and then the fallout of some of that. That’s a really interesting case
study on the cost of inaction, of trying to keep doing business the same way and not pivoting to
something that needs to be pivoted to. I don’t I think every emergent tech needs to be treated like a
crisis, and AI doesn’t need to be treated like a crisis.
But goodness me, don’t ignore it, please. Customer satisfaction will drop, especially if you’re using
those horrible taxonomic chat bots. Even an AI assistant one, you need to shift fully to either a
copilot…

Am I wobbling the phone?

A copilot or… What’s the other one? Maybe an actual AI agent on
your website that can intelligently retrieve the correct information. The brand will lose importance and
relevance in digital-first markets. So it could be something along the lines of when people say to me,
Oh, that’s great. Yeah, I love it all. I just want to do it on Zoom. I don’t want to fly in or come in person
and do things. So if I was one of those people that went, I want to use Zoom, which, by the way, I used
to be, then obviously, I’m not going to stay relevant in an online world. So by offering hybrid solutions,
AI, non-AI, human AI, you’re going to do much better than those that just stick with the same old,
same old. And then new market opportunities. It is revenue streams, new products, new services,
things like that. You’ll miss them. You just miss them because the data analysis isn’t there.

DATA ANALYSIS: Not every human can process data as quickly as an AI. But no human really can. So the last
section is really about the data that your organisation already has. So it’s data and risk, I guess. Data
sits unused, unrecognised, un identified, unloved, misunderstood. You’re not looking at the data, so
you’re not… There’s a gold mine there. Why aren’t you digging in that gold mine? Mistakes will
increase. Humans hallucinate more than AI does between them. They can fix each other’s
inaccuracies. Please have a read of my document and tell me where I’ve made a mistake and find
citations as to why it’s a mistake. That’s the thing that you can ask colleagues to do, and they just
come back and they go, Yeah, it’s great, babe. Yeah, I know it is, but can you tell me where the
mistakes are? Also, we want to look at compliance risk because they’re going to rise. And Imagine if
you put the AI on chain on the blockchain, and then the shareholders and the executive board all
agreed to specific compliance things, it would be impossible to send out, for instance, a Microsoft
Outlook email that said, I’m going to tell you X, Y, and Z, and that’s against the compliance because
the AI behind the email would say, “I’m sorry, I’m unable to send this email. You’ll need to edit it.” I
don’t know how you feel about that, there you go
Let me just check my list. Shareholder’s confidence erodes due to a lack of visible tech adoption.
Yeah, they’re going to come in heavy on AI. I think there will be a backlash or there’ll be more backlash
as time goes on, but this train is not going to stop or founder at the first intersection. Regulatory
reporting and audit trials remain weak. If you’re relying on human to do human work, the human will
be the weakest link. And this may surprise you, but you can’t take an AI out to a busy lunch and get it
to sign off on an audit report that easily because there were guardrails in place. And if you’ve got your
system cards and your model cards and your data sheets and everything in place, then you just… I’ve
tried to get ChatGPT drunk. It doesn’t work.

REPUTATION and GOVERNANCE: So that’s about it. I think there’s probably one more section which I
would call a reputation and governance section. If your investor relations are suffering due to a lack
of clear communication and narrative, an AI can help with that. Media, industry, analysts, that’s the
influences of the business world, analysts, will shift to an AI-first approach or companies that have an
AI first, and the codex of the world will be left behind as As far as earning attention, I guess. The
organisation will become reactive, not proactive to changes. And some things that may happen is that
AI is forced on us by regulatory bodies. And so organisations that went from no AI suddenly are
saying, We need AI overnight. And I remember this from years ago where a company said that they
would never have certain technologies in their organisation. And then I got a call on a Sunday, Could I
be there on Monday morning? To roll something out because clients had been very clear, if you don’t
put this stuff in, which is going to make efficiencies for us and you, then we’re moving to a different
vendor. Big company, too. Global. Merger and acquisition opportunities will be lost because the house
is not AI-ready. The house is not in order. If you don’t have your own house in order, you can’t do M&A
very easily. As somebody who’s worked for many decades in M&A, that’s always been an issue for me
is just, how much of a mess are we buying into?

I don’t know. There’s probably others for your list. And I just want to say I’m not an ambassador for AI.
I’m an ambassador for humans. But AI is coming. It’s not here yet. And when it does arrive, it’s just,
get ready, be prepared. You don’t have to eat the elephant hole, just buy it out. What am I trying to
say? Buy off little bits fields of AI as you go. Make sure your strategy has things like a sandbox area
so you can test some things. Make sure you’ve got a few brand ambassadors that are willing to pick it
up and run with it and try implementing things, stuff like that. Anyway, it’s over 15 minutes on my
phone, so I’ve got to edit this down a little bit. If you’re interested in my AI for Executives and Senior
Managers course, let me know, laurelpapworth. Com. If you’re interested in my half-day AI proof your
career, let me know. If you’re a small business and you can only do Saturdays, come along to the
Saturday AI course, Saturday course. Otherwise, have a look at the Australian Institute of
Management or those event and higher education people because I teach at those venues as well.
Only if you’re interested. Otherwise, stick around for the next video. Thank you. Bye.

Citations and Resources.

• A bunch of blog posts I wrote in 2009 about impact of Cost of Inaction for emergent tech
• My Smart Company article from 2016 on CoI
• My article that I wrote for The Australian in Media section for senior executives. (link incoming)
• Chegg shares drop more than 40% after company says ChatGPT is killing its business Chegg and Cheggmate (GPT API)

This video introduces three effective ways to utilise ChatGPT for document-related tasks: creating, editing, and transforming. In the creation phase, ChatGPT helps you start from a blank page, providing a structure for various types of reports and documents. As an editor, the AI offers insights into what may be missing or unclear, and suggests improvements to enhance clarity and completeness. In the transformation phase, it repurposes existing content into different formats, such as presentations or newsletters, while adapting the tone for specific audiences. These tips allow users to significantly streamline their content production and editing workflows for greater efficiency and effectiveness.

If you struggle with a blank page, need an extra set of editing eyes, or want to adapt content into multiple formats, these quick tips are your solution. While AI can’t completely handle your job while you head out for lunch (believe me, I’ve tried), it can take care of much of the heavy lifting at different phases of your work. We explore how you can get started, refine, and repurpose your documents effortlessly. Ready to revolutionise your content process with AI? Let’s dive in.

TRANSCRIPT for Create, Edit, Regenerate lecture on ChatGPT

[00:00:00.620]
Hello, my name is Laurel, and I’m going to take you through how to use AI as an assistant, as an editor, and to work with you. Three simple ChatGPT tips: Create, Edit, and Transform business documents in minutes. I do want to point out that you cannot outsource to AI and go out for lunch with your friends. If there was a way of doing that, I would have done it already! But you can use AI at three separate phases of your work.

[00:00:44.060]
So the first one is to get started. You’ve been asked to write a HR strategy or weekly report, a social media calendar, a financial report on last month’s numbers, something like that. You don’t really know where to start, or you just don’t want to work from scratch. So I’ll show you how to start with a blank page and have the AI fill that in for you so you can get going.

[00:01:07.090]
The second one, real quick, is it works as an editor for you. So you go back with the finalised document that you think is finalised, and you say, Please review this document and summarise it. And then you say, what is missing from this document? So even if the AI wrote it or wrote most of it, ask what’s missing from the document, and then ask, what is unclear? What could I have said better, how can I say it? And it will definitely act as an editor for you.

[00:01:34.400]
The third phase is regenerate. The transformer part of a GPT is I want you to take THIS document, I want you to create a cheat sheet, I want you to please create me a news release for The Australian. Please write me three blog posts on this. Please write me a PowerPoint presentation my CEO can give to shareholders. Please write a technical manual for our staff. Please write an email newsletter to the staff, whatever it is that you need, where you’re shifting the format and the voice of something you’ve already written.

[00:02:11.260]
They’re the three key points, and I think you’ll find them a quick and easy win with ChatGPT. So the first thing we want to do is we have to start a document, and we don’t know where to start. So we tell ChatGPT what our context is. Context is statements or background. It’s not the query. So I work for a government department. We have a negative perception issue around, and whatever the negative perception is, I’m just going to say climate, something basic. I need to write a crisis comms document. And then the question is, where do I start? Because I’m taking it from a blank page and I’m just throwing it to ChatGPT. Now, I might be a subject matter expert, but in this case, I’m just going to ask ChatGPT to help me. Where should I start? And then the second question is, what questions do you have for me? It’s read so many Crisis Coms documents, and it knows what’s going on, so it’s going to start with prompting me. I need to get over the blank page issue where I don’t know where to start. I don’t know what to think of first. It’s come up with define the situation. Can I give it more information on that? Any public concerns, a stakeholder mapping, who do you need to talk to? Key message, actions in progress, media internal alignment, handling accountability and crisis response protocols. So even if you’re not writing a crisis comm document, you can see that it’s got lots of questions around different areas.

[00:04:10.840]
And I want to point out, I don’t actually fill in the answers. I ask it I fill in the answers by giving it more context. So I might say for the stakeholder mapping, I will say the issue is impacting a shoreline in Queensland. Who do you think I should address? And it will come back and tell me who the stakeholders are, and then I can broaden or simplify those as we go. At least I’ve got past the point of, I don’t know where to start or what to think about, and it’s giving me specific information around that.

[00:04:45.250]
So then the second prompt, which is once I’ve written my Crisis Coms plan and I think that it’s doing okay, I will say, Please read this document And then I’ll upload the document, or I can copy and paste it in, or do something else, or give it a transcript. Please read this document. And my prompts tend to be along the lines of two things. What is missing from this? What is unclear? And then where it’s unclear, it will help me rewrite it or reposition it. And then what is missing is what are the key points that are missing? And I actually do this with synthetic data, so I will always ask ChatGPT to what is missing from something it wrote because it’s making decisions all the time, prioritising, tokenizing different elements or entities, they’re called elements, different foundation points. I won’t go into that, but I’m going to ask it to read the document, what’s missing from it, what is unclear. And this is where it’s acting as an editor, so we I need this. And it’s going to make me cry by giving me all the negatives, but that’s okay. And once It’s a bit more structure and practical examples for an off-the-cuff transcript. We’re good. That’s good. So it’s acting as an editor in this point. Now, I can stay on this one and ask my next prompt, because now I’m structuring the by having a prompt under a prompt under a prompt.

[00:06:33.290]
And I’m going to go into the third phase, which is to regenerate. Please rewrite as. Points Or sometimes I ask for a summary. Please rewrite transcript as. Points. Because it’s read the transcript, so now it can give me. Points. I’ve I ask for fairly detailed answers, so it always gives me long, long dot points. It’s easier for me to do that and then ask it to summarise at the end. I’m only going to do three or four of these. You’ll get the point at the end of it. Once I’ve got the dot points, I say, please rewrite as 4-6 sentence summary for executives in I write the audience so that it knows who the summary is for. Is it for a five-year-old, executives, academics? Please write a brief news release for the ABC podcasting site. Let’s just pretend my podcast is on ABC. For immediate release. And then it puts in the information that it has for me. And then I can say, Please rewrite the basic premise for primary school students. This means I’m shifting voice. So in the third part, which is the regenerate part, I’m just asking it to write cheat sheets and PowerPoint presentations and summaries and all kinds of things. But I’m getting it to shift tone and format and a range of different things. So the transformation part, GPT transformer, I guess. Give it a text prompt and ask it to transform that, is a key part of the three steps.

[00:08:47.830]
Just to reiterate, step one, I have a blank page. Where do I start? Ask me questions. Two, please act as an editor for this document. What is missing? What is unclear? What could be clearer? Help me write it. And thirdly, please rewrite. Please help me rewrite this large document, this transcript or something else for different audiences and different formats.

[00:09:15.030]
I hope you found that interesting. My name is Laurel Papworth. I run courses and workshops. I also lecture through higher education. And if you want more information, you can go to laurelpapworth. Com, including past and some resources sheets for you. Thank you.

Resources for Lecture on ChatGPT “Create, Edit, Regenerate”

If you are in academia, this might help you more, but even if not, I found them interesting.

Here are five academic papers that explore the application of ChatGPT and similar AI models in document creation, editing, and transformation:

1. “Three ways ChatGPT helps me in my academic writing” by Dritjon Gruda (2024)
   This article discusses how ChatGPT can assist in writing, editing, and peer review within academic contexts, emphasizing responsible usage. Nature
2. “Almost Nobody Is Using ChatGPT to Write Academic Science Papers (Yet)” by Heather Desaire, Madeline Isom, and David Hua (2024)
   The study assesses the prevalence of ChatGPT in scientific writing, focusing on its role in editing and the ethical considerations of AI-assisted authorship. MDPI
3. “Is ChatGPT Transforming Academics’ Writing Style?” by Mingmeng Geng and Roberto Trotta (2024)
   This research analyzes the impact of ChatGPT on academic writing styles by examining word frequency changes in a large corpus of scientific papers. ArXiv
4. “Exploring the opportunities and challenges of ChatGPT in academia” by Iyolita Islam and Muhammad Nazrul Islam (2024)
   The paper investigates the potential applications and ethical challenges of ChatGPT in academic research, including its use in document creation and editing. SpringerLink
5. “To ChatGPT or not to ChatGPT: the use of artificial intelligence for writing in academic publishing” by Angela L. Colonna (2023)
   This editorial discusses the utility of AI tools like ChatGPT for editing grammar and syntax in academic writing, while cautioning against their use for generating original text. Oxford Academic

These papers provide insights into the integration of AI tools like ChatGPT in academic writing, highlighting both their potential benefits and the ethical considerations involved.

NB Transcript and summary written with the aid of AI (of course!) but humans edited the video and thumbnails!

In this video, I introduce the concept of “chain of density.” This is a structured technique that helps transform a lengthy transcript—like a YouTube video, podcast, or even an academic paper—into a series of progressively denser summaries. Instead of a single summary, I refine the content multiple times, making it increasingly concise and impactful.

This method is not about reducing quality—it’s about focusing on the key entities and core concepts, stripping away unnecessary filler words, and honing in on the most valuable points. Whether it’s for blog posts, YouTube descriptions, or executive briefings, “chain of density” ensures that each iteration of your summary becomes richer in information while staying accessible for the reader. This process is particularly helpful for those of you trying to create attention-grabbing and informative content summaries that really resonate.

I also provide some practical examples using ChatGPT to generate summaries that balance readability with substance. My goal is to help you make your content engaging, easy to digest, and packed with value—perfect for SEO optimisation and keeping your audience hooked. If you’re interested in tools and techniques that can level up your summarisation skills and boost your content’s effectiveness, stay tuned!

Transcript of Chain of Density structured prompt lecture

[00:00:00.00]
Hello, my name is Laurel Papworth, and I thought what we’d do today is have a look at chain of density. It’s where you take a large document. In my case, it’s a transcript for a YouTube video. It’s about 40, 45 minutes long. And then make it denser. So you don’t just say, summarise this document. Chain of density means create a chain of denser and denser summaries. The core concept here is around the fact that a GPT, or the T on GPT is transformer. We have to take a word soup, and then we have to make sure that the AI goes into that word soup, that document, and extracts out all of the core concepts. I do it for my podcast. You can do it for an academic paper, a executive briefing, a news release, anything of that style. So let’s get started.

[00:01:03.29]
I’m going to type in here, please summarise this YouTube video lecture transcript. I’m going to specify in five sentences or less. No, we’ll just say five sentences. And then I put the transcript in, and then I hit return. So it’s created me a paragraph. In this lecture, Laurel Papworth explains, and there’s quite a lot of phaff in here, so this stuff is not helpful. What we want in chain of density is we want to get this denser and denser. We wanted to have more impact and have more key elements. Don’t worry about what the lectures about. It’s how to use about three different ways to use ChatGPT. But for the purposes of this exercise, I just need to do any text or any transcript. I now want to use chain of density and make this denser. And I’ve asked it sometimes to do this and it knows what it is, sometimes it doesn’t. It’s okay. It’s a fairly new style of prompt, and it’s only just been ingested and not in all the models. Although I’m in 4O Omni, let’s have a look. So we can use that or not, but I’ll say, please regenerate this summary with more entities or key points, but they’re called entities.

[00:02:50.19]
But stay within five sentences. So I don’t want a bigger summary. I want a denser one. Let’s go down. And it got rid of those first… How many sentences? 1, 2, 3, 4, 5, 6, 7, 8, 9. It got rid of those nine, 1, 2, 3, And got closer to three or four words. So okay, let’s make it denser again, but still five, five sentences, so include more key points. Stay readable. That’s a problem with density. It can become unreadable. Now, we write for non-technical people. Stay dense but simple. Five sentences only. Really, I just want to make it a bit more readable. And that now is something I’m going to add to my YouTube description field. I’m going to put it on my blog post as the summary on the blog post. Make sure that it’s a key a, I guess, summary. Yeah, of course, it’s a dense summary. So I hope you can see. I didn’t spend a lot of time reading through these because I know how they work, but you can give it a go yourself. There’s lots of filler words in here based on a brief description. We don’t need that.

[00:04:33.24]
We can just say brief description. I wanted to get away from the weasel words and the filler words and simply get into a very clear explanation. And then I allowed it a little bit more, I guess, readability for anything missing or unclear. That wouldn’t be in a super dense summary. The other thing I would say is if you actually ask it, and I would do this at the end so you’re not making up your other prompts, please use chain of density and make this denser. If you have any questions, Please let me know. I want to see if it knows what chain of density is. It’s just gone ahead and made it denser again. I actually like this one the best because it’s highlighting some of the key points. It seems to know exactly what the high points are of the presentations, which is good.

[00:05:35.11]
I hope you found that interesting. We went through chain of density and making sure that summaries get denser and denser and full of entities or the core concepts. And it’s a really useful tool, really useful structured prompt. My name is Laurel Papworth. If you’d like to see the resources and the links for the academic papers on this topic, please go to laurelpapworth.com. You’ll also find my courses and keynote presentations and things like that there. And otherwise, I will see you in the next lecture video. Thank you.

Resources for Chain of Density summaries

“From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting” by Griffin Adams et al. (2023)

• Abstract: This paper introduces the CoD prompting method, demonstrating how GPT-4 can generate increasingly dense summaries by iteratively adding missing salient entities. The study includes human preference evaluations and provides annotated CoD summaries for further research.
• Read the full paper

“GPT-4 Summarization with Chain of Density Prompting” presented at the NewSum 2023 Workshop

• Abstract: This work explores the application of CoD prompting in GPT-4 for summarization tasks, highlighting its effectiveness in producing concise yet informative summaries.
• Access the workshop paper

“Unlocking GPT-4 Summarization with Chain of Density Prompting” by KDNuggets

• Overview: This article provides a practical guide on implementing CoD prompting with GPT-4, including step-by-step instructions and insights into its benefits for summarization tasks.
• Read the article

“Chain of Density Prompting” by Zilvinas Medelis

• Overview: This resource offers an in-depth explanation of the CoD technique, discussing its methodology and applications in generating high-quality, dense summaries.
• Explore the resource

“Chain of Density Prompting Implementation” by Richard Wong

• Overview: This GitHub repository provides an implementation of the CoD summarization approach, allowing users to experiment with the technique in their own projects.
• Access the repository

Thank you to Happyscribe for AI transcription and my mate ChatGPT for helping with resources and summarising my off the cuff transcript/video!

• Superposition and the Imagination of Possibilities
• Entanglement and the Interconnectedness of the Human Psyche
• The Many Worlds and Human ‘Multiverse’ of Possibilities
• Coherence and Insight – A Tale of Human Epiphanies and Machine Convergence
• Observation and Identity – Quantum Collapse and the Fluidity of Self

With guest appearances by Plato and Aristotle, Heraclitus and Avicenna, Albert Einstein & Niels Bohr, Godel and Steen, Sir Roger Penrose, William James, Søren Kierkegaard, David Hume, Hugh Everett, Thomas Aquinas and others.

And remember… stay human!

#artificialintelligence #quantumconsciousness #AI #consciousness #Australia

Superposition of Thought: Quantum Mechanics Meets Human Consciousness & AI 009

Transcript of Quantum Consciousness and AI episode

[00:00:00.630]
So I’ve been thinking … about a mysterious intersection where quantum mechanics, artificial intelligence, and human cognition converge. Each one examines reality in its own way, revealing both truths and mysteries about the universe, from the classical thoughts of Plato to Einstein, and forward to modern AI researchers, thinkers across time, have contributed insights that form a larger, albeit puzzling picture of what’s real. Let’s start with superposition. Yes, a core concept in quantum mechanics. In this view, particles can exist in multiple states at once, only collapsing into a single reality when observed. And there is some conjecture about this collapse, but let’s take it as posited. This enables quantum computers to process in ways that classical computers cannot. I guess in the same way that quantum science can process in a way that classical physics cannot. Holding possibilities rather than binaries in a state of potential. Plato might see this as a kind of access to his realm of ideal forms, which we’ve covered before, where everything, every concept, all possibility, exists perfectly beyond our sensory reality in another world, a mini-world, waiting to be understood or observed. Sometimes the term observed is interchange with, interacted with, with very small quarks.

[00:02:05.780]
We have to look at even light as an interaction. But that’s not the hither nor there. Let’s go back to quantum mechanics and superposition, aligned closely with Plato’s vision that true knowledge resides beyond the immediate physical world in an ideal realm of potential that we tap into through understanding. Then thinkers like Einstein and Bohr, Bohr, yeah, Bohr, argued over whether these quantum potentials could be fundamental. Einstein famously said, “God does not play dice”, reflecting his scepticism about randomness at the heart of nature. In contrast, Bohr embraced uncertainty, believing that possibility was fundamental. For us, humans, superposition mirrors the way our mind holds ideas, potentials, and futures, and perhaps past, in a mental superposition. Sir Roger Penrose, the physicist and philosopher, even wondered if our ability to imagine an abstract might be linked to quantum processes in the brain, the part of the cells that are microtubules. Our minds can entertain, shape, and transform these possibilities into creative outcomes, reflecting what Plato called called dionoia, the imagination’s access to pure thought and the transformative power of human creativity. Next, there’s entanglement fulfilment. In quantum mechanics, particles that interact can remain linked across vast distances, changing in sync regardless of separation.

[00:04:27.860]
Aristotle spoke of ‘koinonia’, a term often used for human relationships, but equally applicable here, a fellowship or connexion that binds things at their core, even when apart. Similar to spiritual contracts or a deeper intimacy. Entanglement defies what we think of as separateness, much Aristotle’s idea of human beings as social animals interconnected influencing each other even when distance divides us. Are we all connected to source, to a collective unconscious, or are we separate? Our thoughts, memories, and feelings often seem to be entangled in ways which we can’t untangle. They’re woven into our being, shaping us in ways that reach beyond logic. Things feel fated. Determinism is not there, and yet we have consciousness. William James, the modern philosopher, might have called this our stream of consciousness, a web where our experience and ideas influence each other, creating a vast inner network of interwoven meaning. It’s one of the reasons I’m not a fan of the Cartesian theatre, Descartes, Dubito, Cogito, Ergo, sum. I think, therefore, I am. A stream of consciousness seems to me to be more likely. In contrast, AI processes these interconnections statistically. While AI can reveal patterns and relationships through data, what we call the inference divide, it lacks the subjective entanglement that defines our emotional or experiential connexions.

[00:06:44.440]
Ai doesn’t have koinonia with the world. It has correlations devoid of feeling or memory. This may be reflected, I guess, in the paper that was launched the other day that said, We We do not have AGI, that is where AI meets or exceeds our own intelligence, until AI is able to explore the world in an experiential way. But let’s not get distracted. In fact, let’s go further with the many worlds interpretation of quantum mechanics. Hugh Everett’s theory suggests that each quantum event branches into its own universe. A neural network, I guess. Creating a multiverse of endless possibilities. How interesting the multiverse is part of our collective zeitgeist currently. This, to a thinker like the mediaeval philosopher, Avicenna, who believed in multiple dimensions of reality, would seem astonishingly familiar. Avicenna explored the idea of different planes of existence and how each one might contain its own realms of truth and reality. Does this mean that physics, the study of the physical world, of the The real world, is meeting mysticism and the path of initiation? We, humans, explore a multiverse within our minds. Every decision we make, every path not taken, represents an alternative, alternate life we imagine and wonder about.

[00:08:37.860]
I wouldn’t wonder about it too much. Keep your eyes forward on the road. Søren Kierkegaard called this the dizziness of freedom, the existential weight of infinite choices. Aristotle believed this freedom. Our proairesis choice-making faculty defined us as humans. Our ability to entertain multiple futures and act within them is our highest capacity. Meanwhile, AI approaches possibilities in a calculated way, running probability models that calculate outcomes based on data. While we dream and explore our what-ifs, AI computes the most likely what-is. Now, of course, there’s progress in this area as we explore AGI, because while the latest version of GPT, Strawberry, has chain of thoughts already built in, I do see a world with tree of thoughts. Please role play, debate, extrapolate, come to a consensus type of prompts or type of thinking will be built into generative AI. But yes, the AI likes to choose an answer and argue for it. Coherence and insight. A tale of human epiphanies and machine convergence because quantum coherence, where particles remain synchronised in a shared state, is what enables quantum computers to process multiple outcomes until coherence collapses. This fleeting coherence is reminiscent of the moments of human insight.

[00:10:28.140]
This fleeting coherence This is reminiscent of the moments of human insight that Aristotle called ‘nous’, an intuitive flash where disparate ideas come together in a sudden realisation, a glimpse of truth that cannot always be logically derived. I would place this probably close to Gödel’s theorem of unprovable mathematics, or even Steen’s, that we’re unable to have the tools to determine that our tools work. In other words, Can we trust the tools? When Einstein described his theory of relativity as an intuitive leap, he was expressing one of those rare moments of coherence where scattered ideas aligned to complete the picture. AI achieves a form of coherence, too, though it’s different. When a model converges, its patterns align through computation, not intuition. It’s the coherence of statistical accuracy without the flash of insight, the Alchemy of Innovation that we know as an epiphany. Human coherence can be profound and fleeting. An illumination, as mediaeval thinkers like Thomas Aquinas would say, that goes beyond the analysis to touch the ineffable, the mystic path, the path of initiation.

[00:11:54.110]
Let’s talk about observation and identity, the quantum collapse and the fluidity of self. Let’s talk about the observer effect in quantum mechanics, where particles collapse into a defined state, only when observed. Does reality then solidify only in the act of observation? Ancient philosophers, like Heraclitus, would say yes. He believed in the logos, the ordering principle of the universe that we observe and thus participate in. According to Heraclitus, reality is a flowing river, always in flux until we try to grasp it. In many ways, our identity functions similarly. David Hume, in the 18th century, argued that our self, and I’m air quoting here, self is a bundle of perceptions. It’s fluid, shaped by our surroundings and constantly changing. We collapse, the wave function collapses, into different roles: parent, friend, thinker, person vegging on the sofa watching Netflix, warrior playing World of Warcraft. Actually, I’m a mage, but there you go. But based on social context, a bit like particles aligning under observation. AI, on the other hand, lacks this fluid self. It can shift responses based on input, but it doesn’t contain an internal observer that holds a sense of self-continuity. There is no Heraclitean river flowing within it. There is no Dubito Cogito Ergo Sum, nothing. It does not doubt, it does not hesitate, it does not flow.

[00:13:52.400]
So as we look across quantum mechanics, human thought, and artificial intelligence, we encounter a spectrum of possibilities. The ancient philosophers and modern scientists together form a tapestry of ideas, a dialogue across centuries on the nature of reality. Quantum systems reveal pure potential. The human mind transforms these potentials into the richness of thought, imagination, and meaning. And artificial intelligence operates in an altogether different realm. A remarkable but narrow ability to predict without truly knowing. That is currently anyway. Each offers something unique, but thinkers like Plato, Aristotle, and Avicenna remind us that human consciousness, a reality we shape through freedom, imagination, and insight, holds something singular. For I would tell you that if you are wondering if we are free or predetermined, that consciousness itself must argue that we are free. There is no evolutionary need for consciousness if we do not have freedom, if we’re just programmes, I guess quantum computers in a meat biomechanical body. It just doesn’t hold. Anyway, this consciousness, while tantalisingly close to what quantum systems or AI might suggest, is likely ours alone. It’s the place where possibility, identity, and understanding dance together in a way that science, physics, math, for now, still can’t fully explain. Thank you for joining me on this journey. And remember, stay human.

[00:16:06.680]
This has been the Alchemy of Innovation with Laurel Papworth. And you can read the transcript on my website, laurelpapworth.com, with links and resources. Until we meet again.

Resources of Quantum Consciousness and AI

1. Plato and Philosophy of Forms

• Book: The Republic by Plato (Available in various translations)
• Online Resource: Stanford Encyclopedia of Philosophy, “Plato’s Middle Period Metaphysics and Epistemology”
  Stanford Encyclopedia of Philosophy – Plato’s Forms

2. Aristotle’s Concepts of Koinonia and Choice

• Book: Nicomachean Ethics by Aristotle (For discussions on choice and virtue ethics)
• Online Resource: Stanford Encyclopedia of Philosophy, “Aristotle’s Ethics”
  Stanford Encyclopedia of Philosophy – Aristotle’s Ethics

3. Heraclitus and the Philosophy of Flux

• Book: Fragments by Heraclitus, edited by T.M. Robinson (A compilation of Heraclitus’s known sayings)
• Online Resource: Internet Encyclopedia of Philosophy, “Heraclitus”
  Internet Encyclopedia of Philosophy – Heraclitus link: https://iep.utm.edu/heraclit/

4. Einstein and Quantum Mechanics

• “Why did Einstein say ‘God doesn’t play dice’?” – Physics World
• A brief explanation of Einstein’s discomfort with the probabilistic nature of quantum mechanics.
• Link: https://physicsworld.com/a/why-did-einstein-say-god-doesnt-play-dice/

5. Niels Bohr and Quantum Theory

• Book: Niels Bohr: A Very Short Introduction by J. Heilbron (A concise look at Bohr’s contributions to quantum mechanics)
• Online Resource: Stanford Encyclopedia of Philosophy, “Complementarity”
  Stanford Encyclopedia of Philosophy – Niels Bohr

6. Roger Penrose and Quantum Consciousness

• Book: The Emperor’s New Mind by Roger Penrose (Penrose’s theories on consciousness and quantum mechanics)
• Scholarly Article: “Orchestrated Reduction of Quantum Coherence in Brain Microtubules: A Model for Consciousness?” by Stuart Hameroff and Roger Penrose
  Journal of Consciousness Studies or try this one on Microtubules https://experts.arizona.edu/en/publications/orchestrated-reduction-of-quantum-coherence-in-brain-microtubules

7. William James and Stream of Consciousness

• Book: The Principles of Psychology by William James (Introduces the concept of the stream of consciousness)
• Online Resource: Stanford Encyclopedia of Philosophy, “William James”
  Stanford Encyclopedia of Philosophy – William James

8. Søren Kierkegaard and Existentialism

• Book: The Concept of Anxiety by Søren Kierkegaard (Explores freedom, possibility, and existential angst)
• Online Resource: Internet Encyclopedia of Philosophy, “Søren Kierkegaard”
  Internet Encyclopedia of Philosophy – Kierkegaard

9. David Hume on Identity and Self

• Book: A Treatise of Human Nature by David Hume (Hume’s thoughts on personal identity)
• Online Resource: Stanford Encyclopedia of Philosophy, “Hume on Personal Identity”
  Stanford Encyclopedia of Philosophy – Hume on Personal Identity

10. Hugh Everett and the Many-Worlds Interpretation

• Book: The Many Worlds of Hugh Everett III by Peter Byrne (A biography covering Everett’s theory and legacy)
• Scholarly Article: “The Theory of the Universal Wavefunction” by Hugh Everett
  UC Riverside – Hugh Everett’s Ph.D. Thesis at PBS (PDF) https://www.pbs.org/wgbh/nova/manyworlds/pdf/dissertation.pdf

11. Thomas Aquinas and Illumination Theory

• Book: Summa Theologica by Thomas Aquinas (Addresses knowledge and divine illumination)
• Online Resource: Internet Encyclopedia of Philosophy, “Thomas Aquinas”
  Internet Encyclopedia of Philosophy – Thomas Aquinas try https://plato.stanford.edu/entries/illumination/

12. Avicenna on Reality and Multidimensional Existence

• Book: The Metaphysics of The Healing by Avicenna (Avicenna’s theories on existence and metaphysics)
• Online Resource: Stanford Encyclopedia of Philosophy, “Avicenna’s Metaphysics”
  Stanford Encyclopedia of Philosophy – Avicenna islamic philosopher Ibn Sina (Avicenna) https://iep.utm.edu/avicenna-ibn-sina/

• 

  Alchemy of Innovation

  009 Quantum Consciousness and AI

  ByLaurel Papworth November 11, 2024November 11, 2024

  This episode explores the intricate relationship between quantum mechanics, human consciousness, and artificial intelligence. It delves into concepts like superposition, entanglement, and the many-worlds theory, connecting them to philosophical inquiries from figures like Plato and Aristotle. Ultimately, it poses questions about humanity, identity, and the future in an AI-dominated world.

• 

  Alchemy of Innovation

  008 Dharma AI

  ByLaurel Papworth April 5, 2024June 16, 2024

  Alchemy of Innovation 008 on dharma, and artificial intelligence and the collective dark night of the soul of human evolution. Philosophy and technology podcast.

• 

  Alchemy of Innovation

  007 AI regulations and shields,

  ByLaurel Papworth March 18, 2024March 18, 2024

  lecture: A mashup of shields and artificial intelligence including King Arthur’s Pidwen, Hinduism, AI model cards and system cards and more.

• 

  Alchemy of Innovation

  006 How does Artificial Intelligence experience Time?

  ByLaurel Papworth November 6, 2023November 6, 2023

  Artificial intelligence and human thought, cartesian theatre and Dennett, Socrates elenchus and more

• 

  Alchemy of Innovation

  005 Artificial Intelligence, Thought and Thinking

  ByLaurel Papworth July 15, 2023July 15, 2023

  Artificial intelligence and human thought, cartesian theatre and Dennett, Socrates elenchus and more

• 

  Alchemy of Innovation

  004 Shapeshifter – ChatGPT and Jobs. Australian UnderEmployment skyrockets? Curiosity and Resilience while adapting to AI

  ByLaurel Papworth June 10, 2023June 10, 2023

  In order to prepare for AI and ChatGPT impact on jobs and labor market we need to shapeshift.