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Last Updated: September 22, 2025

Introduction

Artificial Intelligence (AI) is a branch of computer science that aims to create machines that mimic human intelligence. This didn’t originally mean creating machines that can think for themselves, but rather, creating machines that can be programmed to think or learn in a way that appears intelligent. However, there seems to be very little, or potentially absolutely no difference at all between “actual intelligence” (whatever that means) and “artificial intelligence” - more on this later.

The language I’m using could be seen as anthropomorphising, however, these models are reflections of some ways the human brain works and learns, and I believe the analogies using human-specific language to be well-accurate enough.

Why Does It Work?

The “intelligence” and “reasoning” abilities of any given AI system are not binary - AI is not either intelligent or not intelligent - these reasoning abilities exist on a sliding scale, similarly to humans. There is only so much reasoning a particular model can perform, and only so much the model can “remember” (or juggle in its “mind”) at any given time. An AI system’s intelligence and working memory size seem to be the constraints on AI ability, however, this is still very high-level, and still hasn’t really answered the question. Funnily enough, we are once again asking "what is intelligence?"

Technically, all these models do is predict the next word (actually “token” instead of “word”, but that’s not too important a distinction at the moment). That said, viewing these models as “only text prediction and nothing more” is majorly missing the point. These Natural Language Processing (NLP) models (like ChatGPT) have been trained on such a large amount of diverse text that they have learned and understood the inherent underlying logic and patterns of human language as presented through text. Language is the vehicle in which we humans use to convey our thoughts, reasoning, feelings, ideas, aspirations, and emotions - these are emissions and admissions of our own intelligence. Hence, intelligence is a fundamental requirement in creating the thoughts we have and the language we use.

Clearly, it seems that by instructing an AI to learn how to effectively model language, it must also learn how to effectively model intelligence itself to be able to excel at modelling language. Stemming from “only” an advanced text prediction algorithm, there is a veritable emergent “ghost-in-the-machine” with a surprisingly organic and authentic display of robust artificial intelligence.

What's On Offer

Large language model assistants keep expanding their skill sets—mixing multimodality, automation, and deeper reasoning. Here’s a snapshot of what the leading platforms offer right now:

The First Pitfall

The first pitfall is assuming every ChatGPT login delivers the same experience. OpenAI now differentiates sharply between free, paid, and workplace offerings—so match your use case to the right plan.

  1. Unpaid / Free: The Free tier keeps limited GPT-5 access, search, voice, file uploads, and Projects, but throttles throughput and omits premium reasoning models—fine for occasional prompting yet easy to outgrow once workflows get heavier.

  2. Individual Paid: Plus ($20/month) and Pro ($200/month) expand limits, unlock Advanced Voice with screen sharing, Deep Research, multiple reasoning models, and early previews like the Codex agent—exactly what you need when you start automating tasks or running longer research sessions.

  3. Business & Enterprise: Business ($25 per user/month billed annually) adds unlimited GPT-5 messaging, connectors into internal tools, SAML SSO, MFA, and compliance guardrails, while Enterprise layers bespoke retention policies, broader governance, and dedicated support for regulated deployments.

Match the tier to the work you need to do—casual prompting, power-user automation, or governed teamwork—then pilot for a week before you roll it out widely.

When we trialled ChatGPT Business, we dropped the ABS employment dataset into a Project, let the Codex agent build out the exploratory code, and returned to the automatically generated visualisation below—a reminder that the higher tiers bundle workflow and automation upgrades, not just faster replies.

A line graph titled 'Difference in Employment Trends: Victoria (VIC) vs Australia (AUS)' displaying three lines representing different sizes of companies by number of employees: 0-19, 20-199, and 200+ employees. The horizontal axis marks the dates from early 2020 to late 2023, while the vertical axis shows the difference in employment index with values ranging from approximately -2 to +6. The lines for all company sizes show fluctuations over time, with notable peaks and troughs, indicating variances in employment trends between Victoria and Australia as a whole.

I'll eventually come back to this page to write more, but for now I'll leave it here. Be sure to check out part 2.

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