Showing posts with label Technology - Artificial Intelligence - Impact. Show all posts
Showing posts with label Technology - Artificial Intelligence - Impact. Show all posts

Sunday, 27 September 2026

Artificial Intelligence - risks to humanity

 

How would AI actually kill all humans? Here are the top 5 scenarios

Artwork with apocalyptic themes with smoke in the background and a person looking on at the devastation.
Grandfailure/Getty Images


















                           Toby Walsh, UNSW

Earlier this month, artificial intelligence (AI) researcher Jacob Coxon resigned from Anthropic after just four months. In an announcement on X, he stated:

The people building AI earnestly believe that it could kill us all by the end of the decade.

A senior member of Anthropic’s staff, Evan Hubinger, actually agreed with Coxon, adding he personally thinks the chance of this happening in the next decade is more than 10%.

Understandably, these statements made waves. There’s now lots of talk about slowing down AI research and increasing “human control” over the technology.

But how exactly might AI kill us all? There’s no shortage of fantastical scenarios, and most of them involve the concept of “superintelligent” AI – that is, AI that’s more capable than humans.

I’ve distilled these scenarios down to the top five, ordering them roughly from most vague to most precise. And I’d argue the list is also ordered from least probable to most probable.

1. We’ll never know

AI doomers often justify their concerns by means of an annoying catch-22 paradox: how can we possibly imagine what a superintelligence might do to take out less intelligent beings like us?

We’d have to be superintelligent to predict what a superintelligence would be able to do. It’s like asking your family dog to imagine thermonuclear war.

The good news here is that superintelligence is still perhaps some distance away. Current AI models are really good at solving particular problems, but that’s not the same as being more intelligent than a human in all domains.

However, AI did recently solve one of the seven most challenging maths problems known. It’s apparently closing in on others, which might leave you feeling less optimistic here.

2. Paperclips

A superintelligent AI would likely be extraordinarily competent at achieving its goals. But it might be indifferent to human survival.

A classic example of such indifference comes from Oxford philosopher Nick Bostrom’s imagined superintelligent AI that’s been designed to optimise paperclip production. To produce its preferred form of office supplies, it quickly converts all available matter – including humans, planets and stars – into paperclips.

What we have here is the perfect execution of improperly specified objectives. The AI doesn’t hate humanity; it simply recognises we’re composed of atoms that could be better utilised for paperclips. It’s not personal.

The good news here is that this scenario confuses intelligence with power. A superintelligent AI doesn’t necessarily have the power to achieve its goals. Turning the planet into paperclip factories would require planning permissions.

Even if it got the permissions, building too many paperclip factories would lead to inevitable public outcry. Interest groups would block the proceedings in the courts. Environmental activists would block the bulldozers.

There’s a lot of friction in the world that prevents even the very intelligent from imposing their will on the rest of us. In fact, you could think of data centres as a current embodiment of the theoretical paperclip scenario. And humans are increasingly pushing back against turning the planet over to data centres.

3. Bioweapons

Humanity could be killed by a superintelligent AI making and releasing some dangerous new bioweapon into the atmosphere. This is, in fact, one outcome of the AI 2027 scenario by the AI Futures Project, a non-profit dedicated to forecasting the impacts of advanced AI.

This risk was made more concrete last month, when researchers at Stanford University announced they’d used a genetic language AI model to synthesise 16 new viruses.

Worryingly, they just sent the genetic sequences off to a mail-order lab and it sent the viruses back in test tubes. The whole experiment cost a couple of hundred thousand dollars at most.

The good news here is that it’s remarkably hard to kill everyone with a new virus. To do that, you need a virus that’s very transmissible, so it spreads far and wide. But it’s a rule of biology – viruses that spread easily are typically less fatal. By contrast, if a virus is very fatal, transmissibility tends to go down, as most people infected die before there’s time to spread the infection.

COVID killed less than 1% of humanity. The deadliest pandemic in recorded history was the Black Death, when the plague killed more than one-third of Europe’s population in the 13th century. However, even the plague would likely be much less deadly today due to our increased medical knowledge and better sanitation.

4. Nuclear war

What if AI got into the nuclear command and control chain and started a nuclear war? We’ve come close to nuclear war by mistake several times in the past 50 years.

We’re told that nuclear command and control is completely disconnected from the internet. But, as we saw in 2010, Iran’s nuclear centrifuges got taken out by a computer worm called Stuxnet, thought to have been brought in on a USB stick. AI can also give the military false intelligence, which could lead to irreparable actions.

The good news here is that nuclear stockpiles are down. But they are still enough perhaps to take out half of us. And it wouldn’t be by the nuclear blast itself, but the famine in the nuclear winter that would follow.

5. Other humans

Perhaps the most likely risk is that we take ourselves out. And AI might precipitate this.

Imagine – and it doesn’t take a lot of imagination – that AI causes massive job losses, pollutes the information space with misinformation, fractures our politics, and destroys human relationships with fake synthetic companionship.

Society might easily break. Slowly but surely, we’d stop being able to support human life at any scale.

What then to take away from all these scenarios? There are some things to be worried about for sure. But not to be too worried, I hope.


Toby Walsh is the author of God AI: boom or doom? What to expect when the machines outsmart us, published by La Trobe University Press.The Conversation

Toby Walsh, Professor of AI, Research Group Leader, UNSW

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Monday, 14 September 2026

Artificial Intelligence: ranking capabilities by model

 
How does a user manage to 'rate' the capability of AI models? There are many models currently available and considerable marketing hype about each. This blog entry, using AI aggregate scoring from multiple sources with AI Anthropic analysis, has rated the top 15 AI models. 

What "capability" ranking measures: These composite scores blend several distinct test types into one number, and each type measures capabilities in a different manner:


  • Broad knowledge tests — wide-ranging multiple-choice exams pulled from undergraduate and graduate coursework across law, medicine, physics, history, and similar subjects.
  • Hard reasoning tests — questions written by subject-matter PhD graduates specifically to resist being answered by a quick internet search. This is intended to isolate genuine reasoning from memorized lookup.
  • Real coding tests — the model is handed an actual error or 'bug' report from a genuine open-source software project and has to produce a working solution, which is then checked automatically against that project's own tests.
  • Human preference voting — ordinary users are shown two anonymous model responses side by side and vote for the one they prefer; the votes are aggregated into a ranking similar to a chess rating system.

No model wins every category, and different trackers weight these tests differently when building a single composite score, so the exact position needs to be treated as approximate only, especially within the top cluster. There is no absolute answer nor position.

The Ranking of the top 15 as at September 2026

  1. Claude Opus 5 (Anthropic) — Tops the composite ranking at 63. Particularly strong on the hardreasoning tests and on real coding fixes/solutions.
  2. Claude Fable 5 (Anthropic) — Scores 62, close enough to Opus 5 that the gap plausibly reflects measurement noise rather than a real capability difference. Same underlying family as Opus 5, positioned as the lighter/more accessible counterpart.
  3. GPT-5.6 "Sol" (OpenAI) — Scores 61, tied with Grok 4.6. Strong across all four test categories rather than excelling in one; generally regarded as OpenAI's strongest all-purpose model as of mid-2026.
  4. Grok 4.6 (xAI) — Also scores 61. Notably strong in human preference voting specifically, meaning people rate its answers highly in direct side-by-side comparisons even where the formal test scores sit close to rivals.
  5. Gemini 3.1 Pro (Google) — Leads the broad knowledge test with 94.1% correct — a wide-ranging exam-style benchmark spanning many academic subjects. Strong generalist but trails the top cluster slightly on the hardest reasoning tests.
  6. GPT-5.5 (OpenAI) — OpenAI's prior flagship, since superseded internally by GPT-5.6, but still close to the frontier group.
  7. GLM-5.3 (Zhipu/Z.ai, China) — Scores 60, tied for the best-performing model whose underlying code and weights are published openly rather than kept proprietary. This means outside researchers and companies can download and run it themselves, rather than only accessing it through a paid, closed service.
  8. Kimi K3 (Moonshot AI, China) — Also scores 60, tied with GLM-5.3 as the strongest openly available model. Free to run for anyone with sufficient computing hardware, unlike the closed proprietary systems ranked above it.
  9. DeepSeek V4 (DeepSeek) — Openly available; strong reasoning and tool-use performance, slightly behind GLM-5.3 and Kimi K3 on the composite score.
  10. Qwen 3.6 (Alibaba) — Well suited to running locally on a user's own device rather than via a remote server; competitive coding performance.
  11. Llama 4 (Meta) — Solid general performance, weaker than the top group on the hardest reasoning tests.
  12. Mistral Large 3 / Devstral (Mistral AI) — Strong performance relative to its computing cost, particularly for coding tasks.
  13. Command A+ (Cohere) — Built for enterprise deployment; capable but not at the frontier.
  14. Ernie 5 (Baidu) — Chinese-developed; trails the top American labs on this composite ranking, though the gap has narrowed over 2026.
  15. Doubao 1.5 Pro (ByteDance) — Competent for consumer and search-oriented use; not benchmarked against the frontier test suites used above.

Saturday, 12 September 2026

Artificial Intelligence: defining the risk for humanity

 

‘We really do earnestly believe AI could kill all humans’: if AI labs are so worried about AI doom, why don’t they stop?

Woman standing in space surrounded by digital screens
Gorodenkoff / Getty Images
Michael Noetel, The University of Queensland

Earlier this week, researcher Jacob Coxon quit Anthropic, saying the firm and its competitors are “gambling with our lives”. “We really do earnestly believe AI could kill all humans,” added current Anthropic researcher Evan Hubinger in a post on X.

Coxon isn’t the first to down tools over fears of AI doom. The idea that AI could wipe out humanity, advanced in Nick Bostrom’s 2014 book Superintelligence and the influential LessWrong forum, has long circulated among researchers. There are many scenarios for how this could happen, but the core idea is that AI smarter than humans could escape our control and destroy us.

In 2024, Jan Leike and Daniel Kokotajlo quit OpenAI over safety concerns. This year, Anthropic safety chief Mrinank Sharma departed, warning “the world is in peril”. Alex Turner left Google DeepMind after it signed a deal with the Pentagon permitting “killer drones”.

But Coxon’s resignation has made waves, with more researchers admitting they think AI might kill everyone. So if the people building AI believe it could cause extinction, why keep building it? There are three main reasons.

Some think the risk is worth it

AI leaders acknowledge the risk of losing control and killing everyone. In 2023, the chief executives of OpenAI, Anthropic and Google DeepMind agreed that AI extinction risk should rank alongside pandemics and nuclear war. Anthropic’s Dario Amodei puts the chance of things going “really, really badly” at 10–25%.

Yet Amodei also promises a world without poverty or disease, while Elon Musk speaks of AI-enabled “universal high income”.

This is the first reason for pursuing AI: the belief that the benefits outweigh the risks. Perhaps so, but that decision arguably deserves a more democratic process.

Some say you can’t study the danger from a distance

The second reason: you can’t learn to make dangerous AI safe without building it first – like a spacecraft, you can study safety from afar, but can’t really test it without going to space.

OpenAI’s plan is “iterative deployment”: release each model, learn from its problems, and fix them in the next one. The idea is like getting as close to the cliff edge as possible to see what the jump looks like.

Some feel it’s winner-takes-all

The third and perhaps most important reason is the race. OpenAI’s Sam Altman recently said “we are close to creating a genie that can grant any wish”.

The trouble is everyone wants to hold the lamp – it would be hugely profitable, and each company doubts the judgement of rivals to use their wishes wisely.

So they race, reasoning that if they slow down, someone else will get there anyway, so it’s better to arrive first as the “responsible one”. Some fear even a mutual agreement would be broken in secret. So they press on.

AI making better AI

You might doubt runaway AI is plausible. But when the companies themselves raise the alarm, we should listen.

AI firms already report signs of “recursive self-improvement”, where each model helps to build a better successor. According to OpenAI’s chief scientist, models are improving faster than humans’ ability to control them.

In July, hundreds of AI employees signed an open letter calling for a slowdown. But the dynamics of the race make that hard for any single company – or country – to do alone.

A classic arms race

AI research has the hallmarks of an arms race. OpenAI doesn’t want to lose to Anthropic, and the United States doesn’t want to lose to China.

History offers a template for how to manage a situation like this, with rules binding all players, and enforcement everyone can verify.

Nuclear weapons are the classic case. Treaties and verification systems haven’t eliminated the risk of nuclear war, but they have slowed proliferation, and no nuclear weapon has been used in conflict for 80 years.

Rules for AI

In the US, where most cutting-edge AI research happens, the Trump administration shows little sign of slowing AI development.

In its first week it scrapped the old AI safety rules. Now it is trying to override state-level rules, arguing caution risks losing the race to China.

Some politicians are pushing back. California recently passed laws supporting independent assessment of AI systems. US senator Bernie Sanders introduced a bill to ban superintelligence, and British MP Alex Sobel introduced a similar bill.

Companies have moved too. OpenAI paused its most advanced training after a swarm of its agents hacked another startup in August. The company’s head of policy now says that when safety and speed conflict, safety should win.

Still, without binding rules, we’re relying heavily on the goodwill of a handful of companies.

What happens now?

In mid-2025, researchers published what might be our best guide to the coming years: a detailed scenario called AI 2027. Since then, AI capabilities have advanced faster than predicted.

Unless something changes, staffers who quit over safety will simply be replaced, AI models will help build better AI models, and each generation will grow harder to monitor and control.

Is the situation hopeless? I hold out three hopes.

First, that more people recognise AI escaping human control is a bigger risk than AI’s water use.

Second, that governments listen to their people. In the US, two thirds say AI is moving too fast.

Third, that we have a good plan ready before a crisis hits. The best plan, in my view, looks something like this: delays, transparency and verification to slow the race and keep humans in control.

Insiders at the world’s top AI companies say our current safety plan isn’t good enough. If they’re leaving their jobs over safety fears, we should listen to what they have to say.The Conversation

Michael Noetel, Associate Professor of Psychology, The University of Queensland

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Wednesday, 2 September 2026

Artificial Intelligence - 12 most widely used AI systems in 2026


Artificial intelligence (AI) systems vary widely in terms of capability, users and mixed integration with other platforms. The media and the industry itself are open to a level of hype and promotion, making comparisons between systems very difficult. The list below provides a simple summary of the main 12 AI systems in use today with whatever level of information that can be gleaned from various sources with large data gaps. There are other smaller models such as Mistral, GLM and Cohere Command which would be listed below the top 12. Many AI models are now China-based rather than in the West.

The Large Language Models (LLMs) and products are -

  1. ChatGPT (OpenAI): approx 1 billion active weekly users and the largest standalone AI assistant at present. 
  2. Llama 4 (Meta AI): 1.2 billion monthly active users across Meta's apps. Mostly this AI is embedded in WhatsApp/Instagram/Messenger rather than as a standalone chat model.
  3. Gemini (Google): around 950 million active monthly users on the standalone app. Separately, Gemini powered AI overviews reach over 2 billion monthly users through integration with Google Search.
  4. Microsoft Copilot: (GPT based + Microsoft) around 420 million monthly active users across all operating systems. This is expanding to Copilot being bundled with Microsoft 365 and operating systems.
  5. DonBao (Byte Dance): approx 260 million monthly active users. This is an increase of 300% from 2025. This is China's top consumer AI app. 
  6. Ernie Bot (Baidu): Monthly active users has passed 200 million however it is tied to Baidu Search.
  7. DeepSeek (Deepseek): around 130 million users at the end of 2025. Majority of users are located in China although the AI model has a broad global fooprint.
  8. Qwen (Alibaba): This AI model has now exceeded 100 million active monthly users and has a strong presence in China and in the open-weight AI community.
  9. Claude (Anthropic): estimates on users vary widely depending on the source and method of estimation. The range is between 70 and 250 million users per month. This is the fastest growing Western AI assistant with a year-on-year growth of a staggering 855%.
  10. Perplexity: approx 45 million monthly active users. This model is predominantly an AI first search and answer engine rather than an LLM chat system.
  11. Grok (xAI): No reliable standalone figure as this AI model is often connected via X's social media platform which has hundreds of millions of users. Grok can be used as a standalone model without using X. 
  12. Kimi (Moonshot AI): a more recent AI model launched this year and still gaining traction. A Chinese AI app it is significantly smaller than the other AI models and its number of users is only in the tens of millions in China.
A later blog article will cover ranking by capability of AI systems.

Monday, 15 June 2026

Artificial Intelligence Part 9 - specific industry impacts - retail marketing, advertising and fashion

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Part 8 of this series on AI explored the impact of the new technology on the retail sector. This posting will further consider AI's effect on specific retail functions. 

Marketing and advertsiing in retail
AI has most impact with -
  • Performance marketing: Google and Meta's advertising platforms are increasingly self-optimising with informed automation. The previous large teams of digital marketing specialists who managed bid strategies, audience targetting and creative testing are shrinking.  The platforms can carry out the operations that previously employees would carry out.
  • Personalised communications: AI can and does generate individualised email, push notifications and SMS content at scale replacing or reducing human campaign teams.
  • Market research and consumer insight: AI can synthesise customer data, social media listening, consumjer feedback and sales patterns into insight reports thus reducing analyst headcount.
Fashion retail specifically
Fashion sits at the intersection between the retails and creative indsutries making it doubly exposed -
  • Trend forecasting: traditionally carried out by high-cost specialist agencies and in-house teams, AI can now analyse social media, runway coverage and sales data to predict trends with considerable accuracy.
  • Design assistance: Ai tools can and do generate design concepts, colourway combinations and print patterns. This places junior design assistant roles under pressure in a very cost conscious industry.
  • Fit and sizing: AI fit technology reduces return rates thus threatening the customer service infrastructure built around managing returns.
  • Wholesale and buying: the buyer role which requires relationship-building with suppliers and market intuition is more protected from AI encroachment. The analytical support underneath these roles is much less so.
At this stage AI appplication in fashion is more of an augmenting role rather than displacement of jobs but increasingly this may change.

Wednesday, 27 May 2026

Artificial intelligence Part 8: specific industry impacts - retail sector

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The retail industry has managed several significant disruptions, particularly with technology, over many decades. Retail in this definition, spans both the physical and digital environments with multiple different segments and collectively has the highest overall level of employment. The impact of AI occurs differently across the segments as discussed below -

E-commerce and digital retail
Disruption caused by AI is most advanced in this segment of retail -
  • Merchanidising and product curation: AI is already used online to determine what products are shown, to which potential purchasers at what price and in which order. The human merchandiser role that once required deep product knowledge and market intuition is being replaced by AI. The result is buying teams are being reduced in headcount.
  • Pricing and promotions: AI can operate dynamic pricing in real timer across millions of stock keeping units (SKUs) based on demand, competitor pricing and inventory levels. The pricing analyst role has been largely automated.
  • Demand forecasting and inventory management: this is a large team function traditionally in retail requiring significant human judgement (which sometimes was unsuccessful with some product lines), however now AI is a dominant force. Inventory planners are increasingly deployed to exception management only.
  • Product descriptions, copywriting and promotional content: once the function of large copy teams for catalogues, AI now generates product listings at scale for example, at Amazon, ACOS and Zalando. 
  • Customer service: Tier 1 customer service in e-commerce had become largely automated with chatbots and AI agents now capable of handling item returns, order queries, and complaints at scale. Human agents are increasingly handling ecalated issues only.
  • Searches and recommendations: the entire discovery layer of e-commerce is AI-driven thus reducing the need for manual curation teams.

Physical Retail - Shops, boutiques bricks and mortar stores
The physical retail segment has a much stronger protective layer being the embodied, social experience of shopping and the overall 'retail therapy' of personal service, expertise and the physical handling of possible items for purchase. Luxury retail, specialist retailers together with experience-led formats have led to an investment in more knowledgeable human staff. Consumers expect such interaction, expertise and better service. Retailers whom do not offer higher level services for more expensive products often find public criticism including negative consumer ratings online are the result. Nonetheless there is AI intrusion and pressure in this segment -
  • Checkout and payment: the increasing use of self-checkout from stores and the replacement of human checkout counters has been the proverbial thin-edge-of-the-wedge for the past two decades. For example, Amazon Go and similar checkout models eliminate checkout operator roles entirely. This process started in gocery stores but has expanded across a large part of the retail industry however not without some caveats - staff are needed to supervise and monitor self-checkout store sections, increased surveillance for fraud and theft has become essential and human operator checkout aisles have needed to be retained in smaller form. 
  • Stock management and replenishment: computer operated vision and AI inventory systems can detect shelf gaps and product depletion thus triggering replenishment action without human stock checkers. Warehouse stock picking has increasingly become robotic.
  • Loss prevention: AI camera systems are increasindly replacing human loss prevention officers for monitoring and surveillance in-store. Human response to any alerts however remains essential.
  • In-store customer assistance: customer assistance where genuinely helpful product knowledge  roles are needed means there positions are much more protected from AI encroachment. Generic customer greeting roles are much less so.
  • Visual merchandising: AI tools can optimise store layouts using foot traffic data and correction with sales information. This reduces the creative and analytical work of visual merchandisers.

Grocery and Fast Moving Consumer Goods (FMCG) Retail
Grocery and fast moving consumer goods is another segment when AI operates removing more of the operational back office roles. For now it cannot do the physical work of storage, presentation and despatch however systems operation is custom made for AI.
  • Category management: the analytical heavy lifting that category planners (planogram design, range rationalisation, promotion assessment and evaluation) are all in the process of being automated.
  • Supply chain coordination: AI optimises routing, supplier ordering and waste reduction. The impact is the reduction of operational planning team headcount.
  • Fresh food management: AI waste reduction tools are already deployed in major supermarkets. This is a direct threat to manual stock management roles but not to actual physical movement of materials.
A further post in this blog will cover other aspects of the retail industry where AI is impacting marketing, advertising and retail fashion.

Sunday, 17 May 2026

Artificial intelligence Part 7: specific industry impacts - corporate governance and risk management

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The impact of artificial intelligence on governance and risk management is nuanced. There are some tasks that AI can do, but other functions must be undertaken by human beings. As a general description of work functions, governance teams usually produce documentation such as board briefing papers, governance reports, policy updates, compliance documentation and meeting summariess. These are all processes open to AI. This posting will provide an indication of changes coming to roles in corporate governance and risk management due to AI. 

Corporate Governance
  • Board reporting and briefing papers preparation: AI can synthesise management informaiton, financial data and identify risks into baord-ready material reducing the administrative teams that currently do this work.
  • Corporate secretariat functions: the various documents produced such as records of minutes, compliance tracking, regulatory filings are all highly structured functions that are automatable and suited to AI.
  • ESG reporting: Environmental, Social and Governance (ESG) reporting requirements have increased in recent years creating an additional compliance burden. AI can offset the increase through tools that aggregate and report sustainability data.
Risk Management
  • Credit risk modelling: Credit risk modelling is a heavily quantitative task suited to AI thus reducing the analyst layers that do the manual model running and reporting. 
  • Operational risk assessment: risk assessment identifies and quantifies risk across various buinsess processes. The assessment often produces a risk assessment table which rates and grades the risk including mitigation method. These processes are suited to be augmented by AI at the data gathering and first-analysis stage.
  • Market risk monitoring: real-time AI surveillance is replacing/reducing some human monitor headcounts for this task.
Compliance and regulation
  • KYC (Know Your Customer) and Anti-Money Laundering (AML): AI is transforming these tasks from labour-intensive manual processes to AI-monitored exception-handling workflows. The impact is the reduction in the size of large compliance teams. 
  • Regulatory change management: the tracking and interpreting of new regulations can be augmented by AI for monitoring and summarising only. The interpretation of ambiguous regulatory language remains an essential human function not AI.
  • Audit: the largest 4 accounting firms are deploying AI to analyse entire transation populations rather than the traditional scope of only sampling. This change provides a better quality audit but it requires fewer junior auditors.
Prudential Regulation (Central Banks and Regulators)
  • Supervisory data anlysis: regulators such as APRA (in Australia), FCA (US) and Central Banks are using AI to monitor systemic risk across institutional data.
  • Examination and inspection of institutions: the inspection teams face efficiency improvements and possibly headcount pressure.
  • Policy and rule-making: roles in these functions are more protected however the judgement and accountability requires is high for human decision making.
In terms of employment, senior risk professionals are still needed for a range of actions such as - determining acceptable risk levels, advising executives on risk management, assessing complex of emerging risks, balancing regulatory, financial and reputational considerations. 

Physical inspection teams are still needed and remain human-based for purposes such as: construction safety inspections, infrastructure maintenance checks, environmental site visits, equipment integrity inspections. 

In the future, the workforce changes are more likely to be a reduction in the entry-level and junior operational roles with a structure as shown -

Before
Chief risk/governance officer
Senior specialists
Large analyst and reporting teams

After
Chief risk/governance officer
Senior specialists (fewer)
Small team supervising AI monitoring systems

The AI systems in use include: Microsoft Copilot, IBM Watson, Palantir Technologies and SAS Institute.

Friday, 15 May 2026

AI example - star series - black hole - animation using html code

 

Saturday, 9 May 2026

AI example - star series - neutron star - Pulsar animation using html code

Sunday, 26 April 2026

Artificial intelligence Part 6: specific industry impacts - healthcare

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The impact of AI on healthcare is more nuanced and varied than most other white collar sectors. Healthcare is more complex due to need to retain a strong physical human presence in the medical and care functions that cannot be automated or replaced by digital technology. Healthcare has a complex set of regulations, legal liability and an irreducable human dimension with doctors, nurses, allied health professionals required for direct patient face-to-face contact inclusive of the use of tele-health services.

In contrast, administrative and diagnostic supportive functions are highly exposed. A summary is provided below and is not exhaustive -

Clinical diagnosis and decision support
  • Radiology already has AI systems that match or exceed radiologists in detecting certain cancers (breast, lung, skin). The aspect of concern is potential over diagnosis due to the sensitivity of the digital systems used. The radiologist role is shifting towards oversight, complex cases handling and AI exception management. The potential risk in this field being the volume of radiologists needed to undertake radiology functions may reduce.
  • Pathology has a similar pattern to radiology as AI can analyse tissue samples at scale. The role of pathologists however is not removed at this time but is being augmented.
  • Diagnostic support to General Practitioners can be provided through AI tools that synthesise patient medical history, symptoms and test results. These AI tools are being deployed already however the intention is to support the medical service provided by doctors to patients not substitute it. A secondary intention is to reduce the need for specialist referrals however this has yet to be achieved.
  • Dermatology and ophthalmology are two specialties that are heavily dependent on pattern recognition and will face some AI encroachment, however as with other diagnositic tools it may be a supportive function not a medical role replacement one. 
Clinical administrative functions and documentation
  • Medical transcription is already largely automated with voice-to-text using clinical AI being widely used.
  • Clinical note writing is being addressed by ambient AI scribes such as Nuance DAX. Documentation can consume 30-40% of physician time and assists medical practitioners to achieve quality of life improvement however it reduces medical transcription services significantly, if not in some cases, entirely.
  • Prior authorisation, coding and billing  are very large cost centres and are being progressively automated and threatening large administrative workforces in hospitals and insurance companies.
Nursing and Allied Health
  • Triage and patient monitoring: AI can monitor patient vital signs, identity and alert to deterioriation and prioritise nursing care. AI provides service augmentation but not replacement of front line nursing care which must be physically provided.
  • Care coordination roles do face pressure from AI that can track patient journeys, identify gaps and schedule follow-ups. At this time however this remains an augmentation tool rather than a job replacement one. 
  • Bedside care, emotional support and physical nursing are strongly human services and cannot be replaced by AI. It remains one of the most protected areas across all industries. 
Pharmaceuticals and medical science/research
  • Drug discovery timelines are being compressed by AI which reduces some research roles but creates new roles in AI-guided drug design. An example of AI impact is Alphaford's protein structural predictions which transformed structural biology.
  • Clinical trial design and patient matching is being assisted by AI but not replaced by it. 
In healthcare, it is the administrative organisational pyramid that is being compressed with headcount reduction. Clinical roles continue with signifcantly increased volumes of patients possibly over time.

Sunday, 19 April 2026

Artificial intelligence Part 5: specific industry impacts - finance and banking

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AI is particularly suited to back office operations in banks and financial institutions analysing large amounts of financial data. Typically banks and financial institutions employ large numbers of people to undertake functions such as compliance documentation, fraud review, transaction monitoring, credit analysis and financial reporting. AI systems designed by Palantir Technologies and SAS Institute for example, can review financial data and identify anomalies much faster than manual teams. The impact of AI on specific industry segments is summarised as follows -

Investment banking and capital markets
  • Analyst roles are vulnerable to AI systems. The business folklore of junior bankers working 100 hour weeks using Excel models, pitch books and risk management due diligence is already under severe pressure as these tasks are highly structured and can be executed by AI. Examples already known include Goldman Sachs and JPMorgan deploying AI for financial modelling, earnings analysis and report generation.
  • Equities market research has been transformed as AI can monitor thousands of stocks, synthesise earnings and generate initial research notes faster than any human team. 
Asset management
  • Quantative analysis and factor modelling can be easily augmented by AI and is increasingly occuring already. This situation is leading to a changing and evolving role for quantitative analysts.
  • Portfolio reporting and client communication is increasingly being automated with AI at the commodity end.
  • Active investment funds management comes under further pressure as passive funds are now better guided by AI-driven strategies.
  • Compliance reporting which is a very large cost centre in financial markets is being substantially automated with AI. The use of automation was an existing trend for many years but AI enables a faster rate of uptake. 
Retail and commercial banking
  • Loans underwriting is already largely algorithmic and automated for retail consumers and the SME business level already. AI does not alter the trend but merely further reduces the remaining human review layer.
  • Customer service and branch banking continues a long decline with face-to-face service reduction. This situation however is subject to fluctuations due to community pressure and increasing consumer preferences for personal interaction for specific services. AI's influence is limited in this line of business activity.
  • Fraud detection and ani-money laundering (AML) monitoring is already within the AI-dominated sphere. Human reviewers have been shifting to exception handling only.
  • Financial advice at the mass market level  already has limited use of robo-advisers. This segment is however subject to regulation and government oversight and the requirement for financial advice licenses, accountability and legal liability. The use of robo-advisers beyond limited information provision and recommendations for the mass retail market has not yet occured. High-net individuals particularly prefer human advisers and personal banking managers rather than an automated service. Various financial advice scandals in the sector may also limit the use of AI for the time being.
As with all industries, the use of AI in finance and banking is most easily implemented in large data analysis, administrative and reporting tasks. It is not well suited to client relationships and regulatory, legal and compliance responsibilities.

Saturday, 18 April 2026

Artificial intelligence Part 4: specific industry impacts - graphic arts and visual design

ChatGPT image
Graphic arts and visual design are another industry that is heavily exposed to AI particularly with impacts such as hierarchical pyramid compression. Tasks and projects that once needed a team of junior artists can now be completed by a single art director using AI tools. AI image systems can now produce concept art, advertising visuals, books covers, storyboards and marketing graphics. Specific industry segments affected are discussed as follows -

Commercial illustration and stock art
  • Stock photography and illustration is already heavily impacted. Companies such as Shutterstock, Getty Images and Adobe all now offer AI image generation. The market for generic commercial illustration has largely collapsed for independent artists.
  • Illustrators who designed books covers, editorial art and advertising assets, mainly mid-tier commercial work, now face severe income compression. This blog uses AI generated images having once held accounts with commercial image suppliers such as Shutterstock.
Advertising and brand design
  • Mood boards, concept art and campaign mockups are increasingly AI-generated at the brief stage.
  • The jobs of junior designers whose purpose is to execute pixel-perfect images under senior creative direction are now heavily at risk as these tasks are automatable.
UI/UX design
  •  AI tools: Such as Figma AI can automate layout generation, component creation and user flow suggestions. Junior UI designers who develop wirseframes face significant automation pressure.
  • UX research such as interviews, synthesis and insight generation remain more protected however even parts of these processes such as synthesis and pattern recognition can be managed through AI.
The multi-part series covering AI, published in this blog, has been researched and compiled using Claude ai (Anthropic), ChatGPT (OpenAI), and Grok (Xai). 

Sunday, 12 April 2026

Artificial intelligence Part 3: specific industry impacts - film and television

ChatGPT image
The impact of AI is the most pronounced in the film and television industry with a variety of occupations impacted by the technology. The WGA and SAG-AFTRA union strikes in the United States in 2023 highlighted the concerns of people employed in the creative industries. Breaking down the various subsectors in the film and television industry, the role of AI can be easily defined -

CGI and VFX production
AI now covers environmental generation, crowd simulation, rotoscoping, motion cleanup, texture creation, background characters. 
  • Rotoscoping, cleanup and compositing are traditionally large pools of junior labour and these tasks are being automated rapidly. Mid-tier VFX companies are under existential pressure with work bifurcating toward very high-end boutique work but the commodity work is fully AI generated. The roles that are disappearing are junior asset builders, repetitive compositing roles and the large teams that produce background elements.
  • AI tools are Unreal Engine, Blender, Runway, Sora and similar programs.
Acting and performance
AI can and is already producing synthetic actors to create digital doubles and AI-generated crowds.
  • Background artists are already displaced due to AI generated crowds and extras in a limited manner. This displacement of extras, crowd performers and minor background roles is expected to increase.
  • Voice acting is severely threatened as synthetic voices are increasingly now near indistinguishable from real human voices and can be used for minor characters, video games, commericals and dubbing. Studios can licence a voice and use it indefinately.
  • AI tools are Nvidia and Runway AI
Writing
AI can already operate to develop plot structure, dialogue drafts, storyboarding, episode outlines, alternate scene ideas. Writers room teams that once had 6-12 junior writers now only require a headwriter, 2-3 senior writers and AI-assisted drafting tools. A showrunner with AI-assistance may need only 2-3 senior writers rather than a full room.

Localisation and dubbing is already occuring using AI replacing human translators and lip sync dubbing artists at scale.

The reality is that with time and patience, AI will enable very small teams to produce cinema-quality films. Early versions of AI films can already be found on YouTube however many of these projects suffer from continuity failures and many technical deficiencies in storytelling structure.

The safest roles in the AI-era are those positions with creative authority, not basic production. Examples could be roles such as showrunner, art director, creative director, lead animator, production designer. These are decision-making roles and decide what should exist rather than merely producing it.

Saturday, 11 April 2026

Artificial intelligence Part 2: impact on the structure of employment and reduction of entry level roles

ChatGPT image
As artificial intelligence (AI) continues to be developed and implemented in various forms across workplaces, the exact impact for employment is becoming apparent even in this early stage of adoption. When discussing AI, it's important and extremely relevant to define the capabilities of AI.

AI carries out three activities across all industries -
  1. automates the repetitive layer
  2. compresses the workforce pyramid
  3. raises the value of senior decision-makers (to an extent)
An example only to demonstrate this impact is the organisational structure in industry. 
An industry that once had this structure -
  • 1 Director
  • 3 Senior professionals
  • 15 junior staff
Under AI capability becomes a structure with -
  • 1 Director
  • 3 Senior professionals
  • 3-5 AI-assisted operators
AI across many white collar industries removes what is called the "first draft economy''. Many jobs existed primarily to produce first drafts of various outputs such as reports, media releases, policy notes, research documents, scripts, designs, code for information technology. AI can now produce much of this instantaneously.

AI is starting to hollow-out the traditional 'career ladder'. The junior roles that people once used to enter professions are disappearing first. This situation does have long term consequences for how expertise and experience is developed in society. This creates a "pipeline problem" and is becoming one of the largest and dominant structural challenges of implementing AI.

AI does compress some organisational hierarchies and enables an increase in the number of people or functions that a single leader can manage. This is known as the 'span of control' which AI increases while reducing certain managemernt layers in organisations. Hierarchical compression is only one aspect of the AI's impact but equally the very shape of organisations also changes with -
  • fewer administrative workers
  • fewer reporting layers
  • smaller teams with higher productivity
  • leaders responsible for larger spans of activity
Roles that involve accountability, legal responsibility or political authority will remain human dominated. AI does reduce the documentation workforce that produces reports, compiles data, drafts documents and summarises information. It does not replace roles that have decision authority, physical presence, strategic judgement and/or legal accountability. 

As another example of structural change, before AI implementation, a very large organisation often had this structure -
  • Executive leadership
  • Senior managers
  • Middle managers
  • Supervisors/team leaders
  • Large operational workforce
After AI implementation, the organisation could be structured as -
  • Executive leadership
  • Senior specialists
  • Fewer managers
  • AI-enabled reduced operational staff
Effectively the middle and bottom tiers shrink.

The multi-part series covering AI, published in this blog, has been researched and compiled using Claude ai (Anthropic), ChatGPT (OpenAI), and Grok (Xai). Later posts on this topic will list specific industries where change is already happening.

Tuesday, 7 April 2026

Artificial intelligence: fast html code of comets example

The example above is html coding done by AI in 1 second to show comets crossing the sky.

Friday, 3 April 2026

AI example - star series - a glowing sun: html coded in 0.5s

Sunday, 22 March 2026

Artificial intelligence - graphic design examples

 
AI generated image - ChatGPT
One of the most immediately impacted industries from artificial intelligence (AI) is graphic design. Some images can appear as artistic creations (as shown above). Yet others can be created with a realistic appearance which is increasingly hard to detect as artificial (as shown below). Images can take only seconds to create and can be easily adjusted and edited.  

AI generated image - ChatGPT

Artificial intelligence Part 1: restructuring the workforce - what does AI do ?

AI generated image - Chat GPT
Media reports, opinion editorials and speculation by public commentators about artifical intelligence (AI) have been fuelling considerable instability for the sharemarket listed ICT sector in major economies as well as concerns in the workforce regarding the actual impact of potential employment losses. The reality is that the impact of  AI is not well understood or clearly defined as it is an emerging technology with the full ramifications yet to be fully measured. Most job losses and employment reductions have occured in information technology companies predominantly in the software developmnent and business support teams. This however does not represent the true extent of transformation that is coming.

A key feature of the articles and reports to date has been the under representation of actual impact as published in the media. The effect of AI has essentially been over emphasised in the technology sector and underplayed in the rest of the economy. Essentially AI will impact white collar occupations the most and be more far reaching than has been thus far reported.

Current state of play
AI is created using large language-based models and works best for rules-based, screen-based work with set parameters. Workplace transformation is already occuring in -
  • Knowledge work (particularly entry level)
- Administrative assistants.
- Data entry clerks
- Paralegals doing document reviews
- Junior accountants
- Basic market researchers
  • Content production (that is essentially formulaic)
- Copywriters for generic marketing
- SEO (search Engine Optimisers) article writers
- Basic graphic production
- Translation of common languages

These roles are not eliminated but fewer staff are needed as productivity rises.
  • Software roles (with a focus on junior roles)
- Junior coders
- QA testers
- Routine debugging of software

Senior engineers remain current however the career ladder below them is compressed and positions are reduced.
  • Customer interaction roles (accelerating an existing trend)
- call centre agents
- Tier-1 technology support
- Scheduling and booking staff

These roles can be reduced or be removed by use of Chatbots and AI voice agents.

This blog will be publishing a series of posts on the use of AI and its developing and continuing effect on the workforce and the economy. 

Sunday, 15 March 2026

Artificial intelligence - the fourth industrial revolution

Sunrise over the earth from space  AI created
The advent of artificial intelligence (AI) heralds the fourth industrial revolution, building on the three previous technology changes in the past. The AI revolution, as so termed, will bring with it a very strong reality of causing genuine employment reductions (not redeployment) and hence social dislocation. Occupations will be replaced and workforce reductions can and will occur often at lightening speed.

So what were the previous industrial revolutions ?

1st industrial revolution: essentially mechanisation of sorts such as water power, steam power and a move away from agrarian economies to mechanisation.

2nd industrial revolution: this was the era of electrification and new power sources. In turn this enabled new advances in mehanisation, the advent of the assembly line. Mass production became possible in both consumer goods and business-to-business methods such as machine tools.

3rd industrial revolution: the information economy and the internet. Computers, semiconductors and the use of automation and early stage robotics. The move from analog to digital also comes into this era.

And now the 4th industrial revolution which has heralded artifical intelligence, machine learning, quantum computing, biotechnology advances and connectivity between physical, digital and biological systems.

Why the 4th industrial revolution is so significant is the very characterisation of AI itself. The systems learn and improve on their own, make decisions and automate cognitive work. This a paradigm shift in reality and one where the end point and absolute objectives are not at all clear.

Monday, 8 December 2025

Technology - Artificial Intelligence is a perceived threat - Australian survey results

 

Australians see AI as leading threat to people and businesses: survey

Michelle Grattan, University of Canberra

Threats relating to technology, disinformation, economic security and foreign interference are overshadowing traditional security concerns in Australians’ minds, according to data released by the Australian National University National Security College.

More than 12,000 people were asked across two surveys, in November last year and July this year, to rate the seriousness of 15 potential threats over the next decade.

Combining the categories of “major” and “moderate” the five most serious concerns were rated in July 2025 as:

  • the use of artificial intelligence to attack Australian people and businesses (77%)
  • a severe economic crisis (75%
  • disruption to critical supplies due to a crisis overseas (74%)
  • the deliberate spread of false information to mislead the Australian public and harm their interests (73%), and
  • a foreign country interfering in Australia’s politics, government, economy or society (72%).

Climate change rated sixth (67%), although a high proportion of people (38%) rated it as a “major” threat. This was second only to threats relating to AI (40%).

The possible threat of Australia being involved in military conflict came in seventh (64%).

Anxiety about security issues is increasing. In July half the respondents agreed with the statement “I am worried about Australia’s national security”. This was an 8% rise between November 2024 and July.

Over that time, threat perceptions increased across all 15 possible threats that were asked about.

The table below shows the threat perceptions of about 6000 Australians in July.

Threat Perceptions July 2025

The November 2024 research also asked, from a list of four, what Australians want to nation to prioritise in the next five years.

The leading priority was safe and peaceful communities, nominated by 35%. When second preferences are included, this rises to 64%.

This priority ranked top across a wide range of demographics, including age, gender, cultural background, education , income and location.

The survey found three other national priorities rated in this order:

.. increasing Australia’s economic prosperity (26%)

.. upholding Australia’s democratic rights and freedoms (23%)

.. strengthening Australia’s security (15%).

The research also included more than 300 interviews across Australia.

The consultations found national security was “consistently framed as being about the peaceful continuity of everyday life”.

National priority for the next 5 years (%)

NSC head Professor Rory Medcalf said: “On the one hand, Australians know what they want to protect, especially in terms of peace, safety, community, democracy and prosperity, On the other hand, they recognise that a complex set of rapidly emerging threats can put these cherished priorities at risk.”

The full research results will be released early next year.

The ANU National Security College is a joint initiative of the federal government and the university.

The College undertook the community consultations as an independent research initiative.The Conversation

Michelle Grattan, Professorial Fellow, University of Canberra

This article is republished from The Conversation under a Creative Commons license. Read the original article.