The AI jobs paradox is creating and eliminating roles at the same time
By Jennifer Friesen
DIGITAL JOURNAL
March 12, 2026
Photo by Ben Iwara on Unsplash
A software developer prepares a pull request and pauses for a moment. Much of the code in the update came from an AI assistant.
The system has already flagged a potential issue and suggested a fix before anyone else on the team reviews the change.
Anyone who has worked around software teams knows pull requests can trigger long review threads and days of back-and-forth. Now an AI assistant often joins the discussion, generating code and flagging problems before another developer even opens the thread.
According to new global research from Snowflake and Omdia, nearly half of the code written inside organizations today is generated with AI assistance.
Technical teams are already reorganizing around that reality, often discovering the same tools creating new roles are eliminating others. The research shows engineering groups both hiring and eliminating roles as AI tools become embedded in everyday workflows.
In Canada, organizations are prioritizing these shifts at the front door, moving faster than global peers to target customer-facing experiences.
For many organizations, the technology shaping how software gets built is now beginning to form how customers interact with the business as well.
Engineering teams are reorganizing around AI systems
The report surveyed more than 2,000 business and technology leaders across 10 countries. Among those organizations, 77% say AI adoption has created jobs somewhere in their workforce, while 46% say roles have been eliminated.
Many companies report both outcomes at once.
This paradox is most visible in technical departments. IT operations, cybersecurity, and software development report some of the largest job gains tied to AI adoption (at 56%, 46%, and 38% respectively). Those same functions also report some of the biggest reductions.
In practice, AI is taking over certain tasks while creating new work around building and operating AI systems.
“Engineering teams are increasingly focused on making AI work in production by deploying AI agents at scale and ensuring they operate reliably and safely in real-world environments,” says Qaiser Habib, head of Canada engineering at Snowflake.
The pattern is already visible in large technology companies.
Salesforce, for example, has publicly discussed “rebalancing” its workforce as it invests in artificial intelligence. CEO Marc Benioff has described cutting some traditional roles while hiring aggressively for AI-focused engineers and teams building the company’s Agentforce platform.
The change mirrors what many organizations in the Snowflake research describe, which is fewer roles tied to routine development tasks, and more demand for engineers who can build, monitor, and scale AI systems.
That focus is changing how teams are structured. Habib says organizations are introducing new responsibilities around system design, evaluation, and control. Companies are also building expertise around the infrastructure that supports AI agents, including integrations, security, and performance.
Anyone who watched the cloud boom of the 2010s will recognize the pattern.
When cloud computing became widespread, companies created new teams responsible for infrastructure, automation, and security. AI is prompting a similar kind of reorganization within engineering groups.
Software development itself is changing
Ask a developer what slows down a release and the answer usually involves code reviews, testing, and a few bugs that appear at the worst possible moment. The modern developer’s workflow is becoming an exercise in “AI orchestration.”
Developers are using AI systems to help generate code, review pull requests, flag potential issues, and monitor performance once software is running. The research suggests the shift is already well underway, with 48% of code now generated with AI assistance, the human role is pivoting toward oversight.
“AI is becoming embedded across the entire software development lifecycle,” says Habib.
Currently, the technology’s strongest foothold is in analytics (71%), code reviews (66%), and generation (65%). Many organizations say the tools are speeding up the work developers already do.
Eighty per cent report faster development velocity, while 76% say AI-assisted development has reduced costs.
The technology is also changing how teams manage quality.
More than eight in 10 organizations report improvements in testing and bug detection when AI coding tools are used, and 80% also say those systems help improve overall code quality.
That changes where developers spend their time. Instead of writing every line manually, many teams now rely on AI systems to generate or suggest code while developers focus on architecture decisions, business logic, and oversight.
“Developer roles are evolving from primarily writing code to defining architecture, business logic, and guardrails for AI-assisted systems,” says Habib.
Testing is moving earlier in the development process as well. AI tools can scan code continuously, flagging issues while software is still being written instead of after a release candidate is built.
AI becomes another coworker, reviewing code, surfacing problems, and helping teams move faster from idea to production.
As engineering teams stabilize these internal tools, the focus is shifting from the back-end to the front office.
Canadian companies are moving faster on customer-facing AI
Once companies begin using AI across engineering teams, the next question is where else could this work?
Attention typically shifts to the customers.
The research suggests Canadian companies are moving quickly in this direction, with 45% of Canadian respondents saying their organizations are prioritizing generative AI tools in front of customers. Globally, that figure sits at 36%.
AI systems can answer routine questions, surface account details, or guide customers through common tasks while human agents step in for more complex issues.
“These areas offer clear, immediate returns,” says Shannon Katschilo, country manager for Canada at Snowflake. “Particularly through service automation and improved customer support, making them a practical entry point for many organizations adopting AI.”
But some companies are already pushing further.
The research shows that 31% of Canadian respondents say their organizations already use agentic AI in production, while another 32% say they’re interested but “early in the adoption process.”
Those systems can complete more complex tasks. Instead of answering a single question, they can analyze data, generate recommendations, and trigger follow-up actions across different systems.
Scaling AI still depends on the foundations
As AI tools move deeper into everyday work, many organizations are discovering that deploying them is one thing. Making them reliable across an entire company is the hard part.
The report found that 96% of organizations face obstacles when trying to expand AI initiatives. In other words, basically everyone.
Most of those challenges have little to do with the algorithms themselves. The difficulty sits within the data companies already own.
Systems hold information in different formats. Quality varies from one dataset to another. Much of the data organizations collect has never been prepared in a way that AI systems can easily use.
Governance quickly enters the conversation as well. When AI systems generate insights, respond to customers, or trigger automated actions, companies need to know exactly what information those systems can access and how decisions are being made.
“Organizations that successfully scale AI combine ambition with strong data foundations,” says Katschilo. “The Canadian companies seeing the most value, especially with autonomous agents, are those investing in high-quality data and the workforce skills needed to turn AI capabilities into measurable business impact.”
Even with the rapid pace of AI development, those fundamentals still determine which organizations see real results.
A developer opens that pull request. An AI assistant suggests a block of code while another system checks the logic and flags a potential bug before anyone else on the team sees it.
These are now routine situations.
The organizations turning those moments into real value are the ones that prepared the groundwork long before the AI arrived.
Final shotsAI is already reshaping engineering teams. Across the organizations surveyed, 77% report job creation tied to AI while 46% report role reductions, often inside the same technical groups.
Canadian companies are pushing AI into customer experiences quickly. Forty-five per cent are prioritizing the use of AI in customer-facing tools, higher than the 35% global average.
Scaling AI still comes down to fundamentals. Ninety-six per cent of organizations report obstacles when expanding AI initiatives, most tied to data quality, governance, and skills.
Written ByJennifer Friesen
Jennifer Friesen is Digital Journal's associate editor and content manager based in Calgary.
By Jennifer Friesen
DIGITAL JOURNAL
March 12, 2026
Photo by Ben Iwara on Unsplash
A software developer prepares a pull request and pauses for a moment. Much of the code in the update came from an AI assistant.
The system has already flagged a potential issue and suggested a fix before anyone else on the team reviews the change.
Anyone who has worked around software teams knows pull requests can trigger long review threads and days of back-and-forth. Now an AI assistant often joins the discussion, generating code and flagging problems before another developer even opens the thread.
According to new global research from Snowflake and Omdia, nearly half of the code written inside organizations today is generated with AI assistance.
Technical teams are already reorganizing around that reality, often discovering the same tools creating new roles are eliminating others. The research shows engineering groups both hiring and eliminating roles as AI tools become embedded in everyday workflows.
In Canada, organizations are prioritizing these shifts at the front door, moving faster than global peers to target customer-facing experiences.
For many organizations, the technology shaping how software gets built is now beginning to form how customers interact with the business as well.
Engineering teams are reorganizing around AI systems
The report surveyed more than 2,000 business and technology leaders across 10 countries. Among those organizations, 77% say AI adoption has created jobs somewhere in their workforce, while 46% say roles have been eliminated.
Many companies report both outcomes at once.
This paradox is most visible in technical departments. IT operations, cybersecurity, and software development report some of the largest job gains tied to AI adoption (at 56%, 46%, and 38% respectively). Those same functions also report some of the biggest reductions.
In practice, AI is taking over certain tasks while creating new work around building and operating AI systems.
“Engineering teams are increasingly focused on making AI work in production by deploying AI agents at scale and ensuring they operate reliably and safely in real-world environments,” says Qaiser Habib, head of Canada engineering at Snowflake.
The pattern is already visible in large technology companies.
Salesforce, for example, has publicly discussed “rebalancing” its workforce as it invests in artificial intelligence. CEO Marc Benioff has described cutting some traditional roles while hiring aggressively for AI-focused engineers and teams building the company’s Agentforce platform.
The change mirrors what many organizations in the Snowflake research describe, which is fewer roles tied to routine development tasks, and more demand for engineers who can build, monitor, and scale AI systems.
That focus is changing how teams are structured. Habib says organizations are introducing new responsibilities around system design, evaluation, and control. Companies are also building expertise around the infrastructure that supports AI agents, including integrations, security, and performance.
Anyone who watched the cloud boom of the 2010s will recognize the pattern.
When cloud computing became widespread, companies created new teams responsible for infrastructure, automation, and security. AI is prompting a similar kind of reorganization within engineering groups.
Software development itself is changing
Ask a developer what slows down a release and the answer usually involves code reviews, testing, and a few bugs that appear at the worst possible moment. The modern developer’s workflow is becoming an exercise in “AI orchestration.”
Developers are using AI systems to help generate code, review pull requests, flag potential issues, and monitor performance once software is running. The research suggests the shift is already well underway, with 48% of code now generated with AI assistance, the human role is pivoting toward oversight.
“AI is becoming embedded across the entire software development lifecycle,” says Habib.
Currently, the technology’s strongest foothold is in analytics (71%), code reviews (66%), and generation (65%). Many organizations say the tools are speeding up the work developers already do.
Eighty per cent report faster development velocity, while 76% say AI-assisted development has reduced costs.
The technology is also changing how teams manage quality.
More than eight in 10 organizations report improvements in testing and bug detection when AI coding tools are used, and 80% also say those systems help improve overall code quality.
That changes where developers spend their time. Instead of writing every line manually, many teams now rely on AI systems to generate or suggest code while developers focus on architecture decisions, business logic, and oversight.
“Developer roles are evolving from primarily writing code to defining architecture, business logic, and guardrails for AI-assisted systems,” says Habib.
Testing is moving earlier in the development process as well. AI tools can scan code continuously, flagging issues while software is still being written instead of after a release candidate is built.
AI becomes another coworker, reviewing code, surfacing problems, and helping teams move faster from idea to production.
As engineering teams stabilize these internal tools, the focus is shifting from the back-end to the front office.
Canadian companies are moving faster on customer-facing AI
Once companies begin using AI across engineering teams, the next question is where else could this work?
Attention typically shifts to the customers.
The research suggests Canadian companies are moving quickly in this direction, with 45% of Canadian respondents saying their organizations are prioritizing generative AI tools in front of customers. Globally, that figure sits at 36%.
AI systems can answer routine questions, surface account details, or guide customers through common tasks while human agents step in for more complex issues.
“These areas offer clear, immediate returns,” says Shannon Katschilo, country manager for Canada at Snowflake. “Particularly through service automation and improved customer support, making them a practical entry point for many organizations adopting AI.”
But some companies are already pushing further.
The research shows that 31% of Canadian respondents say their organizations already use agentic AI in production, while another 32% say they’re interested but “early in the adoption process.”
Those systems can complete more complex tasks. Instead of answering a single question, they can analyze data, generate recommendations, and trigger follow-up actions across different systems.
Scaling AI still depends on the foundations
As AI tools move deeper into everyday work, many organizations are discovering that deploying them is one thing. Making them reliable across an entire company is the hard part.
The report found that 96% of organizations face obstacles when trying to expand AI initiatives. In other words, basically everyone.
Most of those challenges have little to do with the algorithms themselves. The difficulty sits within the data companies already own.
Systems hold information in different formats. Quality varies from one dataset to another. Much of the data organizations collect has never been prepared in a way that AI systems can easily use.
Governance quickly enters the conversation as well. When AI systems generate insights, respond to customers, or trigger automated actions, companies need to know exactly what information those systems can access and how decisions are being made.
“Organizations that successfully scale AI combine ambition with strong data foundations,” says Katschilo. “The Canadian companies seeing the most value, especially with autonomous agents, are those investing in high-quality data and the workforce skills needed to turn AI capabilities into measurable business impact.”
Even with the rapid pace of AI development, those fundamentals still determine which organizations see real results.
A developer opens that pull request. An AI assistant suggests a block of code while another system checks the logic and flags a potential bug before anyone else on the team sees it.
These are now routine situations.
The organizations turning those moments into real value are the ones that prepared the groundwork long before the AI arrived.
Final shotsAI is already reshaping engineering teams. Across the organizations surveyed, 77% report job creation tied to AI while 46% report role reductions, often inside the same technical groups.
Canadian companies are pushing AI into customer experiences quickly. Forty-five per cent are prioritizing the use of AI in customer-facing tools, higher than the 35% global average.
Scaling AI still comes down to fundamentals. Ninety-six per cent of organizations report obstacles when expanding AI initiatives, most tied to data quality, governance, and skills.
Written ByJennifer Friesen
Jennifer Friesen is Digital Journal's associate editor and content manager based in Calgary.
By Dr. Tim Sandle
SCIENCE EDITOR
DIGITAL JOURNAL
March 11, 2026
A street in London. Image by Tim Sandle
With AI-driven job disruption is now spreading beyond traditional technology firms and into Wall Street, where financial giants like Morgan Stanley have begun cutting thousands of roles as artificial intelligence is steadily reducing the need for large operational teams handling manual tasks. This is outlined in a new report exploring the scale of global tech industry layoffs in 2026.
AI = job layoffs?
Mounting warnings from business leaders and economists point to artificial intelligence as a key accelerator of these layoff waves, with companies restructuring around automation, machine learning, and efficiency gains putting not only individual roles but entire job functions at risk.
To determine which companies led 2026’s biggest job cuts, the team at RationalFX compiled layoff data from multiple verified sources, including U.S. WARN notices, TrueUp, TechCrunch, and the Layoffs.fyi tracker, covering announcements made since the start of 2026.
Data shows that around 9,238, or about 20% of the 45,363 tech layoffs recorded worldwide since the start of the year, have been linked to AI implementation and organisational restructuring. The largest contributor to these reductions is the American technology firm Block (4,000 layoffs), whose CEO, Jack Dorsey, said in a post on social media that the decision was not driven by financial difficulty, but by the growing capability of AI tools to perform a wider range of tasks.
As a result, the company is significantly reducing its workforce, from roughly 10,000 employees to about 6,000, as it shifts its strategic focus more heavily towards AI.
Tech Companies With the Most Layoffs Due to AI in 2026
- Block – 4,000 layoffs
- WiseTech Global – 2,000 layoffs
- Livspace – 1,000 layoffs
- eBay – 800 layoffs
- Pinterest – 675 layoffs
- ANGI Homeservices – 350 layoffs
- Oracle – 254 layoffs
- MercadoLibre – 119 layoffs
Following last year’s restructuring at the American technology firm Block, which saw around 8% of its workforce (931 employees) laid off, CEO Jack Dorsey recently announced that the company would be reducing headcount by a further 40%, cutting 4,000 of its current 10,000 roles as part of AI-related automation and restructuring efforts. This represents the most significant wave of AI-driven layoffs so far in 2026.
Australian logistics software developer WiseTech Global announced 2,000 layoffs as part of a sweeping AI-driven restructuring programme aimed at transforming how its logistics platforms are built and maintained. Company leadership argued that advances in generative AI and large language models are dramatically increasing software engineering productivity, with executives stating that traditional approaches to writing and maintaining code are becoming increasingly obsolete.
Singapore-based home design platform Livspace has cut 1,000 jobs as part of its push to accelerate the adoption of AI across its digital interior-design marketplace. Executives have framed the layoffs as part of a shift toward a more technology-driven platform capable of delivering faster and more personalised design services to customers.
In addition, e-commerce platform eBay has also announced 800 layoffs, with the company increasingly investing in AI tools designed to automate product listings, pricing optimisation, and customer-service workflows. Social media platform Pinterest has confirmed around 675 layoffs, affecting roughly 15% of its workforce.
“This trend reflects a broader dynamic: firms are investing heavily in AI‑powered tools and infrastructure to boost efficiency, but the transition is also disrupting traditional job structures, as many entry-level positions have now become obsolete. As AI takes on more responsibilities once handled by humans, the question is no longer if jobs will change, but when and how”, says Alan Cohen, analyst at RationalFX, in the report.
Op-Ed: Atlassian layoffs, Software as a Service, and the scary realities of AI coding
By Paul Wallis
Image: — © Digital Journal
Australian/American Software as a Service (SaaS) giant Atlassian laid off 1600 people this week in what appears to be a reluctant but painful repositioning exercise.
The current state of coding is core business for Atlassian. Software as a Service is a huge sector right in the centre of the storm created by AI coding. Atlassian is naturally trying to adapt to an emerging and somewhat neurotic market.
The big picture is chaotic, to put it politely. Understandably, the many dimensions of AI coding are creating havoc and obvious indecision in business software development. AI can write code, sure, but there are many related, potentially expensive issues.
There’s an irritatingly familiar back story to this mess. The recent big selloff in software development stocks underlines a further fundamental problem. The market seems to think AI will do it all.
It can’t, it won’t, and it shouldn’t. This generation of AI is barely potty-trained. It’s clunky, and it’s error-prone. Just tacking on an LLM and expecting vibe coding to do it all is far beyond absurd. It’s dangerous.
If you think someone’s semiconscious, underqualified level of literacy instantly translates into telling AI to write great code, you’re not doing a lot of thinking. Of course, it won’t turn out pristine, perfect code for all occasions. You might get ballpark, but you’re a long way from business-standard trustworthy code.
AI isn’t particularly literate anyway. Pedantic, yes. Inflexible, yes. Linguistic syntax errors as code are still potential syntax failures. This is nitpicking at a truly obsessive level, but if you don’t pick the nits properly, your code won’t run at all. Imagine an entire language as an opportunity for coding bugs.
Software as a service is essentially the customization of software for business purposes. It can’t be a guessing game. It has to work well within the operational metrics and performance demands of businesses. That’s what SaaS is all about.
There’s a certain karmic irony in the fact that so soon after the software selloff that AI coding is now creating havoc in big businesses like Amazon. If you’re seeing dollar signs heading for the exits, bingo.
Add to this the equally ironic fact that AI has a newly discovered talent for finding coding bugs. At the same time, Anthropic has created a code review tool to manage AI coding quality. What a coincidence.
If you’re somehow getting the impression from this rhapsody of realism that AI needs strict supervision, you’ll at least avoid going broke.
An enchanting narrative for the curious about how much damage a simple glitch in software can do:
I supervised a project that issued notices trying to extract statutory fees and document lodgements from the merry burghers of Sydney. The recipients were accountants, lawyers, and corporate managers. We trustingly issued 40,000 notices to the people who had already paid and lodged their documents, but not the ones who hadn’t. It was as much fun as it sounds.
At the same time, the database was erasing old data when it entered new data. It was bliss, and it took weeks to fix. It almost derailed the project entirely.
We never got an answer as to exactly how this dog’s breakfast happened, but could a few lines of code have done it? Yes. Did we get threatened with lawsuits? Of course. Feeling better about your coding options? Point made.
Meanwhile, back at the software situation:
You don’t have to go back to writing code on stone tablets.
You do need an absolutely idiot-proof, properly tested regime for managing code quality.
You will definitely need SaaS as a built-in fixer.
Do NOT trust AI coding to be some sort of fairy god-agent for your business. Check everything ruthlessly.
_________________________________________________________
Disclaimer
The opinions expressed in this Op-Ed are those of the author. They do not purport to reflect the opinions or views of the Digital Journal or its members.
By Paul Wallis
EDITOR AT LARGE
DIGITAL JOURNAL
March 12, 2026Image: — © Digital Journal
Australian/American Software as a Service (SaaS) giant Atlassian laid off 1600 people this week in what appears to be a reluctant but painful repositioning exercise.
The current state of coding is core business for Atlassian. Software as a Service is a huge sector right in the centre of the storm created by AI coding. Atlassian is naturally trying to adapt to an emerging and somewhat neurotic market.
The big picture is chaotic, to put it politely. Understandably, the many dimensions of AI coding are creating havoc and obvious indecision in business software development. AI can write code, sure, but there are many related, potentially expensive issues.
There’s an irritatingly familiar back story to this mess. The recent big selloff in software development stocks underlines a further fundamental problem. The market seems to think AI will do it all.
It can’t, it won’t, and it shouldn’t. This generation of AI is barely potty-trained. It’s clunky, and it’s error-prone. Just tacking on an LLM and expecting vibe coding to do it all is far beyond absurd. It’s dangerous.
If you think someone’s semiconscious, underqualified level of literacy instantly translates into telling AI to write great code, you’re not doing a lot of thinking. Of course, it won’t turn out pristine, perfect code for all occasions. You might get ballpark, but you’re a long way from business-standard trustworthy code.
AI isn’t particularly literate anyway. Pedantic, yes. Inflexible, yes. Linguistic syntax errors as code are still potential syntax failures. This is nitpicking at a truly obsessive level, but if you don’t pick the nits properly, your code won’t run at all. Imagine an entire language as an opportunity for coding bugs.
Software as a service is essentially the customization of software for business purposes. It can’t be a guessing game. It has to work well within the operational metrics and performance demands of businesses. That’s what SaaS is all about.
There’s a certain karmic irony in the fact that so soon after the software selloff that AI coding is now creating havoc in big businesses like Amazon. If you’re seeing dollar signs heading for the exits, bingo.
Add to this the equally ironic fact that AI has a newly discovered talent for finding coding bugs. At the same time, Anthropic has created a code review tool to manage AI coding quality. What a coincidence.
If you’re somehow getting the impression from this rhapsody of realism that AI needs strict supervision, you’ll at least avoid going broke.
An enchanting narrative for the curious about how much damage a simple glitch in software can do:
I supervised a project that issued notices trying to extract statutory fees and document lodgements from the merry burghers of Sydney. The recipients were accountants, lawyers, and corporate managers. We trustingly issued 40,000 notices to the people who had already paid and lodged their documents, but not the ones who hadn’t. It was as much fun as it sounds.
At the same time, the database was erasing old data when it entered new data. It was bliss, and it took weeks to fix. It almost derailed the project entirely.
We never got an answer as to exactly how this dog’s breakfast happened, but could a few lines of code have done it? Yes. Did we get threatened with lawsuits? Of course. Feeling better about your coding options? Point made.
Meanwhile, back at the software situation:
You don’t have to go back to writing code on stone tablets.
You do need an absolutely idiot-proof, properly tested regime for managing code quality.
You will definitely need SaaS as a built-in fixer.
Do NOT trust AI coding to be some sort of fairy god-agent for your business. Check everything ruthlessly.
_________________________________________________________
Disclaimer
The opinions expressed in this Op-Ed are those of the author. They do not purport to reflect the opinions or views of the Digital Journal or its members.
Op-Ed: Jobs vs AI — Lack of planning equals socioeconomic absurdity
By Paul Wallis
By Paul Wallis
EDITOR AT LARGE
DIGITAL JOURNAL
March 9, 2026
Image: © AFP
Replacing people with AI already doesn’t work on too many levels. Fixing AI-generated problems has become a small global sector overnight. The supposed efficiencies are being eaten up by the uncompromising realities. Yet jobs continue to be lost to AI, with more being shed regularly.
From a purely technological perspective, it’s somehow worse. This generation of AI is primitive. If full automation is a 10, this barely scrapes in as a 2/10.
The fallacies are piling up as AI exposes its own weaknesses daily. Anthropic, parent of Claude AI, has recently published an analysis of the labor market impacts of AI with indicative metrics.
This study is specifically based on displacement risk. The Key Findings section of the study is mercifully brief, but it’s extremely interesting. Critically, they found that a general profile of lower growth in occupations through to 2034. White-collar employees, notably older executive-level females, seem vulnerable.
Anthropic habitually doesn’t sing its own praises. They try to be objective. This is a very useful analysis, with backup from the BLS and includes performance parameters. The probability is that it’s on the money.
Overall exposure to AI across the economy is erratically applied. AI isn’t delivering much in terms of ROI, either. In areas like banking or finance, the AI number-crunching delivers value. In other sectors, not much is happening to the point of anybody reporting it or starting a cult, at least.
I’ve been watching this for some time, and the pattern is simple:
No clearly mapped-out roles and tasks are defined in the preamble
Costing is all over the shop.
Introduction and fanfare as jobs go out the window.
Trying to fit people, businesses, clients, markets, and ROI on the same page in real time.
A mess.
Now imagine this wholesome and tediously effervescent total lack of results applied to a whole global economy. The alarm bells are ringing, but they’re making more noise than sense. There are no visible Exit signs in the dunghill. When committed, you sink or swim.
The problem is a systemic lack of foresight. What’s the big vision?
A stunning tableaux of a smug and smarmy patrician world with everyone else consigned to appropriate levels of squalor as goods and services go comatose? A bit one-track-minded, isn’t it? Or is it just a traditional lack of ideas?
The economy crashes with assets bought up dirt cheap or simply repossessed? But now there’s no economy. Not even anyone to steal from. Oh, hang on. That’s already happening, isn’t it? Ask your helpful local organized criminals or other starry-eyed idealists for details.
No more pesky people doing the work and expecting to get paid for it? Replacing wage outlays with AI is even more naïve. AI, like all technologies, is high maintenance and highly cost intensive. Outlay at this level can be lethal. Obsolescence and innovation will destroy the first acquisitions until a plateau of standardized technologies is reached.
Put it this way – If the economy collapses, so does society and so does the population. A real economic meltdown could be much worse than World War 3. Imbecility incarnate.
The word “socioeconomic” isn’t a glued-together coincidence of terminology. The two are joined at the hip in the real world.
This is the current doomsday scenario, and half-baked as doomsday scenarios are, it’s already looking weird.
Specifically, it’s looking this weird. The UK is looking at Universal Basic Income as an option, according to the Financial Times. The UK is a big economy. It’d be a huge shift. From Thatcherism to a UBI is like America’s Republicans turning communist.
Such a drastic measure also reflects the current collapse of traditional capitalism and neoliberalism as the hopelessly out-of-control cost of living continues to rot away their structures. These two tired old sacred cattle of political self-righteousness aren’t famous for solving problems, just causing them.
The future never gets a word in. This same complete lack of planning has put three generations on the scrapheap. The Millennials, Zoomers, and Gen Alpha are already broke. Mass unemployment is hardly likely to help.
Take off the blindfolds and look where you’re going.
_______________________________________________________
Disclaimer
The opinions expressed in this Op-Ed are those of the author. They do not purport to reflect the opinions or views of the Digital Journal or its members.
March 9, 2026
Image: © AFP
Replacing people with AI already doesn’t work on too many levels. Fixing AI-generated problems has become a small global sector overnight. The supposed efficiencies are being eaten up by the uncompromising realities. Yet jobs continue to be lost to AI, with more being shed regularly.
From a purely technological perspective, it’s somehow worse. This generation of AI is primitive. If full automation is a 10, this barely scrapes in as a 2/10.
The fallacies are piling up as AI exposes its own weaknesses daily. Anthropic, parent of Claude AI, has recently published an analysis of the labor market impacts of AI with indicative metrics.
This study is specifically based on displacement risk. The Key Findings section of the study is mercifully brief, but it’s extremely interesting. Critically, they found that a general profile of lower growth in occupations through to 2034. White-collar employees, notably older executive-level females, seem vulnerable.
Anthropic habitually doesn’t sing its own praises. They try to be objective. This is a very useful analysis, with backup from the BLS and includes performance parameters. The probability is that it’s on the money.
Overall exposure to AI across the economy is erratically applied. AI isn’t delivering much in terms of ROI, either. In areas like banking or finance, the AI number-crunching delivers value. In other sectors, not much is happening to the point of anybody reporting it or starting a cult, at least.
I’ve been watching this for some time, and the pattern is simple:
No clearly mapped-out roles and tasks are defined in the preamble
Costing is all over the shop.
Introduction and fanfare as jobs go out the window.
Trying to fit people, businesses, clients, markets, and ROI on the same page in real time.
A mess.
Now imagine this wholesome and tediously effervescent total lack of results applied to a whole global economy. The alarm bells are ringing, but they’re making more noise than sense. There are no visible Exit signs in the dunghill. When committed, you sink or swim.
The problem is a systemic lack of foresight. What’s the big vision?
A stunning tableaux of a smug and smarmy patrician world with everyone else consigned to appropriate levels of squalor as goods and services go comatose? A bit one-track-minded, isn’t it? Or is it just a traditional lack of ideas?
The economy crashes with assets bought up dirt cheap or simply repossessed? But now there’s no economy. Not even anyone to steal from. Oh, hang on. That’s already happening, isn’t it? Ask your helpful local organized criminals or other starry-eyed idealists for details.
No more pesky people doing the work and expecting to get paid for it? Replacing wage outlays with AI is even more naïve. AI, like all technologies, is high maintenance and highly cost intensive. Outlay at this level can be lethal. Obsolescence and innovation will destroy the first acquisitions until a plateau of standardized technologies is reached.
Put it this way – If the economy collapses, so does society and so does the population. A real economic meltdown could be much worse than World War 3. Imbecility incarnate.
The word “socioeconomic” isn’t a glued-together coincidence of terminology. The two are joined at the hip in the real world.
This is the current doomsday scenario, and half-baked as doomsday scenarios are, it’s already looking weird.
Specifically, it’s looking this weird. The UK is looking at Universal Basic Income as an option, according to the Financial Times. The UK is a big economy. It’d be a huge shift. From Thatcherism to a UBI is like America’s Republicans turning communist.
Such a drastic measure also reflects the current collapse of traditional capitalism and neoliberalism as the hopelessly out-of-control cost of living continues to rot away their structures. These two tired old sacred cattle of political self-righteousness aren’t famous for solving problems, just causing them.
The future never gets a word in. This same complete lack of planning has put three generations on the scrapheap. The Millennials, Zoomers, and Gen Alpha are already broke. Mass unemployment is hardly likely to help.
Take off the blindfolds and look where you’re going.
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Disclaimer
The opinions expressed in this Op-Ed are those of the author. They do not purport to reflect the opinions or views of the Digital Journal or its members.
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