#generativeai

Any news of layoffs from AI taking over entry level roles?

According to this news article for reasons. Yay or Nay?

https://dailyvoice.com/md/elkton/morgan-stanley-to-lay-off-thousands-of-employees-here-are-reasons-why/

Shifting Priorities: The bank is reallocating resources to high-growth areas.

Performance Reviews: Some layoffs are tied to individual job performance.

Operational Efficiency: The bank is managing costs amid economic pressures.

AI, Automation: Increased use of artificial intelligence is automating routine tasks, reducing the need for certain roles.

Dan was right with AI, employees will be replaced.

3 hours ago - Block Inc. announced a 40% workforce reduction to adapt to an AI-driven business model, triggering a 20-24% surge in its stock price.

Forget getting laid off, people will start just leaving on their own if we’re too lazy to write appraisals. Come on, how did they get hired if they can’t pronounce the words AI gave them. And don’t forget that clown last night who was talking all that sm--k about being somewhere outside of NY yet ran sh-t in the CSSC. What trash have we hired for TLs.

The Cisco ELT and multiple layers of bloat beneath them are waking up to the fact that the very AI they are so aggressively promoting will completely destroy their lives.

Each and every one of Cisco's business units are being actively replaced by a small startup using a tech stack that costs them less than $5,000. This is reducing margins.

Every white collar office worker that gets laid off is fewer access points, switches, routers and software seats. This is reducing recurring revenue.

Cisco is, in effect, destroying it's own customer base.

Expect the ELT to start aggressively rewarding themselves with stock buybacks, increased executive compensation, reduced benefits to employees and mass layoffs to provide immediate short term boosts to the stock price.

Got to love it. Another vzw outage and going to be out for 12-14 hours and once again no credits being offered. But hey let’s keep riffing, outsourcing and depending on AI to run the company. It sure seems to be doing wonders for our customers and business model.

I think many people do not get it - AI will impact many jobs for the simple reason that in the past 30 years we created lots of so-called bullsh-t jobs and many of you are doing them. And AI will ki-l those, but that is ok. Personal assistants, auditors, project managers, Excel monkeys, recruiters, HR, etc. to name a few - all will be gone soon.

The Data & AI organization has been in steady decline since Kat H left, and it worsened under Ronke’s leadership. Key talent was laid off without strategic foresight, institutional knowledge was lost, and execution quality collapsed.

For 18 months there has been no clear vision, no strong cadence, and no accountability. After the JC merger, politics and power dynamics overtook delivery.

The team focuses on frameworks and ingestion optics while lacking fundamental data understanding, quality discipline, and business alignment.

There is hope new leadership restores clarity, accountability, and real data leadership — because what we have today is not working.

Let’s break even more stuff by depending on AI for coding and everything else. Nothing like wrong answers.

The U.S. National debt is -

(Currently) $38.7 Trillion (and rising) per U.S. usdebtclock.

AI will take away (most) computer dependent jobs in the future (not all) but enough for the Unemployment rate to spike significantly thus reducing Tax revenue.

So Income Tax, and Corporate Tax will (need) to be Increased (especially) on Corporations, and the wealthy; to be able to cover it.

Reality is, even with the Trump Import tariffs that were nullified ($200.0 Billion a year with refunds of $125.0+ Billion in process back to Corporations-businesses) by the Supreme Court it wouldn't even faze the (current) $990.0 Billion (and rising) a year in Interest paid by U.S. taxpayers to outside Investors that finance the U.S. National debt.

These are the facts.

Here’s where it goes wrong:

1. It ki-ls specificity

Good reviews are specific.
Bad reviews say: “Consistently demonstrates strong teamwork and leadership.”

That’s filler. That’s what AI defaults to if you don’t feed it real input. And vague praise or criticism helps no one grow.

If a manager can’t point to:
• A project
• A missed deadline
• A tough conversation handled well
…then the review is noise.

1. It removes accountability

A performance review is leadership. If you outsource your judgment, you’re outsourcing responsibility. That’s weak management.

Employees can tell when feedback is generic. It erodes trust fast.

1. It avoids hard conversations

The real value of a review isn’t the document. It’s the conversation.

AI makes it tempting to soften, blur, or “corporate-speak” real issues instead of saying:

“You’re strong technically, but you’re not stepping up in meetings.”

Growth requires clarity. Not polish.

1. It creates legal and ethical risk

AI can unintentionally:
• Introduce biased language
• Overstandardize nuance
• Use phrasing that sounds formulaic and defensible instead of human and accurate

That’s risky in performance documentation.

It’s a complete joke. My boss is doing the exact same thing. I genuinely don’t think he’s read anything longer than a text message in his life. At this point, it’s no surprise that intellectual laziness has become a full-blown pandemic in America.

Apparently this, combined with tariff garbage, spooked the market. It is a long read but it helps explain some of investor sentiment right now. It seems like the markets are exceedingly twitchy.

https://www.citriniresearch.com/p/2028gic

And for those wondering about the credibility of Cintrini as a source of financial information, the piece got coverage in its very own front page segment in the WSJ today.

Do they bring in a lot of money? The total comp without stocks is going for 200$k+ for AI roles [i8 and above]

I'm thinking of internally applying but can't seem to shake the feeling there's a catch I'm missing.

Is this department prone to layoffs ? Any thoughts ?

First, there was collaboration.....(we are all in this together)
Then there was automation....(python, ansible rah rah rah!)
Then came agile......(hire scrum masters so we have have two bosses!)
Then came silos (due to agile...we don't care about anything outside our 2 week sprint!)
Now comes AI......(humans BAD, AI is good!)

Meanwhile, BE gets 22M per year, chainsaw gets 40M per year...and
...I get laid off!

Use of Humanas internal AI system will now impact performance reviews. Its recommended to use it at least once a day to avoid negative impact to your end of year.

Our AI su-ks so bad Japan is forcing the use of it.

https://fortune.com/2026/02/17/ai-productivity-paradox-ceo-study-robert-solow-information-technology-age/

Teams that have not begun deploying practical AI solutions for workflow automation, decision support, or cost optimization should expect increasing pressure to do so. Enterprise trends show AI initiatives are quickly shifting from optional innovation projects to core operational expectations tied to efficiency and budget discipline.

Waiting too long typically reduces a team’s ability to influence how AI is implemented. Instead of shaping use cases that fit their processes, they risk having centrally mandated tools introduced on compressed timelines.

Although current AI intake and governance processes may feel slow or overly complex, that friction is temporary. As organizations pursue measurable productivity improvements, broader and more standardized AI deployment is likely approaching. You have been warned!

I'm searching for another job, and that helps. Somewhat. Because it's also made me realize just how hard finding one will be. The constant talk about offshoring and AI is starting to panic me. I have a family. A mortgage. Bills that keep climbing. The fear is almost paralyzing. And please don't tell me to just "deal with it." You all know we're hanging by a thread. So, how are you dealing with it?

“Lenovo is restructuring its Infrastructure Solutions Group (ISG) to sharpen its focus on AI server, storage, and edge computing, driven by a $285 million charge in Q3 FY25/26. This initiative aims to improve profitability, accelerate AI growth, and reduce annual costs by over $200 million within three years”…$200 mil in cost reduction over three years means a whole lot of us are going bye-bye.

We were denied access to chatgpt or other AI sites before except spark. Today they are allowing chatgpt and copilot usage within the enterprise. Is Elevance moving towards AI for everything and see if they can reduce day to day work time or people? Boon or bane for employees

Walk into any corporate office, and you’ll hear the same anxious conversation: Will AI eliminate white-collar jobs?

The optimists insist that new jobs will emerge to replace the ones we lose—after all, it has happened in previous tech revolutions. Pragmatists argue the workforce will simply become more productive with artificial intelligence, creating more value with minimal job cuts. And the pessimists fear entry-level knowledge workers will become obsolete altogether.

But this debate misses a crucial dynamic. Right now, workers are potentially training AI how to make them obsolete. And they often don’t realize it.

The kind of AI used by companies, called an enterprise AI system, can capture everything you do at work and use that information to train itself. These systems can record your interactions within the platform—the prompts you write, the documents you create, the queries you run.

In other words, the company can potentially track—and claim ownership of—every keystroke you make within the system, every idea you document there, every tool you build using that platform. It can identify what approaches worked best, what email language got responses and how you approached those clients. And all that knowledge can become part of the company AI, so it may eventually know, down to increasingly fine details, how you do your job.

Then comes the dangerous part for employees: The AI can pass that information along to anybody else who does your job, or in some cases just do the job itself. Over time, you could become a lot less valuable to your employer—and a lot more replaceable.

This dynamic may fundamentally change the relationship between employer and employee. The stakes are so high and so urgent that both sides are rushing to position (or protect) themselves. Executives are rapidly implementing enterprise AI systems, seeking productivity gains and competitive advantage—and they often aren’t disclosing the implications for job security and privacy. Meanwhile, at least some employees are secretly adopting personal AI tools, sometimes violating corporate policies, so that their employers can’t capture everything they know and do.
Capturing the essence

To understand what’s coming, you need to understand what enterprise AI systems actually are. These are different from the interfaces you use at home. Enterprise AI systems are platforms that integrate directly into corporate workflows—think of Microsoft Copilot embedded in Word, Excel and Outlook, or Salesforce’s Einstein AI woven into customer-relationship management. These systems sit inside the tools where you already work. And they can potentially capture much of your work within the platform, learning from many interactions, and embedding that knowledge into company-owned infrastructure.

What once lived only in employees’ heads, built through years of experience and hard-won expertise, is increasingly being institutionalized in real time. When you leave, at least some of your knowledge stays behind, embedded in systems that will be used by the AI and by your replacement (if a replacement is needed at all).

Imagine that you’re a senior software engineer debugging a system crash. You run a bunch of tests to figure out the problem, and when you discover the solution isn’t in the documentation, you develop a novel workaround. You share the solution with the company, obviously, but the expertise and techniques that you brought to the problem were all yours, in a fundamental way. You figured out the workaround because of what you know and how you work.
That is the way things used to be, anyway. When you do your work through enterprise AI, though, the system doesn’t just record your solution. It can capture your problem-solving approach: which questions you asked first, how you refined the search when initial attempts failed, potentially even the logical steps that led you from symptom to cause. The next time junior engineers face a similar crash, the system may be able to guide them through elements of the methodology you used.

You haven’t lost your expertise. But now the employer also has access to key aspects of that expertise, in a form it controls and can deploy to other employees without you. It has a partial blueprint for how you think, and some of the knowledge that once made you indispensable is now a reproducible company asset.
Making it personal

These revolutionary changes seem to put workers in a tight spot. But I believe employees have an alternative—one that isn’t easy, but could help move the power dynamic back in their favor. Specifically: Employees should consider avoiding their company AI systems when possible and use personal AI tools like ChatGPT, Claude, Gemini, Copilot or dozens of others.

These tools operate on completely different terms than enterprise AI. You access them directly. You own your prompts, your workflows, your customizations. The knowledge you create stays with you. Most critically, when you walk out the door, your AI-enhanced capabilities walk with you.

Maybe you’re required to use your company’s enterprise AI for client work. But all the strategic thinking you do before engaging with clients? Develop that using personal AI tools.

I spoke with a regional vice president at an energy company who does exactly that: He uses his firm’s enterprise system for required compliance and documentation, but develops new analytical approaches and tests complex decisions in personal AI tools. The novel insights stay his.
What can be done?

Using personal AI tools is just the first step employees should take, however. To really change the power dynamic, they can act on other fronts.

• Negotiate upfront. When joining a company, people should treat access to AI tools like intellectual-property ownership. Most employment agreements cover IP created on the job, but employees should dig further into a company’s policies before signing on: What gets captured through enterprise AI? How long is that data retained? Can you use personal AI tools for skill development? Can you request deletion of your contributions if you leave?

Most companies haven’t thought through these questions yet, which means there is room to establish reasonable boundaries before you’re locked in.

• Support collective action. Individual opt-out of AI is often impossible, so unions and professional associations need to pay attention. With collective bargaining, workers could demand transparency about the use of enterprise AI and demand fair compensation for the knowledge it gathers. Without collective power, individual employees will keep clicking “accept” on agreements that restructure their jobs simply because they have no alternative.

Concerted employee action may start to change the AI calculus. Employers may find that enterprise AI systems do capture knowledge, but at a steep cost: They may drive away the most talented employees, ones who realize they can build more valuable, portable capabilities with personal tools.

AI is breaking the traditional model of employment in real time faster than anyone realizes. The companies and employees who understand these dynamics will position themselves to capture AI’s benefits. Those who don’t may find themselves on the losing side of the biggest workplace shift in a generation.

This goes without saying, but everyone’s job is on the chopping block. They are hellbent on getting this AI stuff implemented ASAP. The most infuriating aspect is that these head honchos have NO CLUE about the technology, and they expect everyone to assist in their own demise. They know they want it, but they have no understanding of how it works. Now, these crybabies are complaining about security risks which Google poses AFTER they promoted that as one of the top reasons they are converting, besides “collaboration”. The next time your boss asks you to do something, say to them, “Let AI do it”. Then, when it gets it wrong, you can laugh in their faces and tell them to reap what they sow.

This is exactly what the plan has always been since AI came along.

https://www.wsj.com/lifestyle/careers/ai-knowledge-capture-employees-a69a0e1c

It isn’t whether artificial intelligence is going to replace them. It’s who will control the knowledge that companies capture from their employees.

By: Matthew Call
Feb. 15, 2026 12:00 pm ET

Walk into any corporate office, and you’ll hear the same anxious conversation: Will AI eliminate white-collar jobs?

The optimists insist that new jobs will emerge to replace the ones we lose—after all, it has happened in previous tech revolutions. Pragmatists argue the workforce will simply become more productive with artificial intelligence, creating more value with minimal job cuts. And the pessimists fear entry-level knowledge workers will become obsolete altogether.

But this debate misses a crucial dynamic. Right now, workers are potentially training AI how to make them obsolete. And they often don’t realize it.

The kind of AI used by companies, called an enterprise AI system, can capture everything you do at work and use that information to train itself. These systems can record your interactions within the platform—the prompts you write, the documents you create, the queries you run.

In other words, the company can potentially track—and claim ownership of—every keystroke you make within the system, every idea you document there, every tool you build using that platform. It can identify what approaches worked best, what email language got responses and how you approached those clients. And all that knowledge can become part of the company AI, so it may eventually know, down to increasingly fine details, how you do your job.

Then comes the dangerous part for employees: The AI can pass that information along to anybody else who does your job, or in some cases just do the job itself. Over time, you could become a lot less valuable to your employer—and a lot more replaceable.

This dynamic may fundamentally change the relationship between employer and employee. The stakes are so high and so urgent that both sides are rushing to position (or protect) themselves. Executives are rapidly implementing enterprise AI systems, seeking productivity gains and competitive advantage—and they often aren’t disclosing the implications for job security and privacy. Meanwhile, at least some employees are secretly adopting personal AI tools, sometimes violating corporate policies, so that their employers can’t capture everything they know and do.

Capturing the essence

To understand what’s coming, you need to understand what enterprise AI systems actually are. These are different from the interfaces you use at home. Enterprise AI systems are platforms that integrate directly into corporate workflows—think of Microsoft Copilot embedded in Word, Excel and Outlook, or Salesforce’s Einstein AI woven into customer-relationship management. These systems sit inside the tools where you already work. And they can potentially capture much of your work within the platform, learning from many interactions, and embedding that knowledge into company-owned infrastructure.

What once lived only in employees’ heads, built through years of experience and hard-won expertise, is increasingly being institutionalized in real time. When you leave, at least some of your knowledge stays behind, embedded in systems that will be used by the AI and by your replacement (if a replacement is needed at all).

Imagine that you’re a senior software engineer debugging a system crash. You run a bunch of tests to figure out the problem, and when you discover the solution isn’t in the documentation, you develop a novel workaround. You share the solution with the company, obviously, but the expertise and techniques that you brought to the problem were all yours, in a fundamental way. You figured out the workaround because of what you know and how you work.

That is the way things used to be, anyway. When you do your work through enterprise AI, though, the system doesn’t just record your solution. It can capture your problem-solving approach: which questions you asked first, how you refined the search when initial attempts failed, potentially even the logical steps that led you from symptom to cause. The next time junior engineers face a similar crash, the system may be able to guide them through elements of the methodology you used.

You haven’t lost your expertise. But now the employer also has access to key aspects of that expertise, in a form it controls and can deploy to other employees without you. It has a partial blueprint for how you think, and some of the knowledge that once made you indispensable is now a reproducible company asset.

Making it personal

These revolutionary changes seem to put workers in a tight spot. But I believe employees have an alternative—one that isn’t easy, but could help move the power dynamic back in their favor. Specifically: Employees should consider avoiding their company AI systems when possible and use personal AI tools like ChatGPT, Claude, Gemini, Copilot or dozens of others.

These tools operate on completely different terms than enterprise AI. You access them directly. You own your prompts, your workflows, your customizations. The knowledge you create stays with you. Most critically, when you walk out the door, your AI-enhanced capabilities walk with you.

Maybe you’re required to use your company’s enterprise AI for client work. But all the strategic thinking you do before engaging with clients? Develop that using personal AI tools.

I spoke with a regional vice president at an energy company who does exactly that: He uses his firm’s enterprise system for required compliance and documentation, but develops new analytical approaches and tests complex decisions in personal AI tools. The novel insights stay his.

What can be done?

Using personal AI tools is just the first step employees should take, however. To really change the power dynamic, they can act on other fronts.

• Negotiate upfront. When joining a company, people should treat access to AI tools like intellectual-property ownership. Most employment agreements cover IP created on the job, but employees should dig further into a company’s policies before signing on: What gets captured through enterprise AI? How long is that data retained? Can you use personal AI tools for skill development? Can you request deletion of your contributions if you leave?

Most companies haven’t thought through these questions yet, which means there is room to establish reasonable boundaries before you’re locked in.

• Support collective action. Individual opt-out of AI is often impossible, so unions and professional associations need to pay attention. With collective bargaining, workers could demand transparency about the use of enterprise AI and demand fair compensation for the knowledge it gathers. Without collective power, individual employees will keep clicking “accept” on agreements that restructure their jobs simply because they have no alternative.

Concerted employee action may start to change the AI calculus. Employers may find that enterprise AI systems do capture knowledge, but at a steep cost: They may drive away the most talented employees, ones who realize they can build more valuable, portable capabilities with personal tools.

AI is breaking the traditional model of employment in real time faster than anyone realizes. The companies and employees who understand these dynamics will position themselves to capture AI’s benefits. Those who don’t may find themselves on the losing side of the biggest workplace shift in a generation.

{Matthew Call is an associate professor in the department of management at Texas A&M University’s Mays Business School.}

Thank you, AI - I missed this somehow.

Omar Abbosh's £13m "golden hello" (announced Sept 2023):
A massive one-time buyout package in cash + restricted shares to compensate for what he "lost" leaving Microsoft. On top of his £1m base salary, up to £3m annual bonus, and huge LTIP grants. Pearson's own announcement framed it as "consistent with the 2023 remuneration policy" shareholders approved earlier that year—despite nearly half rejecting the prior CEO's package. Shares dropped ~5% on the news, and outlets like Reuters called it "baiting shareholders" while the company was still restructuring.

Severance policy gutted in 2023: Right around the same time (mid-2023, effective June 15 for some legacy plans like the old National Computer Systems severance), they terminated standard US severance benefits. Hit hard during layoffs—e.g., the former Pearson Online Learning Services (POLS)/Boundless Learning unit axed roughly half its staff in Aug 2023 with zero severance, no PTO payout, abrupt access cutoffs. Long-timers got nothing. Execs acknowledged in town halls that changes (like losing Maryville University contracts) were planned for months, yet rank-and-file got the short end while the new CEO rolls in with kingly compensation.

It's textbook "rules for thee, not for me"—workers lose protections and get zilch during cuts, but the C-suite gets multi-million welcomes to "lead the transformation."

Now Business Insider has learned that the layoffs didn't stop last month, as the company has quietly cut hundreds more jobs starting in February.

After noticing LinkedIn updates from several Salesforce employees, Business Insider sources confirmed widespread cuts, which they said involved fewer than 1,000 roles. Affected roles included marketing, product management, data analytics, and Salesforce's Agentforce AI generative AI product.

https://finance.yahoo.com/news/major-tech-firm-quietly-lays-033300885.html

Goodness, we are down another damn 5 percent on more AI misgivings

Any chance that India or the Philippines could keep Kerrins while she executes her global cheerleading tour? Many I believe would agree that AI could replace her role and save millions . Or a lower paid CIO in India could do the job since much of it is done in India and the person would be smarter.

People are really underestimating how much AI is driving job cuts. Salesforce reportedly used AI agents to lay off 5,000 employees across marketing, product management, data analytics, and even its own Agentforce AI division.

I'm not trying to be a Debbie downer here, but AI is no where near artificial intelligence but more of a language mirror able to apply some logic. But Centene seems overly gung ho to implement it everywhere. My team has been getting Chatgpt responses to emails for over a month and now copilot??? We're just going to ignore the environmental factors??? I'm sure our members will find it so easy to stay health when there's no f***ing water to drink. Well, those members that can still afford their healthcare that is

the reporter asked Eliza which bank has the best AI technology and Eliza answered JPM and GS. oops
lmaooooo

Artificial intelligence significantly impacts the American workforce. AI is cited as a reason for thousands of layoffs across various companies. Amazon, Dow, and Pinterest are among those cutting jobs due to AI. Conversely, demand for workers with generative AI skills is rapidly increasing. These AI-skilled roles offer significantly higher salaries than similar positions.

https://www.cbsnews.com/chicago/news/ai-job-market-workers-resume-hiring/

It summarized:

Dan and Alfonzi are good but the rest of you are facked!

We'll wait a few years for the follow-up article on how IBM became an AI dog.

https://www.economist.com/business/2026/01/29/how-ibm-became-an-ai-darling

It has pulled off yet another striking turnaround

Jan 29th 2026

Throughout its 115-year life IBM has shown itself to be a master of reinvention. In the mid-1990s the mainframe pioneer rescued itself from collapse by shifting its focus to the booming business of IT services. A decade later it sold its struggling PC division to China’s Lenovo.

Over the past half decade or so “Big Blue” has been through another striking transformation. During the 2010s its business was disrupted by the rise of cloud computing, which undermined not only sales of mainframes but also the work of servicing them at a time when low-cost outsourcers from India were pinching share. Revenues and margins shrank, and investors once again lost interest.

That has all changed in the past three years, during which IBM’s share price has more than doubled. As a multiple of net profits, it is now valued similarly to Microsoft and other software champions (see chart). On January 28th it reported that its revenue and net profit rose by 8% and 14% in 2025—a sharp reversal from its years of stagnation. How did IBM pull it off?

The strategy began with the acquisition in 2019 of Red Hat, a platform that, among other things, helps companies manage their workloads across data centres. Rather than trying to compete with Amazon, Google and Microsoft in the so-called public cloud, IBM created a layer between that makes it easier to mix and match among the hyperscalers while continuing to use on-premise mainframes or dedicated private clouds (including those run by IBM) for sensitive tasks. Deals in 2024 and 2025 to buy HashiCorp and Confluent, two more software firms, have solidified IBM’s role as an orchestrator of hybrid clouds.

IBM has also created a space for itself in AI. The company has long dabbled in the technology—including using it to beat Gary Kasparov, the world chess champion, in 1997—but missed the latest wave of large language models. Rather than trying to beat OpenAI and other model-makers at their own game, it has released a series of small language models, under the name Granite, which are tailored to business applications and require less computing power. These and other open-weight models, which make their numerical parameters freely available, are accessible through its watsonx platform, which enterprises can use to build AI agents trained on their own data.

IBM’s growing strength in AI has been helped by another big strategic shift over the past few years—the refashioning of its services arm. In 2021 the company spun off its struggling outsourcing business, now called Kyndryl, which at the time accounted for about a quarter of its workforce. That left it with a smaller consulting division focused on technical expertise, which has come in handy as clients grapple with AI. IBM has booked over $10bn-worth of consulting contracts related to generative AI since the middle of 2023. It has also been using the technology to digitise its own consultants’ work, a move the division’s boss has described as moving to “service as a software”.

Meanwhile, IBM continues to innovate in its original metier of hardware. It is still by far the world leader in mainframes. The z17, released last year, has been a hit. It offers access to IBM’s new Spyre chip, designed for running AI models. Then there is IBM’s work on quantum computers, where it is at the leading edge. McKinsey, a consultancy, reckons the market for the technology could reach nearly $100bn by 2035. IBM thinks it can capture about 20% of the business by selling machines and renting out capacity. It hopes to deliver Starling, a “fault-tolerant” quantum computer that can spot and correct the errors the technology is prone to, by 2029.

Lately investors in legacy technology companies have become increasingly jittery over the prospect of disruption from AI. In the past three months software businesses in America’s S&P 500 index have shed a seventh of their value. Shares in Accenture, the world’s biggest provider of IT services, are down by a quarter over the past year amid fears that bots could soon replace much of what the company does. But IBM, with its unique portfolio of businesses, looks well positioned to make it through the upheaval.

The current layoffs are continuing at the employee level, and there is a possibility that management-level layoffs may occur in the next round.

https://www.forbes.com/sites/tylerroush/2026/02/04/global-software-stock-selloff-oracle-adobe-more-fueled-by-anthropics-new-ai-tools/

This is on top of $50 billion loan problem to finance Open AI venture

Ran into a few customer service reps that may have been AI (based on how they sounded). They were much more knowledgeable than any PSO agent I’ve spoken with. Can we please replace PSO with AI? He-l, I’m willing to work Sundays if it means the calls are filtered by AI instead of PSO

Find humor in this tag line. Layoffs based on xls col autosum for bottom line $$ with a location multiplier. Interesting what AI enabled, data driven analysis of affected employees would tell as far as AI perceived detrimental impact to organizational infrastructure & enterprise level impact, simple prompt using historic comp & bonus, recognition & performance evaluations. Sad, and yes, for the shills, I am an impacted person with a 25 year tenure still at the top of my game, deleted as a line item on said xls spreadsheet. Realistically T-Mobile lost more $$ in the last 12 days putting me on garden leave than the next 5 years of my cost to them. Business will continue, always does.

Maybe someone can pose the AI enabled, Data driven question for the next All Hands on my behalf. Thank you in advance if you do.

Going to be a large number of Microsoft layoffs announced soon.