#machinelearning

Here's the deal: anything you type into Copilot on a company device can be logged and stored under our organization's Microsoft 365 tenant. So if you're asking it about that weird rash, your divorce, or your side hustle… just know that's not exactly private.

And I'll be real with you. I'm on the BTS team, and we can see your prompts. All of them. No one's sitting here reading them for fun, but the access is there, and we just want you to be aware.

So do yourself a favor... Use Copilot at work for work things, and save the rest for home.
Easy rule of thumb: if you'd cringe seeing your prompt on a big screen during a team meeting, don't type it on your work laptop. 😅

WIP: Machine learning + Code + AI - in flight - optimizing refinery scheduling, crude blending... logistics + maintenance sequencing. Now calculate # of jobs lost and $$$ in someone else's pockets

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.

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.

Last quarter, I rolled out Microsoft Copilot to 4,000 employees at $30 per seat per month—about $1.4 million a year. I packaged the whole thing as “digital transformation,” a phrase the board loved so much they approved it in eleven minutes. No one asked what Copilot would actually do, including me. I promised it would “10x productivity,” which isn’t a real metric, but it sounds like one. When HR asked how we’d measure that, I told them we’d “leverage analytics dashboards,” and they promptly stopped asking. Three months later, I checked the usage reports: 47 people had opened it, 12 had used it more than once, and one of them was me. I used it to summarize an email I could have read in 30 seconds; it took 45 seconds and still required correcting hallucinations. Still, I declared the pilot a success—success meaning it didn’t visibly fail. When the CFO asked about ROI, I showed him a graph that moved up and to the right, charting a metric I invented called “AI enablement.” He nodded approvingly. We are now officially “AI-enabled,” whatever that means, and it’s proudly featured in our investor deck.
A senior developer asked why we didn’t just use Claude or ChatGPT, and I replied that we needed “enterprise-grade security.” When he asked what that meant, I said “compliance.” When he asked which compliance, I said “all of them.” His skepticism earned him a “career development conversation,” after which he stopped asking questions. Meanwhile, Microsoft sent a case study team who happily accepted my claim that we “saved 40,000 hours,” a number I produced by multiplying employees by a figure I made up. They didn’t verify it, and they never do. Now we’re featured on Microsoft’s website as a global enterprise achieving massive productivity gains, and the CEO shared it on LinkedIn to 3,000 likes—despite never having used Copilot. None of the executives have; we granted ourselves an exemption to avoid “digital distraction,” a policy I wrote. With licenses renewing next month, I’m requesting an expansion: 5,000 more seats. We haven’t used the first 4,000, but this time we’ll “drive adoption,” which means mandatory training—a 45-minute webinar no one watches but everyone completes, and completion is a metric. Metrics go in dashboards, dashboards go in board presentations, and board presentations get me promoted. I’ll be SVP by Q3. I still don’t know what Copilot actually does, but I know what it’s for: proving we’re “investing in AI.” Investment means spending, spending means commitment, and commitment means we’re serious about the future—the future being whatever I say it is, as long as the graph goes up and to the right.

Last quarter I rolled out Microsoft Copilot to 4,000 employees.

$30 per seat per month.

$1.4 million annually.

I called it "digital transformation."

The board loved that phrase.

They approved it in eleven minutes.

No one asked what it would actually do.

Including me.

I told everyone it would "10x productivity."

That's not a real number.

But it sounds like one.

HR asked how we'd measure the 10x.

I said we'd "leverage analytics dashboards."

They stopped asking.

Three months later I checked the usage reports.

47 people had opened it.

12 had used it more than once.

One of them was me.

I used it to summarize an email I could have read in 30 seconds.

It took 45 seconds.

Plus the time it took to fix the hallucinations.

But I called it a "pilot success."

Success means the pilot didn't visibly fail.

The CFO asked about ROI.

I showed him a graph.

The graph went up and to the right.

It measured "AI enablement."

I made that metric up.

He nodded approvingly.

We're "AI-enabled" now.

I don't know what that means.

But it's in our investor deck.

A senior developer asked why we didn't use Claude or ChatGPT.

I said we needed "enterprise-grade security."

He asked what that meant.

I said "compliance."

He asked which compliance.

I said "all of them."

He looked skeptical.

I scheduled him for a "career development conversation."

He stopped asking questions.

Microsoft sent a case study team.

They wanted to feature us as a success story.

I told them we "saved 40,000 hours."

I calculated that number by multiplying employees by a number I made up.

They didn't verify it.

They never do.

Now we're on Microsoft's website.

"Global enterprise achieves 40,000 hours of productivity gains with Copilot."

The CEO shared it on LinkedIn.

He got 3,000 likes.

He's never used Copilot.

None of the executives have.

We have an exemption.

"Strategic focus requires minimal digital distraction."

I wrote that policy.

The licenses renew next month.

I'm requesting an expansion.

5,000 more seats.

We haven't used the first 4,000.

But this time we'll "drive adoption."

Adoption means mandatory training.

Training means a 45-minute webinar no one watches.

But completion will be tracked.

Completion is a metric.

Metrics go in dashboards.

Dashboards go in board presentations.

Board presentations get me promoted.

I'll be SVP by Q3.

I still don't know what Copilot does.

But I know what it's for.

It's for showing we're "investing in AI."

Investment means spending.

Spending means commitment.

Commitment means we're serious about the future.

The future is whatever I say it is.

As long as the graph goes up and to the right.

All this talk about how AI is going to take this company so far ahead has it really helped you at all? Or just helped you out the door?

I’d love to know how much money is being burned on that hallucinating mess, and how many people were pushed out specifically “because of AI.” Sure, most cuts were really about offshoring, but some people absolutely were replaced in the name of AI, and the productivity expectations tied to it keep creeping up. It’s about time people noticed that, in most cases, AI just drains time and energy. Replace people? Good luck relying on a machine that can’t give you the same answer twice.

Quantum Corp. Sheds 1,500 Jobs Amid AI-Driven Restructuring

• TechCrunch
  Dec 3, 2025, 9:30 AM PT
• Quantum Corp., a leading innovator in AI, announced a significant reduction in its global workforce today, impacting approximately 1,500 employees. The company stated the layoffs are part of a strategic restructuring effort aimed at streamlining operations and shifting focus towards advanced AI development. This move follows a period of aggressive hiring but also increasing automation within its software and data departments. Affected employees will receive severance packages, outplacement services, and extended benefits. Analysts suggest this trend reflects a broader industry pattern where AI advancements are leading to efficiency gains at the cost of human jobs.
  https://techcrunch.com/2025/12/03/quantum-corp-layoffs-ai-restructuring/

AI bubble deflating

https://finance.yahoo.com/news/peter-thiel-dumps-top-ai-143300737.html

https://www.aspistrategist.org.au/microsoft-outlines-ambitious-post-quantum-plans-but-challenges-remain/

What do you think what will happen with AI? Are tens of billions of dollars (we invested in it) ever going to produce any value for us? What about all other AI related companies and products? Do you see this as some kind of a Scam?

And we all know what this means at BNY

https://nypost.com/2025/10/14/business/goldman-sachs-warns-of-looming-layoffs-as-ai-reshapes-operations/

On reddit one user asked if they had implemented an AI Agent in production in a Fusion application and in comments the responses are that this is still in a testing phase on a customer site when they submit SRs and „it’s anything but a success”, they don’t have AI yet and enterprise AI is over promise. If these comments reflect the real state, it’s really hard to understand how can they do the employees huge layoffs at this phase?

with the hype of ai/ml, do you think they will reduce headcount there (both in Ansys and synopsys) or they will layoff revenue making R&D teams at the cost of promise of big returns from their ai/ml offerings in the future?

So far, Intel has not implemented any AI system that can reduce HC. Is LBT going to lead the company in that direction ?

Cisco ELT is essentially forcing you to document your workflows, your decision-making processes and your institutional knowledge into their internal AI systems.

But Why?

They will eventually replicate your work. Every query, every correction, every refined prompt is training data that makes the AI more capable of doing your job. The strategic goal is what economists call "capital labor substitution" which is a gradual replacement of expensive human labor with cheaper AI capabilities while keeping output the same. By mandating internal AI use, Cisco ELT extracts maximum value from you now while capturing your expertise and figuring out which roles can be automated or eliminated. forcing you is necessary because voluntary adoption has been too slow. Cisco needs comprehensive data on how your work actually gets done and you need to train the AI systems.

When Cisco mandates internal AI use, they're forcing you to externalize your expertise and decision-making processes into their corporate-controlled system.
This creates a systematic deskilling effect that also causes you to gradually lose the deep domain knowledge you have. (your brain literally atrophies). You are becoming dependent on AI prompts rather than developing independent problem-solving abilities.

It doesn't matter to them because your tacit knowledge and institutional wisdom is getting captured by the AI system. The end result is a commoditized workforce where new hires need minimal training (the AI contains all the institutional knowledge) and minimal pay (race to the bottom). You can't take critical expertise with you.

Finally, the remaining workers (their friends and family) can easily manage the "you" trained AI

When you get the pink slip, you lose access to the corporate AI systems and your collective knowledge remains permanently owned by the company.

COP AI looks at your writing style in these posts and compared it to your historical company email writing styles. Yes, you will be fired based on your negative views toward the company and leadership.

Yes, Jensen Huang, CEO of Nvidia, personally reviews the compensation of all 42,000 employees every cycle, using machine learning tools to streamline the process. He has stated this is a key part of his management strategy, ensuring fair pay and motivation, often increasing the company’s operational expenses.