Tag: AI

AI – Is The Squeeze Worth The Juice?

There have been a lot of AI-related headlines lately. In this post, I’m questioning whether the investment in Artificial Intelligence will be worthwhile.

The Purpose Of Opinion Posts

Have you ever flicked through the news (or read a newspaper, heaven forbid!) and saw some outrageous headline followed by a very opinionated article? That “opinion editorial” (OpEd) was deliberately written to make you think, read more, and maybe even as clickbait. I used to be paid to write tech articles for Petri.com. Every now and then, I was asked to write an OpEd, something opinionated, that would make people read.

Why I’m Writing This Post

I have seen many news articles about AI lately. Then I saw a tweet by Brad Sams (previously the chief editor at Petri.com) where he wonders what the future of Windows Recall is. That triggered me after reading the news this morning.

The Promise of AI

Where I grew up, AI meant something very different that involved a vet, a long glove, a prime specimen bull, a syringe, and a herd of cattle. But then I went to college and sat through some awful classes on Prolog; I learned about the concept of artificial intelligence back in the mid-1990s. It was just a thing I associated with Hollywood movies until ChatGPT started to run wild a few years ago.

AI is meant to change everything. Huge amounts of data can be analysed in ways that humans cannot accomplish. Insights should be found. Decisions can be made or recommended. The mundane should be automated, freeing up humans to do more responsible tasks or tasks more suited to humans. The world should be better, thanks to AI.

The Realities of AI

I use a combination of generative AI tools for my job:

  • It’s mostly replaced Google as my primary search engine. Google is faster and more accurate, but the commercial ordering of content makes it increasly unproductive.
  • I’ve used it a lot for IaC and scripting. Every one of them makes the same awful, repetitive mistakes, where I’ve ended up writing off half a day and doing it 100% by hand. It’s not all been bad, but it’s very limited.
  • I’ve used it for research on new topics.
  • As a self-employed consultant, I need a reviewer. Copilot has become my chief editor to give me an opinion on my work. Quite honestly, this has been super.
  • I’ve started training an agent to “clone myself” in the way that I work with Azure. This is going slowly.

Copilot is great for explaining a complex JSON-formatted error. I’ve used that a lot to cut to the chase. Copilot sucks at troubleshooting. Everyone knows how much AI hallucinates. We’ve all seen the scenario where it creates a whole “existence” of something that doesn’t exist. I’ve been given PowerShell cmdlets with full syntax explanations – only to find that neither the cmdlet nor the documentation exists! I’ve had troubleshooting tips or root cause suggestions that are complete fabrications that do not fit the fully explained scenario.

I may not be working in Foundry, etc, but I am using the main tools that most are using – and some are paying for.

Which leads me to … return on investment (ROI). It is believed that just 3% of M365 subscribers are paying for Copilot. I suspect that a tiny percentage of those subscribers are paying anything more than the basic amount. I wonder how many, like me, are using free-only SKUs of ChatGPT, Grok, Claude, etc?

Meanwhile, each of the hyperscalers is crippled by capacity issues. In Azure, I cannot deploy services/resources across Availability Zones in the regions that my clients are using. Microsoft Ireland was telling Irish customers to use Sweden Central instead of North Europe (Dublin, Ireland). Now Swedish users are complaining about capacity issues – we’re all eyeing up Denmark East now 🙂

You can bet that the capacity issues are AI-related. The GPUs for AI consume:

  • A lot of space for the rack units and their cooling systems
  • A lot of water
  • A lot of electricity

In Ireland, we have lots of water – please, take some – so that’s not a constraint on data centre expansion here. If you do a little research, you’ll find that Microsoft has ~11 data centres in Grangecastle, Dublin (search on Google Maps for Cuisine De France). Those data centres are nearly full, and the expensive land there is occupied. Microsoft planned a 180MW expansion for North Europe in Jigginstown, Kildare. But those plans have gone nowhere – the last update was in July 2024. Why?

  • Ireland’s electrical transmission grid is full. We have barely tapped our natural generational capacity, but we cannot transmit the electricity. It is estimated that 33% of the grid will be consumed by data centres in 2026! Localised bans have been established for data centre connections to the grid.
  • Data centre manufacturers are building their own carbon-based power stations to counter the grid connection bans. Locals have invested huge amounts in carbon reduction. Those locals are angry that their efforts are being countered by international companies that (a) hire very few locals after construction and (b) pay less than their fair share of local corporation taxes.

AI is filling data centres. And this means that customers who want to use Cloud Computing are not able to get into those data centres. Something has to give.

Redundancies

I did a LinkedIn learning course on generative AI a few years ago. One of the presenters was a woman from Spain who promised that generative AI would free me up from mundane tasks to spend more time being creative. What are we going to do – knit scarves for when we have no electricity to heat our homes?

Microsoft (and others) released free SKUs of their products to “help us” techies with our programming, scripting, and infrastructure-as-code projects. In reality, these products are learning how to code/script from us. Then they are being or will be promised as a way to replace us. The same applies to other generative tasks and skills.

We can see the evidence of this now:

Company20252026
Microsoft15,0009,000
Amazon14,00016,000
Google“Thousands”“Thousands”
Meta3,600Up to 16,000
IBM2,700-8,100“Thousands”
Oracle3,00020,000-30,000

Recent headlines included:

What exactly are those people to do? I know that there were tech skills shortages, but a sudden dump of experienced talent is not natural and will flood the market with more skills than job availability.

A Generation Of Lost Skills

I guess, like many of you, now and then, someone will ask me, “What should I do in college?” I advise them to find a career that involves a lot of human interaction that cannot be automated. For example, I expect that AI will replace most of us in IT infrastructure or software development roles. I read a story yesterday about how one of the redundant Microsoft software engineers took up a career of welding.

I treat Copilot as a junior intern. I will delegate selected small tasks to it, but I have to:

  • Be very selective of the tasks, considering the limited capabilities of generative AI.
  • Review the work to combat hallucinations.

I previously worked in a consulting team where our main way to increase headcount was to hire interns directly from college. We could teach them and give them tasks that increased in complexity and responsibility. Over time, those interns became juniors, and some progressed to seniors. We saw the intern as an investment in future capacity and billing potential. Some made it, some didn’t. But we repouped the investment with those who progressed and stayed. The company, therefore, had a sustainable supply of skills.

Let’s go back to those big tech companies that are replacing skills with generative AI. They probably are not recruiting interns – in fact, it is widely reported that internships are harder to get thanks to AI. Those firms have AI bots that will learn, but can never really be trusted. I have no evidence or insider knowledge, but I suspect that recent cloud outages were caused by AI failures. The experienced humans who supervise the bots will age or move on, but where are the developing interns to replace them? Will they be off somewhere being creative?

Is The Squeeze Worth The Juice?

Ask the stock market what it thinks.

  • Microsoft is down 24-36% year-to-date
  • A $200 billion spend shook the market, and there are near-term cash-burn concerns.
  • Meta has CapEx rising to $115–135B in 2026, raising sustainability questions.
  • IBM experienced a 6.5% stock drop in Feb 2026 amid a broader AI sentiment “reset” and enterprise spending slowdown.
  • Oracle is down ~50% from 2025 highs.

The market’s overall sentiment is worried about massive cap-ex spend. There is, admittedly, long-term optimism. However, I’d question that if:

  • Actual purchases of AI are tiny compared to the investment
  • Capacity issues will force clients to non-hyperscaler platform systems, which will reduce any investment in hyperscaler AI.
  • Software quality will drop, which will force SLA compensation and drive customers elsewhere – not great when large swathes of the market are already dumping American software.

Here’s my crystal ball projection (I have a high fail rate with this stuff!). I think that the market will force a reset. The low ROI on AI, combined with slowing cloud consumption (capacity issues), will cause massive reactions. Skills will plummet. Reduced software quality will cause issues. AI will make bigger mistakes that cost lives/money. CEOs will fall. It may take a decade to regain the lost skills. It may take longer to regain faith in the Cloud if the problem persists too long.

And on that cheery note, I’ll wrap this up!

The Digital Intern – Early Experience with Microsoft Copilot

I will share my early experiences with Microsoft Copilot, the positives and negatives, clear up some false expectations, and explain why I think of Generative AI as a digital intern.

What is Generative AI?

The name gives it away. Generative AI generates or creates something from other known things. Examples are:

  • DALL-E: Creating images, such as Bing Create
  • Chat GPT: A text-based interface for finding things and generating text, such as the Copilot brand from Microsoft.

Pre-Microsoft

There are lots of brands out there but the one that’s grabbing most of the headlines is Open AI because of ChatGPT, which is only on of their products. Like millions of others, I’ve played with ChatGPT. I’ve used it to create Terraform code. It was “OK” but I found:

  • Some of the code was out of date.
  • The structure wasn’t great.

I had to clean up that code to make it usable. But ChatGPT saved me time. I didn’t have to go googling. I was able to create a baseline and use my knowledge and ability to troubleshoot/edit to make the code usable.

I also “ChatGPTd” myself – don’t do it too often or you’ll go blind! Most of what ChatGPT wrote about me was correct. But there were some factual errors. Apparently, I’ve written two books on Azure. Factcheck: I have not published any books on Azure.

Some of the facts were also out of date. I have been “an Azure MVP for 2 years”. That was probably pulled from some online source. ChatGPT didn’t understand the fact (it’s just a calculated set of numbers) and therefore hadn’t the logic to use “2 years” and the publication date to recalculate – or maybe put a date in brackets with the fact.

Copilot

Microsoft has just launched Microsoft 365 Copilot and there is a lot of hoopla and hype which is helping Microsoft shares, even with a bit of a slump in the stock market in general.

I’ve been playing with it and trying things out. First up was PowerPoint. Yes, I can quickly create a presentation. I can add slides. I can change images. But the logic is limited. For example, I cannot change the theme after creating the slides.

The usual fact-checking issues are there too. I used Copilot to create a presentation for my wife on company X in Ireland. The name of company X is also used by companies in the UK and the USA. Even with precise instructions, Copilot tried to inject facts from the UK/USA companies.

However, Copilot did create a skeleton presentation and that saved some time. I played around with it in Word, and it’ll generate a doc nicely. For example, it will write a sales proposal in the style of Yoda. Copilot in Teams is handy – ask it to summarize a chat that you’ve just been added to. Outlook too does a nice job at drafting an email.

Drafting is a good choice of words. Because the text is often just mumbo jumbo that is nothing to do with your or your organisation. It’s filler. In the end, it’s up to you to put in the real information that you want to push.

Bing Enterprise Chat is an option too. You can go into Bing Chat and select the M365 option. You can interrogate facts from “the graph” and M365. You can ask your agenda for the day.

Don’t ask Copilot to tell you how many vacation days are in your calendar. It will search your chat/email history for discussions of vacation time. It does not look at items in your calendar. It will not do maths – more on this next.

Prompt Engineering

Go into Bing Create and ask it to create an image of a countryside scene. Expand the prompt in different ways:

  • Add a run-down building
  • Change the time of day
  • Alter the viewing point
  • Add a background
  • Place some birds in the sky
  • Add a person into the scene
  • Make the foreground more interesting
  • Change the style of image

The image changes gradually as you expand or change the prompt. This is called prompt engineering. Eventually, the final image is nothing like the first image from the basic prompt. What you ask for changes things. Think of the AI as lacking in the “I” part and be as clear and precise as you can be – like how one might instruct a toddler.

Custom Data

I decided to do a mini-recreation of something that I saw the folks from Prodata do with Power BI years ago for presentations. I downloaded publicly available residential property sale information for the Irish market and supplied it to Copilot.

“Tell me how many properties were sold in Dublin in 2023”. No answer because that information was not in the data. Each property sale including address, county, value, and description was in the data, but the “Y properties were sold” fact was not in the data. One would assume that an artificial intelligence would understand the question and know to list/count the items that match the search filter but that is not what happens.

I also found other logic issues. “What was the most expensive property sold in 2023” resulted in a house in Dublin for €1.55 million. I then asked it to list all houses costing more than €1 million. The €1.55m house was not included. I tried other prompts and then returned to my list question – and I got a different answer!

Don’t ask Copilot to do any maths – it won’t tell you averages, differences or sums – because that information was not in the “table” of supplied data.

Data Preparation

You cannot expect to just throw your data at Copilot and for magic to happen. Copilot needs data to be prepared, especially custom (non-Office) data. It needs to be in consumable chunks. You also need to understand what people might ask for – and include that information in the data.

I’m wandering outside of my expertise now, but let’s take my property example. I wanted to analyze property values, do summations, averages, and comparisons. The act of preparing this data for Copilot needs to do these calculations in advance and include the results in the data that is shared with Copilot.

Thoughts

I am not writing off ChatGPT/Copilot. There are problems but it is still very early days and things will be improved.

Right now, we need to understand what Copilot can do, and what it is good at/not good at, and match it up with what will assist the organization.

The most important thing is how we consider Copilot. The name choice by Microsoft was deliberate. They did not call it “Pilot”.

Generative AI is an assistant. It will handle repetitive tasks based on existing data. It has no intelligence to infer new data. It cannot connect two facts that we know are logically connected but are not written down as connected. And Generative AI makes mistakes.

Microsoft called it Copilot because the pilot is responsible for the plane. The user is the pilot. The intention is that Generative AI handles the dull stuff but we add the creativity (prompt engineering/editing) and fact-checking (review/editing).

If you think about it, Copilot is acting like a Digital Intern. How are interns used? You ask them to do the simple things: get lunch, research X and write a short report, write a draft document, and so on. Does the intern produce the final product for a customer/boss? No. Is the intern responsible for what comes out of your team/department? No.

The intern is fresh out of school and knows almost nothing. They will produce exactly what you tell them – if the prompt is too general they get lost in the possibilities. You take what the intern gives you and review/edit/improve it. Their work saves you time, but your knowledge, expertise, and creativity are still required.

I might sound like a downer – I’m not. I’m just not on board the hype train. I’m saying that the train is useful to get from A to B right now, but the line doesn’t go all the way to Z yet. It is still valuable but you have to understand that value and don’t get lost in the hype and the Hollywood-ing of IT.