If my experience with it at work is anything to go by, no. Increasing amounts of my time are spent fixing the sloppy output of coworkers who use AI. Far from being "left behind" I've become one of the few people who is actually able to solve complicated problems.

It's quite good for any problem that nobody cares about. For example, if you have a boss who wants hourly status-updates but does not actually read the updates. Or if you need to fill out security forms, but you were going to lie on all the forms and trust that nobody reads them. Or if you get a bonus for writing >100k lines of code, but the code doesn't need to do anything that people want. Or if you need to have someone answer customer-support questions, but it doesn't matter if the customers get helped or are happy.

https://www.businessinsider.com/uber-coo-andrew-macdonald-ai-token-spending-harder-justify-2026-5

'Nuff said

LLMs cannot do anything new.

The fact that companies are now telling their employees to scale back and be mindful of their token usage shows that it’s not cheaper. It can be efficient yes, usually for automating the mundane stuff, but it’s not cheaper.

Cheaper, prolly

Efficient? Debatable.

Reliable? Nowhere close, and humans aren't even all that reliable.

The thing about knowledge work is it isn't about knowing things, it's about knowing how to put things together. AI lets me accomplish certain tasks faster than I could alone. I can sit in a meeting and spend 30 seconds asking AI to run an analysis and then I can focus back on the meeting. After the meeting, the AI response might be right, but it's probably not. But it has gathered up all the raw data and now I can add calculations to a spreadsheet, or look at the data in one place rather than having to query 8 different systems.

AI is good at rote tasks and grunt work. It'll write you a bit of code slicker than shit, but you have to very specifically tell it exactly what to do and how to do it. That's knowledge work. Actually following the instructions without fucking up is the mundane part. And AI can do that really fast, and if your instructions are very clear, fairly complete, and you understand the likely failure modes that will trip it up, you can really increase your productivity.

But "vibing" anything with AI is bullshit and doomed to failure. If you don't know exactly what you want and exactly how you want it built, you are going to get some rancid garbage. Knowledge work isn't in any danger.

What I do fear is where the next generation of knowledge workers will come from when AI is faster and easier than building up juniors to seniors. I believe they will continue to find their way, but it might be harder and there might be fewer.

AI sucks ass for every serious task. I've used it dozens of times to work on research projects, and consistently it gives me responses that sound good, but have holes, like citing works that don't exist, mis-applying arguments, etc.

these problems never appear if you ask it standard questions, e.g. simple exercise questions from the classes. it always solves those correctly, probably because they exist in the training data. but try to ask it anything where logical inference is non-trivial and it starts splicing facts together in ways that don't end up straight.

anyways, all being said, it's still great for routine tasks, and for informing you about basic knowledge in a new field. since basic knowledge is all written down in books somewhere, AI knows about it quite well. also, coworkers make mistakes, and lots of them. if i sum up all the steps of progress that coworkers make and that AI make while working on a new project, i'd say it's about on the same level. coworkers tend to think about new topics more seriously, but also they often just don't respond, give up, never call back etc. meanwhile AI tries to output something, even if it's wrong.

all that being said, there will still be a decline in knowledge worker jobs, but not so much because of AI being excellent at actively exploring new areas and kinda spreading out to take over jobs, but instead because like 99.999% of our jobs are kinda routine jobs anyways. people always tell themselves they're special, and they are. but at the same time it's mostly routine jobs. these are not mutually exclusive. and that's why a lot of jobs are still going to vanish.

Nope.

“Do the thing. I have a script that does the thing. I made it by listing the things in excel and asking Claude to write a script that does all the things”

The script not only doesn’t run, but it also quietly doesn’t do parts, and breaks stuff too.

So now I’ve got a pile of code that doesn’t work and it would be faster if I’d written it myself than troubleshooting the spaghetti. It’s like having a dumb intern, except the intern is incapable of improvement.

Considering they're raising the prices of it significantly, I'm gonna say no it's not cheaper.

As a programmer: HELL NO!

In 2027 we can probably expect vastly larger bills or reduced usage limits. The price at which some models are being offered right now is in no way sustainable. For example Anthropics subscription plan gives you tons of quota for the most expensive models, while costing relatively little. Github Copilot recently changed its billing to token-based (before it was request-based) and it already produces incredible bills within the company I work for.

It's gotten much more expensive in the past month after AI companies changed their pricing models. They still aren't making money, so expect the price to be jacked up in 2027 as well.

I think eventually we'll reach a price point where companies will realize it's only worth it at certain tasks in certain situations. They're not gonna want to shell out serious cash so middle managers can have their emails written and read by computers.

Right now, the state of the art appears to be that LLM's are far more expensive to run than humans for many tasks. So, yeah, it is a lie.

There are uses for specialty AI that seem to work well, but they are usually bespoke for a certain task.

I expect that, if AI is used in 2027, it is going to be used with a lot more intent in targeted uses. I also expect some companies are going to realize it is better to fully control their own AI on their own hardware than to use a more state of the art AI which will use all their data as training data.

In 2026 you still need expert knowledge to be able to judge whether it's actually generating statements that match reality or just happen to take on a truthy shape.

Web search went from being the most powerful tool in acquiring knowledge to being mostly useless, a crapshoot, because of the difficulty in discerning which articles are useful versus which ones just take on a useful shape.

There seems to be a bit of an odd relation between its value and cost. Building a model and setting up a data center is horrifically expensive, so LLMs as a service have to be just as expensive. But the stuff that can be done by LLMs is low stakes, low thought work, like copy writing, chatbot customer service answering the same 30 FAQs but crashing out on anything else, summarizing this morning's headlines, etc. The price on these things is still being hashed out but it's looking a lot like using AI is like hiring Anthropic for $100,000/yr. to replace a person who only gets paid $45,000/yr.

According to AI Bros, yes.

According to a survey of 782 I&O managers conducted in November and December of 2025 by Gartner, less the 30% work as sold.
https://www.theregister.com/software/2026/04/07/only-28-of-ai-infrastructure-projects-fully-pay-off/5221652

Right now AI companies are doing what they used to say drug dealers do "Hey it's free! Why don't you give it a try?" And once you get hooked they jack up the price.

In all my years I've never come across someone giving away free street drugs, but AI companies do it all the time. The problem is once the funding dries up, I suspect the real price of some of these services is going to be astronomical. They are giving away millions, if not billions of dollars of services free or deeply discounted all in the hopes of "gaining market share".

When the bubble pops we will likely start seeing the actual cost of some of these services.

Mix in the fact that if you've ever used some of these AI services you'll find most of the time you have to babysit the LLM to give you what you want and not go all haywire on you.

With our current level of technology I think we are in a place where we are only really able to augment some of our workers workflow. I really can't see replacing the full staff of a large to mid-sized company considering the cost and efficiency of these products.

That said I think the genie is out of the bottle, and AI services are here for good. What the landscape will look like in the next few years I really can't say. More people/companies may choose to run LLM's locally on their own machines/servers trained on very specific use cases as that might be more cost effective than what our current generation entails.

No, at minimum you still need area experts to babysit the AI. And also to babysit the people who abuse AI way beyond their skill level.

I wouldn't expect it to replace people. It will make workers more productive. However, because it is already pretty well spread through most companies, those productivity gains will only lead to competitive advantages for companies with highly skilled workers.

Think of it like a chainsaw for lumberjacks. A lumberjack with a chainsaw is going to be far more productive than one with just a hand axe. But since every company equips their lumberjacks with chainsaws, they aren't really at an advantage, chainsaws are now just a cost of entry for a company. Also, lumberjacks are required to know how to use a chainsaw. But they are ok.

For knowledge workers, AI is our new chainsaw. We're going to learn to use it. And it's going to be part of our jobs going forward. From my own experience, it has it's uses and is pretty good at certain tasks. It can also be endlessly frustrating at tasks where it's not well suited or the training isn't up to snuff. We just have to learn and adjust to a world where the tool exists and is used everywhere. The genie isn't going back in the bottle.