In many cases the skills are available in house to do the necessary vetting, but these people are already overwhelmed with their existing day to day.
Anyone remember that item a few months back about Amazon now having senior engineers vet generative AI output (https://news.ycombinator.com/item?id=47323017)? I had to LOL when I read that. These folks are already slammed. And the idea that Amazon would allow human bottlenecks to multiply across projects and underlying infrastructure development is ridiculous.
I'm pushing the need for basic engineering principles across whole organisations.
You wouldn't give an engineer 1000 lines of code to review without the original spec of what you're trying to achieve for context (at a minimum, ideally the reviewer was in the room when the work was introduced, and has full context).
So, these docs, they're given as an all or nothing.
Do you push back on the 39th metric that is defined to the utmost detail? Or just resign yourself to the fact that it is what it is?
A one (6 is the goto if we're talking Amazon?!) pager.. "this is what I am proposing" at least gives the skeleton of the idea to push back at the general shape of the idea, refine it, before all the emotional investment of your precious report being complete.
Y'know.. the traditional product running through the spec in a SCRUM* environment.. the engineers doing proper code reviews..
* Yes SCRUM is dead, but that's another thing.
With AI, I have to read through everything, often explain why it's wrong, and then rewrite everything anyways. I mean, I get way more billables, but I think it's symptomatic of how AI loses its advantage of being quick and accessible to those who don't understand the subject matter.
You mean the people they fired and demoralized?
One of the things that "great [wo]men" like about "vibe-coding" (and that includes blindly producing non-code product), is that they, and they alone can now do what used to require the painful process of "passing it to context experts."
Now, the LLM is a "built-in context expert," and they don't need to vet the output anymore.
The problem is that output sometimes take longer to verify than to create in the first place.
That turns AI into a deeply negative ROI system for many applications.
This is an interesting topic. We treat vetting output the same as doing the work ourselves, but that is not the case.
Doing the work is not the same as reviewing work done by others.
I have heard reports of software engineering companies that have gone full agentic. Their seniors only review stuff written by LLMs and it burns them out, because they have to switch context constantly.
I find this interesting because part of being a senior developer is that you are experienced enough that you won‘t make grave mistakes anymore. This is the case in many professions: you are relied upon to not make grave mistakes.
But those same people are now swamped with stuff that they are not able to review, so they will let a grave mistake slip through at some point.
So they really can‘t trust themselves anymore?
I am particularly interested in Education and Human Knowledge Management. I have seen the rate of IT training going to zero. Think about specialized training, where if you make a mistake, the consequence of your errors, are talked about on the tv news of the evening.
The whole idea everybody is just planning to save their butt, using these strings coming out of these numeric matrices, while suspending judgement, just shudders me in horror. A bit like those South Asia Airline companies, that were forbidding their pilots from landing airplanes with manual piloting, leading to an increase loss of skills causing some well known disasters...
If well paid consultants cant even bother to check their links...
I think a lot of the time it's just pure laziness. AI gives people a magical "do all the work for me" button and it can bring out the worst in them.
Now nobody will remember or notice.
It's unsurprising that trying to do more with less results in lower quality.
If you ever needed evidence to not buy “advice” from such outfits, this is exhibit one.
Hopefully they at least fired the partner that published this steaming pile of AI slop.
I had no experience and knew absolutely zero about any of those sectors.
Why would anyone trust these large contractor companies enough to pay them the huge amounts of money to have juniors learning the ropes on their dime?
"Customers" were the content the juniors were trained on in the same way that scraped internet data is what LLMs are trained on, except Customers were paying for the privilege of being 'scraped'.
Now there are no juniors, just LLMs being asked questions that aren't specific enough, and assuming the answer is one-shot correct.
It saves E&Y lots of money though, and their (confusing) reputation will provide a surprising amount of momentum such that plenty of work will keep rolling in for a few years to come.
Performative executives of yesteryear that constantly need external validation and direction and operate through hive mind and groupthink are weak and will die.
I believe some of the biggest problems in today's business leaders are an inability to be open to new information, to think across traditional professional boundaries, or to ask meaningful questions.
AI simply exposes this unapologetically.
Bad management (this includes most government): up your game or get out of the way.
Sycophantic consultant firms: die.
The Economist should do an article on this.
Not to take away from the actually great reporting here, but what they mean is, This approach allows them to milk it for as many clicks as possible.
I don't know but I would expect it to be realtively easy for an LLM to detect "hallucinations".
I’m not entirely convinced this is purely an LLM-related issue. I’ve definitely come across countless misattributed citations in big four reports long before generative AI became widespread.
Did they just prompt ChatGPT with no web search and copy-pasted it?
okay that makes me feel better, I think January's frontier models and beyond are better at this
but check your sources folks
I don't know but I would expect it to be relatively easy for an LLM to detect "hallucinations".
Any person with above average knowledge on a specific topic, can tell when AI starts hallucinating and making things up, or at least introducing new problems due to complexity added rather than solving it, that’s my observation using all top tier ones too, it’s like they are designed to solve a problem regardless so they start making things up or piling workarounds, a person with no deep knowledge in that topic will just copy it all and call it a day.
Just yesterday, I asked claude 4.8 on something specific that I know the answer for, it had a long list of solutions that none were close to the real answer, when I replied with the real answer and pushed back, I got the famous quote “you are right, thanks for pushing back”.
But I guess since EY is a CYA hedge anyway, no one really cares about whether the reports are hallucinations or not. Someone high up spent money on EY, so that they can justify some decision and won't be held responsible that much, when it turns out the decision was shit. All that matters to them is, that it has the appearance of something genuine and then they can base the decision on what they receive from EY, which better be what they already wanted to hear/read anyway.
Slop signalling may be the new power play. Nothing quite says "FU" like a low effort AI hallucinations.