From what I've heard is that this is the same ol story as with any other tech bandwagon for the past 15 years, be it block chain, big data, predictive algorithms, etc. I saw this cycle first hand when I was working on tech projects:

Company execs see at some conference that this is the new big thing and that their business is going to fail if they get in on the ground floor. Then some sales reps from a big corporate tech company (SAP, Oracle, Google, MS) talks them into spending huge amounts of money on all the bells and whistles before even figuring out how to implement the tech in their company. Finally, the consultants come and in the best case scenario, there's a very specific use case for the tech within the company, and it saves/makes quite a bit of money, in the worst case, it's a botched implementation that causes more pain than it solves. Either way, the modest pay-offs are not nearly enough to pay off everything that was spent on the initial investment. Rinse and repeat.

This is like a perfect setup to commit fraud. Just blame AI for cooking your sheets and be done with it.

Dude, Pandas and R exist... And they're incredibly easy to use... Why the fuck was no one even spot checking the numbers

It's very telling that they just implemented the AI without even giving its answers any sanity checks near the beginning. Could have caught it day one but no, it's magic and checking would be a waste of time

There are more guardrails but the company I work for relies heavily on salesforce and I wonder if this is applicable. I don't care I missed my bus and said fuck it and called in sick.

Yeah, the singularity of the economic black hole lmao

That was supposed to be what AI was for in the first place, an excuse to cut staff, smh damn capitalists don't even know their own scams anymore

I actually think around 10% success rate sounds about right here. There are niches where this tech works well, but it's being applied everywhere indiscriminately. So it makes sense that most deployments fail, but a small percentage actually finds the right niche.

I'd say it's more likely that 95% were greedily chasing hype, went a little too all-in, and 7% of them got lucky it didn't burn them.

I agree, it's basically a completely new tool looking for a market fit. It's also worth noting that these companies are basically looking for one stellar application. If they hit on something that works really well, that's gonna be the business model. So, they're perfectly fine with most of the pilots failing if they can find one that works well.

That said, I do think there is a bubble where a lot of companies are implementing these tools without having a good fit for them, and there's a ton of money being wasted in the process. It's kind of the same thing we saw with the dotCom bubble. When it popped, there was an extinction event where most companies went belly up, but we got a ton of useful tech out of it that underpins the internet today.

I expect we'll see a similar thing happen with AI. Except, this time around there's another factor which is that there's direct competition from China. My prediction is that Chinese models will win in the end because Chinese companies aren't looking for direct monetization, they're treating models as infrastructure, sort of what we see with Linux. Most companies don't try to monetize it directly, they build stuff like AWS on top of it and that becomes the product.

I expect American companies are just going to run out of runway in the near future, and they're also getting squeezed by cheap Chinese models that are also open source. Big companies prefer running stuff on prem because they can keep their data private that way, and they can tune the models any way they want. Meanwhile, stuff like DeepSeek is orders of magnitude cheaper than Claude for individual use. So I just don't see a long term business model for models as a service, especially not at pricing like Anthropic or even Google. Vast majority of people aren't gonna pay 20 bucks a month for this stuff, let alone a 100.

But it isn't a niche area, it's the major focus of corporate investment for the last year or two.

the most weird is construction, i don't know what to make of it

Construction usually involves a lot of repetitive, very detail-oriented tasks, like invoicing, writing buy orders, signing off on deliveries, doing payroll, scheduling shifts, project management. A very limited scope LLM can make these tasks much easier I imagine.

How are they measuring productivity? If one of your KPIs is "Accelerate Business Objectives By Leveraging Theft Machine Synergy", the results may be a bit skewed. 😄