America's Top Companies Keep Talking About AI – but Can't Explain the Upsides

https://en.wikipedia.org/wiki/Constructive_dismissal

>In employment law, constructive dismissal occurs when an employee resigns due to the employer creating a hostile work environment.

No employee is resigning when an employer tells the employee they are terminated due to AI replacing them.

The real question is do those unicorns exist or is it all worthless.

> The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows, Challapally explained.

The 95% isn't a knock on the AI tools, but that enterprises are bad at integration. Large enterprises being bad at integration is a story as old as time. IMO, reading beyond the headline, the report highlights the value of today's AI tools because they are leading to enterprises trying to integrate faster than they normally would.

"AI tools found to be useful, but integration is hard like always" is a headline that would have gotten zero press.

As someone who has been in senior engineering management, it's helpful to understand the real reason, and this is definitely not it.

First, these AI subscriptions are usually month-to-month, and these days with the AI landscape changing so quickly, most companies would be reluctant to lock in a longer term even if there were a discount. So it's probably not hard to quickly cancel AI spend for SaaS products.

Second, the vast majority of companies understand sunk cost fallacy. If they truly believed AI wouldn't be a net benefit, they wouldn't force people to use it just because they already paid for it. Salaries for engineers are a hell of a lot more than their AI costs.

The main reason for the push from the top is probably because they believe companies that don't adopt AI strategies now and ensure their programmers are familiar with AI toolsets will be at a competitive disadvantage. Note they may even believe that today's AI systems may not be much of a net benefit, but they probably see the state of the art advancing quickly so that companies who take a wait-and-see approach will be late to the game when AI is a substantial productivity enhancer.

I'm not at all saying you have to buy into this "FOMO rationale", but just saying "they already paid the money so that's why they want us to use it" feels like a bad excuse and just broadcasts a lack of understanding of how the vast majority of businesses work.

They have judgement. They can improve what was generated. They can fix a result when it falls short of the objective.

And they know when to give up on trying to get AI to understand. When rephrasing won't improve next word prediction. Which happens when the situation is complex.

For example I have some product ideas in my head for things to 3D print, but I don't know enough about design to come up with the exact mechanisms and hinges for it. I've tried the chatbots but none of them can really tell me anything useful. But once I already know the answer, they can list all kinds of details and know all about the specific mechanisms. But are completely unable to suggest them to me when I don't mention them by name in the prompt.

The thing that changed it was smartphones (~7 years later). Suddenly, the internet was available everywhere and not just a thing for nerds.

Not sure that AI is quite there yet, currently trying to identify what will be the catalyst that makes it seamless.

Mentioning the mid-1990s' internet boom is somewhat ironic imo, given what happened next. The question is whether "business models mature" with or without a market crash, given that the vast majority of ML money is provided for LLM efforts.

But most AI push is for LLMs, and all the companies you talk about seem to be using other types of AI.

> Failures of workplace pilots usually result from integration challenges, not because the technology lacks value.

Bold claim. Toxic positivism seems to be too common in AI evangelists.

> The fact that 374 S&P 500 companies are openly discussing it shows the opposite of “no clear upside” — it shows wide strategic interest.

If the financial crisis taught me something is that if a company jumps of a bridge the rest will follow. Assuming that there must be some real value because capitalism is missing the main proposition of capitalism, companies will take stupid decisions and pay the price for it.

There was a weird moment in the late noughties where seemingly every big consumer company was creating a presence in Second Life. There was clearly a lot of strategic interest...

Second Life usage peaked in 2009 and never recovered, though it remains somewhat popular amongst furries.

Bizarrely, this kind of happened _again_ with the very similar "metaverse" stuff a decade or so later, though it burned out somewhat quicker and never hit the same levels of farcical nonsense; I don't think any actual _countries_ opened embassies in "the metaverse", say (https://www.reuters.com/article/technology/sweden-first-to-o...).

The AI doesn't carry professional liability insurance, so this is about as good as asking one of the legal subreddits. It's probably fine in this case since the worst case is not getting the money that you were at risk of not getting anyway.

Exactly my experience. I feel like LLMs have potential as Expert Systems/Smart websearch, but not as a generative tool, neither for code nor for text.

You spend more time understanding stuff than writing code, and you need to understand what you commit with or without LLM. But writing code is easier that reviewing, and understanding by doing is easier than understanding by reviewing (bc you get one particular thing at the time and don't have to understand the whole picture at once). So I have a feeling that agents do even have negative impact.

It seems that the smaller the task and the more tightly defined the input and output, the better the LLMs are at one-shotting.

It works best when the target is small and easily testable (without the LLM being able to fudge the tests, which it will do.)

For many other tasks it's like training an intern, which is worth it if the intern is going to grow and take on more responsibility and learn to do things correctly. But since the LLM doesn't learn from its mistakes, it's not a clear and worthwhile investment.

I basically use it for google on steroids for obscure topics, for simple stuff I still use normal search engines.