95% of Companies See 'Zero Return' on $30B Generative AI Spend

> “For instance, an employee can adjust based on new instructions, previous mistakes, and situational needs. A generative AI model cannot carry that memory across tasks unless retrained.”

This is factually false; that is exactly what memory, knowledge, and context can do with no retraining. Not having completely solved self adjustment is not a barrier, merely a hurdle already currently in research. Imagine if, like the human brain, an LLM were to apply training cases identified throughout the day while it “slept”; the author seems to think this would be a massive undertaking of “retraining”. And sorry, if you’ve worked with many of the same types of employees I have over there years, you’d already know that the suggestion employees are more easily adaptable, will remember across tasks, and are good at adjusting to situational needs, can be laughable and even detrimental to think, depending on the person.

The statement seems to be based more on the complaint of a lawyer who has no actual AI technical expertise; hardly the best source for what things AI can and cannot do “currently”. It’s useful to consider that almost all of the subjective opinions expressed in this report come from, effectively, 300 or so (maybe less) individuals, and that it isn’t all that easy to distinguish between the findings that are truly fact-based or opinion-based, especially with the linked post.

It is also important to note that this report seems to focus more on the feedback and data from CEOs who look at P&L, not intrinsic or unquantified values. How do you directly quantify a developer fixing 3 bugs instead of 1 in your internal tool? Unless there are layoffs attributed to this specifically, and not “market changes” or general “reorganizations”, how is this quantified? There are a million things AI might do in the future that may not have a massive, or any, clear return on investment. If I buy a better shovel that saves me an hour on digging a trench in my own backyard, how much money did that save me?

GDP is 29.2t, of which an additional google would find that U.S. labor accounts for an estimated 18.5t. 2.2% of 18.5t, or 29.2t, is still not 61m. In most cases, if the simple part of the math doesn’t fit, there are potentially some bigger logic mistakes at play.

Best of luck on your understanding. As I said, I’d suggest maybe starting with direct statements from factual sources and the report rather than those the author (or you) interpreted.

But I totally get how the GP comment landed the way you describe, but that's why we have guidelines like these:

"Please don't pick the most provocative thing in an article or post to complain about in the thread. Find something interesting to respond to instead."

and (repeating this one) "Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith."

Applying those to the GP comment (https://news.ycombinator.com/item?id=44974675), while it's true that the first sentence could sound like chest-beating, the rest of the comment was making an interesting point about risk tolerance.

The 'strongest plausible interpretation' might go something like this: "Even if the article is correct that 95% of companies are seeing zero return on AI spend so far, that by no means proves that they're on the wrong track. With a major technical wave like AI, it's to be expected that early efforts will involve a lot of losses. Long-term success may require taking early risk, and those with lesser risk tolerance, who aren't willing to sustain the losses associated with these pathfinding efforts, may find themselves losing out in the long run."

I have no idea whether that's right or not but it would make for a more interesting and less hostile conversation! which is basically what we're shooting for here.

Maybe we need to not just say "people aren't adopting it" but actually listen to why.

AI is a new tool with a learning curve. But that means it's a luxury choice-- we can spend our days learning the new tool, trying out toy problems, building a workflow, or we can continue to use existing tools to deliver the work we already promised.

It's also a tool with an absolutely abysmal learning model right now. Think of the first time you picked up some heavy-duty commercial software (Visual Studio, Lotus 1-2-3, AutoCAD, whatever). Yes, it's complex. But for those programs, there were reliable resources and clear pathways to learn it. So much of the current AI trend seems to be "just keep rewording the prompt and asking it to think really hard and add more descriptive context, and eventually magic happens." This doesn't provide a clear path to mastery, or even solid feedback so people can correct and improve their process. This isn't programming. It's pleading with a capricious deity. Frustration is understandable.

If I have to use AI, I find I prefer the Cursor experience of "smarter autocomplete" than the Claude experience of prompting and negotiation. It doesn't have the "special teams" problem of having to switch to an entirely different skill set and workflow in the middle of the task, and it avoids dumping 2000 line diffs so you aren't railroaded into accepting something that doesn't really match your vision/style/standards.

What would I want to see in a prompt-based AI product? You'd have much more documented, formal and deterministic behaviour. Less friendly chat and more explicit debugging of what was generated and why. In the end, I guess we'd be reinventing one of those 1990s "Rapid Application Development" environments that largely glues together pre-made components and templates, except now it burns an entire rainforest to build one React SPA. Has anyone thought about putting a chat-box front end around Visual Basic?

Sorry, this are only the categories. But i have actual products in mind.