Nobody actually wants half the useless tools companies are coming up with because most of the solutions are not really novel. They are just wrapping an LLM.
It's kinda like what I realized with the meta Ray-Bans: I can have these things on my face, they can tell me the answer to virtually any question in 10 seconds or less.
But I, as a human, rarely have questions to ask. When you walk in to your local grocery store - you generally know what you want and where to find it.
A ton of companies are just gluing LLM text boxes into apps and then scratching their heads when people don't use them.
Why?
Because the customer wasn't the user - it was their boss and shareholders. It was all done to make someone else think 'woah, they are following the trend!'.
The core issue with generative AI is that it all works best when focused in a narrow sense. There is like one or two really clever uses I've seen - disappointingly, one of them was Jira. The internal jargon dictionary tool was legitimately impressive. Will it make any more money? Probably not.
addaon · 2h ago
> But I, as a human, rarely have questions to ask.
Wow. This just does not match my personal experience. I do an hour or so walk around the reservoir near my house 4-5 times a week, letting my mind wander freely -- and I find that I stop on average at least five or ten times to take notes about questions to learn the answers to later, and occasionally decide that it's worth it to break pace to start learning the answer right then and there.
alistairSH · 2h ago
But do you need AI for those answers? I sometimes do the same thing, but Google/DDG/whatever works fine for most, and a niche app works for others (IDing a bird = Merlin app, for example).
com2kid · 2h ago
Last year one of my berry bushes had browning leaves with some spots. Google search said infection, treatment plan, etc.
This year I snapped a pic and sent to chat gpt. Normal end of year die off, cut the brown branches away, here is a fertilizer schedule for end of year to support new growth for the next year.
ChatGPT makes gardening so much easier, and that is just one of many areas. Recipes are another, don't trust the math, but chat gpt can remix and elevate recipes so much better than Google recipe blog spam posts can.
jdhzzz · 2h ago
I read that as I-Ding a bird. It was a second of wondering what I-Ding a bird was until I got to "Merlin" and realized it was ID-ing a bird (face-palm emoji here).
poszlem · 2h ago
Not the OP, but I ask way more questions now than I used to. Before, I’d sometimes wonder about things, but not enough to actually go and research them. Now, it’s as simple as asking the AI, and more often than not, I get a satisfying answer.
throwanem · 2h ago
What was the last thing you asked about? What was the answer?
poszlem · 1h ago
The origin of the word calf.
1. Calf (young cow, young of certain other mammals)
Old English: cealf (plural calfru or later calves)
Proto-Germanic: kalbaz or *kalbaz/kalbazō
Cognates: Old Norse kálfr, Old High German kalb, German Kalb, Dutch kalf.
Proto-Indo-European root: often linked to gel- (“to swell, be rounded”), possibly referring to the rounded shape of a young animal. Some etymologists, however, leave it as “origin uncertain” beyond Proto-Germanic.
2. Calf (back of the lower leg)
Old English: caf, cealf (“calf of the leg”) — likely related to the animal term, but the link is uncertain.
Possible origin: Could be from the same gel- “swell” root, referring to the bulging muscle at the back of the leg, or an independent development within Germanic.
Cognates: Old Norse kálfi (“calf of the leg”), Swedish kalv (leg calf), Icelandic kálfi.
throwanem · 1h ago
Can you tell me about the one two before that, without looking it up?
poszlem · 1h ago
Yes, but I’m not going to. You seem to think I owe you a performance or an explanation. Stop circling around trying to trip me up and just make your point, if you have one.
throwanem · 1h ago
You were the one who raised the subject, but sure, if that's the way you want it. You are making a mistake which I believe you will regret, outsourcing future time binding to a machine in this way. You seem to believe you are learning something and I do not think that is true, except for a habit of intellectual laziness that I expect will prove as corrosive for you as lucrative to others.
You're bragging about your calf strength as you habituate to walking with crutches you don't need. Today? Sure, fair enough. Couple years from now? Thank goodness that's not my problem.
poszlem · 42m ago
You’re not here to discuss, you’re here to lecture about “intellectual laziness”, which is exactly why I figured you were just trying to trip me up. I use AI the same way people used dictionaries or encyclopedias: to feed curiosity. I knock out little questions as they pop up, and if even a quarter of it sticks, that’s a win. If you want to twist that into “bragging about calf strength,” that’s just your insecurity talking.
throwanem · 39m ago
"[If] even a quarter of it sticks, that's a win." Sure. Enjoy your day.
sceptic123 · 2h ago
Whether it's correct or not is another question
agloe_dreams · 2h ago
Thats super reasonable - I'm a person with ADHD so if I'm asking questions in a grocery store context - I might fully forget things or take way too long to get things done - Going for a walk in nature is absolutely a much better place for questions like that to me though. I think I would prefer to not have tech in the moment to take me out of the space.
mrandish · 1h ago
As a fellow ADHDer, can confirm. I must aggressively mono-task to ensure things get done. I have to consciously manage which mode I'm in, "Goal" or "Explore". A simple heuristic I sometimes share with others is: "I can either 'think deeply' or 'do/talk/listen'. Doing both modes at once is possible but at reduced throughput and quality of each. Switching modes is laggy." It's not precisely accurate and there are exceptions but it gets the general idea across.
infecto · 2h ago
I am in the same boat. I am always thinking about things and recently often asking ChatGPT for an answer. Having a natural language interface for questions has opened the door for me to many more questions.
svara · 2h ago
I think not having those instant answers available is a big part of why your mind wanders in that setting.
addaon · 2h ago
I have the answers available (I have a phone and good connection), I just am tactical about when to pursue the answer in realtime and when not. If it feels like it's going to open up a wider field of questioning -- or if it feels like I'll learn that this vein is fully mined and goes nowhere -- I'll spend a few minutes searching; otherwise, defer.
mikepurvis · 2h ago
I was going to say the same. It's probably so much healthier to make note of questions for later research than to stop right then and there and either a) fall down a Wikipedia rabbit hole or b) have an AI strapped to your face perform an info-dump.
throwanem · 2h ago
Not everyone wants an imagination. This is good for those who don't.
reactordev · 2h ago
I rarely have questions of others but I always question myself. :shrug:
There’s a difference between asking out loud or another being vs asking yourself internally.
addaon · 2h ago
> I rarely have questions of others but I always question myself.
There's only so many questions I have the ability to answer myself. Of those, there's only so many that I have the lifespan to answer myself. We stand on the shoulders of giants, and even on the shoulders of average people -- really it's shoulders all the way down. Unless the questioning itself is the source of joy (which it certainly sometimes is), I prefer to find out what others have learned when they asked the same questions. It's vanishingly rare that I believe I'm the first to think through something.
reactordev · 2h ago
Absolutely, they usually tend to write about it...
GuB-42 · 1h ago
It happens to me all the time, however, I want to have real answers. And while a LLM is sometimes involved, I usually go deeper, with some cross referencing, fact checking and primary sources. LLMs are great at giving you a starting point, but the problem with them is that it is impossible to distinguish between fact or fiction, so I always have to verify. Really, I have seen my fair share of falsehoods popping up on LLMs, sometimes on simple and uncontroversial topics.
On hot topics like politics, illegal drugs, gender and racial differences, etc... it may be impossible to even get an answer passed the filters.
starik36 · 2h ago
My walk is also around a reservoir, also 4-5 times a week and the length of the walk around it is also 1 hour.
Are you the guy that walks the poodle?
addaon · 2h ago
Negative, just myself. I suspect I've mentioned my physical location on HN previously -- southern Utah.
delusional · 2h ago
I mirror that experience, except for the latter half. I enjoy just being outside and letting my mind wander, letting it wonder about odd questions in the moment. I never actually want or care about the answers, I just like the feeling of thinking.
I already have my phone, I could look up the answers immediately. The reason I don't isn't that I can't. It's that asking the question is the point, not answering it.
WD-42 · 1h ago
I just finished implementing a chatbot in a box for a clients sass. What problem does it solve? None that I can tell, other than now the sass “has ai”.
I still have access to the OpenAI dashboard. I can confirm nobody is actually using it.
brobdingnagians · 1h ago
We recently got a customer support request asking if we were going to "implement AI" on our website and then saying we could use it in our marketing if we did. No suggestion as to why they would find it useful, or what feature could be augmented with it. It's crazy that the hype is so high that random non-tech users suggest adding AI for marketing.
lumost · 56m ago
Embedded AIs are pretty dumb as a product in my opinion. Why would the customer pay you instead of their existing model vendor of choice? Why do they have to learn your chatbox - when it's probably using a crappier model and lacks the context of their preferred vendor.
I really don't want to pay for 5 different AI subscriptions, I want one subscription that works with all my other services (which I already pay for).
BobaFloutist · 1h ago
Now the sass can sass you
palmfacehn · 2h ago
>Because the customer wasn't the user - it was their boss and shareholders. It was all done to make someone else think 'woah, they are following the trend!'.
I'm seeing this again and again. Customers as users seems like the last concern, if it is a concern at all. Adherence to the narrative du jour, fundraising from investors and hyping the useless product up to dump on retail are the primary concerns.
Vaporware or a useless, unlaunched product are advantageous here. Actual users might report how underwhelming or useless it is. Sky high development costs are touted as wins.
sidewndr46 · 2h ago
I've tried to express a similar sentiment to people in the past - that 443rd redesign of the UI for JIRA that moves a button from one side to another. It isn't actually for you. You aren't the user of the software. The user of the software is the product manager (or equivalent role). They need to justify their current role or their next promotion.
Culonavirus · 2h ago
> Because the customer wasn't the user - it was their boss and shareholders.
It's kinda funny that some online shops are now bragging how great their customer support is because they DON'T use LLM bots xD
belter · 2h ago
Dealing with real humans in the future will be the ultimate VIP treatment.
throwanem · 2h ago
It already is.
iib · 2h ago
I think those kind of glasses may be really useful for blind people. I have seen similar glasses targeted at blind people, that at least in theory, seemed to me like a good idea.
I recall the glasses also can write on the screen inside the lens, which makes me think they may be good for deaf people as well.
It's just that these use-cases seem uncool, and big companies seem to have to be cool in order to keep either their status or their profits. But I have a feeling the technology may be really useful for some really vulnerable people.
marcosdumay · 1h ago
Yes, there are people working on image recognition glasses for blind people.
Nobody seems to have been successful yet, and I think the focus on applying LLMs instead of dumb UI and mixed dumb and ML image processing is a large reason why.
com2kid · 2h ago
I use my Meta glasses heavily on vacation, and then occasionally else where. The latest Llama isn't as smart as OpenAI, so after a few wrong answers I gave up on day to day queries.
That said, the scenarios they are good at they are really good at. I was traveling in Europe and the glasses where translating engravings on castle walls, translating and summarizing historical plaques, and just generally letting me know what was going on around me.
raincole · 2h ago
> I, as a human, rarely have questions to ask
This is an eye-opening sentence. It's quite hard to imagine how to live one's daily life with "few questions to ask." Perhaps this is a neurodivergent thing?
throwawaylaptop · 1h ago
I always ponder how many people have a refrigerator in their home their entire life, and what percentage of them don't know how it works.
I've asked several gfs, and they don't have even a hint of how it works. Guy friends do a bit better but not as well as you'd think.
So yes, people live their entire lives not asking obvious questions.
R_D_Olivaw · 2h ago
Oh what a blissful environment the mind that is not full of constant questions begging to be answered and explored must be.
I'll just be over here, floating (often treading water) in a raging river of "what ifs ...", "I wonder ifs..." And, "Hmmms?"
zoeysmithe · 2h ago
I'm autistic and I probably ask many more questions than most people.
I would also argue that ND people seem to be the heavier AI users, at least in my experience. Its a bit like the stereotypical 'wikipedia deep dive' but 10x.
dwb · 1h ago
Don’t try and diagnose people like this please. Even if you’re qualified, and I doubt you are, it’s very insensitive.
tempodox · 2h ago
> … disappointingly, one of them was Jira.
I think this highlights an interesting point: Sensible use cases are unsexy. But the pushers want stuff, however unrealistic, that lends itself to breathless hype that can be blown out of proportion.
thewebguyd · 2h ago
> There is like one or two really clever uses I've seen - disappointingly, one of them was Jira. The internal jargon dictionary tool was legitimately impressive. Will it make any more money? Probably not.
Sounds like Microsoft 365 Copilot at my org. Sucks at nearly everything, but it actually makes a fantastic search engine for emails, teams convos, sharepoint docs, etc. Much better that Microsoft's own global search stuff. Outside of coding, that's the only other real world use case I've found for LLMs - "get me all the emails, chats, and documents related to this upcoming meeting" and it's pretty good at that.
Though I'm not sure we should be killing the earth for better search, there are probably other, better ways to do it.
ljf · 2h ago
Agreed - 95% of the questions I ask Copilot, I could answer myself by searching emails, Teams messages and files - BUT Copilot does a far far better job than me, and quicker. I went from barely using it, to using it daily. I wouldn't say it is a massive speed boost for me, but I'd miss it if it was taken away.
Then the other 5% is the 'extra; it does for me, and gets me details I wouldn't have even known where to find.
But it is just fancy search for me so far - but fancy search I see as valuable.
tasty_freeze · 2h ago
My favorite copilot use is when I join a MS Teams meeting a few minutes late I can ask copilot: what have I missed? It does a fantastic job of summarizing who said what.
kyledrake · 1h ago
> Though I'm not sure we should be killing the earth for better search
They also seem to be coming down in power usage substantially, at least for inference. There's pretty good models that can run on laptops now, and I still very much think we're in the model T phase of this technology so I expect further efficiency refinements. It also seems like they have recently hit a "cap" on the increase in intelligence models are getting for more raw power.
The trendline right now makes me wonder if we'll be talking about "dark datacenters" in the future the same way we talked about dark fiber after the dot com bubble.
michaelfm1211 · 2h ago
> The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.
Makes sense. The people in charge of setting AI initiatives and policies are office people and managers who could be easily replaced by AI, but the people in charge not going to let themselves be replaced. Salesmen and engineers are the hardest to replace, yet they aren't in charge so they get replaced the fastest.
thisisit · 40m ago
There is a reason why sales and marketing is first. It has to do with hallucination.
People have figured out that even if you mess up sales/support/marketing, worse case you apologize and give a gift coupon. And then there is also the verbose nature of LLMs which makes it better suited to write marketing copies etc.
On business process outsourcing like customer support lot of companies are using LLMs, so that part is unclear to me.
Other BPO processes are accounting & finance, IT, human resources etc. And while companies can take that hallucination risk for customers, they see it as a serious risk. If for example, the accounting and finance operations get messed up due to AI hallucination companies will be in real hot water. Same goes for other back office functions like HR, compliance etc. So, most likely this statement is just hogwash.
zoeysmithe · 2h ago
I think this is being overly complimenting to AI. I think the most obvious reason is that for almost all business use cases its not very helpful. All these initiatives have the same problem. Staff asking 'how can this actually help me,' because they can't get it to help them other than polishing emails, polishing code, and writing summaries which is not what most people's jobs are. Then you have to proofread all of this because AI makes a lot of mistakes and poor assumptions, on top of hallucinations.
I dont think Joe and Jane worker are purposely not using to protect their jobs, everyone wants ease at work, its just these LLM-based AI's dont offer much outside of some use cases. AI is vastly over-hyped and now we're in the part of the hype cycle where people are more comfortable saying to power, "This thing you love and think will raise your stock price is actually pretty terrible for almost all the things you said it would help with."
AI has its place, but its not some kind of universal mind that will change everything and be applicable in significant and fundamentally changing ways outside of some narrow use cases.
I'm on week 3 of making a video game (something I've never done before) with Claude/Chat and once I got past the 'tutorial level' design, these tools really struggle. I think even where an LLM would naturally be successful (structured logical languages), its still very underwhelming. I think we're just seeing people push back on hype and feeling empowered to say "This weird text autogenerator isn't helping me."
YetAnotherNick · 1h ago
> MIT found the biggest ROI in back-office automation
Can't find any source to this, even after searching in Google. To me who knows bit of this, I don't find it very believable. Compared to humans, AI struggles in places where a fixed structure and process is required.
K0nserv · 3h ago
I'm arriving at the conclusion that deployments of LLMs is most suitable in areas where the cost of false positives and, crucially, false negatives are low.
If you cannot tolerate false negatives I don't see how you get around the inaccuracy of LLMs. As long as you can spot false positives and their rate is sufficiently low they are merely an annoyance.
I think this is a good consideration before starting a project leveraging LLMs
infecto · 2h ago
Has inaccuracies been an issue for any of the systems you have developed using LLMs? I hear your complaint quite a bit but it does not align with my experience. Definitely one shotting a chatbot around an esoteric problem introduces possible inaccuracies. If I get an LLM to interrogate a pdf or other document that error rate drops significantly and is mostly on the part of the structuring process and not the LLM.
Genuinely curious what others have experienced but specifically those that are using LLMs for business workflows. It is not to say any system is perfect but for purpose driven data pipelines LLMs can be pretty great.
K0nserv · 2h ago
Yes I've seen issues with both, but in part what's tricky about false negatives is also that you don't necessarily realise they are there. In the systems I've worked on we've made it simple for operators to verify the work the LLM has done, but this only guards against false positives, which are less problematic.
I've had pretty good success using LLMs for coding and in some ways they are perfect for that. False positives are usually obvious and false negatives don't matter because as long as the LLM finds a solution, it's not a huge deal if there was a better way to do it. Even when the LLM cannot solve the problem at all, it usually produces some useful artifacts for the human to build on.
simianwords · 1h ago
I completely agree. These are useful in fuzzy cases but we live in a fuzzy world. Most things are fuzzy and nothing is completely true or completely false.
If I as a human deploy code, it is not certain that it necessarily works - just like with LLMs. The extent is different however.
jbreckmckye · 2h ago
I agree, and it's why I think AI is a good $50 billion industry but not a $5 trillion industry.
zahlman · 3h ago
Am I the only one who looked at this shortened headline and wondered why anyone is allowing AIs to fly airplanes?
madcaptenor · 3h ago
No. I also thought that even a 95% success rate wouldn't be good enough for airplanes.
mr_toad · 2h ago
I just assumed it was developed by Boeing.
rigrassm · 2h ago
Thank you for starting my week with a good laugh!
marcosdumay · 1h ago
As a rule of thumb, airplanes subsystems are expected to have 99.99999% reliability, so the whole gets 99.9999%.
Airline airplanes are currently more than one order of magnitude better than this. But if you have that, you can claim your plane works.
Culonavirus · 2h ago
It's very much enough for drones tho... all you need is a tiny Jensen's chip, moped engine, some boom boom play-doh and you're ready to rock. No remote control needed.
jbreckmckye · 2h ago
Drones are expensive. Solid six figures expensive. And they are used around or on things that are even more expensive. You wouldn't want ChatGPT piloting them.
Culonavirus · 2h ago
Under $50k for a Geran-2 level drone.
apwell23 · 2h ago
we can do it once we know how they work. which will be never.
dylan604 · 2h ago
Why not though? Current autopilot just attempts to keep plane on course/speed/altitude. Some can go further with auto-landing, but extreme emergency use only. I could see the airlines wanting to seek any fuel savings possible by possibly allowing AI to test slight changes to altitude/speed/course to conserve fuel based on some live inputs.
lemonwaterlime · 1h ago
The mathematics that LLMs and machine learning are based on started off being developed for aircraft decades ago. It’s called “control theory”. So we had “AI” on airplanes first. Specifically we had adaptive control algorithms explicitly because of the problems introduced by fuel levels changing during the course of a flight.
In physics, we typically start with mass-spring-damper system representation. Elementary physics and engineering typically has assumptions such as mass being constant. You develop all sorts of dynamical models and intuition with that assumption. But an aircraft burns fuel as it flies, meaning its mass changes during the course of the flight. Thus your models drift and you have to adapt to that.
Pilots would have tomes they'd have to switch between at various points of the journey and adaptive control algorithms alleviated this. They still needed the actual reference guide in the cockpit as a risk mitigation.
The difference between that decades old application is that you don’t need a billion parameter model to do flight control. Most people do not understand the historic development of these techniques. The foundation of them has been around for a while. What we have done with the newest batch of "AI" is massively scale them up.
0xCMP · 2h ago
Yes, I wish it was written "Pilot Programs" or something.
layer8 · 2h ago
It certainly made me do a double-take.
atonse · 2h ago
haha I thought the same and also thought "but everyone uses autopilot, what's the problem"
strictnein · 2h ago
> "“Every single Monday was called 'AI Monday.' You couldn’t have customer calls, you couldn’t work on budgets, you had to only work on AI projects.”"
> "Vaughan saw that his team was not fully on board. His ultimate response? He replaced nearly 80% of the staff within a year"
Being that this is Fortune magazine, it makes sense that they're portraying it this way, but reading between the lines there a little bit, it seems like the staff knew what would happen and wasn't keen on replacing themselves.
grahar64 · 2h ago
5% success is actually way higher than I thought it would be. At that rate I suppose there will be actually profitable AI companies with VC subsidies
whymauri · 2h ago
5% success rate might mean: if you get it to work, you are capturing value that the other 95% are not.
A lot of this must come down to execution. And there's a lot of snake oil out there at the execution layer.
5% is not unexpected, as startup success rates are normally about 1:22 over 3 years. lol =3
trenchpilgrim · 3h ago
What's the failure rates if technology pilots in general for comparison?
For example, I heard that SAP has an 80-90% deployment failure rate back in the day, but don't have a citable source for it.
RaftPeople · 2h ago
> I heard that SAP has an 80-90% deployment failure rate
Something to keep in mind is that ERP "failure" is frequently defined as went over budget or over time, even if it ultimately completed and provided the desired functionality.
It's a much smaller percentage of projects that are either cancelled or went live and significantly did not function as the business needed.
kqr · 3h ago
Depends on industry I would think. In my previous industry it was something like 25 %, in my current industry it is closer to 80 %.
alach11 · 2h ago
I think you're on the right track here. Most technology pilots fail. As long as risk/investment is managed appropriately, this is healthy. This seems to follow from Surgeon's Law... 90% of everything is crap [0].
That is not remotely true tbh. The company would have failed long ago if it were
mike_hearn · 2h ago
Not if every manufacturing company in the world decided to use your software anyway.
ERP rollouts can "fail" for lots of reasons that aren't to do with the software. They are usually business failures. Mostly, companies end up spending so much on trying to endlessly customize it to their idiosyncratic workflows that they exceed their project budgets and abandon the effort. In really bad cases like Birmingham they go live before actually finishing setup, and then lose control of their books and have to resort to hiring people to do the admin manually.
There's a saying about SAP: at some point gaining competitive advantage in manufacturing/retail became all about who could make SAP deployment a success.
This is no different to many other IT projects, most of them fail too. I think people who have never worked in an enterprise context don't realize that; it's not like working in the tech sector. In the tech industry if a project fails, it's probably because it was too ambitious and the tech itself just didn't work well. Or it was a startup whose tech worked, but they couldn't find PMF. But in normal, mature, profitable non-tech businesses a staggering number of business automation projects just fail for social or business reasons.
AI deployments inside companies are going to be like that. The tech works. The business side problems are where the failures are going to happen. Reasons will include:
• Not really knowing what they want the AI to do.
• No way to measure improved productivity, so no way to decide if the API spend is worth it.
• Concluding the only way to get a return is entirely replace people with AI and then having to re-hire them because the AI can't handle the last 5% of the work.
• Non-tech executives doing deals to use models or tech stacks that aren't the right kind or good enough.
etc
trenchpilgrim · 2h ago
Not if most of those failures are medium sized businesses with <1000 employees and your successes include a majority of the world's largest corporations that sell goods.
How much money can you pull out as a failed startup founder?
About a mil? Maybe two? Seems realistic…
People have to invent whatever seems reasonable while squinting given how much accumulation of capital there is.
The guys with money are easy to fool. Just lie to them about your „product”, get the cash, get out of the rat race, smooth sailing.
Of course easier said than done. I can’t lie this convincingly, I don’t have the con man skillset or connections.
So I’m stuck in a 9 to 5. Zzz…
antisthenes · 1h ago
> Of course easier said than done. I can’t lie this convincingly, I don’t have the con man skillset or connections.
Isn't the idea that you're not a shitty human being enough in and of itself?
kubb · 1h ago
I am. I'm working for a despicable company for money.
And an incompetent one at that. I can't grab a bag and leave.
brettgriffin · 3h ago
> Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L.
This summer, I built two very sophisticated pieces of software. A financial ledger to power accrual accounting operations and a code generation framework that scaffolds a database from a defined data model to the frontend components and everything in between.
I used ChatGPT substantially. I'm not sure how long it would have taken without generative AI, but in reality, I would have just given up out of frustration or exhaustion. From the outside, it would appear to any domain expert that at least three other people worked on these giving the pace at which they got completed.
The completion of those two were seminal moments for me. I can't imagine how anyone, in any field of information systems, is not multiples more effective than they were five years ago. That directly affects a P&L and I can't think of anything in my career that is even remotely close to having that magnitude.
I don't know what encapsulates an AI pilot in these orgs, and I'm sure they are massively more complex than anything I've done. But to hear 95% of these efforts don't have a demonstrable effect is just wild.
soiltype · 2h ago
> From the outside, it would appear to any domain expert that at least three other people worked on these giving the pace at which they got completed.
Did several domain experts tell you this or are you making it up?
> I can't imagine how anyone, in any field of information systems, is not multiples more effective than they were five years ago.
Perhaps "they are massively more complex than anything I've done"
brettgriffin · 2h ago
> Did several domain experts tell you this or are you making it up?
It's an assertion among eight other engineers on the project with ~15 years of experience in the domain. They are domain experts. This part isn't up for debate.
soiltype · 52m ago
I'm not questioning the credentials of your coworkers - I didn't know they existed!
Just so I'm clear then, because this adds a lot of context: Nobody else worked on the 2 softwares you mentioned, but you are on a team? Are the softwares part of a business, one that's making money?
nemomarx · 2h ago
I think they mean integrating AI into the business system directly and not using it to code things. I can see that having a more neutral impact
brettgriffin · 2h ago
> 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.
Maybe I misunderstood this, but I took this to mean that people inside enterprises are struggling using tools like ChatGPT. They do point out that perhaps the tools are being deployed in the wrong areas:
> The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.
But I've seen some amazing automation does in sales and marketing that directly affected sales efficiency and reduced sales and marketing expenses.
layer8 · 2h ago
“AI pilots” in the article refers to developing AI-based tools, not to using AI for software development. These projects have a 95% failure rate of successfully deploying the AI tool being developed into production.
Regarding use of AI in software development (which is not what the article is about), the proof of the pudding isn’t in greenfield projects, it’s in longer-term software evolution and legacy code. Few disagree that AI saves time for prototyping or creating a first MVP.
ModernMech · 2h ago
> But to hear 95% of these efforts don't have a demonstrable effect is just wild.
Why tho? You used AI to make some software, but did you use AI to achieve rapid revenue acceleration?
That you used AI to build software seems tangential to whether it can increase revenues. Over the years, we've seen many technologies that didn't deliver on promises of rapidly increasing revenues despite being useful for creating software (cough OOP cough), so this new one failing to live up to expectations isn't surprising. Actually given the history of technologies that over promise and under deliver on massive hype, disappointment should be the null hypothesis.
onlyrealcuzzo · 2h ago
These seems like a glass-is-half-empty view.
5% are succeeding. People are trying AI for just about everything right now. 5% is pretty damn good, when AI clearly has a lot of room to get better.
The good models are quite expensive and slow. The fast & cheap models aren't that great - unless very specifically fine-tuned.
Will it get better enough so that that growth rate in success pilots grows from 5% - 25% in 5 years or 20? Who knows, but it almost certainly will grow.
It's hard to tell how much better the top foundation models will get over the next 5-10 years, but one thing that's certain is that the cost will go down substantially for the same quality over that time frame.
Not to mention all the new use cases people will keep trying over that timeline.
If in 10-years time, AI is succeeding in 2x as many use cases - that might not justify current valuations, but it will be a much better future - and necessary if we're planning on having ~25% of the population being retired / not working by then.
Without AI replacing a lot of jobs, we're gonna have a tough time retiring all the people we promised retirements to.
jbreckmckye · 2h ago
> 5% is pretty damn good, when AI clearly has a lot of room to get better.
That depends if the AI successes depended much on the leading edge of LLM developments, or if actually most of the value was just "low hanging fruit".
If the latter, that would imply the utility curve is levelling out, because new developments are not proving instrumental enough.
I'm thinking of an S curve: slow improvements through the 2010s, then a burst of activity as the tech became good enough to do something "real", followed by more gradual wins in efficiency and accuracy.
onlyrealcuzzo · 1h ago
I agree it's an S-curve, but it's anyone's guess where on the S we are.
And regardless, I still see this as very positive for society - and don't care as much about whether or not this is an AI bubble or not.
This resonates. Upskilling to AI tools is perhaps the biggest problem of our day. One idea we have to tackle this problem is to bring onboarding/learning directly into the user's work environment, track struggles and offer targeted support, and create continuous feedback loops. If anyone has faced challenges with increasing activation and retention of users on pilots (or external-facing products), would love to chat and see how we can help .
bilsbie · 3h ago
I can’t help feeling that we’re rapidly heading towards the “trough of disillusionment”.
(How should I invest if I have this thesis)
Davidzheng · 2h ago
short nvidia?
sounds · 2h ago
At this rate, how is it better than pure random chance?
The article mentions 19-20 year old founders, focused on solving single user problems, were the successes.
The sample size is 300 public AI deployments and an undisclosed number of private in-house AI projects. And the survey seems to only consider business applications, as compared with end-user applications like media and software. That's significant but not definitive.
Isn't it more likely that existing problems with low hanging fruit, perhaps unpopular answers, that could be solved by leaning on "AI". And perhaps "AI" wasn't the key to success?
I think for a long time, cutting corners so that the number can go up next quarter has worked surprisingly well. Genuinely, I don't think a lot of corporations view offering a better product as a viable means of competing in the 2025 marketplace.
For them, AI is not the next industrial revolution, it's the next overseas outsourcing; AI isn't a way to bring new value to customers, it's a way to bring roughly the same value (read worse) but at a much cheaper cost to them. If they get their way, everything will get worse, while they make more money. That's the value proposition at play here.
ipnon · 2h ago
This is proof LLMs are viable and productive in my opinion. The baseline rate for business failure over 5 years is around 90%, so they say. With how much hype surrounds LLM wrapper startups this is still an astounding amount of novel business model creation.
Oh god what is this website it gives me a headache with all the pop-ups and auto playing videos.
sitzkrieg · 1h ago
lots of bad partial solutions looking for problems companies rushed to implement
syngrog66 · 1h ago
The title led me to assume it was about the aircraft type of pilot.
scotty79 · 2h ago
I remember when it was being said that computers in business had basically the same impact.
sam0x17 · 2h ago
I mean 5% not failing is pretty standard for any startup-driven thing.
amirkabbara · 3h ago
Why so bad?
longtimelistnr · 3h ago
Because for the typical office - documents are strewn about on random network drives and are not formatted similarly. This combined with the inability to nail down 100% accuracy on even just internal doc search is just too much to overcome for non-tech industry offices. My office is mind blown if i use Gemini to extract data from a PDF and convert it to an .xlsx or .csv
As a technically minded person but not a comp sci guy, refining document search is like staring into a void and every option uses different (confusing) terminology. This makes it extra difficult for me to both do my regular job AND learn the multiple names/ways to do the exact same thing between platforms.
The only solution that has any reliability for me so far are Gemini instances where i upload only the files i wish to search and just keep it locked to a few questions per instance before it starts to hallucinate.
My attempt at RAG search implementation was a disaster that left me more confused than anything.
noddingham · 54m ago
Because you mentioned the use case specifically, I wanted to point you to the fact that Excel has been able to convert images to tables for a while now. Literally screenshot a table from your PDF and it will convert to table. Not trying to diminish any additional capabilities you're getting from Gemini, but this screenshot to table feature has been huge for my finance team.
Turns out that garbage text has very little intrinsic value
ARandumGuy · 3h ago
Any consumer facing AI project has to contend with the fact that GenAI is predominantly associated with "slop." If you're not actively using an AI tool, most of your experience with GenAI is seeing social media or Youtube flooded with low quality AI content, or having to deal with useless AI customer support. This gives the impression that AI is just cheap garbage, and something that should be actively avoided.
troupo · 3h ago
It's in the name: generative AIs.
There are very few use cases at companies where you need to generate something. You want to work with the company's often very private disparate data (with access controls etc.) You wouldn't even have enough data to train a custom LLM, much less use a generic one.
morkalork · 2h ago
In my experience is that LLMs get you 80%of the way to a solution almost immediately but that last 20% when it comes to missing knowledge, data, or accuracy is a complete tar pit and will wreck adoption. Especially since many vendors are selling products that are wrappers and provide generic, non-customised solutions. I hear the same from others doing trials with various AI tools as well.
nathan_compton · 3h ago
I think one reason for this is that LLMs are sort of maximally if accidentally designed to fuck up our brains. Despite all the advancements in the last five years I see them as still, fundamentally, text transformation machines which have only very limited sort of intelligence. Yet because nothing in history has been able to generate language except humans, most of us are not prepared to make rational judgements about their capabilities and those of us that may be also often fail to do so.
The fact that we live in an era where tech people have been so investor pilled that overstating the capabilities of technology is basically second nature does not help.
It's kinda like what I realized with the meta Ray-Bans: I can have these things on my face, they can tell me the answer to virtually any question in 10 seconds or less.
But I, as a human, rarely have questions to ask. When you walk in to your local grocery store - you generally know what you want and where to find it. A ton of companies are just gluing LLM text boxes into apps and then scratching their heads when people don't use them.
Why?
Because the customer wasn't the user - it was their boss and shareholders. It was all done to make someone else think 'woah, they are following the trend!'.
The core issue with generative AI is that it all works best when focused in a narrow sense. There is like one or two really clever uses I've seen - disappointingly, one of them was Jira. The internal jargon dictionary tool was legitimately impressive. Will it make any more money? Probably not.
Wow. This just does not match my personal experience. I do an hour or so walk around the reservoir near my house 4-5 times a week, letting my mind wander freely -- and I find that I stop on average at least five or ten times to take notes about questions to learn the answers to later, and occasionally decide that it's worth it to break pace to start learning the answer right then and there.
This year I snapped a pic and sent to chat gpt. Normal end of year die off, cut the brown branches away, here is a fertilizer schedule for end of year to support new growth for the next year.
ChatGPT makes gardening so much easier, and that is just one of many areas. Recipes are another, don't trust the math, but chat gpt can remix and elevate recipes so much better than Google recipe blog spam posts can.
1. Calf (young cow, young of certain other mammals)
Old English: cealf (plural calfru or later calves)
Proto-Germanic: kalbaz or *kalbaz/kalbazō
Cognates: Old Norse kálfr, Old High German kalb, German Kalb, Dutch kalf.
Proto-Indo-European root: often linked to gel- (“to swell, be rounded”), possibly referring to the rounded shape of a young animal. Some etymologists, however, leave it as “origin uncertain” beyond Proto-Germanic.
2. Calf (back of the lower leg)
Old English: caf, cealf (“calf of the leg”) — likely related to the animal term, but the link is uncertain.
Possible origin: Could be from the same gel- “swell” root, referring to the bulging muscle at the back of the leg, or an independent development within Germanic.
Cognates: Old Norse kálfi (“calf of the leg”), Swedish kalv (leg calf), Icelandic kálfi.
You're bragging about your calf strength as you habituate to walking with crutches you don't need. Today? Sure, fair enough. Couple years from now? Thank goodness that's not my problem.
There’s a difference between asking out loud or another being vs asking yourself internally.
There's only so many questions I have the ability to answer myself. Of those, there's only so many that I have the lifespan to answer myself. We stand on the shoulders of giants, and even on the shoulders of average people -- really it's shoulders all the way down. Unless the questioning itself is the source of joy (which it certainly sometimes is), I prefer to find out what others have learned when they asked the same questions. It's vanishingly rare that I believe I'm the first to think through something.
On hot topics like politics, illegal drugs, gender and racial differences, etc... it may be impossible to even get an answer passed the filters.
Are you the guy that walks the poodle?
I already have my phone, I could look up the answers immediately. The reason I don't isn't that I can't. It's that asking the question is the point, not answering it.
I still have access to the OpenAI dashboard. I can confirm nobody is actually using it.
I really don't want to pay for 5 different AI subscriptions, I want one subscription that works with all my other services (which I already pay for).
I'm seeing this again and again. Customers as users seems like the last concern, if it is a concern at all. Adherence to the narrative du jour, fundraising from investors and hyping the useless product up to dump on retail are the primary concerns.
Vaporware or a useless, unlaunched product are advantageous here. Actual users might report how underwhelming or useless it is. Sky high development costs are touted as wins.
It's kinda funny that some online shops are now bragging how great their customer support is because they DON'T use LLM bots xD
I recall the glasses also can write on the screen inside the lens, which makes me think they may be good for deaf people as well.
It's just that these use-cases seem uncool, and big companies seem to have to be cool in order to keep either their status or their profits. But I have a feeling the technology may be really useful for some really vulnerable people.
Nobody seems to have been successful yet, and I think the focus on applying LLMs instead of dumb UI and mixed dumb and ML image processing is a large reason why.
That said, the scenarios they are good at they are really good at. I was traveling in Europe and the glasses where translating engravings on castle walls, translating and summarizing historical plaques, and just generally letting me know what was going on around me.
This is an eye-opening sentence. It's quite hard to imagine how to live one's daily life with "few questions to ask." Perhaps this is a neurodivergent thing?
I've asked several gfs, and they don't have even a hint of how it works. Guy friends do a bit better but not as well as you'd think.
So yes, people live their entire lives not asking obvious questions.
I'll just be over here, floating (often treading water) in a raging river of "what ifs ...", "I wonder ifs..." And, "Hmmms?"
I would also argue that ND people seem to be the heavier AI users, at least in my experience. Its a bit like the stereotypical 'wikipedia deep dive' but 10x.
I think this highlights an interesting point: Sensible use cases are unsexy. But the pushers want stuff, however unrealistic, that lends itself to breathless hype that can be blown out of proportion.
Sounds like Microsoft 365 Copilot at my org. Sucks at nearly everything, but it actually makes a fantastic search engine for emails, teams convos, sharepoint docs, etc. Much better that Microsoft's own global search stuff. Outside of coding, that's the only other real world use case I've found for LLMs - "get me all the emails, chats, and documents related to this upcoming meeting" and it's pretty good at that.
Though I'm not sure we should be killing the earth for better search, there are probably other, better ways to do it.
Then the other 5% is the 'extra; it does for me, and gets me details I wouldn't have even known where to find.
But it is just fancy search for me so far - but fancy search I see as valuable.
Are we, though? What I have read so far suggests the carbon footprint of training models like gpt4 was "a couple weeks of flights from SFO to NYC" https://andymasley.substack.com/p/individual-ai-use-is-not-b...
They also seem to be coming down in power usage substantially, at least for inference. There's pretty good models that can run on laptops now, and I still very much think we're in the model T phase of this technology so I expect further efficiency refinements. It also seems like they have recently hit a "cap" on the increase in intelligence models are getting for more raw power.
The trendline right now makes me wonder if we'll be talking about "dark datacenters" in the future the same way we talked about dark fiber after the dot com bubble.
Makes sense. The people in charge of setting AI initiatives and policies are office people and managers who could be easily replaced by AI, but the people in charge not going to let themselves be replaced. Salesmen and engineers are the hardest to replace, yet they aren't in charge so they get replaced the fastest.
People have figured out that even if you mess up sales/support/marketing, worse case you apologize and give a gift coupon. And then there is also the verbose nature of LLMs which makes it better suited to write marketing copies etc.
On business process outsourcing like customer support lot of companies are using LLMs, so that part is unclear to me.
Other BPO processes are accounting & finance, IT, human resources etc. And while companies can take that hallucination risk for customers, they see it as a serious risk. If for example, the accounting and finance operations get messed up due to AI hallucination companies will be in real hot water. Same goes for other back office functions like HR, compliance etc. So, most likely this statement is just hogwash.
I dont think Joe and Jane worker are purposely not using to protect their jobs, everyone wants ease at work, its just these LLM-based AI's dont offer much outside of some use cases. AI is vastly over-hyped and now we're in the part of the hype cycle where people are more comfortable saying to power, "This thing you love and think will raise your stock price is actually pretty terrible for almost all the things you said it would help with."
AI has its place, but its not some kind of universal mind that will change everything and be applicable in significant and fundamentally changing ways outside of some narrow use cases.
I'm on week 3 of making a video game (something I've never done before) with Claude/Chat and once I got past the 'tutorial level' design, these tools really struggle. I think even where an LLM would naturally be successful (structured logical languages), its still very underwhelming. I think we're just seeing people push back on hype and feeling empowered to say "This weird text autogenerator isn't helping me."
Can't find any source to this, even after searching in Google. To me who knows bit of this, I don't find it very believable. Compared to humans, AI struggles in places where a fixed structure and process is required.
If you cannot tolerate false negatives I don't see how you get around the inaccuracy of LLMs. As long as you can spot false positives and their rate is sufficiently low they are merely an annoyance.
I think this is a good consideration before starting a project leveraging LLMs
Genuinely curious what others have experienced but specifically those that are using LLMs for business workflows. It is not to say any system is perfect but for purpose driven data pipelines LLMs can be pretty great.
I've had pretty good success using LLMs for coding and in some ways they are perfect for that. False positives are usually obvious and false negatives don't matter because as long as the LLM finds a solution, it's not a huge deal if there was a better way to do it. Even when the LLM cannot solve the problem at all, it usually produces some useful artifacts for the human to build on.
If I as a human deploy code, it is not certain that it necessarily works - just like with LLMs. The extent is different however.
Airline airplanes are currently more than one order of magnitude better than this. But if you have that, you can claim your plane works.
In physics, we typically start with mass-spring-damper system representation. Elementary physics and engineering typically has assumptions such as mass being constant. You develop all sorts of dynamical models and intuition with that assumption. But an aircraft burns fuel as it flies, meaning its mass changes during the course of the flight. Thus your models drift and you have to adapt to that.
Pilots would have tomes they'd have to switch between at various points of the journey and adaptive control algorithms alleviated this. They still needed the actual reference guide in the cockpit as a risk mitigation.
The difference between that decades old application is that you don’t need a billion parameter model to do flight control. Most people do not understand the historic development of these techniques. The foundation of them has been around for a while. What we have done with the newest batch of "AI" is massively scale them up.
> "Vaughan saw that his team was not fully on board. His ultimate response? He replaced nearly 80% of the staff within a year"
Being that this is Fortune magazine, it makes sense that they're portraying it this way, but reading between the lines there a little bit, it seems like the staff knew what would happen and wasn't keen on replacing themselves.
A lot of this must come down to execution. And there's a lot of snake oil out there at the execution layer.
https://www.youtube.com/watch?v=KX5jNnDMfxA
5% is not unexpected, as startup success rates are normally about 1:22 over 3 years. lol =3
For example, I heard that SAP has an 80-90% deployment failure rate back in the day, but don't have a citable source for it.
Something to keep in mind is that ERP "failure" is frequently defined as went over budget or over time, even if it ultimately completed and provided the desired functionality.
It's a much smaller percentage of projects that are either cancelled or went live and significantly did not function as the business needed.
[0] https://en.wikipedia.org/wiki/Sturgeon%27s_law
ERP rollouts can "fail" for lots of reasons that aren't to do with the software. They are usually business failures. Mostly, companies end up spending so much on trying to endlessly customize it to their idiosyncratic workflows that they exceed their project budgets and abandon the effort. In really bad cases like Birmingham they go live before actually finishing setup, and then lose control of their books and have to resort to hiring people to do the admin manually.
There's a saying about SAP: at some point gaining competitive advantage in manufacturing/retail became all about who could make SAP deployment a success.
This is no different to many other IT projects, most of them fail too. I think people who have never worked in an enterprise context don't realize that; it's not like working in the tech sector. In the tech industry if a project fails, it's probably because it was too ambitious and the tech itself just didn't work well. Or it was a startup whose tech worked, but they couldn't find PMF. But in normal, mature, profitable non-tech businesses a staggering number of business automation projects just fail for social or business reasons.
AI deployments inside companies are going to be like that. The tech works. The business side problems are where the failures are going to happen. Reasons will include:
• Not really knowing what they want the AI to do.
• No way to measure improved productivity, so no way to decide if the API spend is worth it.
• Concluding the only way to get a return is entirely replace people with AI and then having to re-hire them because the AI can't handle the last 5% of the work.
• Non-tech executives doing deals to use models or tech stacks that aren't the right kind or good enough.
etc
About a mil? Maybe two? Seems realistic…
People have to invent whatever seems reasonable while squinting given how much accumulation of capital there is.
The guys with money are easy to fool. Just lie to them about your „product”, get the cash, get out of the rat race, smooth sailing.
Of course easier said than done. I can’t lie this convincingly, I don’t have the con man skillset or connections.
So I’m stuck in a 9 to 5. Zzz…
Isn't the idea that you're not a shitty human being enough in and of itself?
And an incompetent one at that. I can't grab a bag and leave.
This summer, I built two very sophisticated pieces of software. A financial ledger to power accrual accounting operations and a code generation framework that scaffolds a database from a defined data model to the frontend components and everything in between.
I used ChatGPT substantially. I'm not sure how long it would have taken without generative AI, but in reality, I would have just given up out of frustration or exhaustion. From the outside, it would appear to any domain expert that at least three other people worked on these giving the pace at which they got completed.
The completion of those two were seminal moments for me. I can't imagine how anyone, in any field of information systems, is not multiples more effective than they were five years ago. That directly affects a P&L and I can't think of anything in my career that is even remotely close to having that magnitude.
I don't know what encapsulates an AI pilot in these orgs, and I'm sure they are massively more complex than anything I've done. But to hear 95% of these efforts don't have a demonstrable effect is just wild.
Did several domain experts tell you this or are you making it up?
> I can't imagine how anyone, in any field of information systems, is not multiples more effective than they were five years ago.
Perhaps "they are massively more complex than anything I've done"
It's an assertion among eight other engineers on the project with ~15 years of experience in the domain. They are domain experts. This part isn't up for debate.
Just so I'm clear then, because this adds a lot of context: Nobody else worked on the 2 softwares you mentioned, but you are on a team? Are the softwares part of a business, one that's making money?
Maybe I misunderstood this, but I took this to mean that people inside enterprises are struggling using tools like ChatGPT. They do point out that perhaps the tools are being deployed in the wrong areas:
> The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.
But I've seen some amazing automation does in sales and marketing that directly affected sales efficiency and reduced sales and marketing expenses.
Regarding use of AI in software development (which is not what the article is about), the proof of the pudding isn’t in greenfield projects, it’s in longer-term software evolution and legacy code. Few disagree that AI saves time for prototyping or creating a first MVP.
Why tho? You used AI to make some software, but did you use AI to achieve rapid revenue acceleration?
That you used AI to build software seems tangential to whether it can increase revenues. Over the years, we've seen many technologies that didn't deliver on promises of rapidly increasing revenues despite being useful for creating software (cough OOP cough), so this new one failing to live up to expectations isn't surprising. Actually given the history of technologies that over promise and under deliver on massive hype, disappointment should be the null hypothesis.
5% are succeeding. People are trying AI for just about everything right now. 5% is pretty damn good, when AI clearly has a lot of room to get better.
The good models are quite expensive and slow. The fast & cheap models aren't that great - unless very specifically fine-tuned.
Will it get better enough so that that growth rate in success pilots grows from 5% - 25% in 5 years or 20? Who knows, but it almost certainly will grow.
It's hard to tell how much better the top foundation models will get over the next 5-10 years, but one thing that's certain is that the cost will go down substantially for the same quality over that time frame.
Not to mention all the new use cases people will keep trying over that timeline.
If in 10-years time, AI is succeeding in 2x as many use cases - that might not justify current valuations, but it will be a much better future - and necessary if we're planning on having ~25% of the population being retired / not working by then.
Without AI replacing a lot of jobs, we're gonna have a tough time retiring all the people we promised retirements to.
That depends if the AI successes depended much on the leading edge of LLM developments, or if actually most of the value was just "low hanging fruit".
If the latter, that would imply the utility curve is levelling out, because new developments are not proving instrumental enough.
I'm thinking of an S curve: slow improvements through the 2010s, then a burst of activity as the tech became good enough to do something "real", followed by more gradual wins in efficiency and accuracy.
And regardless, I still see this as very positive for society - and don't care as much about whether or not this is an AI bubble or not.
(How should I invest if I have this thesis)
The article mentions 19-20 year old founders, focused on solving single user problems, were the successes.
The sample size is 300 public AI deployments and an undisclosed number of private in-house AI projects. And the survey seems to only consider business applications, as compared with end-user applications like media and software. That's significant but not definitive.
Isn't it more likely that existing problems with low hanging fruit, perhaps unpopular answers, that could be solved by leaning on "AI". And perhaps "AI" wasn't the key to success?
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We use generative imagery/video at my job and it's adding value. I see value being added for coders.
There's real innovation happening, but I find it's mostly companies cutting corners making customer service even shittier than it already was.
There's a meme that I think fits: https://i.redd.it/20rpdamxef0f1.jpeg
I think for a long time, cutting corners so that the number can go up next quarter has worked surprisingly well. Genuinely, I don't think a lot of corporations view offering a better product as a viable means of competing in the 2025 marketplace.
For them, AI is not the next industrial revolution, it's the next overseas outsourcing; AI isn't a way to bring new value to customers, it's a way to bring roughly the same value (read worse) but at a much cheaper cost to them. If they get their way, everything will get worse, while they make more money. That's the value proposition at play here.
As a technically minded person but not a comp sci guy, refining document search is like staring into a void and every option uses different (confusing) terminology. This makes it extra difficult for me to both do my regular job AND learn the multiple names/ways to do the exact same thing between platforms.
The only solution that has any reliability for me so far are Gemini instances where i upload only the files i wish to search and just keep it locked to a few questions per instance before it starts to hallucinate.
My attempt at RAG search implementation was a disaster that left me more confused than anything.
https://support.microsoft.com/en-us/office/insert-data-from-...
There are very few use cases at companies where you need to generate something. You want to work with the company's often very private disparate data (with access controls etc.) You wouldn't even have enough data to train a custom LLM, much less use a generic one.
The fact that we live in an era where tech people have been so investor pilled that overstating the capabilities of technology is basically second nature does not help.