11 ms·
80% of AI Projects Crash and Burn, Billions Wasted Says Rand Report
- Eumenes 2y agoRAND wants that money funneled to weapon/missile development instead of chat bots
- hobs 2y agoIt says the problem is management understanding is failing, but its more they do not listen to their technical staff but instead read the equivalent of Cool Stuff Magazine, know their investors read it too, and that the next board meeting is going to revolve around asking them what their grand strategy for AI is. It doesn't matter that they dont have one, frankly most of their data projects fail anyway and you just need one article published about your new vision to sell it for another six months your investor class.
- ms7892 2y agoGetting “ Error establishing a database connection”.
- MattGaiser 2y agoThe site is down, so I can’t read the article, but how is “wasted” defined in the context of these articles? As corporate consulting America has a tendency to call any project, no matter how speculative, wasted if it didn’t succeed.
- dorkwood 2y ago[dead]
- dwallin 2y agoIt's probably better to just link to the Rand Report: https://www.rand.org/pubs/research_reports/RRA2680-1.html https://www.rand.org/pubs/research_reports/RRA2680-1.html
- causal 2y agoFrom the key findings: > First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI. So the #1 problem every startup faces. > Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model. This is interesting, and reinforces the trend towards hoarding data assets. > Third, in some cases, AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users. Makes sense, tough to pick an architecture or model to stick with when better options release weekly. > Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure. Sounds like lack of capital. > Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve. So only a minority of cases? All in all this report seems to be saying "AI is promising but startups are still hard".
- mrweasel 2y ago>> Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model. > This is interesting, and reinforces the trend towards hoarding data assets. Companies completely misunderstand where the data is suppose to come from and attempt to hoard user data. The issue with LLMs is that the problems they are currently best suited for require data generated by the company. Manuals, decision trees, guides, tutorials, expert knowledge in general and companies aren't producing that material, because it's expensive. Also if that data existed, then maybe they wouldn't need an LLM. Tons of LLM implementations are poor attempts to cover up issues with internal processes, lack of tooling and lack of documentation (without which the LLM can't function). I'd say 80% has failed, so far.
- tech_ken 2y ago> > Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure. > Sounds like lack of capital. I actually think this is also an engineering problem, or at least a 'human capital' issue. The skillset for developing an AI model and the skillset for deploying a massive data-based product are highly different, but people who are good at the former often get press-ganged into doing the latter. This is kind of a capital problem (more money means maybe they can hire a second person to manage the operations), but I think it's also just a general lack of awareness that MLOps is really it's own thing. Especially when you're moving fast, tech-debt with these systems builds up really quickly (shockingly quickly). More money lets you hide these problems better, but IMO the solution is only going to come with time as people develop better and better best-practices for this type of project. edit: There's a section in the full report called 'Too Few Data Engineers' that does a better job making this point. Everybody wants to make fancy AI models, nobody wants to be responsible for the 10K lines of uncommented Python and SQL you're using to build your test/train sets
- teqsun 2y ago> Error establishing a database connection site appears to be down
- dudeinjapan 2y agoBecause it's powered by AI
- teqsun 2y agoGuess we can add this article to the 80% that crashed and burned, then.
- paulsutter 2y agoAnd yet progress keeps moving forward. It's almost like the investors understand the risks
- notamy 2y agohttp://web.archive.org/web/20240826091915/https://salesforcedevops.net/index.php/2024/08/19/ai-apocalypse/ http://web.archive.org/web/20240826091915/https://salesforce... Does not appear to be in archive.is
- Oras 2y agoThe website is down, > Error establishing a database connection A bit of irony that salesforcedevops Wordpress can’t manage the traffic from HN
- giancarlostoro 2y agoThey called the article an apocalypse, but the real apocalypse happened to their blog due to lack of caching of content.
- bugbuddy 2y agoMy conspiracy theorist inner voice says the Nvidia longs are DDOSing this because they don’t want any bad AI news before their big revelation today.
- rychco 2y agoI’m shocked; I was certain this would be different than blockchain.
- fkyoureadthedoc 2y agoI'd be shocked if less than 99% of non-shitcoin Blockchain projects crash and burn.
- GaggiX 2y agoDo you think 20% of blockchain projects succeed?
- dudeinjapan 2y agoIn terms of making their conman founders rich, BIG success!
- Xx_crazy420_xX 2y ago80% of AI projects crash, along with this site
- ashryan 2y agoCurrently hugged so I can't read the article, but I can only wonder how this compares to the batting average of any given R&D effort. 20% of projects succeeding on a cutting edge technology might be pretty good, no?
- lolinder 2y agoThe original report is still up (linked to by a sibling) and says this: > By some estimates, more than 80 percent of AI projects fail — twice the rate of failure for information technology projects that do not involve AI. Also, given how early in the hype cycle we are, there are a lot of projects that haven't failed yet but will likely fail in the end.
- rodiger 2y agoAnd in a hype cycle many many more projects get off the ground that normally, outside of a hype cycle, wouldn't have ever received the requisite funding.
- halyconWays 2y ago>Billions wasted Wow gosh. Where does that money go? It just evaporates?
- captainkrtek 2y agoSalaries, AWS bills, laptops purchased for devs
- treprinum 2y agoIsn't that better than normal? It used to be 90% of startups going belly up within 3 years, and out of remaining 10%, 9% becoming zombies and 1% having a proper exit? This looks more like Pareto 80/20 which is way better.
- nemomarx 2y agowe're not really there years into the boom though, so it could regress to that. this would be more like 80 failing so far
- bugglebeetle 2y agoConsider: the difference between past and current interest rates.
- kjkjadksj 2y agoA few percentage points? Should mean nothing for vcs as they look to 10x profit. This is the time to go heavy with investing. Turn the stones others are afraid to turn because of spooky single digit percent interest rates. More likely to find your 10x now than when money was cheaper.
- marcosdumay 2y agoIt's about in-house development on large companies... So, that would make it about 2x worse than normal. What IMO, sounds way too good to be true. (But then, I've seen AI projects being determined complete successes by having the same kind of result that would be considered failures on a normal product: being complete, but nobody using them.)
- AlwaysRock 2y agoUp like 5% from non ai projects?
- whartung 2y agoLet's not discuss the vast successes of the restaurant business.
- deleted 2y ago[deleted]
- jimmar 2y ago"Error establishing a database connection." https://web.archive.org/web/20240826091915/salesforcedevops.net/index.php/2024/08/19/ai-apocalypse/ https://web.archive.org/web/20240826091915/salesforcedevops.... Here's a link to the Rand report: https://www.rand.org/pubs/research_reports/RRA2680-1.html https://www.rand.org/pubs/research_reports/RRA2680-1.html
- mensetmanusman 2y agoFor anyone that knows anything about research and development, a 20% hit rate is actually quite high.
- tech_ken 2y agoFrom the report 80% is "twice the rate of failure for information technology projects that do not involve AI" (https://www.rand.org/pubs/research_reports/RRA2680-1.html https://www.rand.org/pubs/research_reports/RRA2680-1.html) so seems that a 20% hit-rate isn't actually that great. Possibly it's a quirk of how they're normalizing the success/failure count?
- dwallin 2y agoMost of those non-AI projects are likely using well-established practices and technologies. From that perspective a 20% success rate seems pretty good to me.
- tech_ken 2y agoSure that makes sense, overall "success of a technology product" seems like a very fuzzy thing to try and measure so I imagine one could spin the numbers basically however you want
- nerdjon 2y ago> research and development. I think that is the question, how much legitimate R&D is really going on here vs trying to shove an LLM into some random hardware and ship it? Or shove an LLM in some app trying to solve a problem that it isn't capable of. No doubt that creating these models are hard, having the data is hard. But how much of the AI startups is actually that vs just shoving the OpenAI API in something.
- janalsncm 2y agoI think we’d need to dig into the 80% that failed and what kind of “AI project” they were. Is this really R&D? Or were you trying to insert AI into something that didn’t need it, and failed because no one is using your expensive and annoying “chat with us” popup that your VP insisted would keep your company competitive?
- _davide_ 2y agoIs anyone surprised?
- dwallin 2y agoAn interesting note: > By some estimates, more than 80 percent of AI projects fail — twice the rate of failure for information technology projects that do not involve AI. So 60% general success rate vs 20% for an emerging technology that doesn't really have established best practices yet? That seems pretty good to me.
- _heimdall 2y agoTiming will play a role in how that number shakes out. AI projects often stacked huge investments and may not yet have had enough time to burn through all the cash or have it clawed back by investors.
- add-sub-mul-div 2y agoThere's so much LLM shovelware getting spammed here daily that I have a hard time believing 20% of all projects are succeeding. Are we even far enough into the LLM era though for bad projects to have run out of their borrowed time?
- kome 2y agowhat schumpeter called capitalism's creative destruction. move along. ai is still incredible tho.
- AnotherGoodName 2y agoIs anyone else finding their company is asking teams to “insert ai everywhere any way you can”? That’s a sign of a problem imho. The hype is so high the directives are to use ai everywhere regardless of fit. I’m a believer of ai but shoehorning it into everything as that currently boosts stock prices seems insane.
- shmatt 2y agoim finding the exact opposite * blocking every known LLM url due to fear of leaking information to it * not wanting to hire expensive data scientists for any in house development I even asked an Engineering Manager at Meta how much their own team use Llama day to day to multiply their productivity. Their answer was they don't use it at all, and they weren't aware of any internal tooling to utilize it for work
- steveBK123 2y agoThis kind of fits the narrative of some of the Mag7 earnings calls where they more or less say "we aren't sure where the revenue will ever come from.. but its a game theory style arms race where we can't afford to NOT be there if someone figures out how to make revenue in the space". So the big guys are buying GPUs, building out datacenter, developing & training models, etc.. just in case. Maybe LLMs will change some niche dramatically, maybe it will reshape society, or maybe nothing. More prior revolutionary developments end up like crypto, voice assistants, IoT, smart homes than the number that end up like smartphones, web, or the PC.
- chemmail 2y agoI think the only case I can think of where AI will revolutionize positively is self driving cars. Revolutionizing transport will have huge implication. The next thing would be robots, but that's just making people lazy in the household and replacing jobs. Crypto, voice assistants, IoT, smart homes, are bad examples are these have a great chance to grow more still. They will probably replace smartphones as smartphones did with PCs.
- bhawks 2y agoWhat is a project? A startup? Integrating chat bot into your support page? What is AI? Titles like this are often click bait - but since the site is down can't tell.
- mvanbaak 2y agoThis is answered. They only looked at projects that actually implement machine learning etc, and they did not look at projects that use ready to use models (the so called prompt engineering projects)
- gmuslera 2y agoThe problem is not if the 80% of them fail, but if of the remaining 20% you get several black swans that make profit skyrocket for the whole investment set. The problem is if none of them, even the surviving ones, don't worth too much anyway. In that case those billions would had been wasted. But if you invested everything to just one player, and that player failed, then your whole bet failed.
- kjkjadksj 2y agoIt all depends if the rate of black swan is too low to bother with. There is probably a point where spending vc money on lottery tickets starts looking like the more pragmatic investment.
- llamaimperative 2y agoPretty sure this is about AI projects, i.e. potentially career-ending failures, not failed AI companies which in a VC context have a high expected rate of failure.
- marcinzm 2y agoThat's bad management if it's seen as career ending failure. Proper management just does the ROI on the successful ones versus overall cost. And doesn't risk everything on them. Large tech companies test hundreds of model changes and rollouts per year in AB tests for that reason.
- lumost 2y agoAnecdotally, I've never seen a project be a career ender. Most individuals have many projects under their belt, it's rare that an individual has bet the farm on an effort in such a way that others would not employ them. When a project fails, the lessons are often valuable for the next project - when a project succeeds it can often just be do to market position.
- HarHarVeryFunny 2y agoI guess that perspective depends if you are a VC or a company trying to apply AI. The VC expects to lose most of the time and hit it out of the park once in a while, for a large net gain. For companies trying to automate or increase productivity with AI, there are unlikely to be any massively profitable winners that will make up for the failures, so too many failures is going to hurt.
- wrs 2y agoThis movie is familiar…most of this summary in the Rand report applies/applied to any overhyped new technology. E.g., try substituting “NoSQL” for “AI” and see how well most of it reads.
- 2OEH8eoCRo0 2y agoI'm bullish on AI but it was clear to even me that most projects were simple ChatGPT wrappers.
- bob1029 2y agoAppendix A should be mandatory interview questioning for anyone getting into this business. Question 9 is the ultimate test.
- euph0ria 2y agoThe site crashed and burned..
- fidotron 2y agoHistory will repeat itself: https://en.m.wikipedia.org/wiki/Dynamic_Analysis_and_Replanning_Tool https://en.m.wikipedia.org/wiki/Dynamic_Analysis_and_Replann... “DART achieved logistical solutions that surprised many military planners. Introduced in 1991, DART had by 1995 offset the monetary equivalent of all funds DARPA had channeled into AI research for the previous 30 years combined.”
- lyime 2y agoand thats ok
- swalsh 2y agoSite has a database error, so can't read the report. But here's what it probably says. "80% of AI projects don't solve a critical customer need, and find themselves with low usage/sales, and eventually run out of runway" It's the same reason most businesses fail. Sell something people want, and people will buy it. Sell something people don't care about, even if it's powered by cool tech, people still won't buy it. It probably also says something about the high cost of AI... but frankly if you're providing enough value to the customer, you can up your prices to compensate. If your value is too low (ie: not selling something people want) people won't pay it.
- TimPC 2y agoI guess I’m pretty valuable then because my hit rate on AI projects I’ve lead is over triple the industry average.
- bugbuddy 2y agoBut the man in black leather said that people don’t need to learn to code because AI will now do all the coding. Who should we believe? Also, it is funny seeing how all the AI true believers in this thread coping. I am going to go short Nvidia after its earnings whatever the earning results. It is such an obvious trade.
- Vecr 2y agoShorts are never an obvious trade, the downside risk is too high, even if you're eventually directionaly right.
- bob1029 2y agoIt is impossible to overstate how risky options are in this situation. The implied volatility for NVDA is astronomical. Put differently, the OP isn't the first one with the idea to short them, so this incredible demand for options drives the premiums up substantially. That stack of (temporary) paper you are paying for is likely way more expensive than you think it is.
- wnc3141 2y agoShorts I think are only useful if there is an imminent and concrete devaluation event such as defaulting on credit etc. A general predicted market downturn is hard to tie to stock behavior, especially within a concrete time frame.
- HarHarVeryFunny 2y agoNVIDIA may be overvalued, but they can probably "grow into" this valuation with ongoing industry adoption and inference volume, even if LLMs don't get a whole bunch more capable than they currently are. Google now selling corporate Gemini annual licenses for $200/pop to help write e-mails and marketing drivel, etc.
- bugbuddy 2y agoHow many signups is Google getting for that? Will it sustain their capital expenditure and cover the depreciation? All sever equipments come with an expiration date. Based on my experience, all the LLMs have an initial cool factor that wears off pretty quickly. The productivity boost is questionable at best and downright negative in many cases. This is true in many AI projects. Just look at the Computer Vision geniuses that gave us Amazon Go Indian Mechanical Turks.
- tech_ken 2y agoFrom the RAND report "First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI." From personal experience this seems like it holds for most data-products, and doubly so for basically any statistical model. As a data scientist, it seems like my domain partners' vision for my contribution very often goes something like: 0. It would be great if we were omniscient 1. Here's some data we have related to a problem we'd like to be omniscient about 2. Please fit a model to it 3. ???? 4. Profit Data scientists and ML engineers need to be aggressive at early planning stages to actually determine what impact the requested model/data product will have. They need to be ready for the model to be wrong, and need to deeply internalize the concept of error bars, and how errors relate to their use-case. But so often 'the business stuff' gets left to the domain people due to organizational politics and people not wanting to get fired. I think the most successful AI orgs will be the ones that can most effectively close the gap between people who can build/manage models, and the people who understand the problem space. Treating AI/ML tools as simple plug and play solutions I think will lead to lots of expensive failures.
- victor9000 2y agoExcept there is no winning move as an IC in pushing back against a half baked product definition that lacks business rigor. I pushed back in my org against features whose unit economics didn't add up, and I was labeled not a team player, leading to negative professional development. One year later, the entire ML org was laid off because investors lost confidence in our ability to produce a sustainable business model. There is no fix as an IC for unsophisticated product and business leadership.
- seydor 2y agowait till you hear how many research projects crash and burn
- api 2y ago80%+ of all startups fail. Tech is hit driven. The remaining 20% carry the entire industry. Movies, music, and publishing are also hit driven in a similar way.
- winternett 2y agoMost of these projects are too similar in nature to succeed to begin with... Everyone is out to create yet another text chat bot or an image maker and then slap Google Authentication on it and tricks to get people to enroll into a monthly $ubscription... Few are out to be visionary and make products that can be sold to companies that will integrate the tools into their apps
- alexfromapex 2y agoIn my experience, hiring managers worry way too much about finding research engineers with deep math skills when at the end of the day they need software folks to operationalize simple maybe slightly fine-tuned foundation models.
- swalsh 2y agoI'd argue what they REALLY need is domain experts who can write a good prompt.
- Havoc 2y agoIf 20% of AI projects work out that would be massive for humanity. You don’t innovate with 100% odds
- deleted 2y ago[deleted]
- freediver 2y agoWeb Archive to the rescue! https://web.archive.org/web/20240819212746/https://salesforcedevops.net/index.php/2024/08/19/ai-apocalypse/ https://web.archive.org/web/20240819212746/https://salesforc...
- devops000 2y agoError establishing a database connection
- jonplackett 2y ago20% success rate is pretty good no?
- marcosdumay 2y agoYes. It's an unbelievable high number. There's probably a catch somewhere, even if AI is actually being very successful.
- normand1 2y agoJust wait until Rand looks into the success rate of Corporate IT Projects in general...
- herval 2y agohow's that distribution different from any new tech wave? (crypto, past AI waves, robotics, self-driving cars, mobile games...)
- tayo42 2y ago> 1 For this project, we focused on the machine learning (ML) branch of AI because that is the technology underpinning most business applications of AI today. This includes AI models trained using supervised learning, unsupervised learning, or reinforcement learning approaches and large language models (LLMs). Projects that simply used pretrained LLMs (sometimes known as prompt engineering) but did not attempt to train or customize their own were not included in the scope of this work. buried in a footnote. i wasn't sure what "ai project" actually meant I wonder what the failure rate if it actually included "things that use a llm as an api" is too
- atoav 2y agoYeah, as predicted. As a film guy I told my totally hyped colleagues a few years ago that 3D films are not going to stick in the way they expected. When Bitcoin and crypto currencies started to become the next big thing I was the only person in my circles that had actually tried purchasing something with it in a real world setting, years prior. When LLMs became The Shit, I warned against overblown expectations as I had some intuition a out the limitations about it stemming from my own machine learning experiences. And the only reason I was right all these times was because I looked at the technology and the technology did not remotely convince me. Don't get me wrong stereoscopic Films (or 3D as they called it) are impressive in terms of technology. But the effects within movies doesn't bring much. The little distance that remains when people look onto a screen instead of being in a world is something many people need. 3D changes that distance which is not something everybody enjoys.
- tompetry 2y ago>> By some estimates, more than 80 percent of AI projects fail — twice the rate of failure for information technology projects that do not involve AI. So 40% of projects with more proven/experienced technologies fail? That's super high. Replace "AI" with any other project "type" in the root causes and sounds about right. So this feels more of a commentary on corporate "waste" in general than AI.
- marcosdumay 2y agoAFAIK, nearly 60% of software projects fail. That means that 40% don't, what is about double of the 20% they are reporting for AI. That phrase you are quoting is probably a case of journalists being bad with numbers.
- marcinzm 2y agoThis seems like a very strong selling point for B2B AI providers versus in-house enterprise builds of AI. > First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI. The provider at least partially validates that this is a problem space that AI can improve which lowers the risk for the enterprise client. > Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model. The provider leverages it's own proprietary data and/or pre-trained models which lowers the risk for the enterprise client. They also have the cross-client knowledge to best leverage and verify client data. > Third, in some cases, AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users. Provider, especially startups, will lie about using the latest tech while doing something boring under the hood. This, amusingly, mitigates this risk. > Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure. The provider manages this unless it's on-prem although in the latter it can provide support on deployments. > Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve. Still a risk but a VC or big tech budgets covers that so another win.
- axegon_ 2y agoTo be honest, I am really frustrated with what is happening: the hype train killing something which in principle could be a good thing, as usual. In the second half of the 2010s, it was blockchain: Payments - blockchain is the solution. Logistics - blockchain. World hunger - blockchain. Cure for cancer - blockchain. 75% of all job offers from startups were blockchain-related, and admittedly, I worked at such a startup, which, from what I'm able to gather, is a few months away from total collapse. With vision models in the late 2010s, I was seeing AI winter 2.0 just around the corner - it felt like this was the best we could come up with. GANs were, to a very large degree, a party trick (and frankly, they still are). LLMs changed that. And now everyone is shoving AI assistants down our throats, and people are trying to solve the exact same problems they were before, except now it's not blockchain but AI. To be clear: I was never on board with blockchain. AI - I can get behind it in some scenarios, and frankly, I use it every now and then. Startups and founders are very well aware that most startups and founders fail. But most commonly, they fail to acknowledge that the likelihood of them being part of the failing chunk is astronomically high. Check this: a year and a half after ChatGPT came about and a number of very good open-source LLMs emerged, everyone and their dog has come up with some AI product (90% of the time it's an assistant). An assistant which, at large, is not very good. In addition, most of those are just frontends to ChatGPT. How do I know? Glad you asked - I've also been very critical of the modern-day web since people have been doing everything they can to outsource everything to the client. The number of times I've seen "id": "gpt-3.5-turbo" in the developer tools is astronomical. Here's the simple truth: writing the code to train an AI model is not wildly difficult with all the documentation and resources you can get for free. The problems are: Finding a shit load of data (and good data), which is becoming increasingly more difficult and borderline impossible - everyone is fencing their sites, services, and APIs - APIs which were completely free 2 years ago will set you back tens of thousands for even basic data. As I said, the code you need to write is not something out of reach. Training it, on the other hand, is borderline impossible. Simply because it costs A LOT. Take Phi-3, which is a model you can easily run on a decent consumer-grade GPU. And even if you are aiming a bit higher, you can get something like a V100 on eBay for very little. But if you open up the documentation, you will see that in order to train it, Microsoft used 512x H100s. Even renting them out will set you back millions, and you can't be too sure how well you would be able to pull it off. So in the grand scheme of things, what is happening now is the corporate equivalent of pump-and-dump. It's not even fake it till you make it. The big question on my mind is what would happen with the thousands of companies that have received substantial investments, have delivered a product, only for it to crash the second OpenAI stops working. And even not so much the companies, but the people behind these companies. As a friend once said, "If you owe 1M to the bank, you have a problem. If you owe 1B to the bank, the bank has a problem." In the context of startup investments, you are probably closer to 1M than 1B. Then again investors are commonly putting their eggs in different baskets but as it happens with investments and the current situation, all baskets are pretty risky, and the safe baskets are pretty full. We are already seeing tons of failed products that have burned through astronomical amounts of cash. I am a believer in AI as an enhancement tool (not for productivity, not for solving problems, but just as an enhancement to your stack of tools). What I do fear is that sooner or later, people will start getting disappointed and frustrated with the lack of results, and before you know it, just the acronym "AI" will make everyone roll their eyes when they hear it. Examples: "www", "SEO", "online ads", "apps", "cloud", "blockchain".
- sensanaty 2y agoI work on a team doing some shitty AI feature, and as far as I can tell the only reason it's still alive is because our C-level has overdosed on the kool-aid and are adamant that they can squeeze blood out of the AI stone. Pretty much everyone in engineering is telling them it's a monumental waste of time, effort & money (especially money, our AI tooling/provider bills are astronomical compared to everything else we pay for), but to them the word "AI" holds so much power that they just can't resist sinking further and further resources into it. It's really reinforced in me the knowledge that most execs are completely clueless and only chase trends that other execs in their circles chase without ever reflecting on it on their own.
- timcobb 2y ago80/20 rule strikes again! (c'mon folks, this applies to just about everything)...
- ein0p 2y agoWhen it comes to truly novel things, 20% of even modest success is a very high number. I worked in research heavy places (industry labs) over the last decade and if 90% of things you try do not fail, your work is not ambitious enough. That is very hard thing for a SWE to live with, but such is the price of progress. The remaining 10% tend to make it worthwhile. 20% is twice that. It needs to go lower still - you’re not going to succeed by just finetuning yet another llama variant.
- simonsarris 2y ago80% seems far too optimistic. From what I know of projects and development I would think upwards of 90% of all software projects are never shipped. Maybe 95%. Even higher would not surprise me. Maybe this is considered pre-crash or pre-burn by them. Maybe "80% of projects that get publicly acknowledged and are expected to be successful" crash and burn. It must be so much higher.
- Terretta 2y agoThis is an incredible stat. If you only have to explore five time-bound AI* projects to discover one that eradicates recurring costs of toil indefinitely, arguably you should be doing all of them you can. * Nota bene: I'm not using AI as a buzzword for ML, which the article might be doing. In my book, a failed ML project is just a failed big data / big stats project. I'm using AI as a placeholder for when a machine can take over a thing they needed a person for.
- josefritzishere 2y agoOn average proejcts faily about 70% of the time so AI projects are only 10% away from the mean. https://www.projectmanagementworks.co.uk/project-failure-statistics/ https://www.projectmanagementworks.co.uk/project-failure-sta...
- PeterStuer 2y agoJust 80%? Sounds like AI projects are succeeding above average.
- kayge 2y agohttps://archive.is/3p5co https://archive.is/3p5co
- kkfx 2y agoI bet larger part of that was project decided by the management, with unrealistic goals and some external interested party stating they are possible...
- Struyck363 2y ago[dead]