80% of AI Projects Crash and Burn, Billions Wasted Says Rand Report
salesforcedevops.net
salesforcedevops.net
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.
This is my experience in 20 years of SWE as well.
The biggest project failure debacles were obvious from the get-go to all the senior ICs on the team. Generally managers were pushing them for "reasons", and in some cases even wink-wink about the fact they too didn't believe in the project but.. "reasons".
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.
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.
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.
Obvious examples are things like AI-assisted language translation for books if you are a publisher, implementation of cancer screening technologies if you are a healthcare provider, AI-assisted drug discovery if you are a pharma firm, AI-assisted discovery if you are a legal provider, AI trading algo's if you are a hedge fund...
All of these could result in some pretty profitable winners. It's also the sort of thing that can result in 'losers' if you call it wrong - e.g. if there is a 20% chance of success and I don't invest and my competitors do, what happens if I am wrong? You don't want to be the Kodak of your industry. As an example - if I am the worlds biggest provider of call centres, do I really want to bet big that my industry will never be automated, or do I want to start investing now to prepare for that as a possibility?
I don't think your Kodak example works very well here - they were a classic business school case of not realizing what business they were really in, but most uses of AI (whether one means LLMs, or something else like most of your examples) are going to be automation/productivity enhancement, not "pivotal" changes.
See the actual report:
> 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".
> 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.
> 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
I'm unfortunately the guy who that gets dumped on and it's the most hated part of my job. I've tried talking to the people who authored such atrocities but they refuse to acknowledge that's bad code and have huge egos about it and see any slam dunk tools like using a linter to be an impediment to their work.
Unless someone is able to track down the original source of this information I would treat it with great scepticism.
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.
* 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
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.
So it's both outperformed what pessimists might have said (never work) and vastly underperformed what the median enthusiast projected (it's always just a few years away). I'd wager we are still teaching teens how to drive even 20 years from now.
LLMs are ~7 years old, so maybe another decade to being useful if we go by self driving cars learning rate?
Quick skim of Meta engineering's blog only turned up this incident response tool they're "enhancing with AI": https://engineering.fb.com/2024/06/24/data-infrastructure/le...
Management seems to see this as their opportunity to catch up on the cheap.
Rather than having modern tech systems and properly staffed engineering department, let's just uh.. have non-technical people do AI hackathons! Also instead of automating excel jockey jobs with server side data pipelines, what if we.. you guessed it.. gave the excel jockeys AI!
I can imagine this playing out in a lot of industries where the underdogs think its a shortcut and yet..
We had a directive from management to, for political reasons, use AI in the tool as much as possible to show how innovative and forward-thinking the company is. This led to a bunch of poorly-thought-out choices and while the project is in production and has internal users... I don't think it was particularly successful.
Not all of that is due to the "use AI" directive; there were also poor technology and deployment stack choices that made things overly complicated and cost us a bunch of time.
They simultaneously fretted about compliance, and insisted we use an internal wrapper around AI tools, which was not ready for a long time.
That general pattern of security/compliance being at odds with every other part of management happens outside of AI of course.
Shoehorning? Definitely bad.
"There's a new technology out there that makes new things possible, let's explore whether it makes sense to integrate it into what we're doing?" - not only is that the correct attitude, in the long run it's the only attitude that keeps companies alive assuming they have exposure to tech.
See the internet/web revolution, the mobile revolution, etc.
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.)
“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.”
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.
> 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.
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.
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.
Corporate mindset. When I worked at a major cloud provider the platforms group evaluated the ongoing economic cost/benefit of existing machines and every year the decision to retire ancient machines was "not yet". A suit-wearing corporate IT guy gets new hardware every 3 years. GCP will still rent you a Sandy Bridge machine from 2011. EC2 still has Haswell CPUs in its mainstream offering and if you really want them they have Harpertown Xeon from 2008 available.
“Because we still have suckers paying us large cloud bills for ancient hardware.”
I have my doubts about this. Do you have actual good data for this? Most devs including streamers like primeagen and others seem to think Devin is a joke.
The baseline is the same IDE tooling support we see with copilot but with more traditional tech like search engines and API docs search. Dev productivity without relying on AI is a market few cared about until AI money came along looking for problems to solve.
One use I had today... Make all the fields on this record nullable and add these attributes to them. Done in seconds what would really have been at least 30 minutes of work. It's mindless and tedius but I didn't need to do it...
I don't think I would trust the AI to catch edge cases I didn't think about, but I get the feeling for you it's more that dealing with them would be super tedius and they are truly rare. Like an http server unable to create a socket... It's an edge case but realistically let it crash, well the AI can write some more graceful exit with logging I guess...
> Error establishing a database connection
A bit of irony that salesforcedevops Wordpress can’t manage the traffic from HN
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.
> 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.
Does not appear to be in archive.is
> 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.
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
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
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.
That phrase you are quoting is probably a case of journalists being bad with numbers.
Maybe "80% of projects that get publicly acknowledged and are expected to be successful" crash and burn. It must be so much higher.
Movies, music, and publishing are also hit driven in a similar way.
https://web.archive.org/web/20240819212746/https://salesforc...
As corporate consulting America has a tendency to call any project, no matter how speculative, wasted if it didn’t succeed.
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.
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.
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.
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.
There's probably a catch somewhere, even if AI is actually being very successful.
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.
Wow gosh. Where does that money go? It just evaporates?
site appears to be down
You don’t innovate with 100% odds
ai is still incredible tho.
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".