Big Companies That Invest Heavily in AI Also Hire More People, Report Suggests (techcrunch.com) 29
"Companies spending heavily on AI are growing headcount faster, even in the entry-level roles that many fear are doomed," writes TechCrunch. That's the conclusion of new report tracking AI spending from Ramp's corporate card/bill pay data as well as Revelio Labs' workforce records from 21,599 U.S. firms:
According to the report, "high-intensity adopters" — firms that spend on average $30 per employee per month on AI in the first three months — saw headcount increase 10.2%. Headcount also rose across functions, including engineering, sales, administration, customer service, finance, marketing, and scientist roles. The strongest job growth among high-intensity adopters was in the information sector, which includes software, internet, media, and tech-adjacent firms.
Despite these positive signals, the data isn't as rosy as it seems. It skews heavily toward tech-forward, knowledge-work firms — ones that might have VC-backing and are growing fast anyway, making it difficult to say whether AI is contributing to the hiring or just showing up at companies that are expanding anyway. "This paper does not show that AI universally creates jobs," the paper's authors admit, "but it does counter claims that AI will lead to broad job losses."
It also counters claims that AI is killing all junior jobs. Recent research from Goldman Sachs found that AI has already erased about 16,000 net jobs per month over the past year, with Gen Z and entry-level workers taking the brunt of the burden. But in tech-forward firms, the report finds that entry-level headcount actually rose by 12%... "For software and technology firms, AI can make core output cheaper or faster to produce: writing code, debugging, building internal tools, producing technical documentation, and supporting product development," the report reads. "Lower production costs in these workflows can raise the return to expanding the whole firm, not just the engineering team."
But companies that buy subscriptions and run pilots, yet did not go on to make sustained investments, don't tend to see any gains in headcount, per the report. That sets up the potential for a widening gap between firms that have the resources — like capital, technical staff, founder networks, and management bandwidth — to turn AI adoption into actual business gains and those that are stuck experimenting with subscriptions. In other words, this report suggests that firms that already have the resources are the ones that will see the largest gains.
CNBC argues another AI "narrative" was challenged this week: that open source can't make money. "The assumption was that giving your model away for free meant no business. That's breaking too, as open-model companies start posting real revenue and enterprises move from renting AI to running their own."
Despite these positive signals, the data isn't as rosy as it seems. It skews heavily toward tech-forward, knowledge-work firms — ones that might have VC-backing and are growing fast anyway, making it difficult to say whether AI is contributing to the hiring or just showing up at companies that are expanding anyway. "This paper does not show that AI universally creates jobs," the paper's authors admit, "but it does counter claims that AI will lead to broad job losses."
It also counters claims that AI is killing all junior jobs. Recent research from Goldman Sachs found that AI has already erased about 16,000 net jobs per month over the past year, with Gen Z and entry-level workers taking the brunt of the burden. But in tech-forward firms, the report finds that entry-level headcount actually rose by 12%... "For software and technology firms, AI can make core output cheaper or faster to produce: writing code, debugging, building internal tools, producing technical documentation, and supporting product development," the report reads. "Lower production costs in these workflows can raise the return to expanding the whole firm, not just the engineering team."
But companies that buy subscriptions and run pilots, yet did not go on to make sustained investments, don't tend to see any gains in headcount, per the report. That sets up the potential for a widening gap between firms that have the resources — like capital, technical staff, founder networks, and management bandwidth — to turn AI adoption into actual business gains and those that are stuck experimenting with subscriptions. In other words, this report suggests that firms that already have the resources are the ones that will see the largest gains.
CNBC argues another AI "narrative" was challenged this week: that open source can't make money. "The assumption was that giving your model away for free meant no business. That's breaking too, as open-model companies start posting real revenue and enterprises move from renting AI to running their own."
That leaves the problem that LLM may not stay... (Score:5, Insightful)
First, this is good, because it means companies have realized (well, some of them), that the "review" and "prompt with actual insight" parts of LLM use are non-optional for reasonable (not clear yet whether they will be "good", that will take some time) outcomes.
But it still does not look like LLMs will be a long-term thing, because it seems they will not be able to get the cost down. There are now several findings that LLMs with current token prices (which are still too low to cover cost) are not cost-effective compared to humans doing the full job. This does not (!) take into account external costs, like having to feed people that are unemployed because of LLM use. TCO is a thing for societies, because they, unlike individual enterprises, cannot escape it.
Sure, much simpler LLMs and, in particular, specialist LLMs will probably remain a thing, but the general ones? Does not look like it. Hence, as all previous AI hypes, 95% hot air, 5% actual substance. No surprise. Sure, that 5% substance has value, but why always the 95% crap?
Re:That leaves the problem that LLM may not stay.. (Score:4, Insightful)
Or this could be an example of Jevons Paradox [wikipedia.org] .
As AI makes employees more productive, and thus more profitable, would you fire them or hire more?
Re: (Score:3)
There's no indication of improved productivity for general LLMs beyond being an expensive pirating engine.
Only specialised LMMs that are carefully curated with experienced human made accurate, and presumably paid for, data have been making inroads on productivity.
Re: (Score:3)
Indeed. And there are a lot of known problems with LLMs that may or may not have really bad effects on top of what is known. For example, reviewing LLM generated code seems to cause high stress and is really bad for motivation. That will likely increase burnout among the (already far too few) people that can do these reviews competently. There is a ton of more such potential problems.
That said, specialist models with clear focus, well-crated training data and, depending on application, fact-checkers verifyi
Re: (Score:2)
https://arxiv.org/abs/2601.202... [arxiv.org]
Re: (Score:2)
That reference is pretty damning. Basically makes LLM use dangerous unless you are very careful. I wonder how many people are actually careful enough and how many will damage their cognitive and on-target skills badly ...
Well, I guess we will see some really bad long-term problems from LLM use. Probably makes the whole endeavor negative-worth.
Report reads like a political posturing document (Score:2)
The report does read like a paid editorial to do political spin control on the AI, datacenter and jobs discussion.
It's going to be a large issue once one political party or the other makes AI, datacenter, and consumers pay significantly higher electricity rates due to datacenters.
https://hls.harvard.edu/today/... [harvard.edu]
How data centers may lead to higher electricity bills
The public is paying for the energy infrastructure used to power Big Tech, argues Harvard Law expert
Sep 03, 2025
By Rachel Reed
Re: (Score:2)
A growth mindset would say, hire more. If LLM's are a force multiplier then you increase productivity dramatically by burning the candle at both ends. The bottleneck then becomes sales.
Re: (Score:2)
Every new technology starts out expensive, but then the price comes down, often dramatically. This will be true of AI as well. There is no reason to think AI will *remain* more expensive than human labor in a large number of white-collar occupations.
There is also no reason to think LLMs aren't a "long-term thing." They are already better than previous iterations of software that does things like language translation and closed captioning. They certainly are better than call center script readers who barely
Re: (Score:2)
Every new technology starts out expensive, but then the price comes down, often dramatically.
Only that this is not new technology. It is old technology scaled up. There are no easy wins to be had.
Re: (Score:2)
By what definition is it not "new"? ChatGPT wasn't available to the public until late 2022. That's less than four years ago. Sure, some forms of AI have been around for decades. But those were mostly experimental, certainly not useful for commercial adoption. Watson AI was first sold to business customers in 2014, and for most, the price was prohibitive.
The "personal computer" in the form of the IBM PC burst on the scene in 1981, and was quite expensive, starting at $1,500 (nearly $7,000 in today's dollars)
Re: (Score:2)
By the definition of "are the base mechanisms used well-known and well optimized"? Seriously, do you even begin to think about the claims you make?
Re: (Score:2)
I would argue that the "base systems" are not well-optimized. There are wide variations between the various LLMs in terms of processing and energy costs, for example. Claude Sonnet is among the most processor-and-energy hungry models, while DeepSeek is at the lower end. Others, like GPT and Gemini, are somewhere in the middle. If the mechanisms were so well-optimized, we wouldn't expect to see such dramatic differences between them.
Re: (Score:2)
Here is an example of an innovation that will help bring down prices: SMALL language models.
https://slashdot.org/story/26/... [slashdot.org]
Not Surprising! (Score:2)
Big spenders have hired people (Score:2)
That's all they're saying. With a big shrug on productivity.
My report: trust your common sense (Score:2)
Re: (Score:2)
So, perhaps their best intentions...aren't for you.
There is a direct correlation between how much "progress" is not for you and may well be fake on one side, and the amount of money involved on the other. Too many people become complete assholes and stop seeing anything else but the dollars when a lot of money is involved. It is basically a assured you will get ripped off in such cases.
Let me point out... (Score:5, Informative)
This report is being touted on "PR Newswire" (emphasis mine). And the "study" was done by Ramp, whose primary business is selling AI-supported tools to businesses.
Re: (Score:2)
Indeed. Caveat lector.
This is just spin (Score:2)
As the comment above points out, PR Newswire is just an industry shill.
And it's coming from another front here:
https://it.slashdot.org/story/... [slashdot.org]
It's all of the ilk "There's no problem with AI, just bad publicity - and we can fix that."
new party line (Score:2)
A new party line. Previous party line was bad for business.
more tech - more people to support the tech (Score:2)
as someone who works in the technology field, it sure seems like every time we want to implement new/more technology, it requires more people to support it...
Original "Report" Done By PR Newswire (Score:2)
PR Newswire being a public relations amplification service - and look - presto chango - it arrives at Techcrunch and /.
Media literacy rule #1. always check the source.
Companies spending on AI, spend on people. (Score:2)
News at 11.