As much as I love to hate on Uber, that website is from 2022. Uber has been profitable since 2023.
It's profit margin seems to have stabilized around 10%.
The real economic crime is losing at least $40bn over 10 years scaling a business that ended up having retail profit margins (i.e. low profit margins).
This is actually a very old AI insight, acknowledged at least as early as the 80s, let me see if I can find the quote.
Found it:
> Rodney Brooks explains that, according to early AI research, intelligence was "best characterized as the things that highly educated male scientists found challenging", such as chess, symbolic integration, proving mathematical theorems and solving complicated word algebra problems. "The things that children of four or five years could do effortlessly, such as visually distinguishing between a coffee cup and a chair, or walking around on two legs, or finding their way from their bedroom to the living room were not thought of as activities requiring intelligence. Nor were any aesthetic judgments included in the repertoire of intelligence-based skills.
> "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility."
The things like proving complicated theorems are things that are acquired by education within a lifetime, and that's why they're easy for AI.
The things a child can do are acquired through millions of years of evolution. While they don't require much explicit education, that doesn't mean they're easier.
Fair enough but even thing acquired within a lifetime have a hierarchy. Many societies, for example, assume that the kids who are good in Math are smart but the ones who write well or are exemplary in "co-curricular" subjects simply aren't that bright.
As an example, the kid who can solve Math problems has less of an edge over AI than the kid who automatically becomes the captain of the neighbourhood football team but older human beings often assume that the former is smarter.
I've always found that weird, do people really use plungers for that?
The toilet brush is a much better tool for unclogging the average toilet.
The plunger is actually meant to unclog sinks as far as I can tell, since it can attach much better to the sink and through its action can create pressure to unclog the much smaller sink drain pipe.
> In the past "computer" was a job description and mechanical power came from serfs.
Serfs, all right, but in what world do you live where "computers", people who did manual computing (i.e. mechanical additions/multiplications/... with very large numbers) are the same as actual research mathematicians, who are basically pure logicians?
The only perspective where it makes sense to root for mathematicians to go away is if you're a misandrist that thinks humanity should be replaced by robots (for reasons...). Or isn't logic something that's a defining human trait, and one of the main reasons we became the dominant species on the planet?
I don't think that "root[ing] for mathematicians to go away" is the problem. The problem (if there is one) is that the process by which that occurs is economically determined. No amount of complaining will stop AI from being useful in mathematics or erase the incentives to make it better. It's automatic process, like photography sidelining painting or shoe factories sidelining cobblers. We go through this with every technological advance and the outcomes are pretty much determined. No cheerleaders are needed.
It's a mix. If the current wave of LLM businesses crater, demand for LLM specific hardware (and related hardware) will crater. GPUs were propped up by crypto currencies and now by LLMs. They're still great at doing fundamental math operations, but for their value to stay up another massive business opportunity involving matrix multiplication and the like would need to rise as soon as the current business cycle winds down.
Infrastructure is massively complex and multi cloud is super hard to do. Switching LLMs is... a drop down.
Now, that doesn't mean running your own LLM will be easy, but this will mean it's a lot more likely that there will be at least regional LLMs, in my opinion. I.e. there will be Google, whichever (if any) is left standing of OpenAI or Anthropic, and then there will be Chinese hosted LLMs, probably Indian hosted LLMs, European hosted LLMs, plus LLMs hosted on managed services (i.e. Bedrock). For sure I see large banks on the like being able to host the best OSS or even licensed LLMs on their own cloud infrastructure accounts (i.e. at AWS, Azure, etc).
And that's on top of the LLMs running on owned server infrastructure plus actual local, on device LLMs.
You're using the future tense, but all of those things already exist. Google exists, Amazon Bedrock exists, DeepSeek's cloud product exists, etc. etc. But this isn't relevant to what the post you are replying to said, which is that "cloud-based, metered AI being a dominant work mode [is a] fad". Since all of those things are cloud-based, metered AI.
I was talking more about on-premises, on private cloud and on-device stuff, as I said.
If you look at what Uber is spending per developer per month, they clearly have some headroom to consider whether more-local, unmetered AI tools on device, on premises, in private cloud, can be cost-effectively used to cut down how much money they are pouring into Anthropic and OpenAI. Not least because a bit of centralised effort might lead them to distilled models that are better for their purposes. Some of that budget could go into simply putting a bit more capacity on a developer's desk.
Can they do it now for everything? Obviously not. But IMO there is no reason at all for planning and scaffolding tasks to be done with cloud models, and there are many reasons why it might be better to do document processing without leaving the premises.
The incentives are there on the technical, operations and particularly on the business levels, and the relative disruption of the switch really small, considering that all the tooling can use different models for different tasks already. They must at least be investigating the possibility; it's irresponsible not to.
Uber's not really a good example because they deliberately incentivized their engineers to spend as many tokens as possible, which was silly. But even assuming that every developer uses the full $1,500 a month of tokens that they are now allowing, that's actually not a lot of money relative to the cost of a single developer for them. It's less than 1/10 of a junior engineer's fully loaded salary.
Where I would expect to see people invest in local models is in cases where a company has regulatory requirements to keep data local, or where they're doing some specialized kind of work. Neither of those really apply to Uber. In 2026, it would absolutely be irresponsible for a taxi company to try to build a better Claude than Claude.
Now what this looks like 5 or 10 years from now, it's hard to say. A lot will depend on whether China keeps releasing open weight models and whether people can still run those open weight models on commercially available hardware.
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