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Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

reuters.com

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Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#61

Post by TheOtherHobbes »

erwald wrote: post #24
InsideOutSanta wrote: post #10

Earlier quoted context omitted.

The problem is that the dramatic improvement in capabilities is not translating to a dramatic increase in revenue.

"Anthropic and OpenAI generate a lot of revenue with relatively few employees – an estimated $9M and $5.5M in revenue per employee (RPE), respectively. If either company were to go public, it would have a higher RPE than any public tech company on Forbes’ Global 2000 list." https://epoch.ai/data-insights/revenue-per-employee-ai-compa...

They've replaced employees with compute, so RPE is irrelevant.

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#62

Post by lenerdenator »

toomuchtodo wrote: post #27
erwald wrote: post #24

Earlier quoted context omitted.

"Anthropic and OpenAI generate a lot of revenue with relatively few employees – an estimated $9M and $5.5M in revenue per employee (RPE), respectively. If either company were to go public, it would have a higher RPE than any public tech company on Forbes’ Global 2000 list." https://epoch.ai/data-insights/revenue-per-employee-ai-compa...

This assumes they do not have to increase prices to be profitable, and that they will continue to have customers when customers can switch to open models at similar performance. As an analogy, Uber could crank up rates after the VC growth play was over to stoke revenue and profits because they have a duopoly with Lyft. LLM consumers can switch to Kimi models fairly trivially today, and whatever the frontier open mode…

The question will be whether customers can switch.

Can you install a near-SOTA model on a cluster in a data center? Of course. Compliance and operations are the sticking points. I work in healthcare IT, and it's amazing how tight the data compliance requirements are. I can't have someone in Canada look at prod data. If we told hospitals that we were handing off PHI/PII to Chinese models, they'd end our relationship due to the long history China has of hacking Western networks and computers. They don't care how open and cheap things are.

Then, you have to keep up-to-date on the latest technology and right-size things in a very fluid market. If you sign a contract for hosting the model on a data center that's running what the SOTA is now in hardware, and someone comes through with a data center hardware or software product that makes that data center contract a disadvantage (maybe it's too expensive and the other party won't budge on the price), you might have to factor that into your offering's price, and that could put you at a disadvantage in your marketplace.

Google, MS, etc. all want to leverage the cloud model to make this be less of an issue for you, for a price. They have the ability to update you with the SOTA stuff in the data centers, because they're the ones driving that SOTA. They can say they host in the US and develop most of their stuff in the US.

Will that be enough of a moat?

Probably not for the levels of spending that are happening now, but over the long term, probably.

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#63

Post by budsniffer952 »

vrganj wrote: post #51
budsniffer952 wrote: post #42

Earlier quoted context omitted.

[flagged]

Sure, and my nephew is building nice little trucks with Legos. The point is, is anyone getting any value from it?

>The point is, is anyone getting any value from it?

No, you're right, no one is getting any value from it.

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#64

Post by saberience »

paxys wrote: post #2

These alarms have been going off for a long time now. Everyone is already in too deep to admit that there’s a problem.

What problem? What alarms?

I see everyone around me doing way more work, of way more depth, than they ever did before using AI models. I see my company and friends of mine all paying large sums of money to Anthropic, Google, OpenAI to use AI models, and do more work than we did before.

So Google is investing in infrastructure which is HIGHLY in demand, there is much more demand than supply, and then they are making money from this infrastructure...

That's a good thing for Google, and as an investor in Google, I am glad they are making these investments.

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#65

Post by TheOtherHobbes »

budsniffer952 wrote: post #28
InsideOutSanta wrote: post #10

Earlier quoted context omitted.

The problem is that the dramatic improvement in capabilities is not translating to a dramatic increase in revenue.

> not translating to a dramatic increase in revenue. Completely false. AI and AI related revenues are growing exponentially .

Expenditure on compute is growing even more exponentially.

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#66

Post by lightbendover »

650 wrote: post #40

I've been seeing quite a few companies juicing short term margins and quarter to quarter maxxing even more than before, one such example: https://x.com/MaxAnderson/status/2080229375773941871 https://xcancel.com/MaxAnderson/status/2080229375773941871 --- As someone who has personally spent $500k / mo+ on Google Ads for years, I can tell you with certainty: This revenue growth in Search is artificial & extremely unheal…

[dead]

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#67

Post by malfist »

vrganj wrote: post #25
mynameisjonny_ wrote: post #6

Earlier quoted context omitted.

> Everyone is in too deep to now admit that there’s a problem I'm not sure how to square this with the dramatic improvement in LLM capabilities in the last 8-9 months. If anything, it makes the earlier investments look prescient?

I'm not sure I've seen what I would call dramatic improvement since maybe GPT4? Sure, things got better. But I'd call it iterative more than revolutionary. I still wouldn't trust any of the models to do anything meaningful unattended. They all still do dumb shit all the time. Plus, even if they were genuinely dramatically better, the businesses sure as hell aren't. They're burning money left and right, they have no m…

It sure is funny how everyone claims the current model is a "dramatic improvement" over the models from X months ago.

You'd think if there had been that many dramatic improvements I'd have to babysit an LLM less frequently.

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#68

Post by saberience »

gonzalohm wrote: post #18
FartyMcFarter wrote: post #4

Why does it raise alarm? Pretty sure all this spending was planned.

I'm pretty sure they didn't plan to just spend cash without any return. It raises an alarm because there is no end in sight for the money burning

Since when is investing in infrastructure burning money?

If there is a huge demand for shipping goods internationally, investing in ships and planes isn't burning money.

There is massive demand for compute in the world right now, Google is investing in that area. That's a good thing.

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#69

Post by toomuchtodo »

lenerdenator wrote: post #62
toomuchtodo wrote: post #27

Earlier quoted context omitted.

This assumes they do not have to increase prices to be profitable, and that they will continue to have customers when customers can switch to open models at similar performance. As an analogy, Uber could crank up rates after the VC growth play was over to stoke revenue and profits because they have a duopoly with Lyft. LLM consumers can switch to Kimi models fairly trivially today, and whatever the frontier open mode…

The question will be whether customers can switch. Can you install a near-SOTA model on a cluster in a data center? Of course. Compliance and operations are the sticking points. I work in healthcare IT, and it's amazing how tight the data compliance requirements are. I can't have someone in Canada look at prod data. If we told hospitals that we were handing off PHI/PII to Chinese models, they'd end our relationship d…

My primary role is cybersecurity in a regulated entity in a regulated industry, I am highly confident it is straightforward to do so based on work accomplished in only a couple of weeks. Stand up a router, stand up a Kubernetes cluster if you don't have one, stand up the necessary VMs and compute for serving inference. Two pizza team, in my experience.

Customers can switch (although we can argue the speed and pain of doing so), and the speed at which they do will be a function of cost efficiency and demonstrable value (imho). A recent example of this is Broadcom and VMware [1], for example. When motivated, it can be done. If there is no objective, measured value being delivered, the spend will be cut. If the value delivered is measured, it will be enabled at a lower cost through cost optimization measures (ie self hosting) [2].

This is all to say: there is no moat, the revenue of inference providers is volatile and not assured in any measure. Caveat emptor.

[1] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

[2] Microsoft considers replacing ChatGPT and Claude with Kimi K3 to save $600M - https://news.ycombinator.com/item?id=49022984 - July 2026

Re: Alphabet's cash burn raises alarm for Big Tech as AI spending climbs

#70

Post by 8organicbits »

kibwen wrote: post #37
lotsofpulp wrote: post #16

Earlier quoted context omitted.

Keeping in mind that Jan 1 2026 to Jul 22 2026 is an arbitrary and meaningless time period to analyze.

Are you asserting that there exists a time period to analyze that is not arbitrary and meaningless? If so, which? The reason why 2026 specifically is interesting is because it wasn't until late December of last year that AI models started to demonstrate particularly interesting capabilities, while we finally got IPO announcements for OpenAI and Anthropic. Assuming that the market works at all, it should be pricing in…

> wasn't until late December of last year that AI models started to demonstrate particularly interesting capabilities

What are you referring to here?

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