[mark_l_watson]

I was also wondering if Google would try to make profit from selling TPUs, but they probably won’t because:

At least for me, Google has some real cachet and deserves kudos for not losing money selling Gemini services, at least I think it is plausible that they are already profitable, or soon will be. In the US, I get the impression that everyone else is burning money to get market share, but if I am wrong I would enjoy seeing evidence to the contrary. I suspect that Microsoft might be doing OK because of selling access to their infrastructure (just like Google).

[alephnerd]

There's no point selling TPUs when you can bundle TPU access as part of much more profitable training services. The margins are much higher providing a service as part of GCP versus selling.

[mark_l_watson]

I agree. Amazon and I think Microsoft are also working on their own NVIDIA replacement chips - it will be interesting to see if any companies start selling chips, or stick with services.

[alephnerd]

From what I'm hearing in my network, the name of the game is custom chips hyperoptimized for your own workloads.

A major reason Deepseek was so successful margins wise was because the team heavily understood Nvidia, CUDA, and Linux internals.

If you have an understanding of the intricacies of your custom ASIC's architecture, it's easier for you to solve perf issues, parallelize, and debug problems.

And then you can make up the cost by selling inference as a service.

> Amazon and I think Microsoft are also working on their own NVIDIA replacement chips

Not just them. I know of at least 4-5 other similar initiatives (some public like OpenAI's, another which is being contracted by a large nation, and a couple others which haven't been announced yet so I can't divulge).

Contract ASIC and GPU design is booming, and Broadcom, Marvell, HPE, Nvidia, and others are cashing in on it.

[coredog64]

I wouldn't be surprised if a fair portion of Amazon's Bedrock traffic is being served by Inferentia silicon. Their margins on Anthropic models are razor thin and there's a lot of traffic, so there's definitely an incentive. Additionally, every model that's served by Inferentia frees up Nvidia capacity for either models that can't be so served or for selling to customers.

[Mistletoe]

Do you have a link or references showing Google isn’t losing money on Gemini?

[mark_l_watson]

Earning report does not break out profit from Gemini separately, but this is still useful https://abc.xyz/assets/34/fa/ee06f3de4338b99acffc5c229d9f/20...

A long time ago I worked as a contractor at Google, and that experience taught me that they don’t like things that don’t scale or are inefficient.

[brazukadev]

That's the same as saying that Google is winning the AI race because they don't like losing. They won't win anything if we are in a bubble that burst tho

[GoatInGrey]

A hypothetical AI bubble bursting doesn't mean that every single AI vendor fails completely. Like the Dot-Com Bubble, the market value drops precipitously and many companies fold, but because the market value does not fall to zero, the survivors (i.e. Amazon) still win.

[noduerme]

Websites were still mostly selling goods and services in 2001. Not giving away hot takes and hallicinated summaries in exchange for eyeballs. In other words, after stuff like pets.com collapsed, people still found it useful to have pet food delivered, and the business model evolved. LLMs, on the other hand, don't seem to have a lot of public appeal. Most of the use cases are being shoved down the public's throat. Their appeal is to corporations as cost saving replacements for workers. But an AI bubble bursting would look like corporations rolling back their exuberance for the AI craze. What's already only speculatively profitable and requires enormous capex would probably become too toxic for anyone to try again for a generation.