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Comment by xnx | Hacker News Reader

> Nobody has a moat.

Custom hardware, data centers, huge cash reserves, deep/broad talent pool, and non-AI customer base are all huge advantages if not moats.

Google, Microsoft, or Amazon are more likely to be the AI leaders than OpenAI or Anthropic.

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The technical crowd will always overvalue the recent technical advantage of software over the available customer base and business model math.

Having a non-trivial cost advantage is part of business-model math.

Overvalue, not no value

Crossing the Chasm book talked about this, innovators/early adopters care more about stuff than later portions of the market https://en.wikipedia.org/wiki/Crossing_the_Chasm

> Google, Microsoft, or Amazon are more likely to be the AI leaders than OpenAI or Anthropic.

If not now, then when will these companies be AI leaders?

Even Google, with its staggering advantages in cash, compute, real estate, training data, and having basically invented the field only manages to briefly claim a 1-2 week lead once or twice a year.

The financials for Anthropic and OpenAI are likely borderline suicidal, google and co are publicly traded. Moreover, all innovations downstream to them dont they? Why not just stay slightly behind, especially given many have stake in those other companies?

Microsoft owns 51% of OpenAI, so they just have to wait for them go bankrupt then they come in and clean house.

The Internet says they have a 27% stake. Where'd you get your number from?

Thanks for fact-checking me. It used to be 49% and it was only the "share of profits under the old capped-profit structure".

Better to stay in the game and keep pace with the startups until they've burned up all their VC money and employees cash out after IPO.

Have you done a google search recently? The response time beats any anthopic or openai stuff, by an order of magnitude. They already are.

You understand that most people use AI not for coding ???

Google literally has billions of user that simply integrating it all with Gemini is massive undertaking

sure gemini is not frontier for coding but you know that is doesn't matters for google consumer

There are many companies that have data centers. They are conceptually easy to build. An ASIC is difficult enough that if you make one someone will leapfrog you while you are still making it (at least so far), though once you have one your costs will be enough lower than the competition that you can perhaps undercut them.

True, but have other hyperscalers caught up to Google's AI data centers?: fully liquid cooled, torus networking(?), 100,000+ TPUs interconnected, etc.

Google is already on gen 8 of its TPUs and is certainly already working on the next version or two.

Does it really matter? Those are interesting technologies, but off-the-shelf computers with some good fans and HVAC cool just fine. Likewise, 10 gigabit networking is not particularly expensive these days. Google's stuff is better and may or may not be cheaper. But wherever we can buy off the shelf now works just fine.

> 10 gigabit networking is not particularly expensive these days.

I am not a data center expert, but TPU chip interconnect reaches 13 terabytes per second: https://introl.com/blog/google-tpu-v6e-vs-gpu-4x-better-ai-p...

No single computer can handle that much. Your routers need that ability.

>non-AI customer base are all huge advantages if not moats.

This is the interesting part to me. People talk about a “SaaSpocalypse” because AI makes SaaS features cheap to copy, yet deeply embedded SaaS still accumulates integrations, data, and switching costs. Gemini is a good example: Google can put AI directly into Gmail, Docs, Drive, Search, etc., where people already work. Meanwhile, frontier-model performance leads often seem to disappear within months. Could model quality itself actually be a less durable moat than workflow and distribution? Curious where people who’ve worked in ML for a long time see the moat actually compounding.

If Moore's law continues, then in less than 10 years today's state of the art model will be able to run on a cell phone. How much smarter do we actually need AI to be? Would it still require datacenters and custom hardware?

They probably said the same thing about social media back in the day.

I'm sure the thinking out there, and hence investment, is all about how to tether the user to the most addictive, network-effected, incredibly deep, server-side, moat-able version of AI possible.

Moore's law stalled ~2015. Unfortunately, no way current models will run on the <100W thermal budget of a cell phone. Printing the weights directly into a chip would help efficiency a lot, but not enough.

In all likelihood in a few years we'll get ~200-400bA~4-6 MoE models that are on chip, and they'll be better than the current frontier.

I hate it when people pick up a phone while I'm using the modem.

Is it conceivable that in 10 years time we’ll have 7B models that have the same level performance as modern frontier ones?

That would be a stretch. Gemma 4 is 31B and not frontier.

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