OpenAI builds first chip with Broadcom and TSMC, scales back foundry ambition

BRCM is a leader in SerDes along with Marvell and other companies. There is so many IPs from Synopsys and Cadence that in turn many of these chip companies use. Not sure what is hoopla here. Nobody is going to completely vertically integrate all these IPs.

I wonder how this squares with the exclusivity contract with Microsoft. Even the OpenAI/Oracle deal requires Oracle to run Azure stack on their datacenter so MSFT can mediate the relationship. The AMD chips mentioned are also purchased by MSFT.

I wonder if this really means that OpenAI is accepting the risk/capital expense while providing a variety of hardware to Microsoft, or if there are other terms at play.

Yeah, that's um, wild.

You don't just acquire $7T.

The ENTIRE US domestic Net Investment isn't even $1T: https://fred.stlouisfed.org/series/W790RC1Q027SBEA

Gross Fixed Capital Formation (Net Investment + Deprecation) isn't much more: https://fred.stlouisfed.org/series/NFIRSAXDCUSQ

Google, Apple, and Microsoft together don't even spend $100B on CapEx per year. And they're worth almost $10T put together.

Asking for $7T when you're a $100B company is so ridiculous it's beyond belief.

Could it be possible that OpenAI's new autocomplete will be as transformative to the global economy as WeWork's short term office rentals?

Reminds me of:

Musk flew there with Cantrell, prepared to purchase three ICBMs for $21 million. But to Musk's disappointment, the Russians now claimed that they wanted $21 million for each rocket, and then taunted the future SpaceX founder. As Cantrell recounted to Esquire: “They said, 'Oh, little boy, you don't have the money?”

Source?

when i read that news back then, i read it as "billions" and thought "that sounds very ambitious, hardly he'll get that money". Man, i'm 3 orders of magnitude behind the current state of the art in tech, and need to start thinking in trillions (while it was only recently when a billion dollars was large money).

>>OpenAI launched o1 — codenamed Strawberry — on Thursday night, with all the excitement of a dentist’s appointment. Across a series of tweets, Sam Altman described o1 as OpenAi’s “most capable and aligned models yet.” Though he conceded that o1 was “still flawed, still limited, and it still seems more impressive on first use than it does after you spend more time with it,” he promised it would deliver more accurate results when performing the kinds of activities where there is a definitive right answer — like coding, math problems, or answering science questions.

it seems to me like they nerfed the regular free ai model to give me imperfect replies on my benchmark consistently while the o1 model nails it consistently. before the free model would, depending on the day, give me more accurate replies to my benchmark .

thus widening the divide to make the o1 model look more useful. openai can do whatever they want and thats fine but all it seems to me they did was make 01 look good by comparison by nerfing the free product.

You can throw more money at it to make it go faster, but it also might fuck it up and take longer.

Once they get it right, they will increase the gap with other LLM providers.

Ditching it might mean ditching the tooling that makes it so effective.

Apart from people in education, a whole SEO industry has grown around LLMs and they love the newly found powers to produce spam at scale.

In business / government space people are cautiously introducing LLMs to improve productivity in the comms area where slight hallucinations or robotic corporate language are acceptable, because the final text is still produced by a human. On the customer support / sales side companies are investing money in "AI" versions of the good old phone menu trees that inevitably make you scream "I want to speak to a human" before you get connected to a human being.

Elsewhere adoption is slow, because we expect deterministic results and these are just not to be expected. I write software that interacts with LLMs via APIs and have to implement defensive coding techniques I haven't used for over a decade, because the output may switch from the requested structure at random. This makes use of structured output or function calling problematic.

Once all outlook users have buttons to summarize, extract into, improve the email, then you have hundreds of millions using LLMs everyday.

I also used to be in disbelief, but i'm seeing more and more unexpected people use LLMs , and they even figure out how to prompt them right.

Particularly o1-preview is really good at writing broad strokes mini review style articles that discuss some question you're interested in.

It only works in cases where there's a lot of text out there already that discusses the topic and that needs to be sort of remixed to answer your question specifically.

The chat format is much more efficient for this than a web search or reading a few related Wikipedia articles.

It's truly glass half full vs. glass half empty. I'm super impressed and excited about how useful this is, and yet it clearly isn't AGI.

A highly advanced content retrieval system (but lacking in accuracy, so ok for situations where that may be ok).

I wouldn't say it's revolutionary -- certainly not as revolutionary as search, but it's a major step forward in being able to retrieve information. Ultimately will allow you to interact with computers the way you see in the old Star Trek movies, where you're speaking in plain English and receiving information back in the same (providing the systems which are the source of the needed information are interconnected and fed into the training set).