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In Machines we Trust

OpenAI's $121B Funding Round Explained

In Machines we Trust

In Machines we Trust

Technology

4.36 Ratings

🗓️ 3 April 2026

⏱️ 13 minutes

🧾️ Download transcript

Summary

In this episode, we explore OpenAI's record-setting $121 billion funding round at an $852 billion valuation and discuss the implications of this massive investment. We also touch on Anthropic's recent setbacks and Huawei's competitive new AI chip entering the market amidst the ongoing US-China tech tensions.

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Chapters
00:00 Introduction
00:04 OpenAI's Funding Round
00:50 Huawei's AI Chip
04:03 Anthropic's Source Code Leak
06:46 Investment Insights
12:07 Market Outlook See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Transcript

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0:00.0

Welcome to the podcast. I'm your host, Jaden Schaefer. Today on the podcast, we're talking about

0:04.0

opening eye that just closed the largest private funding round in tech history, $121 billion,

0:10.6

at an $852 billion valuation. We also need to talk about what that means, who's writing the

0:16.6

checks, where all this money is actually going. And then something else I thought was pretty terrible was

0:22.1

Anthropics' rough week and a month they have been having. On the one hand, they're having a

0:27.1

generational run. I, you know, I am using Claude and Anthropic more than ever before for basically

0:32.1

everything with Claude Co-Cowork and Claude code. But at the same time, they've had a bunch of

0:36.5

bad PR perhaps. And the most recent is that their Claude code. But at the same time, they've had a bunch of bad PR, perhaps, and the most recent

0:39.6

is that their Claude code source code just got exposed to a public NPM registry. They

0:46.8

accidentally shipped about 500,000 lines of this. In addition, I want to talk about where hardware

0:52.1

is at. Huawei's new 950 PR chip is picking up real orders from ByteDance and Alibaba. I think this means a lot in the context of U.S.-China Chip Wars. So let's get into all of it. Before we do, if you are someone who uses AI tools regularly, which I'm guessing is most of you, if you're listening to this, you should absolutely check out AIbox at AIbox.AI. It is my own startup and it gives you access to over 80 AI models in one place. So instead of paying for separate subscriptions to chat, GPT, Cloud, and Gemini and everything else, you have one platform. The thing that I think is actually the most useful is that you can build automations just by describing what you want in plain language. So no coding is required. I'm not a developer. I built it for people like myself. It's $8.99 a month is the starting price, which is way less than stacking three or four different subscriptions on top of each other. The link is in the description. I think this is something that will be super worth trying out because it will save you money and it will also keep all of your files and logins in one place. So if you want to go try it out, A.Ibox.a. link in the description. Let's talk about Huawei first. Their 950 PR chip is getting a lot of traction and it is a serious competitor to Nvidia's AI chips. They've been working on this for a while. We know China obviously has been working on this because they don't want to get left behind. And the United States, there's a whole bunch of chip export controls. Routers reported last week that both BightDance and Alibaba are planning to place orders. And customer testing has apparently gone really well. Huawei right now, they previously had kind of their flagship, Ascend 910C. It was struggling to get any adoption from some of the big private sector tech companies.

2:21.2

Invideo was still basically dominating everywhere there.

2:23.7

So I think the main complaint that it got was software compatibility.

2:27.4

If your whole stack is built around Nvidia's CUDA ecosystem, then switching is super, super painful.

2:33.1

The 950 PR apparently is going straight after that.

2:36.8

And I guess they're sort of addressing it.

2:39.4

It's a lot more compatible with Kuda workflows, and the response time is a lot better.

2:43.3

But by basically integrating with the software that NVIDIA uses, they're able to get into

2:47.2

that same ecosystem without people having to completely rebuild everything from scratch. The pricing on it is really interesting too, the standard version with DDR memory. It comes in

2:55.6

around $6,900 per card. The premium HBM version is about $9,600. For comparison,

3:04.7

Nvidia's high-end AI chip sells for significantly more than that.

3:08.4

So I think that there's a big cost advantage.

...

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